Semantic Applications Research Articles

WDBT: Non-volatile memory wear characterization and mitigation for DBT systems

Emerging high-capacity and byte-addressable non-volatile memory (NVM) is promising for the next-generation memory system. However, NVM suffers from limited write endurance, as an NVM cell will wear out very soon after a certain number of writes, making NVM undependable. To address this issue, many wear reduction and leveling mechanisms have been proposed. Nevertheless, most of these mechanisms are developed without the knowledge of application semantics and behaviors. In this paper, we advocate application-level wear management, which allows us to create effective and flexible wear reduction and leveling techniques for specific application domains. Particularly, we find that applications running with dynamic binary translation (DBT) exhibit significantly more writes. This is because DBT systems need to handle architectural differences when translating instructions across different architectures. In this paper, we present WDBT, which focuses on wear reduction and leveling for DBT systems on NVM. WDBT is designed based on common practices of DBT systems to reduce the majority of writes introduced by DBT. We also implement a prototype of WDBT using a real-world DBT system, QEMU, for multiple popular instruction sets. Experimental results on SPEC CPU 2017 show that WDBT can effectively reduce writes by 52.09% and 34.48% for x86-64 and RISC-V, respectively. Moreover, the performance overhead of WDBT is negligible.

ArEnAlg: A Pedagogical Mobile Application for Algorithms Linguistics Based on a Semantic Bilingual Ontology

Ontologies are knowledge structures that serve as the foundation for semantic applications. These semantic artifacts organize and classify knowledge to define the semantic relations among the concepts. Some ontologies are constructed for a single natural language, whereas others are built for dual or multi-natural languages. The goal of this study is to design and implement a mobile educational application to help students learn more effectively and to increase the learner’s acquisition of computational linguistics in the Algorithm domain. The methodology is to develop an application with unique features that can display the terms and the concepts in both Arabic and English languages in two different layouts: a list form and a flashcard form. The created app “ArEnAlg” can generate the terms in the algorithm domain. The major objective of the exercise modules is to examine the user’s knowledge of the concepts in the domain of algorithms. The evaluation results tested three factors: the learner’s enjoyment, the acquisition of the computational linguistics, and the application presentation, which showed that behavioral responses support the purpose of the developed application with a max-min range of 99% -81%.

Thoshihiko Izutsu’s Descriptive Significance in the Story of Maria in The Qur’an and His Approuch Techniques | Al-dala>lah Al-Was}fiyah li Tusyihi@ku> i>zutsu> ‘an Qis}s}oti Maryam Fi> Al-Qura>n Wa Taqniyatu Madkholiha>

In Toshihiko Izutsu's book entitled Ethico-Religious Concept in the Qur'an, he states that his semantic study contains three parts. explain the methodological principles of semantic analysis, the positive and negative relationship of pre-Islamic tribal moral rules with Islamic Qur'anic ethics and the consistent application of the methodological rules that have been described. Basically, this thesis concludes that the study of the meaning of the Qur'an must be examined critically because the Qur'an presents its contents globally, so a critical study is needed to understand the message it contains. This research is a type of qualitative literature. The method used is to search for all Maryam words in the Qur'an and then group them after being divided into several groups, This research is qualitative literature. The method used is to search for all Maryam words in the Qur'an and then group them after being divided into several groups. The results of this study indicate that Toshihiko Izutsu's semantic approach is more effective than relying on translated text. This study also tries to apply this approach to Arabic learning, especially in meanings such as translation skills and Reading Comprehension. This approach seems suitable to be applied in order to minimize errors experienced by students in interpreting reading texts.
 Keywords: Story of Maryam, Semantics of Toshihiko Izutsu, influence and application.

