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The present study employs density functional theory-based first principle calculation to investigate the electron transport properties of polyaniline following exposure to acidic and alkaline pH. In-situ deposited polyaniline-based paper device maintains emeraldine salt form while it is exposed to acidic pH and converts to emeraldine base when it is subjected to alkaline pH solutions. These structural changes at acidic and alkaline pH are validated experimentally by Raman spectra. Furthermore, the Raman spectra computed from density functional theory are validated with the experimental spectra. The changes in the theoretical energy band gap of polyaniline obtained from first principle calculations were correlated with the changes in the experimental impedimetric response of the sensor after exposure to acidic and alkaline solutions. Finally, the impedimetric responses were used to predict urine pH through a machine learning based smart and interactive web application. Different machine learning based regression models were implemented to acquire the best possible outcome. Gradient Boosting Regressor with least square loss model was selected as it showed lowest mean square, mean absolute, and root mean square error than other models. The smart sensing platform successfully predicts the unknown pH of urine samples with an average accuracy of more than 98%. The locally deployed smart web app can be accessed within a local area network by the end-user, which holds promise towards effective detection of urinary pH. [Display omitted] [ABSTRACT FROM AUTHOR]

• Machine learning classifier to support space traffic management decision making. • Intelligent classification of conjunction events under aleatory and epistemic uncertainty. • Combination of machine learning and Dempster-Shafer theory of evidence for robust decision making. • Optimal and robust impulsive and low-thrust collision avoidance manoeuvre design. This paper presents a decision support system that can automatically allocate collision avoidance manoeuvres in the event of a high risk close encounter between two space objects. Decisions are supported by an Intelligent Classification System that combines Dempster-Shafer theory of evidence with Machine Learning to automatically classify conjunctions according to the probability of collision, the uncertainty on the probability of collision, the time to close approach and the cost of a collision avoidance manoeuvre. We propose a simple analytical model that allows for the fast and robust computation of both impulsive and low-thrust manoeuvres under a mix of aleatory and epistemic uncertainty. Aleatory uncertainty is the non-reducible randomness in observation data, dynamic model and parameters, while epistemic uncertainty is the lack of knowledge on system dynamics and observation data, including the model of aleatory uncertainty itself. Dempster-Shafer theory of evidence is used to model the epistemic uncertainty in the calculation of the probability of collision. Some numerical examples are included to show the performance of the collision avoidance manoeuvre optimisation strategy and of the intelligent decision support system. [ABSTRACT FROM AUTHOR]

• Machine learning classifier to support planetary defense decision making. • Intelligent classification of deflection strategies under aleatory and epistemic uncertainty. • Combination of machine learning and Dempster-Shafer theory of evidence to robust decision making. This paper presents an Intelligent Decision Support System (IDSS) that can automatically assess the suitable robust deflection strategies to respond to a Near Earth Objects (NEO) impact scenario. The input to the IDSS is the warning time, the orbital parameters and mass of the NEO and the corresponding uncertainties. The output is the deflection strategies that are more likely to offer a successful deflection. Both aleatory and epistemic uncertainties on ephemerides and physical properties of the NEO are considered. The training data set is produced by generating thousands of virtual impactors, sampled from the current distribution of NEO. For each virtual impactor we perform a robust optimisation, under mixed aleatory/epistemic uncertainties, of the deflection scenario with different deflection strategies. The robust performance indices is considered by the deflection effectiveness, which is quantified by Probability of Collision post deflection. The IDSS is based on a combination Dempster-Shafer theory of evidence and a Random Forest classifier that is trained on the data set of virtual impactors and deflection scenarios. Five deflection strategies are modelled and included in the IDSS: Nuclear Explosion, Kinetic Impactor, Laser Ablation, Gravity Tractor and Ion Beam Shepherd. Simulation results suggest that the proposed decision support system can quickly provide robust decisions on which deflection strategies are to be chosen to respond to a NEO impact scenario. Once trained the IDSS does not require re-running expensive simulations to make decisions on which deflection strategies are to be used and is, therefore, suitable for the rapid pre-screening or reassessment of deflection options. [ABSTRACT FROM AUTHOR]

The computational theory of cognition, or computationalism, holds that cognition is a form of computation. Two issues related to this view are comprised by the goal of this paper: A) Computing systems are traditionally seen as representational systems, but functional and enactive approaches support non-representational theories; B) Recently, a sociocultural theory against computationalism was proposed with the aim of ontologically reducing computing to cognition. We defend, however, that cognition and computation are in action, thus cognition is just a form of computing and that cognition is the explanatory basis for computation. We state that: 1. Representational theories of computing recurring to intentional content run into metaphysical problems. 2. Functional non-representational theories do not incur this metaphysical problem when describing computing in terms of the abstract machine. 3. Functional theories are consistent with enactive in describing computing machines not in a strictly functional way, but especially in terms of their organization. 4. Enactive cognition is consistent with the computationalism in describing Turing machines as functionally and organizationally closed systems. 5. The cognitive explanatory basis for computing improves the computational theory of cognition. When developed in the human linguistic domain, computer science is seen as a product of human socionatural normative practices, however, cognition is just an explanatory, not ontological, basis for computing. The paper concludes by supporting that computation is in action, that cognition is just one form of computing in the world and the explanatory basis for computation. [ABSTRACT FROM AUTHOR]

The requirements for machining large size and high precision optical components are increasing with the rapid development of optical systems. In this paper, the process parameters (polishing paths, polishing path pitch and polishing inclination, etc.) of machining large-size optical convex lenses are optimized. The results show that the spiral path is the best pattern for uniformity of material removal at the corners, followed by Hilbert, while the scanning path has serious material removal non-uniformity at the corners. The influence of polishing path pitch on the uniformity of material removal was analyzed by theoretical calculations and the adjacent path pitch is optimized. Moreover, the effect of polishing inclination on machining accuracy is analyzed. In addition to the above parameters, the process parameters (spindle speed and feed speed, etc.) of ultrasonic vibration-assisted polishing were also optimized. A diameter of 300 mm large-size optical convex lens was polished, and excellent surface roughness (8 nm) was obtained by optimized process parameters. The novel method not only provides a theoretical basis for achieving deterministic removal, but also provides guidance for machining large-size workpieces. It also provides the basic theory for machining large-size, high-precision optical components. [ABSTRACT FROM AUTHOR]

