Semantic Management Research Articles

An efficient quantification of COVID‐19 in chest CT images with improved semantic segmentation using U‐Net deep structure

AbstractThe worldwide spread of the coronavirus (COVID‐19) outbreak has proven devastating to public health. The severity of pneumonia relies on a rapid and accurate diagnosis of COVID‐19 in CT images. Accordingly, a computed tomography (CT) scan is an excellent screening tool for detecting COVID‐19. This paper proposes a deep learning‐based strategy for recognizing and segmenting a COVID‐19 lesion from chest CT images, which would introduce an accurate computer aided decision criteria for the physicians about the severity rate of the patients. Two main stages have been proposed for detecting COVID‐19; first, a convolutional neural network (CNN) deep structure recognizes and classifies COVID‐19 from CT images. Second, a U‐Net deep structure segments the COVID‐19 regions in a semantic manner. The proposed system is trained and evaluated on three different CT datasets for COVID‐19, two of which are used to illustrate the system's segmentation performance and the other is to demonstrate the system's classification ability. Experiment results reveal that the proposed CNN can achieve classification accuracy greater than 0.99, and the proposed U‐Net model outperforms the state‐of‐the‐art in segmentation with an IOU greater than 0.92.

DisP+V: A Unified Framework for Disentangling Prototype and Variation From Single Sample per Person.

Single sample per person face recognition (SSPP FR) is one of the most challenging problems in FR due to the extreme lack of enrolment data. To date, the most popular SSPP FR methods are the generic learning methods, which recognize query face images based on the so-called prototype plus variation (i.e., P+V) model. However, the classic P+V model suffers from two major limitations: 1) it linearly combines the prototype and variation images in the observational pixel-spatial space and cannot generalize to multiple nonlinear variations, e.g., poses, which are common in face images and 2) it would be severely impaired once the enrolment face images are contaminated by nuisance variations. To address the two limitations, it is desirable to disentangle the prototype and variation in a latent feature space and to manipulate the images in a semantic manner. To this end, we propose a novel disentangled prototype plus variation model, dubbed DisP+V, which consists of an encoder-decoder generator and two discriminators. The generator and discriminators play two adversarial games such that the generator nonlinearly encodes the images into a latent semantic space, where the more discriminative prototype feature and the less discriminative variation feature are disentangled. Meanwhile, the prototype and variation features can guide the generator to generate an identity-preserved prototype and the corresponding variation, respectively. Experiments on various real-world face datasets demonstrate the superiority of our DisP+V model over the classic P+V model for SSPP FR. Furthermore, DisP+V demonstrates its unique characteristics in both prototype recovery and face editing/interpolation.

The modularity codes

The hypothesis presented here is that codes as described by Marcello Barbieri are the fundamental principle behind biological modularity. Modularity has been studied in different life science disciplines, especially in the fields of evolution and development, as well as in network biology, yet there is still no consensus on how modularity evolved itself. Modularity is basically the functional integrity of multiple molecular players involved in a common process. Codes as defined by Barbieri describe a tripartite relation involving an adapter molecule connecting two other independent types of molecules to each other in an arbitrary, but semantic manner. This form of interaction goes beyond predictable mere physical or chemical one-to-one interactions and always relates three molecules to each other. A code of three topologically related molecules interacting in a defined order may be considered a minimal module on its own, but when one regards a set of multiple, overlapping tripartite, coded interactions, this paves the way towards logically and functionally consistent coherence of multiple participants of a certain, modular process. A theoretical outline of how to identify and describe such modular structures is given.

Cortical Vessel Segmentation for Neuronavigation Using Vesselness-Enforced Deep Neural Networks

We propose in this paper an efficient method to segment cortical vessels in craniotomy images acquired by the surgical microscope. Our method uses a vesselness-enforced convolutional neural network to classify each pixel of a craniotomy image as a vessel or surrounding tissue. This permits training the network not only on appearance-based features but also on geometrical-based constraints that will ensure the continuity of the vascular trees. Our solution uses neural style transfer to generate new instances of images from manually labeled data leading to augment the training dataset in an anatomically semantic manner. The generated images improve the generalization of our model to various types of cortical surface appearances and vascular geometries. We conducted experiments on real images from human patients that demonstrate that accurate intraoperative cortical vessel segmentation can be achieved.

