Semantics Interaction Control for Constructing Intelligent Ecology of Internet of Things and Critical Component Research

Intelligent equipment is a kind of device that is characterized by intelligent sensor interconnections, big data processing, new types of displays, human-machine interaction and so on for the new generation of information technology. For this purpose, in this paper, first, we present a type of novel intelligent deep hybrid neural network algorithm based on a deep bidirectional recurrent neural network integrated with a deep backward propagation neural network. It has realized acoustic analysis, speech recognition and natural language understanding for jointly constituting human-machine voice interactions. Second, we design a voice control motherboard using an embedded chip from the ARM series as the core, and the onboard components include ZigBee, RFID, WIFI, GPRS, a RS232 serial port, USB interfaces and so on. Third, we take advantage of algorithms, software and hardware to make machines “understand” human speech and “think” and “comprehend” human intentions to structure critical components for intelligent vehicles, intelligent offices, intelligent service robots, intelligent industries and so on, which furthers the structure of the intelligent ecology of the Internet of Things. At last, the experimental results denote that the study of the semantics interaction controls based on an embedding has a very good effect, fast speed and high accuracy, consequently realizing the intelligent ecology construction of the Internet of Things.

Semantic model and optimization of creative processes at mathematical knowledge formation

The aim of this work is mathematical education through the knowledge system and mathematical modeling. A net model of formation of mathematical knowledge as a deductive theory is suggested here. Within this model the formation of deductive theory is represented as the development of a certain informational space, the elements of which are structured in the form of the orientated semantic net. This net is properly metrized and characterized by a certain system of coverings. It allows injecting net optimization parameters, regulating qualitative aspects of knowledge system under consideration. To regulate the creative processes of the formation and realization of mathematical know- edge, stochastic model of formation deductive theory is suggested here in the form of branching Markovian process, which is realized in the corresponding informational space as a semantic net. According to this stochastic model we can get correct foundation of criterion of optimization creative processes that leads to “great main points” strategy (GMP-strategy) in the process of realization of the effective control in the research work in the sphere of mathematics and its applications.

A Natural Language Generation Algorithm for Greek by Using Hole Semantics and a Systemic Grammatical Formalism

This work is about the progress of previous related work based on an experiment to improve the intelligence of robotic systems,with the aim of achieving more linguistic communication capabilities between humans and robots.In this paper,the authors attempt an algorithmic approach to natural language generation through hole semantics and by applying the OMAS-III computational model as a grammatical formalism.In the original work,a technical language is used,while in the later works,this has been replaced by a limited Greek natural language dictionary.This particular effort was made to give the evolving system the ability to ask questions,as well as the authors developed an initial dialogue system using these techniques.The results show that the use of these techniques the authors apply can give us a more sophisticated dialogue system in the future.

Using ontology and rules to retrieve the semantics of disaster remote sensing data

Remote sensing data plays an important role in natural disaster management.However,with the increase of the variety and quantity of remote sensors,the problem of“knowledge barriers”arises when data users in disaster field retrieve remote sensing data.To improve this problem,this paper proposes an ontology and rule based retrieval(ORR)method to retrieve disaster remote sensing data,and this method introduces ontology technology to express earthquake disaster and remote sensing knowledge,on this basis,and realizes the task suitability reasoning of earthquake disaster remote sensing data,mining the semantic relationship between remote sensing metadata and disasters.The prototype system is built according to the ORR method,which is compared with the traditional method,using the ORR method to retrieve disaster remote sensing data can reduce the knowledge requirements of data users in the retrieval process and improve data retrieval efficiency.

Enhancing Deep Learning Semantics:The Diffusion Sampling and Label-Driven Co-Attention Approach

The advent of self-attention mechanisms within Transformer models has significantly propelled the advancement of deep learning algorithms,yielding outstanding achievements across diverse domains.Nonetheless,self-attention mechanisms falter when applied to datasets with intricate semantic content and extensive dependency structures.In response,this paper introduces a Diffusion Sampling and Label-Driven Co-attention Neural Network(DSLD),which adopts a diffusion sampling method to capture more comprehensive semantic information of the data.Additionally,themodel leverages the joint correlation information of labels and data to introduce the computation of text representation,correcting semantic representationbiases in thedata,andincreasing the accuracyof semantic representation.Ultimately,the model computes the corresponding classification results by synthesizing these rich data semantic representations.Experiments on seven benchmark datasets show that our proposed model achieves competitive results compared to state-of-the-art methods.

Formal Semantics of OWL-S with Rewrite Logic

SOA is built upon and evolving from older concepts of distributed computing and modular programming, OWL-S plays a key role in describing behaviors of web services, which are the essential of the SOA software. Although OWL-S has given semantics to concepts by ontology technology, it gives no semantics to control-flow and data-flow. This paper presents a formal semantics framework for OWL-S sub-set, including its abstraction, syntax, static and dynamic seman-tics by rewrite logic. Details of a consistent transformation from OWL-S SOS of control-flow to corresponding rules and equations, and dataflow semantics including “Precondition”, “Result” and “Binding” etc. are explained. This paper provides a possibility for formal verification and reliability evaluation of software based on SOA.