Since the robotic capturing of a space tumbling object requires a rapidly accurate prediction of the object motion, this paper addresses the nonlinear multi-step motion prediction issue of the object based on machine learning theory and proposes a novel prediction approach, in which the prediction efficiency and the prediction accuracy are main focuses. To improve the prediction efficiency, sparse Gaussian processes are employed for the multi-step motion prediction, only providing the historical data of the object motion states. The sparse pseudo training data continuously optimized from real historical data makes it feasible for real-time predictions. To improve the prediction accuracy, a heuristic optimizer, Markov chain Monte Carlo, is integrated within sparse Gaussian processes to overcome the sensitivity problem caused by random initializations during the training process, thus avoids the local optimal training results. Then, a high-quality sparse pseudo training data is obtained to construct the predictive distributions during the testing process. The benchmark example and comparisons of several motion prediction applications illustrate the correctness and effectiveness of the proposed approach, and the experimental validation demonstrates its real-world potential. [ABSTRACT FROM AUTHOR]

A standard assumption of the theory of machine learning is the data are generated from a fixed but unknown probability distribution. Although this assumption is based on the foundations of the theory of probability, however, for most learning problems we usually technically random shuffle the original datasets, such as random split into training and test datasets before the training model, to satisfy the assumption, and then we use the shuffled training dataset to train a machine learning model. However, honestly, for real-life learning applications, the data pairs are observed batch by batch under their own original order and it is not necessary to randomly shuffle the original order in advance. From a mathematical point of view, we test if the random shuffling will play a non-negligible influence on the generalization of learning machines. We reduce the problem of random shuffling into the problem of distribution-shift detection. This paper is devoted to testing the null hypothesis that random shuffling does not affect the generalization of learning machines and introduces a distributions-free martingales method against the null hypothesis. We report the five real-life benchmarks of experimental performance with the help of Support Vector Machines and a multi-layer perceptron model. The results show a bonafide fact that the distribution shift in itself of the data is an inescapable reality when we build machine learning algorithms as the original order. [ABSTRACT FROM AUTHOR]

Signals used for Earth observation, when travelling through the atmosphere, are sensitive to refractivity; especially high spatio-temporal variations of water vapor are difficult to model and correct. Remaining unmodeled tropospheric delays deteriorate the positioning solution and therefore limit the accuracy of sensing and navigation applications. These delays are usually computed with empirical models based on ground meteorological parameters (pressure, temperature and water vapor partial pressure). However, existing models are not accurate enough for high-precision applications such as GNSS, where in consequence the so-called zenith total delay (ZTD) has to be estimated together with other unknown parameters (coordinates etc.). For decades, the Institute of Geodesy and Photogrammetry at ETH Zurich has been studying collocation methods for the modeling of tropospheric delays using meteorological parameters, successfully interpolating pointwise or integral atmospheric observations. Meanwhile, machine learning has become a widely used and valuable alternative, when big datasets are available for the training process. Indeed, we have already successfully predicted ZTDs based on meteorological parameters with an accuracy of 1–2 cm for locations (GNSS stations) already used in the training phase. However, difficulties arise to predict delays at new locations. In this work, we take a step forward in investigating the combination of machine learning algorithms and physical models used in a collocation approach to derive atmospheric delay fields at a very high resolution. Thus, without processing any GNSS data we can predict tropospheric delay fields everywhere in the area of investigation. In this paper, we firstly describe the designed architecture of the neural network, secondly, the combination of least-squares collocation and artificial neural networks for high-resolution prediction of tropospheric delays. We benefit from the complementary characteristics of these algorithms. While machine learning is capable of successfully predicting the variation of time series for given points, empirical models based on collocation are well suited for describing spatial variations within the area of investigation. Finally, we report the achieved performance for the entire territory of Switzerland (1–2 cm in terms of RMS), showing that the synergic combination of these algorithms can overcome the individual drawbacks of each method and provide more accurate delay estimates than either method individually. Datasets of 11 years, covering the territory of Switzerland, consisting of GNSS ZTDs from 72 permanent AGNES/COGEAR (swisstopo, ETHZ) stations and meteorological data from MeteoSwiss were used for this research. • Machine learning to predict time series of GNSS tropospheric delays. • The prediction of tropospheric delays at new locations is difficult. • Least-squares collocation can successfully interpolate in space tropospheric delays. • Combination of machine learning and collocation (physical models) methods. • High-resolutions spatio-temporal tropospheric fields. [ABSTRACT FROM AUTHOR]

In recent years, information systems have become large-scale and complicated. The demand for research on the cause analysis of information systems that can fail and the construction of countermeasures has been increasing. Systems Theoretic Accident Model and Processes (STAMP) is an accident model based on systems theory. STAMP has the feature that it can analyze not only failures of system components and human errors but also hazards caused by unsafe interaction between components and between components and humans. An analysis method based on the STAMP model System-Theoretic Process Analysis (STPA) is a method for analyzing in advance the possibility of a system accident for the interaction between a controller and a controlled device. As a preliminary study, we have performed STAMP/STPA to 'Fallen Barrier Trap at Railroad Crossing," which is an example of STAMP/STPA analysis. We have analyzed with the time automaton model checker UPPAAL in cooperation with STAMP Workbench, a STAMP/STPA support tool. From the preliminary study, we consider that the analysis procedure can be automated in order to reduce the burden on analysts. In this paper, we propose a method for deriving hazard transition sequences as a STAMP/STPA support method. Specifically, the model definition is described in STAMP Workbench, the method of deriving files that can be used in UPPAAL from the definition statement is described, and in order to automate model checking, the SAT/SMT solver is used. The constraints for the clock variables in each component are automatically derived. This paper describes a method for deriving the shortest path as a counterexample that does not satisfy the safety constraint equation by using the calculation method of the concrete value the constraints of and the binary search method. The proposed method is applied manually as the evaluation experiment. We confirmed that an effective sequence corresponding to some hazard scenarios derived by the conventional procedure could be derived. [ABSTRACT FROM AUTHOR]

