Information structures in language directed architectures

U-Net is a widely adopted neural network in the domain of medical image segmentation. Despite its quick embracement by the medical imaging community, its performance suffers on complicated datasets. The problem can be ascribed to its simple feature extracting blocks: encoder/decoder, and the semantic gap between encoder and decoder. Variants of U-Net (such as R2U-Net) have been proposed to address the problem of simple feature extracting blocks by making the network deeper, but it does not deal with the semantic gap problem. On the other hand, another variant UNET++ deals with the semantic gap problem by introducing dense skip connections but has simple feature extraction blocks. To overcome these issues, we propose a new U-Net based medical image segmentation architecture R2U++. In the proposed architecture, the adapted changes from vanilla U-Net are: (1) the plain convolutional backbone is replaced by a deeper recurrent residual convolution block. The increased field of view with these blocks aids in extracting crucial features for segmentation which is proven by improvement in the overall performance of the network. (2) The semantic gap between encoder and decoder is reduced by dense skip pathways. These pathways accumulate features coming from multiple scales and apply concatenation accordingly. The modified architecture has embedded multi-depth models, and an ensemble of outputs taken from varying depths improves the performance on foreground objects appearing at various scales in the images. The performance of R2U++ is evaluated on four distinct medical imaging modalities: electron microscopy, X-rays, fundus, and computed tomography. The average gain achieved in IoU score is 1.5 ± 0.37% and in dice score is 0.9 ± 0.33% over UNET++, whereas, 4.21 ± 2.72 in IoU and 3.47 ± 1.89 in dice score over R2U-Net across different medical imaging segmentation datasets.

In Section I, we mentioned many abstract data structures which we feel should be available in high level programming languages, including various kinds of queues, stacks, and mapping functions. This list is far from complete. We would imagine other programmers to have their own lists. The ones we chose hopefully form a basis for others. We hope that future systems will not only include the information structures mentioned, but will provide extension mechanisms to allow the user to define new information structures. These extension mechanisms should allow representations for user defined structures to be chosen much the same way as for built-in information structures. In our demonstration system, we have limited ourselves to those abstract data types already available in LEAP: sets, sequences and a single ternary relation. Let us now consider these information structures.

The main concern dealing with content-based image retrieval (CBIR) is to bridge the semantic gap. The high level query posed by the user and low level features extracted by the machine illustrates the problem of semantic gap. To solve the problem of semantic gap, this paper presents a hybrid technique using an emergence index and fuzzy logic for efficient retrieval of images based on the colour feature. Emergence index (EI) is proposed to understand the hidden meaning of the image. Fuzzy similarity measure is developed to calculate the similarity between the target image and the images in the database. The images were ranked based on their similarity along with the fuzzy similarity distance measure. The preliminary experiments conducted on small set of images and promising results were obtained.

Information and argument structures in Sociology research abstracts

In this thesis, we explore techniques for the development and verification of programs in a high-level, expressive, and safe programming language. Our programs can express problems over unbounded domains and over recursive and mutable data structures. We present an implementation language flexible enough to build interesting and useful systems. We mostly maintain a core shared language for the specifications and the implementation, with only a few extensions specific to expressing the specifications. Extensions of the core shared language include imperative features with state and side effects, which help when implementing efficient systems. Our language is a subset of the Scala programming language. Once verified, programs can be compiled and executed using the existing Scala tools. We present algorithms for verifying programs written in this language. We take a layer-based approach, where we reduce, at each step, the program to an equivalent program in a simpler language. We first purify functions by transforming away mutations into explicit return types in the functions' signatures. This step rewrites all mutations of data structures into cloning operations. We then translate local state into a purely functional code, hence eliminating all traces of imperative programming. The final language is a functional subset of Scala, on which we apply verification. We integrate our pipeline of translations into Leon, a verifier for Scala. We verify the core functional language by using an algorithm already developed inside Leon. The program is encoded into equivalent first-order logic formulas over a combination of theories and recursive functions. The formulas are eventually discharged to an external SMT solver. We extend this core language and the solving algorithm with support for both infinite-precision integers and bit-vectors. The algorithm takes into account the semantics gap between the two domains, and the programmer is ultimately responsible to use the proper type to represent the data. We build a reusable interface for SMT-LIB that enables us to swap solvers transparently in order to validate the formulas emitted by Leon. We experiment with writing solvers in Scala; they could offer both a better and safer integration with the rest of the system. We evaluate the cost of using a higher-order language to implement such solvers, traditionally written in C/C++. Finally, we experiment with the system by building fully working and verified applications. We rely on the intersection of many features including higher-order functions, mutable data structures, recursive functions, and nondeterministic environment dependencies, to build concise and verified applications.

The level of support for high level programming languages (HLPLs), despite the claim of support by some of the newly introduced microprocessors, is inadequate despite the falling (rising) cost of hardware (software). Computer systems that are to be used to run programs written exclusively in HLPLs have a need for a multiprocessor/multiarchitecture microprocessor design (M3D); the personal/desk-top computers are prime examples. The HLPLs that will be used in programming these systems will be grouped according to their structures. Each group of languages will require their own instruction set; the hardware and software needs will determine the type of architecture configuration, i.e. aspects such as: register-to-register, memory-to-memory, stack, or a combination of these. The overall system should be divided up into functional units, e.g. ALU, compiler/translator, etc. Each functional or subfunctional unit will be implemented in software, firmware, hardware, or a combination of these with implementation decision based on the speed criticality, cost, technology, and the need to modify the unit. This approach will lead to a cop-down-design, and will also reduce the semantic gap, software and system cost, and down-time.

