Public AOriented Personalized Health Care Platform based on web service

In this paper, we are using web service technologies in order to store data and also giving guideline line to people ,and that information is very confidentiality of patient data. Web service is playing a vital role in present scenario. Now days we are seeing web service have a more importance and so many technologies are existing .But in this paper we are using SOA and WSC. SO A means service oriented architecture which makes a communication between the two service and simple pass the data. WSC which means web service coordination which distributed the application actions. The main aim health care application development but health care industry is lagging behind other sectors

Synthesis of positive logic programs for checking a class of definitions with infinite quantification

We describe a method based on unfold/fold transformations that synthesizes positive logicprograms P(r)with the purpose of checking mechanically definitions of the form D(r) =∀X(r(X) ⇔QYR(X, Y))where ris the relation defined by the formula QYR(X, Y), Xis a set of variables to be instantiated at runtime by ground terms, QYis a set of quantifiedvariables on infinite domains (Qis the quantifier) and R(X, Y)a quantifier-free formulain the language of a first-order logic theory. This work constitutes a first step towards theconstruction of a new type of assertion checkers with the ability of handling restrictedforms of infinite quantification

Machine learning for function synthesis

Function synthesis is the process of automatically constructing functions that satisfy a given specification. The space of functions as well as the format of the specifications vary greatly with each area of application. In this thesis, we consider synthesis in the context of satisfiability modulo theories. Within this domain, the goal is to synthesise mathematical expressions that adhere to abstract logical formulas. These types of synthesis problems find many applications in the field of computer-aided verification. One of the main challenges of function synthesis arises from the combinatorial explosion in the number of potential candidates within a certain size. The hypothesis of this thesis is that machine learning methods can be applied to make function synthesis more tractable. The first contribution of this thesis is a Monte-Carlo based search method for function synthesis. The search algorithm uses machine learned heuristics to guide the search. This is part of a reinforcement learning loop that trains the machine learning models with data generated from previous search attempts. To increase the set of benchmark problems to train and test synthesis methods, we also present a technique for generating synthesis problems from pre-existing satisfiability modulo theories problems. We implement the Monte-Carlo based synthesis algorithm and evaluate it on standard synthesis benchmarks as well as our newly generated benchmarks. An experimental evaluation shows that the learned heuristics greatly improve on the baseline without trained models. Furthermore, the machine learned guidance demonstrates comparable performance to CVC5 and, in some experiments, even surpasses it. Next, this thesis explores the application of machine learning to more restricted function synthesis domains. We hypothesise that narrowing the scope enables the use of machine learning techniques that are not possible in the general setting. We test this hypothesis by considering the problem of ranking function synthesis. Ranking functions are used in program analysis to prove termination of programs by mapping consecutive program states to decreasing elements of a well-founded set. The second contribution of this dissertation is a novel technique for synthesising ranking functions, using neural networks. The key insight is that instead of synthesising a mathematical expression that represents a ranking function, we can train a neural network to act as a ranking function. Hence, the synthesis procedure is replaced by neural network training. We introduce Neural Termination Analysis as a framework that leverages this. We train neural networks from sampled execution traces of the program we want to prove terminating. We enforce the synthesis specifications of ranking functions using the loss function and network design. After training, we use symbolic reasoning to formally verify that the resulting function is indeed a correct ranking function for the target program. We demonstrate that our method succeeds in synthesising ranking functions for programs that are beyond the reach of state-of-the-art tools. This includes programs with disjunctions and non-linear expressions in the loop guards