The mixed information stream of reliable, semi-reliable and obviously false data observed in the network is a data flow in the so-called facilitated format, the task of which is to somehow attract attention, influence the emotional sphere in the form of hype-information, which helps to make hasty decisions and harm-actions. A growing variety of forms of technological push-impact, which are successfully used for commercial purposes. Nevertheless, the study of various push-forms shows that they are based on a single semantic component that uses the offset of actual concepts to achieve the benefit of the information provider. The consumer of push services receives an apparent benefit that is far from his actual interests and intentions. A semantic computational model is proposed that captures the emerging bias of concepts. The concept of a semantic broadcasting channel is developing. The paper considers the task of constructing specialized semantic systems in the form of information graphs. The information graphs are considered as structured sets of vertices that define the method of processing information and are connected by arcs, describing the sequence of processing. In its turn, an information graph can be used as an arc, which gives hyper-graph nature to the network in the whole. The information graphs are compared with other data flow control tools. The tools for constructing transformational semantics for calculations above information graphs are offered, which assume the specialization for the class of computations associated with the vertices of the graph. A method for making an abstract computing machine with a given semantics is proposed. The paper considers an example of making an abstract machine for the case of deterministic semantics, which is illustrative for certain fragments of antimonopoly legislation. [ABSTRACT FROM AUTHOR]

The thermal property of the scum layer (soil-rock mixtures) has dominant influence on the heat exchange efficiency between the lower rock layer and the upper environment in the open-pit mines of the cold regions. This paper presents a series of thermal conductivity tests (560 samples) on the scum particle to investigate the coupling effects of ice (moisture) content, temperature, and particle size distribution on the thermal properties. Previously reported models (47 empirical or theoretical models) were adopted to predicate the thermal conductivity of soil-rock mixtures in order to validate the evaluation ability of these models under the wide testing ranges. The comparison results indicate that the theoretical models, normalized model and linear/non-linear models all can not fully predict experimental results under the wide testing conditions. Three machine learning algorithms were used in the assessment presentation for the thermal properties of soil-rock mixtures. The performance of three machine learning algorithms were contrastively examined by using three important indexes (the coefficient of determination (R2), the root mean square error (RMSE) and the relative error (RE)). Based on the evaluation results, the performance ranking of three machine learning algorithms can be listed (GA-BP > SVR > RFR). This investigation is a beneficial attempt for the large data analysis to introduce the machine learning method into the determination of the thermal conductivity of soil-rock mixture under complex conditions. • A series of thermal conductivity tests were conducted to study the coupling effects of moisture content, temperature, and particle size distribution on the thermal properties of soil-rock mixtures. • 47 empirical or theoretical models were adopted to predicate the thermal conductivity of soil-rock mixtures in order to validate the evaluation ability of these models under the wide testing ranges. • Three machine learning algorithms were used in the assessment presentation for the thermal properties of soil-rock mixtures. [ABSTRACT FROM AUTHOR]

Abstract In ultra-precision machining, vibration is the key factor which restricts the machining accuracy and surface quality of a workpiece. Using the traditional active negative-stiffness vibration control technology, the simultaneous suppression of the floor vibration interference and payload direct interference is difficult. The ability of the conventional vibration isolation system to inhibit the direct disturbance of a payload deteriorates during the suppression of the low-frequency interference, and the residual low-frequency vibration severely restricts further improvement of the machining accuracy. To solve this problem, this paper proposes a novel active vibration control method of absolute displacement feedback based on the blending of infinite and zero stiffness. The absolute displacements of the payload and floor are considered as the feedback signals, and through the series and parallel connections of positive and negative stiffness, the equivalent stiffness between an isolated payload and reference point and between the isolated payload and floor tends to infinity and zero, respectively. The infinite and zero-stiffness blending control is realized, and the direct interference of the payload and floor vibrations at low frequencies (< 2 Hz) is effectively suppressed simultaneously. Finally, the effectiveness of the active vibration control method based on infinite and zero stiffness blending is verified by experiments. It is demonstrated to provide a good working environment for the precision and surface quality of ultra-precision workpieces. The active vibration control method proposed in this paper provides a feasible way to improve the performance of ultra-precision machine tools. Highlights • Payload disturbances and floor vibrations are suppressed simultaneously. • The equivalent stiffness tends to infinity and zero, respectively. • Absolute displacement is estimated by integrating the absolute velocity. [ABSTRACT FROM AUTHOR]

Abstract A fusion of learning automata and Petri nets, referred to as APN-LA, has been recently introduced in the literature for achieving adaptive Petri nets. A number of extensions to this adaptive Petri net have also been introduced; together we name them the APN-LA family. Members of this family can be utilized for solving problems in the domain of graph problems; each member is suitable for a specific category within this domain. In this paper, we aim at generalizing this family into a single framework, called generalized APN-LA (GAPN-LA), which can be considered as a framework for solving graph-based problems. This framework is an adaptive Petri net, organized into a graph structure. Each place or transition in the underlying Petri net is mapped into exactly one vertex of the graph, and each vertex of the graph represents a part of the underlying Petri net. A vertex in GAPN-LA can be considered as a module, which, in cooperation with other modules in the framework, helps in solving the problem at hand. To elaborate the problem-solving capability of the GAPN-LA, several graph-based problems have been solved in this paper using the proposed framework. [ABSTRACT FROM AUTHOR]

Abstract In multi-view modeling (MVM) the system under development is described by distinct models, called views, which capture different perspectives of the system. Possible overlaps of the views may give rise to inconsistencies. Following the formal MVM framework of [33] , the view consistency problem asks to check the consistency of a given set of views with respect to a given set of abstraction functions. Existing work checks view consistency of discrete systems (transition systems or finite automata) with respect to two types of abstraction functions: (1) projections of state variables and (2) projections of an alphabet of events onto a subalphabet. In this paper, we study view consistency with respect to timing abstractions, specifically, periodic sampling , for automata and transition systems. Highlights • Multi-view modeling in a multi-periodic sampling abstraction setting. • Necessary and sufficient conditions for view consistency. • Sound and complete method to check view consistency on Buchi automata. • Sound and complete method to check view consistency on symbolic transition systems. • Complexity analyses for the various algorithms in the paper. [ABSTRACT FROM AUTHOR]