A Survey of Binary Code Fingerprinting Approaches: Taxonomy, Methodologies, and Features

Binary code fingerprinting is crucial in many security applications. Examples include malware detection, software infringement, vulnerability analysis, and digital forensics. It is also useful for security researchers and reverse engineers since it enables high fidelity reasoning about the binary code such as revealing the functionality, authorship, libraries used, and vulnerabilities. Numerous studies have investigated binary code with the goal of extracting fingerprints that can illuminate the semantics of a target application. However, extracting fingerprints is a challenging task since a substantial amount of significant information will be lost during compilation, notably, variable and function naming, the original data and control flow structures, comments, semantic information, and the code layout. This article provides the first systematic review of existing binary code fingerprinting approaches and the contexts in which they are used. In addition, it discusses the applications that rely on binary code fingerprints, the information that can be captured during the fingerprinting process, and the approaches used and their implementations. It also addresses limitations and open questions related to the fingerprinting process and proposes future directions.

WEB PAGE RECOMMENDATION SYSTEM BY INTEGRATING ONTOLOGY AND STEMMING ALGORITHM

In this research, we offer a customized-recommendation system that uses item representations and user profiles based on the ontologies that provide personalized services to semantic applications. To develop and implement the personalized-recommendation system, a system that uses the representations of the items and the user profiles based on the ontologies to provide the semantic applications with personalized services. Recommendation systems can use semantic reasoning capabilities to overcome present system limits and increase the quality of recommendations. The recommender makes use of domain ontologies to improve personalization: on the one hand, a domain-based inference method is used to model user interests more effectively and accurately; on the other hand, a semantic similarity method is used to improve the stemmer algorithm, which is used by our content-based filtering approach, which provides a measure of the affinity between an item and the user. In recommender systems and web personalization, Web Usage Mining is crucial. This study presents an effective recommender system based on ontology and web usage mining. The approach's first step is to extract features from online documents and build on related ideas. Then, they create an ontology for the website using the concepts and relevant terms retrieved from the records. The semantic similarity of web documents is used to group them into multiple semantic themes, each with its own set of preferences. The suggested solution incorporates ontology and semantic knowledge into Web Usage Mining and personalization procedures, as well as a stemming algorithm, and gets an overall accuracy of 90%.

A Semantic NoSQL Application Program Interface for Big Data

Complexity, heterogeneity, schemaless-ness, data visualization, and extraction of consistent knowledge from Big Data are the biggest challenges in NoSQL databases. This paper presents a general semantic NoSQL Application Program Interface that integrates and converts NoSQL databases to semantic representation. The generated knowledge base is suitable for visualization and knowledge extraction from different Big Data sources. The authors use a case study of the COVID-19 pandemic prediction and other weather occurrences in various parts of the world to illustrate the suggested API. The Authors find a correlation between COVID-19 spread and deteriorating weather. According to the experimental findings, the API's performance is enough for heterogeneous Big Data.

Towards Dependency-Aware Cache Management for Data Analytics Applications

Memory caches are being used aggressively in today's data analytics systems such as Spark, Tez, and Piccolo. The significant performance impact of caches and their limited sizes call for efficient cache management in data analytics clusters. However, prevalent data analytics systems employ rather simple cache management policies-notably Least Recently Used (LRU) and Least Frequently Used (LFU)-that are oblivious to the application semantics of data dependency, expressed as directed acyclic graphs (DAGs). Without this knowledge, cache management can, at best, be performed by guessing the future data access patterns based on history, which frequently results in inefficient, erroneous caching with a low hit rate and a long response time. Worse still, the lack of data dependency knowledge makes it impossible to retain the all-or-nothing cache property of cluster applications, in that a compute task cannot be sped up unless all the dependent data has been kept in the main memory. In this paper, we propose a novel cache replacement policy, named Least Reference Count (LRC), which exploits the application's data dependency information to optimize the cache management. LRC keeps track of the reference count of each data block, defined as the number of dependent child blocks that have not been computed yet, and always evicts the block with the smallest reference count. Furthermore, we incorporate the all-or-nothing requirement into LRC by coordinately managing the reference counts of all the input data blocks for the same computation. We demonstrate the efficacy of LRC through both empirical analysis and cluster deployments against popular benchmarking workloads. Our Spark implementation shows that, the proposed policies well address the all-or-nothing requirement and significantly improve the cache performance. Compared with LRU and a recently proposed caching policy called MEMTUNE, LRC improves the caching performance of typical workloads in production clusters by 22% and 284%, respectively.

