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In this short paper, we take a hint from biology about forming collectives. Some mammal heart muscle cells, when isolated in the laboratory, become viable single-cell animals, with many observable traits in common with other "wild" single-cell animals. That is to say, these autonomous systems (the isolated cells) give up an enormous amount of their capability (movement, certain kinds of...
Natural language-based use cases remains the main means of requirements elicitation and specification, despite the well-known problems that accompany natural language specifications, namely their incompleteness, inconsistency and ambiguity. This paper presents a novel approach for tackling the textual description problems by using FrameNet frames, which are linguistics patterns for concept description...
Rounding errors are introduced pervasively when using floating-point arithmetic to approximate real arithmetic. The accumulation or catastrophic cancellation of rounding errors in numerical programs may produce high inaccuracy results, which can cause serious software failures once being triggered. High inaccuracies are known hard to debug and fix manually for developers. Hence, the automated techniques...
Word sense disambiguation is the process of identifying existence of polysemous words in the text and disambiguating the appropriate sense satisfying the given context in Kannada language. The proposed methodology uses the synonyms of the target word and its surrounding words' gloss in combination with part of speech tagging to determine the overlap between the senses of the polysemous word or the...
Ontology Matching is a process to find correspondences between semantically related entities of two ontologies. Most matching systems do evaluation by comparing the correspondences with reference alignment. Since 2010 another method has been used to measure a logic-based of correspondence or mapping, called incoherent mapping measurement. The more incoherent of the mapping the lower quality of mapping...
In many applications, such as data integration and big data analytics, one has to integrate data from multiple sources without detailed and accurate schema information. The state of the art focuses on matching attributes among sources based on the information derived from the data in those sources. However, a best join result according to a method's own pre-determined criteria may not fit a user's...
The various techniques are proposed for better semantic segmentation. The neuro-fuzzy technique is proposed for learning common nature between object and structure. The proposed technique work better for robotic environment for fast and efficient results. The proposed technique provides better accuracy as compared to previous technique and work better in semantic segmentation as compared to previous...
Program invariant is formal description of properties that should hold at certain program location in every valid execution. It is very useful for program analysis and verification. In this paper, we introduce an abstraction interpretation approach for generating program invariant efficiently and precisely. A polynomial interval domain is proposed for representing abstract state and precise loop effect...
Different from current researches on Flickr group recommendation approaches that recommend groups to either users or images, this work proposes a unified framework that recommends groups to both users and images. Four types of entities in the Flickr system (users, tags, images, and groups) are integrated into a tetradic model, and then we uses tetradic decomposition to discover the latent semantic...
Despite reusability being a Learning Objects (LO) core feature, sometimes they demand modifications to fit the new use context. This adaptation process must be easy and rapid. However, several LO are produced in a way that changes need to be done at source code, requiring technical knowledge. Guided Customization (GC) aims at enabling the user to execute adaptations without this requirement. It allows...
While there is a large amount of text data on the Internet, people need to organize the text data with experienced category. However, the flat structure of categories could not satisfy the modern information management. To solve this problem, we propose a hierarchical classification process with a strategy, called candidates, used to relieve the blocking problems. Besides, we establish the description...
In this paper, we propose a reconstruction-based supervised hashing (RSH) method to learn compact binary codes with holistic structure preservation for large scale image search. Unlike most existing hashing methods which consider pair-wise similarity, our method exploits the structural information of samples by employing a reconstruction-based criterion. Moreover, the label information of samples...
Semantic instance segmentation remains a challenge. We propose to tackle the problem with a discriminative loss function, operating at pixel level, that encourages a convolutional network to produce a representation of the image that can easily be clustered into instances with a simple post-processing step. Our approach of combining an offthe- shelf network with a principled loss function inspired...
To develop with lower costs information systems that do not violate regulations, it is necessary to elicit requirements compliant to the regulations. Automated supports allow us to avoid missing requirements necessary to comply with regulations and to exclude functional requirements against the regulations. In this paper, we propose a technique to detect goals relevant to regulations in a goal model...
In ubiquitous IoT environment, recommending services are getting popular in daily livings that depend on user identification, location, activity, situation and preferences. To provide scalable and dynamic solutions for smart spaces service features, Web of Objects facilitates real world object virtualization using semantic ontology. Cognitive functionalities in Web of Objects combines intelligence...
Data-parallel jobs transfer massive amounts of data between a series of successive stages. The coflow abstraction is proposed to represent a group of parallel flows between two stages and efficiently improves stage-level performance. However, state-of-the-art coflow scheduling techniques are agnostic to the jobs' inter-coflow semantics and thus are suboptimal in reducing the average job completion...
In this paper we present a new approach to implementing causal consistency in geo-replicated data stores, which we call Optimistic Causal Consistency (OCC). The optimism in our approach lies in that updates from a remote data center are immediately made visible in the local data center, without checking if their causal dependencies have been received. Servers perform the dependency check needed to...
Recently, much attention has been paid to music retrieval. Yet, it is not easy to conduct high-performance music retrieval due to the semantic gap. Therefore, this paper presents an effective and efficient method to partially solve this problem. In terms of effectiveness, the acoustic features are improved to increase the precision of retrieval. In terms of efficiency, a depth-first-search strategy...
Memory caches are being aggressively used in today's data-parallel systems such as Spark, Tez, and Piccolo. However, prevalent systems employ rather simple cache management policies — notably the Least Recently Used (LRU) policy — that are oblivious to the application semantics of data dependency, expressed as a directed acyclic graph (DAG). Without this knowledge, memory caching can at best be performed...
We study mobile user behavior from cell-level location trace (CLLT). Since CLLT contains no GPS coordinates of mobile users, we infer approximate user locations from the cell locations they visit. We build upon the vast literature on user behavior analysis and demonstrate the ability to extract user behavior in the absence of the more precise GPS information. We focus on the “leisure time” behavior,...
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