Highlights • And-cross transitions are a useful abstraction that can make some constructs easier to specify for the modeler. • They can be transformed to and from alternative modeling solutions that require a greater number of transitions. • With fewer transitions, the time taken and memory required to certify a model to be deterministic using formal methods can be much reduced compared to other approaches. This is related to the time and memory required to compute the set of pairs of potentially conflicting transitions. Abstract In this paper, we present an approach to formally encode state machines expressed in Umple for symbolic verification. We illustrate this with a real-world modeling example that encodes and analyzes and-cross transitions. This paper discusses a formal description of our approach to represent state machine systems under analysis (SSUAs); a systematic approach to certifying that SSUAs are deterministic; and an evaluation of performance (memory usage and execution time) on the case study. Method We describe a formalization of state machines in Umple that enables their translation to model checking tools and also to code that is consistent with this. We present three alternative modeling solutions for a sample problem and a solution based on the use of and-cross transitions. State machine models corresponding to these solutions are represented in Umple, a model-oriented programming language. These are automatically transformed to SMV, the input language of the nuXmv (or NuSMV) model checker. By cleanly separating concerns, we systematically integrate components of hierarchical state systems as opposed to the traditional flattening approach, yet manage the complexity introduced by concurrency and and-crossing. We then compose and verify a set of requirements (e.g., correctness, safety, liveliness, etc.) on the resulting systems of all the modeling approaches to empirically compare the different modeling alternatives with the use of and-cross transitions. Results We can encode and formally analyse complex state machines with and-cross transition(s). We observed a large reduction in the number of required transitions for encoding the SSUA, as opposed to the alternative approaches. We asserted that solutions derived from the approaches are identical behavior-wise even though each approach models the SSUA differently. Each of the approaches yielded the same result for potentially conflicting pairs which is a false positive (i.e., the SSUAs are deterministic). We observe that each approach maintains the same global state-space irrespective of the variations in their number of transitions. Furthermore, we observe that it is untrue that a more abstract method applied to an SSUA outperforms its less abstract counterpart whenever parameters (such as execution time, memory usage and the number of Binary Decision Diagrams - BDDs) are the factors under consideration. Contributions A systematic approach to encode state machines with and-cross transitions (including unusual transitions). An enhanced but fully automated approach to discovering nondeterminism in state machines even in the presence of unbounded domains and multiple and-cross transitions within the same enclosing orthogonal state. An empirical study of the impact of abstraction on some performance parameters. We also present an extended formalization of Umple state machines. [ABSTRACT FROM AUTHOR]

Time series anomaly detection aims to find specific patterns in time series that do not conform to general rules, which is one of the important research directions in machine learning. However, most existing time series anomaly detection methods solely consider the original observed state space. In this paper, we investigate the different views of the original signals by reconstructing them from the different transformations to learn better comprehensive representations of normal patterns. Specifically, we propose a time series anomaly detection model based on self-supervised multi-transformation learning while jointly learning the noise and filter transformation of the normal time series and capturing the anomaly simultaneously in both transformation patterns. Firstly, the model randomly adds noise to the original signal to construct the noisy signal, and the Kalman filter is applied to the original signal to construct the filtered signal. The noisy and filtered signals are subsequently used to reconstruct the original signal, and the randomly added noise is classified. Finally, the model captures the two transformation patterns of normal time series signals and transforms the anomaly detection problem into the reconstruction metric problem of time series signals under noise and filter transformations. The effectiveness of the proposed method is verified on seven time series datasets. The source code is available at https://github.com/hh2668790143/MTAE. • More comprehensive learning of time series features. • Multi-transformation patterns are constructed by adding noise and filtering. • Multi-transformation learning can learn time series more comprehensively. • Self-supervised noise classification tasks can optimize the learning process. • The effectiveness of the method is verified on seven time series datasets. [ABSTRACT FROM AUTHOR]

In this paper we present a novel approach for solving the consequent part of neuro-fuzzy modeling with an emphasis on the forgetting factor in the multi-class learning problem. Our solution is based on recursive least squares (RLS) for online and incremental learning applications, where the data stream is not necessarily uniformly distributed over time. Such a setup can lead to forgetting of specific classes that have not been used for a period of time. In this work we present a reasoned and detailed description of elastic memory learning (EML) and EML with the use of confidence interval (EML+) to avoid unnecessary treatment of the forgetting factor. We present the experimental results and evaluation of our methods in order to show their usefulness not only against forgetting of unused classes, but also for dealing with the lowered recognition rate after all classes have been learned. We note that by using EML, forgetting is significantly eliminated and the recognition rate is slightly affected as well, while EML+ puts more emphasis on keeping the recognition rate higher than the forgetting. Thus, this paper presents two methods that significantly eliminate the forgetting factor for incremental learning with a different focus on its importance, i.e. high recognition rate vs high immunity to forgetting; both of these methods perform significantly better than RLS for these aspects. [ABSTRACT FROM AUTHOR]

The modal mu-calculus is a very expressive formalism extending basic modal logic with least and greatest fixpoint operators. In the seminal paper introducing the formalism in the shape known today, Kozen also proposed an elegant axiom system, and he proved a partial completeness result with respect to the Kripke-style semantics of the logic. The problem of proving Kozen's axiom system complete for the full language remained open for about a decade, until it was finally resolved by Walukiewicz. In this paper we develop a framework that will let us clarify and simplify parts of Walukiewicz' proof. Our main contribution is to take the automata-theoretic viewpoint, already implicit in Walukiewicz' proof, much more seriously by bringing automata explicitly into the proof theory. Thus we further develop the theory of modal parity automata as a mathematical framework for proving results about the modal mu-calculus. Once the connection between automata and derivations is in place, large parts of the completeness proof can be reformulated as purely automata-theoretic theorems. From a conceptual viewpoint, our automata-theoretic approach lets us distinguish two key aspects of the mu-calculus: the one-step dynamics encoded by the modal operators, and the combinatorics involved in dealing with nested fixpoints. This “deconstruction” allows us to work with these two features in a largely independent manner. More in detail, prominent roles in our proof are played by two classes of modal automata: next to the disjunctive automata that are known from the work of Janin & Walukiewicz, we introduce here the class of semi-disjunctive automata that roughly correspond to the fragment of the mu-calculus for which Kozen proved completeness. We will establish a connection between the proof theory of Kozen's system, and two kinds of games involving modal automata: a satisfiability game involving a single modal automaton, and a consequence game relating two such automata. In the key observations on these games we bring the dynamics and combinatorics of parity automata together again, by proving some results that witness the nice behaviour of disjunctive and semi-disjunctive automata in these games. As our main result we prove that every formula of the modal mu-calculus provably implies the translation of a disjunctive automaton; from this the completeness of Kozen's axiomatization is immediate. [ABSTRACT FROM AUTHOR]

