Multi-granularity

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A Node Localization Algorithm Based on Multi-Granularity Regional Division and the Lagrange Multiplier Method in Wireless Sensor Networks

A Node Localization Algorithm Based on Multi-Granularity Regional Division and the Lagrange Multiplier Method in Wireless Sensor Networks

A multi-granularity partition method is proposed in this paper. Firstly, according to the characteristics of Thiessen polygons, positioning areas were preliminarily divided using the anchor node location and communication. Secondly, we can get overlapping portions by combining the adjacent nodes triangle with a polygon area. By gradually narrowing the scope, we will lock target nodes into the possible regional positioning. Finally, the coordinate is calculated using the localization algorithm.

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Multi Granularity Self Attention for Neural Machine Translation

Multi Granularity Self Attention for Neural Machine Translation

Current state-of-the-art neural machine trans- lation (NMT) uses a deep multi-head self- attention network with no explicit phrase in- formation. However, prior work on statis- tical machine translation has shown that ex- tending the basic translation unit from words to phrases has produced substantial improve- ments, suggesting the possibility of improv- ing NMT performance from explicit model- ing of phrases. In this work, we present multi-granularity self-attention (M G -S A ): a neural network that combines multi-head self- attention and phrase modeling. Specifically, we train several attention heads to attend to phrases in either n-gram or syntactic for- malism. Moreover, we exploit interactions among phrases to enhance the strength of structure modeling – a commonly-cited weak- ness of self-attention. Experimental results on WMT14 English-to-German and NIST Chinese-to-English translation tasks show the proposed approach consistently improves per- formance. Targeted linguistic analysis reveals that M G -S A indeed captures useful phrase in- formation at various levels of granularities.
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Self-adjusting multi-granularity locking protocol for object-oriented databases

Self-adjusting multi-granularity locking protocol for object-oriented databases

Liou, "A Multi-granularity Locking Model for Concurrency Control in Object- Oriented Database Systems," IEEE Transactions on Knowledge and Data Engineering, vol. Ram, " To[r]

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A multi granularity pattern based sequence classification framework for educational data

A multi granularity pattern based sequence classification framework for educational data

More particularly, we formulate a multi-granularity framework for classifying sequences of discrete events. The framework consists of three phases: feature generation, feature selection, and model construction. The proposed feature generation tech- nique can effectively capture the inherent temporal structure of the sequences by mining frequent sequential patterns at different window sizes. The extracted features capture not only the temporal aspects of the underlying sequences, but also their variability at multiple levels of time granularity. Next, the most important features are identified by applying standard variable importance algorithms for feature selection. The classification model is then constructed by using the selected features.
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Complex Big Data Analysis Based on Multi-granularity Generalized Functions

Complex Big Data Analysis Based on Multi-granularity Generalized Functions

MGGF can reflect the prototype of the linear feature pattern in the data set with a relatively small number of multi-granularity pattern classes, which has the integrity and globality in the recognition of the data prototype. Therefore, it has relatively strong noise data processing ability and data generalization ability. However, GFDM can only rely on the grid like partition of the domain of discourse space of the data set in advance to approximate a certain data class from the local fine grid point. The smaller the block shapes of the grid point, the higher the data partition accuracy, hence the more the required number of points of division. However, the increase in the number of the points of division will not only lead to the sensitivity of the model to the data noise, but also result in the exponential increase in the number of the GFDM models to be calculated, hence causing great computational burden.
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Multi Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering

Multi Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering

This paper describes a novel hierarchical attention network for reading comprehen- sion style question answering, which aims to answer questions for a given narrative paragraph. In the proposed method, atten- tion and fusion are conducted horizontally and vertically across layers at different levels of granularity between question and paragraph. Specifically, it first encode the question and paragraph with fine-grained language embeddings, to better capture the respective representations at semantic level. Then it proposes a multi-granularity fusion approach to fully fuse information from both global and attended representa- tions. Finally, it introduces a hierarchical attention network to focuses on the answer span progressively with multi-level soft- alignment. Extensive experiments on the large-scale SQuAD and TriviaQA datasets validate the effectiveness of the proposed method. At the time of writing the pa- per (Jan. 12th 2018), our model achieves the first position on the SQuAD leader- board for both single and ensemble mod- els. We also achieves state-of-the-art re- sults on TriviaQA, AddSent and AddOne- Sent datasets.
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Elastic Resource Allocation for Multi Granularity Multicasting Traffic  in OFDM Based Optical Networks

