Formal methods use mathematical models for analysis and verification at any part of the program life-cycle. We describe the state of the art in the industrial use of formal methods, concentrating on their increasing use at the earlier stages of specification and design. We do this by reporting on a new survey of industrial use, comparing the situation in 2009 with the most significant surveys carried out over the last 20 years. We describe some of the highlights of our survey by presenting a series of industrial projects, and we draw some observations from these surveys and records of experience. Based on this, we discuss the issues surrounding the industrial adoption of formal methods. Finally, we look to the future and describe the development of a Verified Software Repository, part of the worldwide Verified Software Initiative. We introduce the initial projects being used to populate the repository, and describe the challenges they address.

/pdf/formal-methods-practice-and-experience-2ooioh81ba.pdf

Formal methods: Practice and experience

The B Book

Today, science is passing through an era of transformation, where the inundation of data, dubbed data deluge is influencing the decision making process. The science is driven by the data and is being termed as data science. In this internet age, the volume of the data has grown up to petabytes, and this large, complex, structured or unstructured, and heterogeneous data in the form of “Big Data” has gained significant attention. The rapid pace of data growth through various disparate sources, especially social media such as Facebook, has seriously challenged the data analytic capabilities of traditional relational databases. The velocity of the expansion of the amount of data gives rise to a complete paradigm shift in how new age data is processed. Confidence in the data engineering of the existing data processing systems is gradually fading whereas the capabilities of the new techniques for capturing, storing, visualizing, and analyzing data are evolving. In this review paper, we discuss some of the modern Big Data models that are leading contributors in the NoSQL era and claim to address Big Data challenges in reliable and efficient ways. Also, we take the potential of Big Data into consideration and try to reshape the original operationaloriented definition of “Big Science” (Furner, 2003) into a new data-driven definition and rephrase it as “The science that deals with Big Data is Big Science.”

https://www.jstage.jst.go.jp/article/dsj/13/0/13_14-041/_pdf

A Brief Review on Leading Big Data Models

Recently, researchers have paid escalating attention to studying the emotional state of an individual from his/her speech signals as the speech signal is the fastest and the most natural method of communication between individuals. In this work, new feature enhancement using Gaussian mixture model (GMM) was proposed to enhance the discriminatory power of the features extracted from speech and glottal signals. Three different emotional speech databases were utilized to gauge the proposed methods. Extreme learning machine (ELM) and -nearest neighbor (NN) classifier were employed to classify the different types of emotions. Several experiments were conducted and results show that the proposed methods significantly improved the speech emotion recognition performance compared to research works published in the literature.

/pdf/improved-emotion-recognition-using-gaussian-mixture-model-4ext4ciw4r.pdf

Improved Emotion Recognition Using Gaussian Mixture Model and Extreme Learning Machine in Speech and Glottal Signals

UML-B is a `UML-like' graphical front end for Event-B that provides support for object-oriented modelling concepts. In particular, UML-B supports class diagrams and state machines, concepts that are not explicitly supported in plain Event-B. In Event-B, refinement is used to relate system models at different abstraction levels. The same abstraction-refinement concepts can also be applied in UML-B. This paper introduces the notions of refined classes and refined state machines to enable refinement of classes and state machines in UML-B. Together with these notions, a technique for moving an event between classes to facilitate abstraction is also introduced. Our work makes explicit the structures of class and state machine refinement in UML-B. The UML-B drawing tool and Event-B translator are extended to support the new refinement concepts. A case study of an auto teller machine (ATM) is presented to demonstrate application and effectiveness of refined classes and refined state machines.

/pdf/language-and-tool-support-for-class-and-state-machine-59mmeeyl6m.pdf

Language and Tool Support for Class and State Machine Refinement in UML-B

Emotion expression is an essential function for daily life that can be severely affected some psychological disorders. In this paper we identified seven emotional states anger,surprise,sadness ,happiness,fear,disgust and neutral.The definition of parameters is a crucial step in the development of a system for emotion analysis.The 15 explored features are energy intensity,pitch,standard deviation,jitter,shimmer,autocorrelation,noise to harmonic ration,harmonic to noise ration,energy entropy block,short term energy,zero crossing rate,spectral roll-off,spectral centroid and spectral flux,and formants In this work database used is SAVEE(Surrey audio visual expressed emotion).Results by using different learning methods and estimation is done by using a confidence interval for identified parameters are compared and explained.The overall experimental results reveals that Model 2 and Model 3 give better results than Model 1 using learning methods and estimation shows that most emotions are correctly estimated by using energy intensity and pitch.

