Semantic Vocabulary Research Articles

Regulatory Compliance Modelling Using Risk Management Techniques

We describe a novel approach to regulatory compliance decision support that leverages an asset-based cyber security risk management approach following ISO 27005, and illustrate this with an example based on the General Data Protection Regulation (GDPR). Previous work in regulatory compliance modelling and decision support utilises semantic vocabularies and reasoning techniques, and our approach has the primary benefit that regulatory compliance is integrated with other domains of risk management, so that insight from domains such as cyber security combined with regulatory compliance for privacy can be gained based on a single model of the user’s socio-technical infrastructure. The main contributions of this paper are: to show how regulatory requirements may be modelled as “compliance threats” in an asset-based risk management framework; to illustrate mapping from the GDPR’s legal text to domain assets, processes and relationships, compliance threats and control strategies to mitigate the threats; to show how the threats are trigged via recognition patterns based on asset configurations; to illustrate how the different types of regulatory requirement, e.g. obligations, prohibitions and derogating conditions, are represented in such a scheme; and finally to describe how to model causal dependencies between choices made to address a compliance threat and downstream additional compliance requirements.

Semantic Annotation of Web of Things Using Entity Linking

The web of things (WoT) improves syntactic interoperability between internet of things (IoT) devices by leveraging web standards. However, the lack of a unified WoT data model remains a challenge for the semantic interoperability. Fortunately, semantic web technologies are taking this challenge over by offering numerous semantic vocabularies like the semantic sensor networks (SSN) ontology. Although it enables the semantic interoperability between heterogeneous devices, the manual annotation hinders the scalability of the WoT. As a result, the automation of the semantic annotation of WoT devices becomes a prior issue for researchers. This paper proposes a method to improve the semi-automatic semantic annotation of web of things (WoT) using the entity linking task and the well-known ontologies, mainly the SSN.

A Novel Dissection Gesture Classification to Characterize Robotic Dissection Technique for Renal Hilar Dissection.

Deconstruction of robotic surgical gestures into semantic vocabulary yields an effective tool for surgical education. In this study we disassembled tissue dissection into basic gestures, created a classification system, and showed its ability to distinguish between experts and novices. Videos of renal hilum preparation during robotic assisted partial nephrectomies were manually reviewed to identify all discrete surgical movements. Identified dissection movements were classified into distinct gestures based on the consensus of 6 expert surgeons. This classification system was then employed to compare expert and novice dissection patterns during the renal hilum preparation. A total of 40 robotic renal hilum preparation videos were reviewed, representing 16 from 6 expert surgeons (100 or more robotic cases) and 24 from 13 novice surgeons (fewer than 100 robotic cases). Overall 9,819 surgical movements were identified, including 5,667 dissection movements and 4,152 supporting movements. Nine distinct dissection gestures were identified and classified into the 3 categories of single blunt dissection (spread, peel/push, hook), single sharp dissection (cold cut, hot cut and burn dissect) and combination gestures (pedicalize, 2-hand spread, and coagulate then cut). Experts completed 5 of 9 dissection gestures more efficiently than novices (p ≤0.033). In consideration of specific anatomical locations, experts used more peel/push and less hot cut while dissecting the renal vein (p <0.001), and used more pedicalize while dissecting the renal artery (p <0.001). Using this novel dissection gesture classification system, key differences in dissection patterns can be found between experts/novices. This comprehensive classification of dissection gestures may be broadly applied to streamline surgical education.

Research and Practice of Cultural Creative Product Design Teaching Based on Semantic Innovation

This topic makes an exploratory research on the teaching mode of cultural and creative product design. Besides, via the research method of semantic combination, we make a product redesign of Liaoning traditional culture. Through the cognitive construction and semantic innovation of the semantic vocabulary of regional characteristics, crowd characteristics, innovation characteristics and metaphorical features, it provides new ideas for the innovation and dissemination of regional cultural product design. Verify the design hypothesis through the practice of design model, so as to obtain the solution of Liaoning cultural product design.