Elastic Resource Allocation for Multi Granularity Multicasting Traffic in OFDM Based Optical Networks

Traditional WDM network assigns a fixed spectral grid (50 GHz or 100 GHz) for each traffic demand. When the multicast traffic is lower than the entire wave- length capacity, it results in inefficient resource utilization because of the rigid fixed-grid and coarse bandwidth allocation. However, elastic optical networks based on optical OFDM technology adopt flexible bandwidth allocation to match multi-granularity traffic by elastically accommodating multiple subcar- riers [13], and the spectrum gird is set 12.5 GHz or 6.25 GHz. Lower spectrum gird is easier to provide fine-granularity capacity to satisfy the traffic demands, and the bandwidth utilization is improved. Therefore, optical OFDM technology has been regarded as a promising optical transmission technology because of its high-speed transmission, high spectral efficiency and flexible spectrum allocation. Optical OFDM technology can satisfy multi-granularity traffic demand by adjusting the number of OFDM multiplexing subcarrier. Figure 1 illustrates the routing and spectrum resource allocation scheme for three parallel multi-granu- larity traffic demands.
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Dynamic Multi Granularity Service Composition

Dynamic Multi Granularity Service Composition

The trend is for enterprises to outsource parts of their services, in order to concentrate on their own core `businesses. Meanwhile, users usually need to compose multiple different services to create a sophisticated application. Through the service-oriented architecture paradigm, users can compose elementary services to form new value added services through the process of service composition. In template-based service composition, an abstract composite service, consisting of a collection of abstract services orchestrated by workflow patterns, is first defined and then instantiated and executed at run time by binding abstract services to concrete ones. This dynamic binding ensures a loose - coupling of services and all so-called QoS-aware service composition problem. In existing work, to expand the selection scope using the concept of generalized component services, a backtracking-based algorithm and an extended genetic algorithm(GA) has been applied for finding an optimized solution and near-optimal solution respectively in composition service The proposed work, will adopt the multi-granularity service composition automatically at run time. This will be useful to study how to extend other Meta-heuristic algorithms along with Tabu-search algorithm used for efficient optimization service selection.
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Multi Layer, Multi Dimensional and Multi Granularity Network Model to Measure Network Security

Multi Layer, Multi Dimensional and Multi Granularity Network Model to Measure Network Security

Abstract. Recently, network security is more and more important and the network security measure is the premise to build a robust and secure network. However, the methods of measuring the network security are limited. For example, the most methods are not comprehensive, which only consider a part of the network ignoring the overall network. Therefore, this paper proposes a new multi-layer, multi-dimensional and multi-granularity network model based on the attack graph and CVSS. The model divides the network into four layers. The four layers can measure the network security completely and effectively. For each layer, two dimensions are quantified. The measure value of each layer is rated score 0-10. In addition, this model takes the different network granularities into account, making the network security model more comprehensive. In order to examine the validity of the network model, this paper carries out two experiments by configuring five networks with different security configurations. It is found that this model not only can identify the network security level effectively but also can quickly locate the security problems.
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Multi Granularity Chinese Word Embedding

Multi Granularity Chinese Word Embedding

This paper considers the problem of learning Chinese word embeddings. In contrast to En- glish, a Chinese word is usually composed of characters, and most of the characters them- selves can be further divided into components such as radicals. While characters and radical- s contain rich information and are capable of indicating semantic meanings of words, they have not been fully exploited by existing word embedding methods. In this work, we propose multi-granularity embedding (MGE) for Chi- nese words. The key idea is to make full use of such word-character-radical composition, and enrich word embeddings by further incorpo- rating finer-grained semantics from characters and radicals. Quantitative evaluation demon- strates the superiority of MGE in word sim- ilarity computation and analogical reasoning. Qualitative analysis further shows its capabili- ty to identify finer-grained semantic meanings of words.
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An I vector Based Approach to Compact Multi Granularity Topic Spaces Representation of Textual Documents

An I vector Based Approach to Compact Multi Granularity Topic Spaces Representation of Textual Documents

4 Compact multi-view representation In this section, an i-vector-based method to represent automatic transcriptions is presented. Initially introduced for speaker recognition, i- vectors (Kenny et al., 2008) have become very popular in the field of speech processing and re- cent publications show that they are also reli- able for language recognition (Martınez et al., 2011) and speaker diarization (Franco-Pedroso et al., 2010). I-vectors are an elegant way of re- ducing the imput space dimensionality while re- taining most of the relevant information. The technique was originally inspired by the Joint Factor Analysis framework (Kenny et al., 2007). Hence, i-vectors convey the speaker characteris- tics among other information such as transmission channel, acoustic environment or phonetic content of speech segments. The next sections describe the i-vector extraction process, the application of this compact representation to textual documents (called c-vector), and the vector transformation with the EFR method and the Mahalanobis met- ric.
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Multi Granularity Representations of Dialog