Understanding and estimation of emotional expression using acoustic analysis of natural speech

System availability is improved by the replication of data objects in a distributed database system However, during updates, the complexity of keeping replicas identical arises due to failures of sites and race conditions among conflicting transactions Fault tolerance and reliability are key issues to be addressed in the design and architecture of these systems Event B is a formal technique which provides a framework for developing mathematical models of distributed systems by rigorous description of the problem, gradually introducing solutions in refinement steps, and verification of solutions by discharge of proof obligations In this paper, we present a formal development of a distributed system using Event B that ensures atomic commitment of distributed transactions consisting of communicating transaction components at participating sites This formal approach carries the development of the system from an initial abstract specification of transactional updates on a one copy database to a detailed design containing replicated databases in refinement Through refinement we verify that the design of the replicated database confirms to the one copy database abstraction

/pdf/rigorous-design-of-fault-tolerant-transactions-for-4ygynegpl2.pdf

Rigorous design of fault-tolerant transactions for replicated database systems using event b

Lane detection is one of the most challenging problems in machine vision and still has not been fully accomplished because of the highly sensitive nature of computer vision methods. Computer vision depends on various ambient factors. External illumination conditions, camera and captured image quality etc. effect machine vision performance. Lane detection faces all these challenges as well as those due to loss of visibility, types of roads, road structure, road texture and other obstacles like trees, passing vehicles and their shadows. There are several lane detection methods having their own working principles and backgrounds, merits and demerits.

Analysis of lane detection techniques using openCV

Model of Causal Order First Refinement VARIABLES sender , receive , order INVARIANT order e MESSAGE 1 MESSAGE If M1 k M2 and P has received M2, then P must have received M1 If M1 k M2 and P has sent M2, then P must have sent or received M1 order is transitive INITIALISATION sender := 0 || receive := 0 || order := 0 Abstract Model of Causal Order First Refinement VARIABLES sender , receive , order INVARIANT order e MESSAGE 1 MESSAGE (m1am2)eorder ¶ (pam2)ereceive ¶ pIsender(m1) fi (p am1) e receive ) (m1am2)eorder ¶ (m2ap) e sender fi ((m1ap) esender v (p am1) ereceive ) (m1 a m2) e order ¶ (m2 am3) e order fi (m1 a m3) e orderModel of Causal Order First Refinement VARIABLES sender , receive , order INVARIANT order e MESSAGE 1 MESSAGE (m1am2)eorder ¶ (pam2)ereceive ¶ pIsender(m1) fi (p am1) e receive ) (m1am2)eorder ¶ (m2ap) e sender fi ((m1ap) esender v (p am1) ereceive ) (m1 a m2) e order ¶ (m2 am3) e order fi (m1 a m3) e order INITIALISATION sender := 0 || receive := 0 || order := 0 Operations OPERATIONS Send (pp,mm) = SELECT mm ‰ dom(sender) THEN order := order U ( (sender~[{pp}] * {mm}) U ( receive[{pp}] * {mm})) || sender := sender U {mm a pp} END; Receive (pp,mm) = SELECT mm e dom(sender) ¶ (pp a mm) ‰ receive ¶ pp Isender(mm) ¶ Am.( m e MESSAGE ¶ (m amm) e order ¶ pp I sender(m) fi (pp a m) e receive) THEN receive := receive U {pp a mm} END END Buffering of Messages Second Refinement To ensure globally ordered delivery of messages at a recipient process, early message need be buffered. For any two message M1, M2 where M1 is ordered before M2 (M1kM2), If M2 arrives early at a process then M2 is buffered until M1 is received. Buffering of Messages Second Refinement INVARIANT buffer e PROCESS 1 MESSAGE ¶ ran(buffer) z dom (sender) ¶ ran(receive) I ran(buffer) = 0 SETS PROCESS ; MESSAGE VARIABLES sender , receive , order ,buffer INITIALISATION sender := 0 || receive := 0 || order := 0 || buffer := 0 OPERATIONS Arrive (pp,mm) = SELECT mm e dom(sender) ¶ (pp a mm) ‰ buffer ¶ (pp a mm ) ‰ receive ¶ pp I sender(mm) THEN buffer := buffer U {pp a mm} END ; Introducing a new event Arrive Logical Clocks : Vector Clock Vector Clock uses a vector of Integers of size N, where N is number of processes in system. Process Pi maintains a vector clock VTi. VTi [ i ] is process Pi’s own logical time. VTi [ j ] is process Pi’s best knowledge of time at process Pj.. Proposed by Fidge and Mattern and based on Lamport’s scalar clocks P2 P3 P1 1,0,0 1,0,0 1,0,0 1,0,0 Applying Vector Clocks to Broadcast System

Application of Event B to Global Causal Ordering for Fault Tolerant Transactions

This paper presents liveness properties that need to be preserved by Event-B models of distributed systems. Event-B is a formal technique for development of models of distributed systems related via refinement. In this paper we outline how enabledness preservation and non-divergence are related to the liveness properties of the B models of the distributed systems. We address the liveness issues related to our model of distributed transactions and outline the construction of proof obligations that need to be discharged to ensure liveness.

/pdf/verification-of-liveness-properties-in-distributed-systems-4numpr8s1b.pdf

Verification of Liveness Properties in Distributed Systems

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