Semantics and Interpretation

本文通过语义学的发展简史,阐述了语义学与词汇、语义学与文化学等语境之间的关系,在不同的文化语言之间进行翻译时应注意的语义差别。同时,文章从另一个角度阐释了语言中常出现的多种歧义现象,表达同一语言现象在不同的文化背景下必须使用不同的表达方式。语义学作为一门新兴学科,有许多不足和有待于发展的地方,需要求语言学界进一步地开拓和发展。

Towards a Unified Framework of Syntax, Semantics and Logic

1. IntroductionHumans have the ability (or competence) to think logically, and this is an undeniable fact. However,what this ability consists in is a difficult question. It might be said that logical ability consists in theknowledge of a set of logic rules. But what are those logic rules? For centuries logicians have devel-

A Contrastive Study on Folk Custom Semantics of Chinese and English Words of Plants and Animals

This paper attempts to make a comparison of folk custom semantics on Chinese and English words of plants and animals.

A Brief Introduction to Phonology, Morphology,Lexicology, and Semantics

Phonetics is a fundamental branch of linguistics and itself has three different aspects.Articulatory Phonetics—describes how vowels and consonants are produced or'articulated'in various parts of the mouth and throat.Acoustic Phonetics—a study of how speech sounds are transmitted:when sound travels through the air from the speaker’s mouth to the hearer’s ear.

Building Requirements Semantics for Networked Software Interoperability

Naturally, like the web, integrated software systems in Internet will have to be distributed and heterogeneous. To im-prove the interoperability of services for SAAS, it is crucial to build requirements semantics that will cross the entire lifecycle of services especially on requirements stage. In this paper, a requirements semantics interoperability extend-ing approach called Connecting Ontologies (CO) that will act as semantics information carrier designing to facilitate the requirements identification and services composition is proposed. Semantic measurement of Chinese scenario is explored. By adopting the approach, a series of tools support for transport domain are developed and applied based on CO and DPO (Domain Problem Ontology) to enforce requirements engineering of networked software efficiently.

Aristotelian Semantics, Homo Economicus, Images, and Brands

Aristotle＇s general theory of meaning is describing for the first time relations among linguistic signs, mental images, and real things. Centuries later, the triangle of meaning or the semiotic triangle became a model of how objects interact with signs and interpreters （C. S. Peirce） or how linguistic symbols are related to the objects they represent （Ogden and Richards 1923）. However, these triangles can be traced back to the 4th century BC, in Aristotle＇s Organon, when it was first mentioned the importance of images and signs in the creation of meaning. The nature of universals as mental images and their relation to the objects is still debated and, recently Lambert Wiesing＇s The Philosophy of Perception challenges current theories of perception. Taking perception to be real is in the core of the new debates about concept of mind. What the reality means for a subject is a central philosophical question （Meztinger, The Ego tunnel）. The new triangle of meaning is not only a relation among objects, realities, signs but a relation among real, objectified entities, irrespective if they are in the mind or outside it. In this new approach, the question of how human perception is possible is reformulated by questions about what perception induces us to be and do. Perceptions are embodied, to be visible, and to continually participate in the public and physical world we perceive. Looking back to Aristotle＇s work from these new approaches our paper argues that Aristotelian images were conceived by him as entities strongly related to action. As mind perceptions which determine us to act, they do not have a passive role but rather taking the lead in our life. This is very much in line with modem philosophical thinking. His thoughts about images and dynamics of reality based on perception and images had important consequences in economics, marketing and branding, giving to perceptions an active role in turning potential reality in actual reality. Brands are in fact images and perceptions in action and interaction and are built in order to compel us to act either to influence or to be influenced.

Intelligent Agent Based Mapping of Software Requirement Specification to Design Model

Automatically mapping a requirement specification to design model in Software Engineering is an open complex problem. Existing methods use a complex manual process that use the knowledge from the requirement specification/modeling and the design, and try to find a good match between them. The key task done by designers is to convert a natural language based requirement specification (or corresponding UML based representation) into a predominantly computer language based design model—thus the process is very complex as there is a very large gap between our natural language and computer language. Moreover, this is not just a simple language conversion, but rather a complex knowledge conversion that can lead to meaningful design implementation. In this paper, we describe an automated method to map Requirement Model to Design Model and thus automate/partially automate the Structured Design (SD) process. We believe, this is the first logical step in mapping a more complex requirement specification to design model. We call it IRTDM (Intelligent Agent based requirement model to design model mapping). The main theme of IRTDM is to use some AI (Artificial Intelligence) based algorithms, semantic representation using Ontology or Predicate Logic, design structures using some well known design framework and Machine Learning algorithms for learning over time. Semantics help convert natural language based requirement specification (and associated UML representation) into high level design model followed by mapping to design structures. AI method can also be used to convert high level design structures into lower level design which then can be refined further by some manual and/or semi automated process. We emphasize that automation is one of the key ways to minimize the software cost, and is very important for all, especially, for the “Design for the Bottom 90% People” or BOP (Base of the Pyramid People).