Multi Granularity Representations of Dialog

Recent work has focused on improving latent rep- resentations of language through the use of large- scale self-supervised pre-training on very large corpora. Kiros et al. (2015) trains a sequence-to- sequence model (Sutskever et al., 2014) to predict the surrounding sentences, and uses the final en- coder hidden state as a generic sentence represen- tation. ELMo (Peters et al., 2018) trained a bi- directional language model on a large corpus in or- der to obtain strong contextual representations of words. OpenAI’s GPT (Radford et al., 2018) pro- duces latent representations of language by train- ing a large transformer (Vaswani et al., 2017) with a language modelling objective. Devlin et al. (2018) further improves on this line of research by introducing the masked language modelling objec- tive and a multi-tasking pre-training loss. Each of these methods has obtained state-of-the-art results on the GLUE benchmark (Wang et al., 2018), sug- gesting that they are strong and general represen- tations of language.
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A Multilingual, Multi style and Multi granularity Dataset for Cross language Textual Similarity Detection

A Multilingual, Multi style and Multi granularity Dataset for Cross language Textual Similarity Detection

Note that the performances of the methods using external resources such as ontologies, dictionaries or corpora, are extremely dependent of these resources. It is also impor- tant to note that the confidence intervals are larger on the document-level (with an average of 1.37% against 0.61% for the sentence-level and 0.76% for the chunk-level) be- cause during the evaluation of this granularity, the number N of evaluated units is such that N 6= |S| but N = 2,000. There is a strong correlation between the results of meth- ods on the three granularities (average of 0.938), except be- tween the chunk- and sentence- level for CL-CTS (0.757) and between the sentence- and the document- level for CL-ASA (0.493). Some methods on some sub-corpora are more efficient on fairly small textual units (CL-C3G on Wikipedia sub-corpus) while other methods are more effi- cient on longer units (CL-C3G on TALN sub-corpus), al- though the average best results are obtained at the chunk- level. Generally, all the methods see their performances gradually deteriorate as the granularity of compared doc- uments increases, however we also see that many meth- ods see their performances stagnated between the sen- tence and document level (CL-CTS or CL-ESA for exam- ple). Also, the results tend to be better on Wikipedia, APR and Europarl corpora because the ratio of named entities present in these corpora is more important (see Table 2). The trend of the results on parallel corpora commonly used in evaluation tasks (e.g. JRC, APR and Europarl), at the sentence- and document- level, correlate very well (0.875) with scientific papers sub-corpus (TALN). This suggests that a method efficient on JRC and Europarl corpora should be useful for cross-language similarity detection on scien- tific papers.
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Granular computing with multiple granular layers for brain big data processing

Granular computing with multiple granular layers for brain big data processing

MGrC considers multiple levels of IG when solving a problem, and there have been a lot researches in this regard [41–45, 62–69]. Three basic mechanisms of MGrC can be summarized from these research works with regard to the way in which multi-granular levels are used in problem solving. They are granularity optimization, granularity conversion, and multi-granularity joint computation. In granularity optimization, the most suitable granular level of a domain is chosen for the multi-granular information/ knowledge representation model (MGrR), and the most efficient and satisfactory enough solution is generated on it [41–43]. Granularity conversion means the working gran- ularity layer will be switched between adjective layers or jump to a higher or lower granular layer, in accordance with the requirements of solving a problem [44, 45]. Multi- granularity joint computation takes a problem-oriented MGrR as input, and every layers of the MGrR are employed jointly to achieve a correct solution to the problem. Each of the three mechanisms has its particular type of problem to deal with.
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Lattice CNNs for Matching Based Chinese Question Answering

Lattice CNNs for Matching Based Chinese Question Answering

How LCNs utilizes Multi-Granularity To investigate how LCNs utilize multi-granularity more intuitively, we analyze the MRR score against granularities of overlaps between questions and answers in DBQA dataset, which is shown in Figure 3. It is demonstrated that CNN-char performs better than CNN-CTB impressively in first few groups where most of the overlaps are single characters which will cause serious word mismatch. With growing of the length of overlaps, CNN-CTB is catching up and finally overtakes CNN-char even though its overall performance is much lower. This results show that word information is com- plementary to characters to some extent. The LCN-gated is approaching to the CNN-char in first few groups, and outperforms both character and word level models in next groups, where word level information become more pow- erful. This demonstrates that LCNs can effectively take ad- vantages from different granularities, and the combination will not be harmful even when the matching clues presents in extreme cases.
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The problem of granularity for scientific explanation