Self-Supervised Color-Concept Association via Image Colorization.

The interpretation of colors in visualizations is facilitated when the assignments between colors and concepts in the visualizations match human's expectations, implying that the colors can be interpreted in a semantic manner. However, manually creating a dataset of suitable associations between colors and concepts for use in visualizations is costly, as such associations would have to be collected from humans for a large variety of concepts. To address the challenge of collecting this data, we introduce a method to extract color-concept associations automatically from a set of concept images. While the state-of-the-art method extracts associations from data with supervised learning, we developed a self-supervised method based on colorization that does not require the preparation of ground truth color-concept associations. Our key insight is that a set of images of a concept should be sufficient for learning color-concept associations, since humans also learn to associate colors to concepts mainly from past visual input. Thus, we propose to use an automatic colorization method to extract statistical models of the color-concept associations that appear in concept images. Specifically, we take a colorization model pre-trained on ImageNet and fine-tune it on the set of images associated with a given concept, to predict pixel-wise probability distributions in Lab color space for the images. Then, we convert the predicted probability distributions into color ratings for a given color library and aggregate them for all the images of a concept to obtain the final color-concept associations. We evaluate our method using four different evaluation metrics and via a user study. Experiments show that, although the state-of-the-art method based on supervised learning with user-provided ratings is more effective at capturing relative associations, our self-supervised method obtains overall better results according to metrics like Earth Mover's Distance (EMD) and Entropy Difference (ED), which are closer to human perception of color distributions.

التَّکْرَارُ وَأثَرُهُ فی تَماسُکِ النَّصِّ الشّعریّ القدیم مِیمیَّةُ حُمَیدِ بنِ ثَوْرٍ أُنمُوذَجًا

الملخص تسعى هذه الدراسة إلى الوقوف على أسرار التکرار ودوره في التماسک النصي في ميمية حميد بن ثور الهلالي، بوصفها من أطول القصائد في الشعر القديم؛ إذ تصل أبياتها إلى قرابة مائتي بيت؛ فيثير هذا الطول سؤالا جوهريا ملحا: کيف استطاع حميد أن يحقق هذا التماسک لهذه القصيدة الطويلة؟ وکان للبحث أن يتوقف عند عنصر واحد، لطبيعته، وهو عنصر التکرار بوصفه من أهم عناصر التماسک النصي الذي يتجاوز السبک إلى الحبک ؛ لکونه ينتقل من الإطار الشکلي في السبک ليتجاوزه إلى النسق الدلالي العام في الحبک. Abstract This study seeks to identify the secrets of repetition and its role in textual coherence in Mimia of Humaid ibn thor Al-Hilali, As one of the longest poems in old poetry. Its verses reach two hundred. This length raises an essential and insistent question: How can he achieve this cohesion for this long poem? This research was to stop at one element because of its nature, this element is the repetition as one of the most important textual coherence elements, which exceeds cohesion to coherence. because it moves from the formal frame in cohesion and exceeds the general semantic manner in coherence.

Measuring semantic distances using linked open data and its application on music recommender systems

PurposeMeasuring the similarity between two resources is considered difficult due to a lack of reliable information and a wide variety of available information regarding the resources. Many approaches have been devised to tackle such difficulty. Although content-based approaches, which adopted resource-related data in comparing resources, played a major role in similarity measurement methodology, the lack of semantic insight on the data may leave these approaches imperfect. The purpose of this paper is to incorporate data semantics into the measuring process.Design/methodology/approachThe emerged linked open data (LOD) provide a practical solution to tackle such difficulty. Common methodologies consuming LOD mainly focused on using link attributes that provide some sort of semantic relations between data. In this work, methods for measuring semantic distances between resources using information gathered from LOD were proposed. Such distances were then applied to music recommendation, focusing on the effect of various weight and level settings.FindingsThis work conducted experiments using the MusicBrainz dataset and evaluated the proposed schemes for the plausibility of LOD on music recommendation. The experimental result shows that the proposed methods electively improved classic approaches for both linked data semantic distance (LDSD) and PathSim methods by 47 and 9.7%, respectively.Originality/valueThe main contribution of this work is to develop novel schemes for incorporating knowledge from LOD. Two types of knowledge, namely attribute and path, were derived and incorporated into similarity measurements. Such knowledge may reflect the relationships between resources in a semantic manner since the links in LOD carry much semantic information regarding connecting resources.