There Is No Standard Model of ZFC and ZFC_(2) with Henkin Semantics

In this article we proved so-called strong reflection principles corresponding to formal theories Th which has omega-models or nonstandard model with standard part. A possible generalization of Löb’s theorem is considered. Main results are: 1) , 2) , 3) , 4) , 5) let k be inaccessible cardinal then .

ST-SIGMA:Spatio-temporal semantics and interaction graph aggregation for multi-agent perception and trajectory forecasting

Scene perception and trajectory forecasting are two fundamental challenges that are crucial to a safe and reliable autonomous driving(AD)system.However,most proposed methods aim at addressing one of the two challenges mentioned above with a single model.To tackle this dilemma,this paper proposes spatio-temporal semantics and interaction graph aggregation for multi-agent perception and trajectory forecasting(STSIGMA),an efficient end-to-end method to jointly and accurately perceive the AD environment and forecast the trajectories of the surrounding traffic agents within a unified framework.ST-SIGMA adopts a trident encoder-decoder architecture to learn scene semantics and agent interaction information on bird’s-eye view(BEV)maps simultaneously.Specifically,an iterative aggregation network is first employed as the scene semantic encoder(SSE)to learn diverse scene information.To preserve dynamic interactions of traffic agents,ST-SIGMA further exploits a spatio-temporal graph network as the graph interaction encoder.Meanwhile,a simple yet efficient feature fusion method to fuse semantic and interaction features into a unified feature space as the input to a novel hierarchical aggregation decoder for downstream prediction tasks is designed.Extensive experiments on the nuScenes data set have demonstrated that the proposed ST-SIGMA achieves significant improvements compared to the state-of-theart(SOTA)methods in terms of scene perception and trajectory forecasting,respectively.Therefore,the proposed approach outperforms SOTA in terms of model generalisation and robustness and is therefore more feasible for deployment in realworld AD scenarios.

Real-time collaborative design based on operation semantics in distributed CAD environment

This paper proposes a collaborative design model based on operation semantics in a distributed computer-aided design (CAD) environment. The goal is to reduce time consumption in data format conversion and the requirement of network bandwidth so as to improve the cooperative ability and the synchronization efficiency. Firstly, real-time collaborative design is reviewed and three kinds of real-time collaborative design models are discussed. Secondly, the concept of operation semantics is defined and the framework of an operation semantics model is presented. The operation semantics carries the original design data and actual operation process to express design intent and operation activity in conventional CAD systems. Finally, according to the operation semantics model, a CAD operation primitive is defined which can be retrieved from and mapped to the local CAD system operation commands; a distributed CAD collaborative architecture based on the model is presented, and an example is given to verify the model.

The “Prototype and Stereotype” Theory in Lexical Semantics Teaching Methodology

Cognitive linguistics studies the relationship between language and cognition,which claims that semantics structures reflect the mental categories which people have formed their experiences of growing up and acting in the world.The lexical and semantics combined together to be the foundations of language which makes into people' s communication.As the main branch of cognition,the prototype and stereotype theory have a close relationship to the lexical semantics.Thus,it is essential and useful to make a research on the lexical semantics under the influence of the "prototype and stereotype theory" and it will help a lot in teaching methodology.

A Limited Negative-based Three Value Semantics of Inductive Logic Programs

Inductive logic programming adopts the standard horn lope program as its logic framework for inductivelearning. Due to the fact, however, that the expressive power of horn logic is relatively limited and the mechansm ofnegation is mostly that of negation as failure, it is difficult to make full use of negative information and consequentlynot suitable for inductive learning. This Paper adopts nounal lope program as me language of inductive logic programsand presents accordingly a kind of semantics called Limited Negation semantics. The issues of direct denotation andinference of negation in concept induction are solved. The paper shows that LN is directly generalized for the semantics of Well-Founded in die significance Of optional negation and has superior theoretical features, especially the capability Of expressing and processing negation by introducing the constant ’false’. ExperimentS also show that the inductive concepts in learning are accurately interpreted with LN.

Universal Semantics in Translation

What and how we translate are questions often argued about. No matter what kind of answers one may give, priority in translation should be granted to meaning, especially those meanings that exist in all concerned languages. This research defines them as universal sememes, and the study of them as universal semantics, of which applications are also briefly looked into.