Improved Visual-Semantic Alignment for Zero-Shot Object Detection

Zero-shot object detection is an emerging research topic that aims to recognize and localize previously ‘unseen’ objects. This setting gives rise to several unique challenges, e.g., highly imbalanced positive vs. negative instance ratio, proper alignment between visual and semantic concepts and the ambiguity between background and unseen classes. Here, we propose an end-to-end deep learning framework underpinned by a novel loss function that handles class-imbalance and seeks to properly align the visual and semantic cues for improved zero-shot learning. We call our objective the ‘Polarity loss’ because it explicitly maximizes the gap between positive and negative predictions. Such a margin maximizing formulation is not only important for visual-semantic alignment but it also resolves the ambiguity between background and unseen objects. Further, the semantic representations of objects are noisy, thus complicating the alignment between visual and semantic domains. To this end, we perform metric learning using a ‘Semantic vocabulary’ of related concepts that refines the noisy semantic embeddings and establishes a better synergy between visual and semantic domains. Our approach is inspired by the embodiment theories in cognitive science, that claim human semantic understanding to be grounded in past experiences (seen objects), related linguistic concepts (word vocabulary) and the visual perception (seen/unseen object images). Our extensive results on MS-COCO and Pascal VOC datasets show significant improvements over state of the art.1

Constrained BERT BiLSTM CRF for understanding multi-sentence entity-seeking questions

AbstractWe present the novel task of understanding multi-sentenceentity-seekingquestions (MSEQs), that is, the questions that may be expressed in multiple sentences, and that expect one or more entities as an answer. We formulate the problem of understanding MSEQs as a semantic labeling task over an open representation that makes minimal assumptions about schema or ontology-specific semantic vocabulary. At the core of our model, we use a BiLSTM (bidirectional LSTM) conditional random field (CRF), and to overcome the challenges of operating with low training data, we supplement it by using BERT embeddings, hand-designed features, as well as hard and soft constraints spanning multiple sentences. We find that this results in a 12–15 points gain over a vanilla BiLSTM CRF. We demonstrate the strengths of our work using the novel task of answering real-world entity-seeking questions from the tourism domain. The use of our labels helps answer 36% more questions with 35% more (relative) accuracy as compared to baselines. We also demonstrate how our framework can rapidly enable the parsing of MSEQs in an entirely new domain with small amounts of training data and little change in the semantic representation.

A DEEP AUTOENCODER-BASED REPRESENTATION FOR ARABIC TEXT CATEGORIZATION

Arabic text representation is a challenging assignment for several applications such as text categorization and clustering since the Arabic language is known for its variety, richness and complex morphology. Until recently, the Bag-of-Words remains the most common method for Arabic text representation. However, it suffers from several shortcomings such as semantics deficiency and high dimensionality of feature space. Moreover, most existing methods ignore the explicit knowledge contained in semantic vocabularies such as Arabic WordNet. To overcome these shortcomings, we proposed a deep Autoencoder based representation for Arabic text categorization. It consisted of three stages: (1) Extracting from Arabic WordNet the most relevant concepts based on feature selection processes (2) Features learning via an unsupervised algorithm for text representation (3) Categorizing text using deep Autoencoder. Our method allowed for the consideration of document semantics by combining both implicit and explicit semantics and reducing feature space dimensionality. To evaluate our method, we conducted several experiments on the standard Arabic dataset, OSAC. The obtained results showed the effectiveness of the proposed method compared to state-of-the-art ones.Arabic text representation

FUNGSI SEMANTIK KOSAKATA ALAT DAN TEKNIK PENANGKAPAN IKAN MASYARAKAT NELAYAN DI KAWASAN BOULEVARD MANADO

The purpose of this study is to describe the function of semantic vocabulary tools and fishing techniques in fisherman communities in the area of Boulevard Manado. This research uses qualitative descriptive method and uses lexical semantic study approach to analyze semantic function of vocabulary tool and fishing technique in fisherman communities. This research was conducted along the coastal area of Boulevard, Sario Sub-district, Manado City, North Sulawesi. Sources of data in this study were taken from those who work as fishermen, who know about the vocabulary tools and fishing techniques.The data collection techniques are direct observation techniques and interviews. The data collection tool was the researcher as a key instrument. Based on the results of data analysis research found in fishing gear and techniques in Bolevard Manado area, there are 30 vocabulary in fishing gear, and 14 vocabulary in fishing technique in the form of these results collected in this study. The researcher hopes for this research continued on the vocabulary of fisherman fishing tools and techniques in the Bolevard Manado area, from different perspectives, so that further research can complement the study of semantics, especially lexical semantics with other problems. Keywords: Function of Semantic, vocabulary tools, fishing techniques ,Fisherman communities.