B Methodにおける部品再利用によるソフトウェア合成と高信頼ソフトウェア部品の整備

本研究では信頼性を定理証明により定性的に評価可能な高信頼ソフトウェアを形式仕様から自動生成する手法の提案を目的とする．また，この手法の実現にあたり課題となる，高信頼部品整備を容易にするため，既存の高信頼ソフトウェアからソフトウェア部品群を自動生成する手法を提案する． 近年のソフトウェアの大規模複雑化に伴い，開発コストの増大と信頼性の低下が問題となっている．高信頼なソフトウェアを容易に開発することはソフトウェア工学の大目標の一つといえる．これに対するドラスティックなアプローチとしては入力仕様や設計から人手によらずコードを生成する自動コード生成が挙げられる．自動コード生成は開発コストの低減や開発期間の短縮だけでなく，人による誤りが混入せず信頼性向上にも寄与する．一方で，生成ソフトウェアの信頼性が部品の信頼性に依存し，高信頼なソフトウェア部品を開発対象ごとに整備する必要があり，このコストが手法適用の妨げとなる． 本研究では数学を基盤としてソフトウェアの信頼性を定性的に保証する形式手法BMethod の枠組みをソフトウェア部品と再利用の自動化に応用することで，高信頼ソフトウェア部品の自動生成とその再利用による新たなソフトウェア開発手法「モデル充足ソフトウェア合成（MSSS）手法」を提案する．B Method はJ.R.Abrial らにより提案された形式手法であり，大きな特徴として数学的仕様と命令型言語による実装間の整合性を定理証明に保証できる点である．MSSS 手法ではこのB Method の信頼性保証の枠組みを応用してモデル充足ソフトウェア部品(MSFC) を定義する． MSSS 手法は基盤とする数学的仕様の性質上，集合論と述語論理で記述できる範囲内でしかソフトウェアの仕様を記述できず，非同期処理やユーザインタフェースは自動合成の対象外となる．一方で，ソフトウェアの信頼性とMSFC の信頼性を数学的に定義することで，MSSS 手法では部品生成手法とソフトウェア合成手法の信頼性を数学的に定義でき，定性的な評価が可能である．本研究ではソフトウェア部品の生成手法自体に定理証明を適用し，その信頼性を定性的に保証する事を試みた．これにより，細分化モデルの信頼性を保証するのに必要な推論器の性質と，制約条件の抽出条件を定理証明により得られた．ソフトウェア合成についても提案した合成手順で部品の実装間の制約条件の矛盾以外は保証できることを示し，合成結果に矛盾が生じた際の低コストな解決手段を提案した． MSSS 手法のように数学的判定を必要とする部品自動再利用では膨大な部品群に対して数学的判定を行うため，計算コストの低減が問題となる．本研究ではこの問題に対して，数学的に意味の等しい数学的仕様の字面が一致し，また，含意関係となる字面が完全部分一致となるようモデル細分化手法を提案した．これにより，文字列一致による効率的な部品検索を可能にした． 本研究ではMSSS 手法の適用例として銀行口座システムなどに対するMSFC 生成とレンタカーシステムなどの自動合成を行った．これにより生成されたMSFC やソフトウェアに対してB Method の証明器を適用し，定義どおりの信頼性が得られることを確認した．ただし，B Method により記述されたソフトウェアは現状では広く公開されておらず，より実践的な手法の適用が今後の課題となる． 近年では，システムの不具合が莫大な賠償や会社の信用問題に発展するケースが相次いでおり，一般企業にも高信頼なシステム開発が求められている．しかし，高信頼ソフトウェア開発手法はその高い開発コストゆえに普及していないのが実情である．このため，MSSS 手法により高信頼ソフトウェア開発を自動化することは，高信頼ソフトウェア開発の導入コストを低減し，それを普及する為にも重要であると考える．また，高信頼ソフトウェア開発の自動化により人間はデバッグやコーディング作業から解放され，より上流工程の創造的作業に専念できると期待できる．電気通信大学201

Automatic synthesis of component & connector software architectures with bounded combinatory logic

Combinatory logic synthesis is a new type-based approach towards automatic synthesis of software from components in a repository. In this thesis we show how the type-based approach can naturally be used to exploit taxonomic conceptual structures in software architectures and component repositories to enable automatic composition and configuration of components, and also code generation, by associating taxonomic concepts to architectural building blocks such as, in particular, software connectors. Components of a repository are exposed for synthesis as typed combinators, where intersection types are used to represent concepts that specify intended usage and functionality of a component. An algorithm for solving the type inhabitation problem in combinatory logic - does there exist a composition of combinators with a given type? - is then used to automate the retrieval, composition, and configuration of suitable building blocks with respect to a goal specification. Since type inhabitation has high computational complexity, heuristic optimizations for the inhabitation algorithm are essential for making the approach practical. We discuss particularly important (theoretical and pragmatic) optimization strategies and evaluate them by experiments. Furthermore, we apply this synthesis approach to define a method for software connector synthesis for realistic software architectures based on a type theoretic model. We conduct experiments with a rapid prototyping tool that employs this method on complex concrete ERP- and e-Commerce-systems and discuss the results

Suojareleiden mittausketjun signaalinkäsittely

Since the early days of programming and automated reasoning, researchers have developed methods for systematically constructing programs from their specifications. Especially the last decade has seen a flurry of activities including the advent of specialized conferences, such as LOPSTR, covering the synthesis of programs in computational logic. In this paper we analyze and compare three state-of-the-art methods for synthesizing recursive programs in computational logic. The three approaches axe constructive/deductive synthesis, schema-guided synthesis, AA and inductive synthesis. Our comparison is carried out in a systematic way where, for each approach, we describe the key ideas and synthesize a common running example. In doing so, we explore the synergies between the approaches, which we believe are necessary in order to achieve progress over the next decade in this field