Die Kunst der Einfachheit: Standortbestimmungen in der deutschen Gegenwartsliteratur: Judith Hermann — Peter Stamm — Robert Seethaler by Nadine Wisotzki

Reviewed by: Die Kunst der Einfachheit: Standortbestimmungen in der deutschen Gegenwartsliteratur: Judith Hermann — Peter Stamm — Robert Seethaler by Nadine Wisotzki Heide Kunzelmann Die Kunst der Einfachheit: Standortbestimmungen in der deutschen Gegenwartsliteratur: Judith Hermann — Peter Stamm — Robert Seethaler. By Nadine Wisotzki, Transcript Verlag, 2021. 224 pp. €40. ISBN 978–3-8376–5832–3. This book by literary and cultural historian Nadine Wisotzki concentrates on Einfachheit, simplicity, as an aesthetic currency and strategy for re-evaluating a problematic struggle against ambiguity identified in contemporary cultural criticism (see for example Thomas Bauer, Die Vereindeutigung der Welt, 2018). The study takes a focused approach by looking at selected fiction in German in the new millennium. Wisotzki posits with Hans-Ulrich Gumbrecht, one supervisor of this doctoral thesis-turned-book, that the present, fragmented and oddly removed from a human experience, is defined by latency, a curious delay between an event and our reaction to it. As a result of this, ontologically essential concepts like complexity or simplicity require a more differentiated, deeper exploration before we can discuss our present times with the heightened sense of an 'offene Zukunft' that Wisotzki claims they might provide (p. 47). Consequently, Wisotzki attempts a cultural-historical survey of the usage of the term Einfachheit in recent aesthetic history, with the aim of developing 'eine Ästhetik für die Gegenwart' (p. 77) that is based on artistic representation of Einfachheit as a central paradigm. Literature, as so often, serves as a third space where an exemplary analysis of Einfachheit as a cultural, socio-philosophical concept and aesthetic strategy becomes possible. The study consists of five chapters and is generally well-balanced in its presentation of the historical aesthetic usage of the term Einfachheit and its three main semantic applications in public cultural discourse. This is [End Page 216] complemented by analysis and close readings of three contemporary authors, Judith Hermann, Peter Stamm and Robert Seethaler. Chapter 1 provides an illuminating panoramic view of the way the term 'simplification' figures today in Western European intellectual and aesthetic discourse. The author highlights its use in the context of lifestyle ('Rustikalität', p. 19), values ('Idee des Unverfälschten und Wertstabilen', ibid.) and the existential authenticity for which humans strive. Here, Wisotzki formulates her main goal, which is to examine simplicity as a topos that is no longer too vague for rigorous literary criticism. Chapter 2 pursues this goal convincingly with a highly condensed yet inspired summary of how the term Einfachheit has been employed in the arts either to celebrate order, clarity and modesty or, conversely, to scorn banality and stupidity. Wisotzki's lexical and semantic overview of the term's usage (pp. 43–47) and her pairing of typologies of Einfachheit ('einfach-vielfach'/'eindeutigmehrdeutig'/'klar-undurchsichtig', p. 49) are particularly useful. Chapter 3 is meant to offer the bridge between cultural history and the close reading in the following chapters and provides my only point of criticism of an otherwise excellently executed study. Wisotzki's approach here is somewhat reductive in parts and too much interdisciplinary referencing (from Bauhaus via Voltaire to Malewitsch) is packed into too few pages. Also, the rather broad reach from Kafka's 'littérature mineure' (Guattari/Deleuze) via an excursion into Englishspeaking short story writing (E. A. Poe, Hemingway, Carver) to a summative critique of postmodern superficiality and the verdict that 'Einfachheit hat offensichtlich ganz unterschiedliche Qualitäten' [simplicity clearly has very different qualities, p. 75] could have been refocused on German-language writing traditions for more stringency. Ingo Schulze's eponymous novel Simple Stories (1998) should at least have been mentioned and could have been used as a segue into a more detailed discussion of the conflation of genres which characterizes German writing at the intersection of complex historical developments and social utopias of simplicity. What follows is a varied discussion of aspects of reduction, silence and linguistic austerity as narrative strategy in Judith Hermann's novels and short story collections (Chapter 4). By analysing the mixed reception of Hermann's work, Wisotzki emphasizes our society's inability to cope well with the absence and withholding of explanations and reasoning. The insightful reading of Peter Stamm's novels or stories through the...