The problem of granularity for scientific explanation

Suppose that I form the desire to move my arm, and subsequently move my arm. There are at least two ways of describing this process. On the one hand, a mental event (I form the desire) causes an action (I move my arm). On the other hand, a neural event (a physical process occurs in my brain that accounts for my forming the desire) causes me to move my arm. This multi-level picture is not unique to the relationship between psychology and neurobiology. One could tell a similar story with respect to the relationship between economics and psychology, biology and chemistry, chemistry and physics, and so on. Putative multi-level causal relations can cross disciplines: one could tell a coarse-grained story about El Niño weather patterns having a particular effect on the GDP of Australia, or one could tell a fine-grained story about certain combinations of sea surface temperatures and zonal winds having the same impact. In each instance, the level of causal description that we give for an event corresponds to the level at which we seek to explain that same event. When we ask why I moved my arm, or why the growth rate of Australian GDP slowed, it seems that we can give causal explanations at different levels of granularity. As discussed in the introduction to this dissertation, the thesis that one can often give an adequate causal explanation of the same event at varying levels of granularity is defended by Jackson and Pettit (1988, 1990, 1990, 1992, see also Pettit 2017). This thesis is also implicit in Davidson’s (1970) philosophy of mind. Following Jackson and Pettit, I label this view causal ecumenism.
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Granularity Effects in Tense Translation

Granularity Effects in Tense Translation

The paper is organized as follows. In sections 2 through 4 we present the problem and discuss the linguistic factors involved, always keeping an eye on their exploitation for disambiguation. Sections 5 and 6 are devoted to an abstract def- inition of temporal granularity and a discussion of granularity eects on scope resolution. In sec- tion 7 the actual disambiguation algorithm is presented, while section 8 describes its perfor- mance on the Verb mobil test data. A summary

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National Urban Climate Clustering Analysis for Cluster Analysis of Container Handling Stations

National Urban Climate Clustering Analysis for Cluster Analysis of Container Handling Stations

In summary, for regional clustering, the spatial granularity used is the prefecture-level city, and the time granularity is the month, because the minimum statistical time period of rain[r]

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Generalization in Qualitative IS Research - Approaches and their Application to a Case Study on SOA Development

Generalization in Qualitative IS Research - Approaches and their Application to a Case Study on SOA Development

Moreover, based on only one case study, it is impossible to determine the extent to which a single factor contributes to certain outcomes. After conducting more case studies, a factor analysis could help to understand more reliably how the combination of identified factors affects the use of methods. Since there is already a large body of literature that provides a comprehensive list of context factors for domains different from SOA, future research should concentrate on identifying those that have a significant impact on SOA implementation projects and, in doing so, reason about the mechanisms more deeply. Quantitative research should aim at validating the resulting hypotheses and models. The model of soft factor transition links experience with SOA design and implementation aspects (flexibility and reusability) and provides a description of a generative mechanism which is likely to be at work. One question for future research that arises from this model is if a technical (resp. business- oriented) understanding of services necessarily leads to fine-grained (resp. coarse-grained) services and a bottom up (resp. top down) approach. Most likely, more experience would enable the project team to flexibly adapt granularity, create services on different levels of granularity, or apply a top down (resp. a hybrid) approach. Further case studies would help to shed light on these cause-and- effect relationships.
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Online Full Text

Online Full Text

Zarrin et al. [6] present a model to assess maintainability of SOA systems. The idea is quite similar to ours in that service structural properties in the design phase as well as service management mechanism structures in the operation phase are considered as effective factors in assessing service maintainability. In the design phase, the factors are three design properties from [4], i.e., coupling, cohesion, and granularity, and in the operation phase, the factors are the ITIL processes that are practiced in the system. However, their model is only conceptual, without specific assessment details. Other work focuses merely on structural properties of the design, such as the work by Perepletchikov [7] which focuses on cohesion and coupling as useful predictors of maintainability of service-oriented software. Leotta et al. [8] take a different approach and compare maintainability of non-SOA and SOA systems. They focus only on changeability at the architectural level, i.e., maintainability is determined by the number of architectural components of the system which are affected by the change requests as well as the level of efforts put to respond to the change requests.
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