URBAN IDENTITY AND IRANIAN NEW TOWNS

In an era when urbanization on a large scale and at a rapid pace has become a dominant trend, particularly in developing countries, the fundamental issue of urban spaces identity is always being ignored. This is an important issue in two respects. First, it refers to the weakening of the identity elements over time, coinciding with the new development; second, it regards the lack of attention to identity stimulants, and failure in using them in a figurative and semantic manner in a small-scale structure to the macro-scale of urban tissues. The relative identity crisis is particularly apparent in new towns and it is one of the obstacles to attract population into these soulless towns. The main purpose of this paper is to evaluate the identity of 10 new towns in Iran to achieve a conceptual-cognitive framework for reviewing the above mentioned problem. We conducted this research based on qualitative and quantitative methods and by the use of the Goeller matrix and of a qualitative survey questionnaire. Findings indicate that in terms of selected measures, the new town of Baharestan achieved the first place and the other two towns “Andisheh and Pardis” occupied the next ranks. Results indicate that among new towns under investigation, the new town of Baharestan got the highest rank because its principles of identity were inspired by the principles of the Isfahan School.

Engaging Building Automation Data Visualisation Using Building Information Modelling and Progressive Web Application

Abstract The integration of the building information modelling (BIM) and assets database has enabled a potential pathway for building stakeholders to add value to the building lifecycle management (BLM) processes. However, the obtaining, storing, processing, and distributing of data have not been ratified in a detailed and semantic manner in any official guideline. This paper suggests a framework for the efficient development of an interoperable visualisation of a building “digital twin” through an intuitive interface, to contribute to the idea above. The framework was applied in two case studies as examples, where rich visualisations of two buildings in the Kanta-Häme region of Finland were constructed using their architectural models and building indoor climate metrics. By constructing an engaging interface, relevant metrics and constant feedback from buildings’ occupants are gathered, meaningful data is selected, processed, and displayed to improve the facility management (FM) process. The yielded result is a progressive web application (PWA), where valuable sets of building performance data are visualised and a promptly communicable channel between owners/occupants and building system is delivered. Further development of this application in practice and research work is also proposed to harness the data-driven monitoring and automation in buildings to the greatest extent.

Adaptive and Optimization of Personalized Information Retrieval Model in Semantic Web

The recognition of user’s visited set of web pages for the prediction of web page is a key drawback. Thus the work is employed with the web access log files which is stored in the server. To understand the user interest patterns, the web access log files are extracted that depicts the user behavior. Various applications can be employed to predicting user’s behavior while serving the web. During this work, the proposed framework analyze the user usage, reinforced the content and the content retrieved with the semantic manner. The semantic information retrieval supported the user access pages are preprocessed and the web log data of the particular user is analyzed to identify the user profile. Then the retrieved information is graded with clustering the semantic content based results. The ranked content is then analyzed with the user profile to produce an optimized search results for the users based on the user classification.

Towards an Effective Syntax and a Generator for Deterministic Standard Regular Expressions

Abstract Deterministic regular expressions are a core part of XML Schema and used in other applications. But unlike regular expressions, deterministic regular expressions do not have a simple syntax, instead they are defined in a semantic manner. Moreover, not every regular expression can be rewritten to an equivalent deterministic regular expression. These properties of deterministic regular expressions put a burden on the user to develop XML Schema Definitions and to use deterministic regular expressions. In this paper, we propose a syntax for deterministic standard regular expressions (DREGs), and prove that the syntax of DREGs is context-free. Based on the context-free grammars for DREGs, we further design a generator for DREGs, which can generate DREGs randomly, and be used in applications associated with DREGs, e.g. benchmarking a validator for DTD or XML Schema, and inclusion checking of DTD and XML Schema. Experimental results demonstrate the efficiency and usefulness of the generator.