Semantization of The Web-Oriented Advisory Software: Models, Methods And Technologies

We consider actual trends in use of semantic technologies for development of the Web-oriented advisory software. Requirements and expressiveness of knowledge representation in this domain cause use of ontological knowledge model, and the Semantic Web standards and tools provide the technological foundation for creation of intelligent advisory applications that use external ontologies as sources of domain knowledge. Personification of advisory services needs in analysis of learning outcomes of informal and non-formal learning, and therefore we propose method of their formalization with use of background knowledge about with classifications of professions and qualifications in agricultural domain. This method is based on matching of semantic properties of information objects with different structure represented by atomic competencies. Services provided by advisory system are considered from the point of view of semantic technologies. Proposed approach is used for development of applied advisory system AdvisOnt aimed to combine the market of educational services with the labor market. AdvisOnt characteristics satisfy requirements to distributed semantic application.

Ontology Versioning Driven by Instance Evolution in the τOWL Framework

Like other components of Semantic Web-based applications, ontologies are evolving over time to reflect changes in the real world. Several of these applications require keeping a full-fledged history of ontology changes so that both ontology instance versions and their corresponding ontology schema versions are maintained. Updates to an ontology instance could be non-conservative that is leading to a new ontology instance version no longer conforming to the current ontology schema version. If, for some reasons, a non-conservative update has to be executed, in spite of its consequence, it requires the production of a new ontology schema version to which the new ontology instance version is conformant so that the new ontology version produced by the update is globally consistent. In this paper, we first propose an approach that supports ontology schema changes which are triggered by non-conservative updates to ontology instances and, thus, gives rise to an ontology schema versioning driven by instance updates. Note that in an engineering perspective, such an approach can be used as an incremental ontology construction method driven by the modification of instance data, whose exact structure may not be completely known at the initial design time. After that, we apply our proposal to the already established [Formula: see text]OWL (Temporal OWL 2) framework, which allows defining and evolving temporal OWL 2 ontologies in an environment that supports temporal versioning of both ontology instances and ontology schemas, by extending it to also support the management of non-conservative updates to ontology instance versions. Last, we show the feasibility of our approach by dealing with its implementation within a new release of the [Formula: see text] OWL-Manager tool.

Exploring graph neural networks for semantic enrichment: Room type classification

Semantic enrichment of Building Information Modeling (BIM) models supplements models with the implicit semantics for further applications. In this paper, we use the room classification task to develop, test and illustrate a novel approach to semantic enrichment of BIM models - representation of models as graphs and application of graph neural networks (GNNs). A dedicated graph dataset consisting of 224 apartment layouts with nine room types and node/edge features was compiled. An improved GNN algorithm, SAGE-E, was developed for processing both node and edge features and a batch method was used to improve efficiency. The experiments showed that (1) The novel approach of adopting graphs and GNNs was feasible. (2) SAGE-E achieved higher accuracy (79%) and more balanced prediction (F1 = 0.79) when compared with other machine learning algorithms. (3) SAGE-E shortened the training and validation process. This work11A part of EU CBIM project at https://cbim2020.net.technion.ac.il/ (accessed on 17/8/2021). pioneers the application of GNNs for semantic enrichment and opens the door to other possible applications.