Image Classification Model Using Visual Bag of Semantic Words

In the image classification field, the visual bag of words (BoW) has two drawbacks. One is low classification accuracy because a visual BoW is typically extracted from local low-level visual feature vectors via key points, without considering the high-level semantics of an image. The other is excessive time consumption because the size of the vocabulary is very large, especially for images with explicit backgrounds and object content. To solve these two problems, we propose a novel image classification model based on a visual bag of semantic words (BoSW), which includes an automatic segmentation algorithm based on graph cuts to extract major semantic regions and a semantic annotation algorithm based on support vector machine to label the regions with a visual semantic vocabulary. The proposed BoSW model refines image semantics by introducing user conceptions for extracting semantic vocabularies and reducing the size of the vocabulary. Experimental results demonstrate the superiority of the proposed algorithm through comparisons with state-of-the-art methods on benchmark datasets.

Supporting Vocabulary Acquisition in Physical Education Settings

Vocabulary is the basis for all content area learning, including physical education. This article presents several easily implemented vocabulary strategies that support content learning within the cognitive domain of physical education by reinforcing conceptual understanding and relationships. These strategies include: concept circles, list-group-label with concept maps, semantic feature analysis, vocabulary squares (Frayer Model), and word webs. The article will discuss how these strategies can be used within different aspects of lesson planning (e.g., activating prior knowledge or assessment). It will also describe how these strategies can be easily modified for age-group or unit focus. Following discussion of the different strategies, an overview chart is provided for easy reference of the tools and their overarching purpose.

A semantic parliamentary multimedia approach for retrieval of video clips with content understanding

Digital videos of parliamentary activity play an important role in enhancing transparency and accountability for open e-government. The rapid growth in these videos and the lack of semantic annotations and relationships between video and knowledge resources make it increasingly difficult to find accurate video clips with contextual information for content understanding. To overcome this problem, we highlight the need for building multimedia systems based on a semantic vision. With this aim, we focus on (1) how to address the knowledge representation for automatic extraction of contextual information for video content understanding; (2) how to link the parliamentary knowledge structures within video resources to provide accurate video clips retrieval; and (3) how to perform semantic annotation on video resources. The methodology applied is focused on a systematic approach that uses techniques from ontology engineering. This approach is based on the definition of two models: the semantic model and the reference architecture. The semantic model is composed of a reference ontology and a semantic video annotation framework. The ontology provides the support for video content understanding and the semantic vocabulary for annotating video resources. The video annotation framework is based on an RDF-powered semantic video annotation to effectively relate low- and mid-level visual features, corresponding to speakers’ interventions, to high-level parliamentary concepts. To evaluate the proposed system, a prototype for the Canary Islands Parliament (Spain) has been carried out. The results show how semantic enhancement is a key enabler for improved video retrieval on parliamentary multimedia content.

Cross-environment activity recognition using a shared semantic vocabulary

Effectively recognizing activities in smart environments requires either matching sensors to semantic models or labeled training data from the target environment for machine learning. Combining knowledge-driven and data-driven approaches improves activity recognition (AR) by providing the benefits of each while also mitigating their challenges. In this paper we present the Semantic Cross-Environment Activity Recognition (SCEAR) system which is a novel method for creating semantic feature spaces and enables data-driven AR systems to transfer AR models across environments. We evaluate SCEAR using 22 datasets from real-world smart environments. Transferred model performance is compared to models trained in the target environment and shown to provide a 39% average per-class improvement.

