Classification and regression are most interesting problems in the fields of pattern recognition. The regression problem can be changed into binary classification problem and least squares support vector machine can b...
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In this paper, we introduce the condition of distributed computing at present firstly. On this foundation, according to the analysis of particular case of distributed computing network, we implement a distributed netw...
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In this paper, we introduce the condition of distributed computing at present firstly. On this foundation, according to the analysis of particular case of distributed computing network, we implement a distributed network environment of P2P whose bottom layer frame is based on JXTA. It improves issuing method of message based on pipeline decomposition mechanism of task based on usage ratio of processor and distributed mechanism of object based on serialization. We build an improved distributed computing network platform on the platform of Linux and Windows and simulate a distributed computing environment. The simulated experiment proves the feasibility and the validity of the distributed network computing platform that is constructed in this paper
The integration of database and information retrieval techniques provides users with a wide range of high quality services. We present a prototype system, called NUITS, for efficiently processing keyword queries on to...
The integration of database and information retrieval techniques provides users with a wide range of high quality services. We present a prototype system, called NUITS, for efficiently processing keyword queries on top of a relational database. Our NUITS allows users to issue simple keyword queries as well as advanced keyword queries with conditions. The efficiency of keyword query processing and the user-friendly result display will also be addressed in this paper.
The constraint problem can be transformed to an optimization problem. Particle swarm optimization (PSO) is a new evolutionary computation technique. Even PSO has many attractive properties, but it lacks global search ...
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The constraint problem can be transformed to an optimization problem. Particle swarm optimization (PSO) is a new evolutionary computation technique. Even PSO has many attractive properties, but it lacks global search ability at the end of the run. This paper introduce a hybrid approach called the TPSO that simultaneously applies particle swarm optimization (PSO), and tabu search (TS) to create a generally well-performing search heuristics, and combat the problem of premature convergence. The new algorithm considers candidate solutions and their fitness as individuals, which are based on their recent search progress. The tabu search makes each particle to reset its record of its best position, to avoid making direction and velocity decisions on the basis of outdated information. The feasibility of the proposed method is demonstrated on Solving Geometric Constraint Problems.
Recently, ontology learning is emerging as a new hotspot of research in computer science. In this paper the issue of ontology learning is divided into nine sub-issues according to the structured degree (structured, se...
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Recently, ontology learning is emerging as a new hotspot of research in computer science. In this paper the issue of ontology learning is divided into nine sub-issues according to the structured degree (structured, semi-structured, non-structured) of source data and learning objects (concept, relation, axiom) of ontology. The characteristics, major approaches and the latest research progress of the nine sub-issues are summarized. Based on the analysis framework proposed in the paper, existing ontology learning tools are introduced and compared. The problems of current research are discussed, and finally the future directions are pointed out.
This paper presents a new model to incorporate decision theory into Graphplan framework, which enables our planner to handle uncertainty and make decision to choose the optimal one among a set of hypothesis valid plan...
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This paper presents a new model to incorporate decision theory into Graphplan framework, which enables our planner to handle uncertainty and make decision to choose the optimal one among a set of hypothesis valid plans. This planer, called UTDP is tested on several experimental domains. And the experimental results show that UTGP is sound and efficient and performs better than the famous probabilistic planner Buridan.
Smart contracts are programs that permanently store and automatically execute on the blockchain system such as Ethereum. Due to the non-tamperable nature of the underlying blockchain, smart contracts are difficult to ...
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Smart contracts are programs that permanently store and automatically execute on the blockchain system such as Ethereum. Due to the non-tamperable nature of the underlying blockchain, smart contracts are difficult to update once deployed, which requires redeploying the contracts and migrating the data. It means that the observation of smart contract evolution in the real world makes more sense. Hence, in this paper, we conducted the first large-scale empirical study to characterize the evolution of smart contracts in Ethereum. For evolution identification, we presented a contract similarity-based search algorithm, digEvolution, and evaluated its effectiveness with five different search strategies. Then we applied this algorithm to 80,152 on-chain contracts we collected from Ethereum, to dig out the evolution among these contracts. We then explored three research questions. We first studied whether the evolution of smart contracts is common (RQ1), then we studied how do the Gas consumption (RQ2) and the vulnerability (RQ3) of smart contracts vary during the evolution. Our research results show that the evolution of smart contracts is not very common. There are some contract components that have vulnerability but still be called by users. The Gas consumption of most smart contracts doesn’t vary during the evolution, contract is Gas-efficient before and after the evolution. The vulnerability of most smart contracts doesn’t vary during the evolution, both are secure before and after the evolution.
Unsupervised visible-infrared person re-identification (USVI-ReID) aims to match a person across two modalities without annotations. Current research primarily addresses the modality gap by establishing cross-modality...
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Unsupervised visible-infrared person re-identification (USVI-ReID) aims to match a person across two modalities without annotations. Current research primarily addresses the modality gap by establishing cross-modality correspondences through matching algorithms and utilizing memory banks for contrastive learning. However, the inherent noise in pseudo labels and neglect of hard samples often limit the efficacy of cross-modality learning. In this paper, we propose a Dual-modality-shared Learning and Label Refinement (DLLR) algorithm for USVI-ReID. First, we leverage a Cluster Similarity Matching (CSM) module and a Cluster Relationship-based Label Refinement (CRLR) algorithm to create and refine pseudo labels. Then, we adopt a Weighted Modality-shared Memory (WMM) to construct memory banks by jointly considering sample distribution and feature differences, thereby enhancing the effectiveness of cross-modality learning. Extensive experiments on three publicly available datasets validate the effectiveness of our proposed method, which outperforms state-of-the-art methods. Code is available at https://***/CharRic/DLLR.
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