The primary purpose of this paper is to realize robust place recognition algorithms towards simultaneous viewpoint and condition changes, and provide satisfactory computational efficiency. In this paper, we significantly improve the viewpoint invariance of the SeqSLAM algorithm by using state-of-the-art deep learning techniques to generate robust feature representations of images and develop the SeqCNNSLAM . Experimental results show that SeqCNNSLAM outperforms state-of-the-art place recognition systems in most cases, such as, when precision is maintained at 100%, the maximum recall obtained by SeqCNNSLAM is 50% higher than SeqSLAM on the Norland dataset with simultaneous condition change and 12.5% viewpoint change. Besides, we develop an acceleration method called A-SeqCNNSLAM , which exploits the location relationship between the matching images of adjacent images to reduce the matching range of the current image. Experimental results demonstrate that an acceleration of  ∼  5 times is achieved with minimal accuracy degradation of  ∼  5%. Finally, to enable A-SeqCNNSLAM adaptability in new environments, O-SeqCNNSLAM is devised for the online parameter adjustment in A-SeqCNNSLAM. [ABSTRACT FROM AUTHOR]

Distributed algorithms have become a rapidly growing field of research due to the advances of the network technologies. However, they are very difficult to implement correctly because they must meet many requirements. In this paper, we follow the guidelines of systematic literature reviews to provide a survey of the existing works ensuring the formal verification of distributed algorithms in static and dynamic networks. Then, we develop a taxonomy of these solutions based on some criteria. Also, a discussion on each criterion is shown with a focus on constraints, requirements and challenges. Finally, we identify some recommendations and open research areas which can motivate the development of more efficient solutions. So, throughout this present paper, we provide information for researchers and developers to understand the contributions and challenges of the existing solutions to pave the way for enhancing their reliability. [ABSTRACT FROM AUTHOR]

The increasing demands of quality and productivity, together with the Internet of Things (IoT) technology, promote the development of intelligent manufacturing where supervisory system plays an increasing role. Verification of supervisory rules to ensure the system is error-free becomes essential. A timed automata (TA) formalism is one of the most widespread approach appropriate for the system modeling and verification. This paper proposes an approach for modeling and verification supervisory rules in production process. The behavior models of entities are translated into timed automata models. The timed automata models of all entities in workspace construct a synchronized network, which can simulate the occurrences in production process. In addition, the paper presents a method of building checking automata. Comparison of the result from supervisory system with the result from checking automata can test the supervisory rules, and analysis of the simulation process can discover the reasons of existing problems. Experiment result shows that the approach is effective in the motivating scenario by finding the potential deficiency in supervisory rules. [ABSTRACT FROM AUTHOR]

Nowadays, industries are using product quality certifications to promote their products. This is a time taking process and requires the assessment given by human experts which makes this process very expensive. This paper explores the usage of machine learning techniques such as linear regression, neural network and support vector machine for product quality in two ways. Firstly, determine the dependency of target variable on independent variables and secondly, predicting the value of target variable. In this paper, linear regression is used to determine the dependency of target variable on independent variables. On the basis of computed dependency, important variables are selected those make significant impact on dependent variable. Further, neural network and support vector machine are used to predict the values of dependent variable. All the experiments are performed on Red Wine and White Wine datasets. This paper proves that the better prediction can be made if selected features (variables) are being considered rather than considering all the features. [ABSTRACT FROM AUTHOR]

Real time anomaly detection is important to performance and efficiency in many areas. This paper offers a complex method for detecting abnormal telecommunication traffic. The proposed method includes components based on entropy analysis, signature analysis and machine-learning detection of anomalies using fractal and recurrence analysis. Datasets containing realizations of normal traffic and realizations of attacks of different types were used as input data. Traffic containing the attack is considered to be abnormal. The multifractal and recurrence methods are briefly described. The results of multifractal and recurrence analysis of data-set traffic showed significant differences between normal and attacked realizations. Based on the obtained results, machine-learning algorithms are proposed. The classifiers random forest and neural network were used and showed good classification accuracy. The methods signature and entropy analysis are briefly described, and the results of their application showed a high degree of anomaly detection. The complex anomaly detection method combines entropy analysis, signature analysis, and machine-learning using the multifractal and recurrence analysis. The proposed complex method is tested on virtual network and is compared pervious methods. Simulation results showed that the proposed complex method has the highest percentage of detected attacks, the lowest percentage of undetected attacks and lost data. [ABSTRACT FROM AUTHOR]

Natural Language Processing (NLP) applications such as text categorization, machine translation, sentiment analysis, etc., need annotated corpora and lexicons to check quality and performance. This paper describes the development of resources for sentiment analysis specifically for Arabic text in social media. A distinctive feature of the corpora and lexicons developed are that they are determined from informal Arabic that does not conform to grammatical or spelling standards. We refer to Arabic social media content of this sort as Dialectal Arabic (DA) - informal Arabic originating from and potentially mixing a range of different individual dialects. The paper describes the process adopted for developing corpora and sentiment lexicons for sentiment analysis within different social media and their resulting characteristics. The addition to providing useful NLP data sets for Dialectal Arabic the work also contributes to understanding the approach to developing corpora and lexicons. [ABSTRACT FROM AUTHOR]