Зоолексика в семантическом аспекте (на примере отзоонимных существительных)

The article is devoted to semantic analysis zoonymic vocabulary of the Russian language. The paper presents definitions of terms zoonym and zoolexis . The characteristic of Russian nouns derived from zoonyms is given from the point of view of grammatical semantics. In accordance with the nature of grammatical semantics of nouns derived from zoonyms their semantic classes are distinguished. The focus is on the description of the whole semantic groups of nouns derived from zoonyms with a particular meaning. The relevance of the study is determined by the fact that nouns derived from zoonyms with the meaning of a particular item represent a considerable variety among all nouns derived from zoonyms (383 of 489 recorded in derivational dictionary) and are an important part of zoonymic vocabulary in nominative and communicative aspect. On the basis of definition and contextual analysis of lexical units the authors divided them into groups. It is shown that the semantic grouping of nouns derived from zoonyms with the specific meaning depend on belonging to a lexico-grammatical category of animate or inanimate nouns, as lexical representatives of each category form different semantic groups that have certain characteristics. The novelty of the research is seen in the fact that for the first time the Russian nouns derived from zoonyms and naming animals fully analysed from the point of view of semantics.

Semantic Verbs Are Intensional Transitives

In this paper I show that we have strong empirical and theoretical reasons to treat the verbs we use in our semantic theorizing—particularly ‘refers (to)’, ‘applies (to)’, and ‘is true (of)’—as intensional transitive verbs (ITVs). Stating our semantic theories with intensional vocabulary allows us to partially reconcile two competing approaches to the nature and subject-matter of semantics: the Chomskian approach, on which semantics is non-relational, internalistic, and concerns the psychology of language users, and the Lewisian approach, on which semantics is fully relational, specifies truth-conditions, and has metaphysical implications. ITVs have two readings: an intensional, de dicto reading, and a relational, de re reading. A semantic theory stated with the de dicto readings of our semantic verbs captures the core insights of the Chomskian approach to semantics, in part because it allows us to assign extremely fine-grained semantic values to expressions, even when those expressions are empty. On the other hand, the de re reading yields a theory that is fully relational, and issues in truth-conditions. The resulting theories are related—and compatible—in that they are expressed by two different readings of the very same semantic vocabulary, and plausibly, the distinction between these two readings is one of scope.

On the Costs of Nonclassical Logic

Solutions to semantic paradoxes often involve restrictions of classical logic for semantic vocabulary. In the paper we investigate the costs of these restrictions in a model case. In particular, we fix two systems of truth capturing the same conception of truth: (a variant) of the system KF of Feferman (The Journal of Symbolic Logic, 56, 1–49, 1991) formulated in classical logic, and (a variant of) the system PKF of Halbach and Horsten (The Journal of Symbolic Logic, 71, 677–712, 2006), formulated in basic De Morgan logic. The classical system is known to be much stronger than the nonclassical one. We assess the reasons for this asymmetry by showing that the truth theoretic principles of PKF cannot be blamed: PKF with induction restricted to non-semantic vocabulary coincides in fact with what the restricted version of KF proves true.

Analysis of the Structure of Surgical Activity for a Suturing and Knot-Tying Task.

BackgroundSurgical tasks are performed in a sequence of steps, and technical skill evaluation includes assessing task flow efficiency. Our objective was to describe differences in task flow for expert and novice surgeons for a basic surgical task.MethodsWe used a hierarchical semantic vocabulary to decompose and annotate maneuvers and gestures for 135 instances of a surgeon’s knot performed by 18 surgeons. We compared counts of maneuvers and gestures, and analyzed task flow by skill level.ResultsExperts used fewer gestures to perform the task (26.29; 95% CI = 25.21 to 27.38 for experts vs. 31.30; 95% CI = 29.05 to 33.55 for novices) and made fewer errors in gestures than novices (1.00; 95% CI = 0.61 to 1.39 vs. 2.84; 95% CI = 2.3 to 3.37). Transitions among maneuvers, and among gestures within each maneuver for expert trials were more predictable than novice trials.ConclusionsActivity segments and state flow transitions within a basic surgical task differ by surgical skill level, and can be used to provide targeted feedback to surgical trainees.