The effects of syntactic and information structures on sentence processing load were investigated using two reading comprehension experiments in Japanese, a head-final SOV language. In the first experiment, we discovered the main effects of syntactic and information structures, as well as their interaction, showing that interaction of these two factors is not restricted to head-initial languages. The second experiment revealed that the interaction between syntactic structure and information structure occurs at the second NP (O of SOV and S of OSV), which, crucially, is a pre-head position, suggesting the incremental nature of the processing of both syntactic structure and information structure in head-final languages.

Fuzzy sets theory provides a good theoretical basis to represent imprecision of the information, at different levels of representation. It constitutes an unified framework for representing and processing both numerical and symbolic information, as well as structural information (constituted mainly by spatial relationships in image processing). We show that this theory can achieve tasks at several levels, from low level (e.g. grey-level based classification) to high level (e.g. model based structural pattern recognition and scene interpretation). We describe the use of fuzzy set at low level, for processing the basic numerical information contained in the images. Then we go to the object level, and show how to represent objects or structures in the images as fuzzy objects, and we present some operations on such fuzzy objects. At higher level, we are concerned by structural information and spatial relationships between objects, as distances, adjacency, and relative position between fuzzy objects. Finally, we address the problem of combining and fusing information. We show examples at low level for multi-source classification, and at high level for recognition of brain structures based on an atlas.

In production automation, field level control software is executed on programmable logic controllers and implemented according to the IEC 61131-3 standard. In contrast to sophisticated architectural capabilities in business software and high level programming languages, the IEC 61131 standard provides only limited support for architectural designs. Nevertheless, architectural decisions are made during implementation. Within this paper, a novel approach to discover implemented architectural designs within existing field level automation software is proposed and a framework for analyzing field level automation software, following a sophisticated viewpoint-based software engineering paradigm, is presented.

Using information systems while performing complex tasks: an example from architectural design

The aim of this paper is to show that some computational models inspired from biological membranes, such as P systems, can be simulated on digital computers in an efficient manner. To this aim, it is necessary to characterize non-determinism and parallel execution of evolution rules inside regions. Both these issues are formally described here in order to obtain a feasible description in terms of data structures and operations able to be implemented in a functional programming language. Static and dynamic structures of transition P systems are formalised in order to define a bio-language to represent them. Finally, a draft of a language for describing a transition P systems is presented. It will facilitate the description of transition P systems in terms of sentences in a high level programming language; such sentences will define a program. A process of compilation will parse the program to appropriate data structures and will launch the execution of the simulation process.

Code comments are essential for programming comprehension. Nevertheless, developers often neglect to update comments after modifying the source code. Wrong code comments may lead to bugs in the maintenance process, thus affecting the reliability of the software. So, timely comment mismatch detection is crucial for software development and maintenance. However, existing works have the following two limitations: 1) the lack of use of code structural and sequential information, and 2) the ignorance of existing associations between code and comments. In this paper, we propose a new model called ASKDetector (AST-Semantic and Key features fusion based mismatch Detector). For the first limitation, we encode code with an attention-based preorder traversal abstract syntax tree sequence to obtain both order and structural information. And CodeBERT is utilized to capture contextual semantic features further. For the second one, we encode extracted association information between the code snippets and comments to reduce the semantic gap. The correlations between the encoders are learned through a fusion layer and a multi-layer perceptron. The experimental results prove that our detector outperforms the state-of-the-art model in evaluation metrics, where our F1 and accuracy exceed an average of 3.4%.

Aiming at the problem that virtual machine information cannot be extracted incompletely, we extend the typical information extraction model of virtual machine and propose a perception mechanism in virtualization system based on storage covert channel to overcome the affection of the semantic gap. Taking advantage of undetectability of the covert channel, a secure channel is established between Guest and virtual machine monitor to pass data directly. The Guest machine can pass the control information of malicious process to virtual machine monitor by using the VMCALL instruction and shared memory. By parsing critical information in process control structure, virtual machine monitor can terminate the malicious processes. The test results show that the proposed mechanism can clear the user-level malicious programs in the virtual machine effectively and covertly. Meanwhile, its performance overhead is about the same as that of other mainstream monitoring mode.

We execute the quantum eraser, the Elitzur–Vaidman bomb, and the Hardy’s paradox experiment using high-level programming language on a generic, gate-based superconducting quantum processor made publicly available by IBM. The quantum circuits for these experiments use a mixture of one-qubit and multi-qubit gates and require high entanglement gate accuracy. The results aligned with theoretical predictions of quantum mechanics to high confidence on circuits using up to 3 qubits. The power of quantum computers and high-level language as a platform for experimenting and studying quantum phenomena is henceforth demonstrated.

Визначено предметну галузь управління проектами редевелопменту як засобу якісного перетворення міської території. Проаналізовано напрями сучасних досліджень у редевелопменті, що дало змогу виділити їх переваги та недоліки. Визначено необхідність проектування та реалізації інформаційно-аналітичної системи (ІАС) підтримки рішень "Редевелопмент" щодо проекту редевелопменту, зважаючи на багатокритеріальність, багатоваріантність задач редевелопменту, потребу оброблення та зберігання великих масивів кількісної та якісної інформації. Розроблено інфологічну модель ІАС "Редевелопмент", яка містить множину функцій оброблення даних, обчислювальних функцій та функцій забезпечення інтерфейсу з користувачем, що передбачають використання як високорівневої мови програмування C#, так і переваг відомого інструментального засобу MATLAB, необхідні функції якого імплементовано в програму на C#. Представлено схему бази даних, що дозволило здійснювати зберігання та обмін інформацією між користувачами системи, а саме стейкхолдерами проекту: інвестором, замовником, підрядником, управляючою компанією. Базовими функціями аналітичного блоку розробленої системи є такі: автоматизація процедури експертного оцінювання характеристик проекту редевелопменту, прогнозування попиту та пропозиції щодо певного функціонального призначення проекту редевелопменту.