To numerically solve the Burgers’ equation, in this paper we propose a general method for constructing wavelet bases on the interval [ 0 , 1 ] derived from symmetric biorthogonal multiwavelets on the real line. In particular, we obtain wavelet bases with simple structures on the interval [ 0 , 1 ] from the Hermite cubic splines. In comparison with all other known constructed wavelets on the interval [ 0 , 1 ] , our constructed wavelet bases on the interval [ 0 , 1 ] from the Hermite cubic splines not only have good approximation and symmetry properties with extremely short supports, but also employ a minimum number of boundary wavelets with a very simple structure. These desirable properties make them to be of particular interest in numerical algorithms. Our constructed wavelet bases on the interval [ 0 , 1 ] are then used to solve the nonlinear Burgers’ equation. Our method is based on the finite difference formula combined with the collocation method. Therefore, our proposed numerical scheme in this paper is abbreviated as MFDCM (Mixed Finite Difference and Collocation Method). Some numerical examples are provided to demonstrate the validity and applicability of our proposed method which can be easily implemented to produce a desired accuracy. [ABSTRACT FROM AUTHOR]

Most of the proposed algorithms to solve the dynamic clustering problem are based on nature inspired meta-heuristic algorithms. In this paper a different reinforcement based optimization approach called continuous action-set learning automata (CALA) is used and a novel dynamic clustering approach called ACCALA is proposed. CALA is an optimization tool interacting with a random environment and learn the optimal action from the environment feedbacks. In this paper the dynamic clustering problem considered as a noisy optimization problem and the team of CALAs is used to solve this noisy optimization problem. To build such a team of CALAs this paper proposed a new representation of CALAs. Each automaton in this team uses its continuous action-set and defining a suitable action-set for each automaton has a great impact on the CALAs search behavior. In this paper we used the statistical property of data-sets and proposed a new method to automatically find an action-set for each automaton. The performance of ACCALA is evaluated and the results are compared with seven well-known automatic clustering techniques. Also ACCALA is used to perform automatic segmentation. The experimental results are promising and show that the proposed algorithm produced compact and well-separated clusters. [ABSTRACT FROM AUTHOR]

In a fundamental paper in polyhedral combinatorics, Queyranne describes the complete facial structure of a classical object in combinatorial optimization, the single machine scheduling polytope. In the same paper, he answers essentially all relevant algorithmic questions with respect to optimization and separation. In the present paper, motivated by recent applications in the design of optimal incentive compatible mechanisms, we address an algorithmic question that was apparently not addressed before. Namely, we turn Carathéodory’s theorem into an algorithm, and ask to write an arbitrary point in the scheduling polytope as a convex combination of the vertices of the polytope. We give a combinatorial O ( n 2 ) time algorithm, which is linear in the naive encoding of the output size. We obtain this result by exploiting the fact that the scheduling polytope is a zonotope, and by the observation that its barycentric subdivision has a simple, linear description. The actual decomposition algorithm is an implementation of a method proposed by Grötschel, Lovász and Schrijver, applied to one of the subpolytopes of the barycentric subdivision. We thereby also shed new light on an algorithm recently proposed for a special case, namely the permutahedron. [ABSTRACT FROM AUTHOR]

Protein-protein interactions (PPIs) are of biological interest for their active participation in coordinating a number of cellular processes in living organisms. This paper attempts to formulate PPIs as an optimization problem with an aim to independently maximize (a) the stability of a complex formed by two proteins predicted to be interacting, (b) the difference between their individual accessible solvent area and that of the corresponding protein-protein complex, (c) their functional similarity and d) the occurrence of their interacting domain pairs. The novelty of the paper lies in ranking the set of PPI networks, obtained through independently optimizing individual objectives, using two approaches. The first approach is concerned with identifying the equally good PPI networks based on their fitness-based ranks with respect to individual four objectives. The second approach aims at sorting the PPI networks based on their fuzzy memberships to satisfy individual four objectives. The paper also proposes a novel single objective optimization algorithm to optimize individual objectives, influencing the true prediction of a PPI network. The proposed algorithm is realized by an amalgamation of the differential evolution and the stochastic learning automata, where the former is employed to globally explore the search space and the latter for the adaptive tuning of the control parameter of the algorithm. The proposed technique outperforms the existing methods, including relative specific similarity, domain cohesion coupling, random decision forest, fuzzy support vector machine and evolutionary/swarm algorithm based approaches, with respect to both sensitivity and specificity. [ABSTRACT FROM AUTHOR]

This paper focuses on the blocking flow shop scheduling problem with the objective of total flowtime minimisation. This problem assumes that there are no buffers between machines and, due to its application to many manufacturing sectors, it is receiving a growing attention by researchers during the last years. Since the problem is NP-hard, a large number of heuristics have been proposed to provide good solutions with reasonable computational times. In this paper, we conduct a comprehensive evaluation of the available heuristics for the problem and for related problems, resulting in the implementation and testing of a total of 35 heuristics. Furthermore, we propose an efficient constructive heuristic which successfully combines a pool of partial sequences in parallel, using a beam-search-based approach. The computational experiments show the excellent performance of the proposed heuristic as compared to the best-so-far algorithms for the problem, both in terms of quality of the solutions and of computational requirements. In fact, despite being a relative fast constructive heuristic, new best upper bounds have been found for more than 27% of Taillard’s instances. [ABSTRACT FROM AUTHOR]

The sentiment classification method is a research field that is proliferating in Indonesia since it is fast in extracting public opinion and provides essential and valuable information for stakeholders. Of the best-performing sentiment classification approaches, machine learning is one of them that has excellent performance. However, the method has several problems, such as noisy features and high dimensionality of features that significantly affect the sentiment classification performance. Therefore, to overcome the problems, this paper presents a novel feature selection using a combination of Query Expansion Ranking (QER) and Genetic Algorithm-Support Vector Machine (GA-SVM) for improving sentiment classification performance. Based on the experimental results, the proposed method could significantly improve sentiment classification performance, outperform all state-of-the-art algorithms, and decrease computational time. The method achieved the best performance in average precision, recall, and f-measure with the value of 96.78%, 96.76%, and 96.75%, respectively. [ABSTRACT FROM AUTHOR]