Task-Level vs. Segment-Level Quantitative Metrics for Surgical Skill Assessment

Task-level metrics of time and motion efficiency are valid measures of surgical technical skill. Metrics may be computed for segments (maneuvers and gestures) within a task after hierarchical task decomposition. Our objective was to compare task-level and segment (maneuver and gesture)-level metrics for surgical technical skill assessment. Our analyses include predictive modeling using data from a prospective cohort study. We used a hierarchical semantic vocabulary to segment a simple surgical task of passing a needle across an incision and tying a surgeon's knot into maneuvers and gestures. We computed time, path length, and movements for the task, maneuvers, and gestures using tool motion data. We fit logistic regression models to predict experience-based skill using the quantitative metrics. We compared the area under a receiver operating characteristic curve (AUC) for task-level, maneuver-level, and gesture-level models. Robotic surgical skills training laboratory. In total, 4 faculty surgeons with experience in robotic surgery and 14 trainee surgeons with no or minimal experience in robotic surgery. Experts performed the task in shorter time (49.74s; 95% CI = 43.27-56.21 vs. 81.97; 95% CI = 69.71-94.22), with shorter path length (1.63m; 95% CI = 1.49-1.76 vs. 2.23; 95% CI = 1.91-2.56), and with fewer movements (429.25; 95% CI = 383.80-474.70 vs. 728.69; 95% CI = 631.84-825.54) than novices. Experts differed from novices on metrics for individual maneuvers and gestures. The AUCs were 0.79; 95% CI = 0.62-0.97 for task-level models, 0.78; 95% CI = 0.6-0.96 for maneuver-level models, and 0.7; 95% CI = 0.44-0.97 for gesture-level models. There was no statistically significant difference in AUC between task-level and maneuver-level (p = 0.7) or gesture-level models (p = 0.17). Maneuver-level and gesture-level metrics are discriminative of surgical skill and can be used to provide targeted feedback to surgical trainees.

Reproducibility of execution environments in computational science using Semantics and Clouds

In the past decades, one of the most common forms of addressing reproducibility in scientific workflow-based computational science has consisted of tracking the provenance of the produced and published results. Such provenance allows inspecting intermediate and final results, improves understanding, and permits replaying a workflow execution. Nevertheless, this approach does not provide any means for capturing and sharing the very valuable knowledge about the experimental equipment of a computational experiment, i.e., the execution environment in which the experiments are conducted. In this work, we propose a novel approach based on semantic vocabularies that describes the execution environment of scientific workflows, so as to conserve it. We define a process for documenting the workflow application and its related management system, as well as their dependencies. Then we apply this approach over three different real workflow applications running in three distinct scenarios, using public, private, and local Cloud platforms. In particular, we study one astronomy workflow and two life science workflows for genomic information analysis. Experimental results show that our approach can reproduce an equivalent execution environment of a predefined virtual machine image on all evaluated computing platforms.

Hierarchical abstract semantic model for image classification

Semantic gap limits the performance of bag-of-visual-words. To deal with this problem, a hierarchical abstract semantics method that builds abstract semantic layers, generates semantic visual vocabularies, measures semantic gap, and constructs classifiers using the Adaboost strategy is proposed. First, abstract semantic layers are proposed to narrow the semantic gap between visual features and their interpretation. Then semantic visual words are extracted as features to train semantic classifiers. One popular form of measurement is used to quantify the semantic gap. The Adaboost training strategy is used to combine weak classifiers into strong ones to further improve performance. For a testing image, the category is estimated layer-by-layer. Corresponding abstract hierarchical structures for popular datasets, including Caltech-101 and MSRC, are proposed for evaluation. The experimental results show that the proposed method is capable of narrowing semantic gaps effectively and performs better than other categorization methods.

Using Text Models In Diagnostic Tasks.

Abstract This paper contains developing of a method of solving diagnostic tasks for complex technical objects (STO) based on using text models (TMi) to describe the functioning of STO. A TMi model is a text description, in normalized form, of all fragments of STO functioning process. The description of TMi is for med using semantic vocabularies of different types, which are generated on the basis of usage of information about all the aspects of STO construction and functioning. Such interpretation description is a subject area for tasks of STO diagnostics. Detection of malfunction and deviations of a functioning process of STO from an established functioning mode is implemented on the basis of analysis of semantic parameters of text description of the STO functioning process in order to determine semantic anomalies which occur in the descriptions of the STO functioning process, as well as in the descriptions of fragments of its functioning. Semantic anomalies occur in case when values of semantic parameters go beyond their established limits.