FAIR data representation in times of eScience: a comparison of instance-based and class-based semantic representations of empirical data using phenotype descriptions as example

BackgroundThe size, velocity, and heterogeneity of Big Data outclasses conventional data management tools and requires data and metadata to be fully machine-actionable (i.e., eScience-compliant) and thus findable, accessible, interoperable, and reusable (FAIR). This can be achieved by using ontologies and through representing them as semantic graphs. Here, we discuss two different semantic graph approaches of representing empirical data and metadata in a knowledge graph, with phenotype descriptions as an example. Almost all phenotype descriptions are still being published as unstructured natural language texts, with far-reaching consequences for their FAIRness, substantially impeding their overall usability within the life sciences. However, with an increasing amount of anatomy ontologies becoming available and semantic applications emerging, a solution to this problem becomes available. Researchers are starting to document and communicate phenotype descriptions through the Web in the form of highly formalized and structured semantic graphs that use ontology terms and Uniform Resource Identifiers (URIs) to circumvent the problems connected with unstructured texts.ResultsUsing phenotype descriptions as an example, we compare and evaluate two basic representations of empirical data and their accompanying metadata in the form of semantic graphs: the class-based TBox semantic graph approach called Semantic Phenotype and the instance-based ABox semantic graph approach called Phenotype Knowledge Graph. Their main difference is that only the ABox approach allows for identifying every individual part and property mentioned in the description in a knowledge graph. This technical difference results in substantial practical consequences that significantly affect the overall usability of empirical data. The consequences affect findability, accessibility, and explorability of empirical data as well as their comparability, expandability, universal usability and reusability, and overall machine-actionability. Moreover, TBox semantic graphs often require querying under entailment regimes, which is computationally more complex.ConclusionsWe conclude that, from a conceptual point of view, the advantages of the instance-based ABox semantic graph approach outweigh its shortcomings and outweigh the advantages of the class-based TBox semantic graph approach. Therefore, we recommend the instance-based ABox approach as a FAIR approach for documenting and communicating empirical data and metadata in a knowledge graph.

An Adaptive Application Framework with Customizable Quality Metrics

Many embedded environments require applications to produce outcomes under different, potentially changing, resource constraints. Relaxing application semantics through approximations enables trading off resource usage for outcome quality. Although quality is a highly subjective notion, previous work assumes given, fixed low-level quality metrics that often lack a strong correlation to a user’s higher-level quality experience. Users may also change their minds with respect to their quality expectations depending on the resource budgets they are willing to dedicate to an execution. This motivates the need for an adaptive application framework where users provide execution budgets and a customized quality notion. This article presents a novel adaptive program graph representation that enables user-level, customizable quality based on basic quality aspects defined by application developers. Developers also define application configuration spaces, with possible customization to eliminate undesirable configurations. At runtime, the graph enables the dynamic selection of the configuration with maximal customized quality within the user-provided resource budget. An adaptive application framework based on our novel graph representation has been implemented on Android and Linux platforms and evaluated on eight benchmark programs, four with fully customizable quality. Using custom quality instead of the default quality, users may improve their subjective quality experience value by up to 3.59×, with 1.76× on average under different resource constraints. Developers are able to exploit their application structure knowledge to define configuration spaces that are on average 68.7% smaller as compared to existing, structure-oblivious approaches. The overhead of dynamic reconfiguration averages less than 1.84% of the overall application execution time.