A Synergy of Semantic and Context Awareness for Service Composition in Ubiquitous Environment

Service composition in an important facet in service oriented architecture, it’s about the idea of assembling atomic services to satisfy a demand rather than building new applications from ‘scratch’, From the user’s perspective it’s a complex task due to the increasing number of services in the web and their heterogeneity. This complexity is increasing in the internet of Things era wehere computing devices are everywhere. In this work we propose an approach for composition of context aware services in a semantic manner, Artificial Intelligence planning is used to automate the composition starting from a defined objectif containing user request and context parameters. Service are described by extending OWL-S with contextual conditions. The proposed architecture was evaluated through an e-health scenario where chronic patients can benefit from a remote and automated medical supervision and emergency handling.

Enhanced hierarchical attribute based encryption with modular padding for improved public auditing in cloud computing using semantic ontology

The problem of public auditing has been well studied in many research articles. The methods described earlier suffer to achieve higher result in public auditing. To overcome the challenges in public auditing, the author propose a hierarchical attribute based encryption based modular padding scheme. The method organizes the cloud data in hierarchical manner and each level of attributes has been stored in semantic manner. The semantic relations of the attributes are used to select the key parameter and the same is used to encrypt or decrypt the cloud data. The semantic ontology maintains the category of data attributes and for each data attribute the ontology refers a secret key. The key from the semantic ontology is selected and the same is used to perform verification. Again the encrypted data is performed with modular padding, which selects the random number and based on the value of random number, a padding of 1 or 0 is performed. The proposed approach improves the performance of public auditing and security in cloud environment.

QoS-Aware Multigranularity Service Composition: Modeling and Optimization

Quality of service (QoS)-aware optimal service composition aims to maximize the overall QoS value of the resulting composite service instance while meeting user-specified global QoS constraints. Traditional methods only consider as candidates service instances that implement one abstract service in the composite service and neglect those that could perform multiple abstract services. To overcome this shortcoming, this paper proposes the concept of generalized component services (GCSs), which is defined in a semantic manner, to expand the selection scope so as to achieve a better solution. A QoS-aware multigranularity service composition model is formulated and how to identify all the GCSs for a composite service is elaborated. A backtracking-based algorithm and an extended genetic algorithm are proposed to optimize the resulting composite service instance. Lastly, evaluation results of these algorithms are described.

Natural Deduction Calculi and Sequent Calculi for Counterfactual Logics

In this paper we present labelled sequent calculi and labelled natural deduction calculi for the counterfactual logics CK + {ID, MP}. As for the sequent calculi we prove, in a semantic manner, that the cut-rule is admissible. As for the natural deduction calculi we prove, in a purely syntactic way, the normalization theorem. Finally, we demonstrate that both calculi are sound and complete with respect to Nute semantics [12] and that the natural deduction calculi can be effectively transformed into the sequent calculi.

Library Towers and the Vertical Dimension of Knowledge

Verticality, and related figures such as the tower, stack, or mountain, are commonly used as spatial metaphors to express the hierarchy that we apply to information and knowledge. But these metaphors that transform the vertical dimension of knowledge into words are also translated into library architecture. Different libraries include, or have been built in the form of, a tower. In these cases, verticality as a spatial metaphor is folded back onto the spatial and architectural field where it originated. Library towers transform verticality as a concept that conveys relations in knowledge into architectural language. The translation of verticality as a dimension of knowledge into architecture thus forms a strange double bind between space and knowledge. This article analyzes how libraries have expressed the vertical dimension of knowledge in their architecture and identifies different approaches that make the vertical dimension of knowledge architecturally present. The library of Ghent University (Belgium), by Henry van de Velde, includes a storehouse of books that has been completely accommodated in a tower. The architecture of the French National Library, by Dominique Perrault, plays with the metaphor of the tower in a semantic manner. Other libraries, such as the “Book Mountain” by MVRDV in Spijkenisse, exploit the book stack architecturally; and some libraries, such as The Netherlands Institute for Sound and Vision, by Neutelings Riedijk architects, do not build up but down, in the underground, to house their collections.