Random field Monte Carlo (MC) reliability analysis is a robust stochastic method to determine the probability of failure. This method, however, requires a large number of numerical simulations demanding high computational costs. This paper explores the efficiency of machine learning (ML) models and Artificial Neural Networks used as surrogate models trained on a limited number of random field slope stability simulations in predicting the results of large datasets. The paper explores the efficiency of the predictions on the probability of failure using databases with and without factor of safety (FOS) computations. An extensive range of soil heterogeneity and anisotropy is examined on unstratified and layered slopes. On datasets requiring only the examination of failure or non-failure class of slopes (without FOSs), the performance of ML classification of the random field slope stability results generally reduces with higher anisotropy and heterogeneity of the soil. However, using the probability summation method proposed here, ML prediction of the probability of failure is shown to be highly accurate for the whole range of soil heterogeneity and anisotropy. The errors in the predicted probability of failure using 5% of MC data is only 0.46% in average for the prediction of the remaining unseen 95% of data. Offering such accuracies, the approach accelerates the computations for about 100 folds. The models also proved similarly efficient in predicting FOSs for stratified random field anisotropic heterogenous slopes. [ABSTRACT FROM AUTHOR]

This paper considers a class of multi-objective production–distribution scheduling problem with a single machine and multiple vehicles. The objective is to minimize the vehicle delivery cost and the total customer waiting time. It is assumed that the manufacturer’s production department has a single machine to process orders. The distribution department has multiple vehicles to deliver multiple orders to multiple customers after the orders have been processed. Since each delivery involves multiple customers, it involves a vehicle routing problem. Most previous research work attempts at tackling this problem focus on single-objective optimization system. This paper builds a multi-objective mathematical model for the problem. Through deep analysis, this paper proposes that for each non-dominated solution in the Pareto solution set, the orders in the same delivery batch are processed contiguously and their processing order is immaterial. Thus we can view the orders in the same delivery batch as a block. The blocks should be processed in ascending order of the values of their average workload. All the analysis results are embedded into a non-dominated genetic algorithm with the elite strategy (PD-NSGA-II). The performance of the algorithm is tested through random data. It is shown that the proposed algorithm can offer high-quality solutions in reasonable time. [ABSTRACT FROM AUTHOR]

Semaphores were introduced by Dijkstra as a tool for modeling concurrency in computer programs. In this paper we provide a formal definition of PV-programs, i.e. programs using semaphores, their state spaces and execution spaces. The main goal of this paper is to prove that every finite homotopy type may appear as a connected component of the execution space of a PV-program. [ABSTRACT FROM AUTHOR]

This paper proposed a TRIZ-based systematic Device Trimming Algorithm with theory and application. The method can be used to resolve process-machine problems by re-designing the problematic processing machines or simply used to trim components for various purposes. The trimming process is orchestrated by a trimming plan which consists of sequenced trimming tasks. Elements of each trimming task include function carrier, useful functions, objects, trimming rules, new carriers, trimming problem statement, and trimming method. A 2-loop depth-first recursive trimming process is proposed to maximize the trimming effect. Applied on a slit-valve failure of a chemical vapor deposition equipment in one of major Taiwanese foundry companies, the proposed problem solving process successfully identified the critical key disadvantages of the problem and solved the slit-valve failure with breakthrough results. A number of solutions were generated by the integrated process which involves a number of TRIZ tools. This paper describes only the solution by the trimming process. The main contributions of this paper include:1) Establishing an integrated trimming process consistent with TRIZ problem-solving model and capable of breakthrough problem solving and cost savings; 2) Solving the slit-valve problem with 83.3% component count reduction, 95% component cost reduction, 99% operational energy reduction, and completely designed-out the original failure mode. The results have been converted into a patent pending approval. [ABSTRACT FROM AUTHOR]

The study of some close connections between the combinatorial properties of words and the performance of the automata minimization process constitutes the main focus of this paper. These relationships have been, in fact, the basis of the study of the tightness and the extremal cases of Hopcroft's algorithm, that is, up to now, the most efficient minimization method for deterministic finite state automata. Recently, increasing attention has been paid to another minimization method that, unlike the approach proposed by Hopcroft, is not based on refinement of the set of states of the automaton, but on automata operations such as determinization and reverse, and is also applicable to non-deterministic finite automata. However, even for deterministic automata, it was proved that the method incurs, almost surely, in an explosion of the number of the states. Very recently, some polynomial variants of Brzozowski's method have been introduced. In this paper, by using some combinatorial properties of words, we analyze the performance of one of such algorithms when applied to a particular infinite family of automata, called standard word automata , constructed by using standard sturmian words. In particular, Θ ( n log ⁡ n ) is the worst case time complexity when the algorithm is applied to the infinite family of word automata associated to Fibonacci words representing also the worst case of Hopcroft's minimization algorithm. [ABSTRACT FROM AUTHOR]

In software industry, automation plays a major role for improving the test efficiency of software test organisation. For ensuring test coverage satisfaction and thereby reducing risk, projects use more people for manual testing or use automation tools or techniques to improve the level of test automation. The choice of selection depends on reduction in project cycle time or reduction in time for test. This paper discusses about software automation steps and explains an algorithm for addressing the issues faced in system management automation of various services like DHCP, DNS, Firewall etc. Paper is written based on author's experience using various automation frame works. [ABSTRACT FROM AUTHOR]

It is known that a nondeterministic input-driven pushdown automaton (IDPDA) (a.k.a. visibly pushdown automaton; a.k.a. nested word automaton) with n states can be transformed to an equivalent deterministic automaton with 2 Θ ( n 2 ) states (B. von Braunmühl and R. Verbeek, 1983 [8] ), and that this size is necessary in the worst case (R. Alur and P. Madhusudan, 2009 [4] ). This paper demonstrates that the same worst-case 2 Θ ( n 2 ) size blow-up occurs when converting a nondeterministic IDPDA to an unambiguous one, and an unambiguous IDPDA to a deterministic one. In addition, the methods developed in this paper are used to demonstrate that the descriptional complexity of complementation for nondeterministic IDPDAs is 2 Θ ( n 2 ) , and that the descriptional complexity of homomorphisms for deterministic IDPDAs is 2 Θ ( n 2 ) . [ABSTRACT FROM AUTHOR]