A Survey of Semantic Construction and Application of Satellite Remote Sensing Images and Data

With the rapid development of satellite technology, remote sensing data has entered the era of big data, and the intelligent processing of remote sensing image has been paid more and more attention. Through the semantic research of remote sensing data, the processing ability of remote sensing data is greatly improved. This paper aims to introduce and analyze the research and application progress of remote sensing image satellite data processing from the perspective of semantic. Firstly, it introduces the characteristics and semantic knowledge of remote sensing big data; Secondly, the semantic concept, semantic construction and application fields are introduced in detail; then, for remote sensing big data, the technical progress in the study field of semantic construction is analyzed from four aspects: semantic description and understanding, semantic segmentation, semantic classification and semantic search, focusing on deep learning technology; Finally, the problems and challenges in the four aspects are discussed in detail, in order to find more directions to explore.

Corpus-based cognitive semantics: Extended units of meaning and their implications for Translation Studies

Traditional lexical semantics focuses on the meaning of individual lexemes. Firth (1957) brought our attention to collocations and the fact that meaning is not isolated in the lexeme. In 1996 Sinclair argued for the existence of extended units of meaning which, as the expression indicates, go beyond the lexeme. In recent years Stubbs (2001b), and other corpus linguists have convincingly shown that meaning is a phraseological phenomenon to a high degree. Corpus searches allow us to study lexemes in their immediate context, study their most frequent collocates and thus help us reveal their semantic preferences (Sinclair 1987, 1996) and semantic prosodies (evaluation) (Louw 1993). Some of the findings confirm intuitions, whereas some make us aware of connotations which we have never before consciously known the existence of. In this article, I shall argue for the application of corpus-based cognitive semantics as a tool for researchers within translation studies (TS) who are particularly interested in revealing evaluative aspects of the units of meaning of source texts and their translations. What may formerly have been described as something intangible like an ‘atmosphere’, now becomes tangible because of the patterns emerging from large numbers of examples. I shall provide empirical examples in various langua ges of such evaluative patterns which are of course not automatically generated but come about as the result of computer-generated concordance lines and thorough manual analysis.

A semantic model for enterprise application integration in the era of data explosion and globalisation

ABSTRACT This paper presents a model for Enterprise Application Integration (EAI) in the modern era of data explosion and globalisation. Application here refers to software, which is in essence data system, and data refers to both information and knowledge (data serves as a vehicle for information as well as knowledge). The salient features of the model are: (1) separation of business functions from applications and enterprises, (2) three-layer architecture of the model (conceptual or semantic level, external or application level, internal or realisation level), and (3) integration of structured, semi-structured and non-structured data. To our best knowledge, the existing model or solution to EAI does not hold all the three features. A case study is presented to illustrate how the model works. The model can be used by an individual enterprise or a group of enterprises that form a network, e.g., a holistic supply chain network.

Sequential linked data: The state of affairs

Sequences are among the most important data structures in computer science. In the Semantic Web, however, little attention has been given to Sequential Linked Data. In previous work, we have discussed the data models that Knowledge Graphs commonly use for representing sequences and showed how these models have an impact on query performance and that this impact is invariant to triplestore implementations. However, the specific list operations that the management of Sequential Linked Data requires beyond the simple retrieval of an entire list or a range of its elements – e.g. to add or remove elements from a list –, and their impact in the various list data models, remain unclear. Covering this knowledge gap would be a significant step towards the realization of a Semantic Web list Application Programming Interface (API) that standardizes list manipulation and generalizes beyond specific data models. In order to address these challenges towards the realization of such an API, we build on our previous work in understanding the effects of various sequential data models for Knowledge Graphs, extending our benchmark and proposing a set of read-write Semantic Web list operations in SPARQL, with insert, update and delete support. To do so, we identify five classic list-based computer science sequential data structures (linked list, double linked list, stack, queue, and array), from which we derive nine atomic read-write operations for Semantic Web lists. We propose a SPARQL implementation of these operations with five typical RDF data models and compare their performance by executing them against six increasing dataset sizes and four different triplestores. In light of our results, we discuss the feasibility of our devised API and reflect on the state of affairs of Sequential Linked Data.