Intent-Based User Segmentation with Query Enhancement

With the rapid advancement of the internet, accurate prediction of user's online intent underlying their search queries has received increasing attention from online advertising community. This paper aims to address the major challenges with user queries in the context of behavioral targeting advertising by proposing a query enhancement mechanism that augments user's queries by leveraging a user query log. The empirical evaluation demonstrates that the authors' methodology for query enhancement achieves greater improvement than the baseline models in both intent-based user classification and user segmentation. Different from traditional user segmentation methods, which take little semantics of user behaviors into consideration, the authors propose a novel user segmentation strategy by incorporating the query enhancement mechanism with a topic model to mine the relationships between users and their behaviors in order to segment users in a semantic manner. Comparing with a classical clustering algorithm, K-means, the experimental results indicate that the proposed user segmentation strategy helps improve behavioral targeting effectiveness significantly. This paper also proposes an alternative to define user's search intent for the evaluation purpose, in the case that the dataset is sanitized. This approach automatically labels users in a click graph, which are then used in training an intent-based user classifier.

COMPARING THE PERFORMANCE OF SEMANTIC IMAGE RETRIEVAL USING SPARQL QUERY, DECISION TREE ALGORITHM AND LIRE

The ontology based framework is developed for representing image domain. The textual features of images are extracted and annotated as the part of the ontology. The ontology is represented in Web Ontology Language (OWL) format which is based on Resource Description Framework (RDF) and Resource Description Framework Schema (RDFS). Internally, the RDF statements represent an RDF graph which provides the way to represent the image data in a semantic manner. Various tools and languages are used to retrieve the semantically relevant textual data from ontology model. The SPARQL query language is more popular methods to retrieve the textual data stored in the ontology. The text or keyword based search is not adequate for retrieving images. The end users are not able to convey the visual features of an image in SPARQL query form. Moreover, the SPARQL query provides more accurate results by traversing through RDF graph. The relevant images cannot be retrieved by one to one mapping. So the relevancy can be provided by some kind of onto mapping. The relevancy is achieved by applying a decision tree algorithm. This study proposes methods to retrieve the images from ontology and compare the image retrieval performance by using SPARQL query language, decision tree algorithm and Lire which is an open source image search engine. The SPARQL query language is used to retrieving the semantically relevant images using keyword based annotation and the decision tree algorithms are used in retrieving the relevant images using visual features of an image. Lastly, the image retrieval efficiency is compared and graph is plotted to indicate the efficiency of the system.

Goal oriented recognition of composed activities for reliable and adaptable intelligence systems

The emerging availability of already deployed sensors that can be utilized for activity and context recognition raised a new paradigm. This paradigm called opportunistic sensing utilizes the available sensing infrastructure for activity and context recognition. This work focuses on utilizing this dynamically varying sensing infrastructure to recognize high-level composed activities in an adaptable way. The proposed methods use activity relations modeled in an ontology. This domain knowledge is used to dynamically configure hidden Markov models (HMM) and evidential networks. These models are popular in activity and context recognition systems due to their high recognition accuracy. A goal oriented approach is proposed to dynamically create and instantiate these models. The goal encapsulates the recognition purpose of the activity and context recognition system and is expressed in an abstracted and semantic manner. This flexible approach utilizes the opportunistic sensing principles. It directs the dynamic configuration of the activity and context recognition system during runtime. The configured recognition models are based on the recognition purpose of the system, and the configured sensing ensemble depends on the available sensing infrastructure. This enables the dynamic configuration and adaption of the activity and context recognition system during runtime to detect composed and time sequenced activities using HMMs or evidential networks in an opportunistic way.

Active Self-aspects as a Basis for Encoding Specificity Effects in Memory

The self-concept is known to be multifaceted, containing self-aspects that vary in activation (Linville & Carlston, 1994; McConnell, 2011). However, little is known about how these self-aspects function in memory. The present study tested whether variations in active self-aspects are capable of eliciting encoding specificity effects (similar to state-dependent memory). The results revealed that people exhibit superior memory when their active self-aspect is the same at both encoding and retrieval of information, relative to when different self-aspects are active at encoding and retrieval. This study provides additional evidence that self-aspects are important and variable memory structures. Importantly, encoding specificity was found regardless of whether the information was encoded in a self-referential or semantic manner, providing the first evidence that self-aspects are also implicated in memory that is not self-referential.