This paper proposes an approach to verifying context free properties of programs. In this approach, the system to be verified is modeled as a program m in Modeling, Simulation and Verification Language (MSVL), and the desired property is also specified by an MSVL program m ′. Then program m and formula ¬ m ′ are interpreted by means of executing programs m and m ′. If an acceptable execution path is generated, a counterexample is found, otherwise the property is valid. To show how the proposed approach works, an example is given. [ABSTRACT FROM AUTHOR]

The pulsar is a highly magnetized rotating neutron star that provides the first indirect evidence for the existence of gravitational waves and also provides the possibility to reveal extreme phenomena in neutron star astrophysics. Therefore, the identification of pulsars in the universe is a prerequisite for the study of pulsars and gravitational waves. At present, a large number of pulsar searches have produced millions of pulsar candidates. In the face of these large-scale data, if only relying on manual visual classification by experts in related fields, it will be a huge project. Since the emergence of machine learning, its theory and technology have become increasingly mature, and has been successfully applied to astronomical research fields such as pulsar candidate screening. This paper introduces the related machine learning theory of pulsar candidate recognition firstly, and then reviews the research status of pulsar candidate recognition based on machine learning in recent years. Finally, we discuss and prospect the identification of pulsars in the future. [ABSTRACT FROM AUTHOR]

The paper investigates the closure of the language family defined by input-driven pushdown automata (IDPDA) under the following operations: insertion ins (L , K) = { x y z | x z ∈ L , y ∈ K } , deletion del (L , K) = { x z | x y z ∈ L , y ∈ K } , square root L = { w | w w ∈ L } , the first half 1 2 L = { u | ∃ v : | u | = | v | , u v ∈ L } and cyclic shift Image 1. For K and L recognized by nondeterministic IDPDA, with m and with n states, respectively, insertion requires exactly m n + 2 m states, as long as K is well-nested; deletion requires exactly 2 n states, for well-nested K ; square root requires n 3 − O (n 2) states, for well-nested L ; the well-nested subset of the first half is representable with 2 O (n 2) states; the well-nested subset of the cyclic shift requires exactly 2 n 2 states. Without the well-nestedness constraints, non-closure is established in each case. [ABSTRACT FROM AUTHOR]

A Conditional Simple Temporal Network with Uncertainty (CSTNU) is a formalism able to model temporal plans subject to both conditional constraints and uncertain durations. The combination of these two characteristics represents the uncontrollable part of the network. That is, before the network starts executing, we do not know completely which time points and constraints will be taken into consideration nor how long the uncertain durations will last. Dynamic Controllability (DC) implies the existence of a strategy scheduling the time points of the network in real time depending on how the uncontrollable part behaves. Despite all this, CSTNUs fail to model temporal plans in which a few conditional constraints are under control and may therefore influence (or be influenced by) the uncontrollable part. To bridge this gap, this paper proposes Conditional Simple Temporal Networks with Uncertainty and Decisions (CSTNUDs) which introduce decision time points into the specification in order to operate on this conditional part under control. We model the dynamic controllability checking (DC-checking) of a CSTNUD as a two-player game in which each player makes his moves in his turn at a specific time instant. We give an encoding into timed game automata for a sound and complete DC-checking. We also synthesize memoryless execution strategies for CSTNUDs proved to be DC and carry out an experimental evaluation with Image 1 , a tool that we have designed for CSTNUDs to make the approach fully automated. [ABSTRACT FROM AUTHOR]

Stock forecasting plays a pivotal role in time series forecasting as it enables informed and effective investment decisions by minimizing risks. In this paper, a novel hybrid model for stock price forecasting is proposed to explore the impact of a decomposition-reconstruction method fused with machine learning models, aiming to enhance the predictive ability of the model. Following the decomposition-prediction-reconstruction principle, the hybrid model incorporates wavelet transform, integrating Encoder Forest (EF) with Informer. To mitigate the influence of long-time series noise on stock forecasting, the original data is decomposed into high-frequency signal components (CD) and low-frequency signal components (CA). The Informer and Encoder Forest are trained to predict the future CA and CD, respectively. The hybrid model is implemented for all stocks in three industries of the china stock market. Several models including MLP, RNN, LSTM, Informer, WT + RNN + DT, WT + LSTM + RF, EMD + Informer + EF, VMD + Informer + EF, CEEMD + Informer + EF, and CEEMDAN + Informer + EF are designed as compared methods to verify the superiority and advancement of the proposed technique. Evaluating the performance of individual models reveals that the prediction accuracy follows the ranking: MLP < RNN < LSTM < Informer. Comparing the results of the hybrid model with those of the individual models demonstrates that the hybrid model improves prediction accuracy. This comparison also indicates that the wavelet transform and tree models can enhance the accuracy of the model without altering the initial ranking of the prediction effect. It is worth noting that WT and EMD-like methods employ different data decomposition approaches, leading to diverse outcomes. Experimental results indicate that WT is better suited for a hybrid model that combines two distinct methods. All experimental results indicated that the proposed hybrid model has higher prediction accuracy, stronger generalization ability, and stronger practicability, which is more suitable for Stock forecasting problems. [ABSTRACT FROM AUTHOR]

[Display omitted] Combustible gas is widely used in industry and daily life but presents an unavoidable explosion hazard. Predicting explosion overpressure and guiding engineering design is an effective way to reduce the consequences of explosions. This paper presents a novel approach that integrates explosive theory and data science. Starting from the first principle, a dimensionless mathematical formula was constructed based on the explosive theory. Gas species, gas concentration, container parameters, and ignition position were fully considered by the formula, and built into data sets using machine learning techniques as characteristic parameters. The SVR algorithm, the BP algorithm, and the DNN algorithm all attest to the superiority. And the predictive physical consistency of this method has been proven. Simultaneously, the dataset constructed by this method effectively reduces the number of features. The PSO algorithm was used for parameter optimization based on the DNN algorithm guided by theory for building F-P (First principle) PSO-DNN Model. In the comparison of prediction results, the mean relative error of the F-P PSO-DNN model is 15%, 19% lower than that of the best performing Molkov model. [ABSTRACT FROM AUTHOR]