Acquisition of Semantics for AI-based Applications in Medical Technologies

Abstract For the development, training, and validation of AIbased procedures, such as the analysis of clinical data, prediction of critical events, or planning of healthcare procedures, a lot of data is needed. In addition to this data of any origin (image data, bio-signals, health records, machine states, …) adequate supplementary information about the meaning encoded in the data is required. With this additional information - the semantic or knowledge - a tight relation between the raw data and the human-understandable concepts from the real world can be established. Nevertheless, as the amount of data needed to develop robust AI-based methods is strongly increasing, the assessment and acquisition of the related knowledge becomes more and more challenging. Within this work, an overview of currently available concepts of knowledge acquisition are described and evaluated. Four main groups of knowledge acquisition related to AI-based technologies have been identified. For image data mainly iconic annotation methods are used, where experienced users mark or draw depicted entities in the images and label them using predefined sets of classifications. Similarly, bio-signals are manually labelled, whereby important events along the timeline are marked. If no sufficient data is available, augmentation and simulation techniques are applied yielding data and semantics at the same time. In applications, where expensive sensors are replaced by low-cost devices, the high-grade data can be used as semantics. Finally, classic rule-based approaches are used, where human factual and procedural knowledge about the data and its context is translated into machine-understandable procedures. All these methods are depending on the involvement of human experts. To reduce this, more intelligent and hybrid approaches are needed, shifting the focus from the-human-in-the-loop to the-machine- in-the-loop.

Evaluation of taxonomic and neural embedding methods for calculating semantic similarity

AbstractModelling semantic similarity plays a fundamental role in lexical semantic applications. A natural way of calculating semantic similarity is to access handcrafted semantic networks, but similarity prediction can also be anticipated in a distributional vector space. Similarity calculation continues to be a challenging task, even with the latest breakthroughs in deep neural language models. We first examined popular methodologies in measuring taxonomic similarity, including edge-counting that solely employs semantic relations in a taxonomy, as well as the complex methods that estimate concept specificity. We further extrapolated three weighting factors in modelling taxonomic similarity. To study the distinct mechanisms between taxonomic and distributional similarity measures, we ran head-to-head comparisons of each measure with human similarity judgements from the perspectives of word frequency, polysemy degree and similarity intensity. Our findings suggest that without fine-tuning the uniform distance, taxonomic similarity measures can depend on the shortest path length as a prime factor to predict semantic similarity; in contrast to distributional semantics, edge-counting is free from sense distribution bias in use and can measure word similarity both literally and metaphorically; the synergy of retrofitting neural embeddings with concept relations in similarity prediction may indicate a new trend to leverage knowledge bases on transfer learning. It appears that a large gap still exists on computing semantic similarity among different ranges of word frequency, polysemous degree and similarity intensity.

New End-to-End Strategy Based on DeepLabv3+ Semantic Segmentation for Human Head Detection.

In the field of computer vision, object detection consists of automatically finding objects in images by giving their positions. The most common fields of application are safety systems (pedestrian detection, identification of behavior) and control systems. Another important application is head/person detection, which is the primary material for road safety, rescue, surveillance, etc. In this study, we developed a new approach based on two parallel Deeplapv3+ to improve the performance of the person detection system. For the implementation of our semantic segmentation model, a working methodology with two types of ground truths extracted from the bounding boxes given by the original ground truths was established. The approach has been implemented in our two private datasets as well as in a public dataset. To show the performance of the proposed system, a comparative analysis was carried out on two deep learning semantic segmentation state-of-art models: SegNet and U-Net. By achieving 99.14% of global accuracy, the result demonstrated that the developed strategy could be an efficient way to build a deep neural network model for semantic segmentation. This strategy can be used, not only for the detection of the human head but also be applied in several semantic segmentation applications.