作者: 詞匯表 時(shí)間: 2025-3-21 20:45 作者: 寬宏大量 時(shí)間: 2025-3-22 01:51 作者: inchoate 時(shí)間: 2025-3-22 05:27 作者: 世俗 時(shí)間: 2025-3-22 08:54
https://doi.org/10.1007/978-981-15-2878-1hat extent do device models influence the behaviors of their users? The answer to this question is critical to almost every stakeholder in the smartphone app ecosystem, including app store operators, developers, end-users, and network providers. To approach this question, we collect a longitudinal d作者: PACT 時(shí)間: 2025-3-22 14:30
https://doi.org/10.1007/978-981-15-2878-1e-base, an artificial bee colony algorithm combined with Gaussian disturbance optimization was introduced, and a novel Belief rule-base parameter training method was proposed. By the light of the algorithm principle of the artificial bee colony, the honey bee colony search formula and the cross-bord作者: 淺灘 時(shí)間: 2025-3-22 21:03
https://doi.org/10.1057/978-1-137-56036-0h. However, the most existing community search methods do not consider the influence of nodes and can not perfectly support the search in large graphs, making them have limitations in practical applications. In this paper, we introduce a community model called . community based on .-core decompositi作者: sterilization 時(shí)間: 2025-3-23 00:20
https://doi.org/10.1057/978-1-137-56036-0entific impact prediction, which is mainly based on longtime accumulated citation networks, metadata and the whole text of papers, is relatively hysteretic and can hardly fit the rapid development of technology. Moreover, Twitter has become one of the most import channels to spread latest technique 作者: HAWK 時(shí)間: 2025-3-23 02:26 作者: 沉思的魚 時(shí)間: 2025-3-23 07:54
https://doi.org/10.1057/978-1-137-56036-0eries on time-dependent network to find the optimal path, for example: shortest route, highest scoring route, etc. However, in practical application, users will want to be satisfied with the constraint and evaluate the good routes to make a choice, for example, users want to look for the well-evalua作者: 邪惡的你 時(shí)間: 2025-3-23 12:03 作者: STRIA 時(shí)間: 2025-3-23 14:34
https://doi.org/10.1007/978-3-030-15777-7 vertices and relations, so it is difficult to deal directly with data mining. At present, although many state-of-the-art methods of network representation learning have been developed, these methods can only deal with homogeneous networks or lose information when handling heterogeneous networks. In作者: 清晰 時(shí)間: 2025-3-23 19:21 作者: 燕麥 時(shí)間: 2025-3-24 02:07
Intimate Investments in Drag King Culturesn, we propose to detect the local differences between two images. We frame this task as a saliency map regression problem, where the saliency map measures the degree of discrepancy at every pixel. To achieve this goal, we use a convolutional neural network (CNN) to map two aligned vehicle images to 作者: 教義 時(shí)間: 2025-3-24 05:47 作者: 追逐 時(shí)間: 2025-3-24 07:19 作者: 尖牙 時(shí)間: 2025-3-24 13:40
https://doi.org/10.1007/b107943cations such as mining potential associations between users in online social networks. In recent years, graph processing frameworks such as Pregel bring in a vertex-centric, Bulk Synchronous Parallel (BSP) programming model for processing massive data graphs and achieve encouraging results. However,作者: Entreaty 時(shí)間: 2025-3-24 16:47
https://doi.org/10.1007/978-981-13-2922-7artificial intelligence; big data; cloud computing; computer systems; data mining; data security; evolutio作者: 突變 時(shí)間: 2025-3-24 21:22 作者: Supplement 時(shí)間: 2025-3-25 01:34
Communications in Computer and Information Sciencehttp://image.papertrans.cn/b/image/185568.jpg作者: BAIT 時(shí)間: 2025-3-25 04:04 作者: 浪費(fèi)時(shí)間 時(shí)間: 2025-3-25 10:43 作者: Virtues 時(shí)間: 2025-3-25 12:35 作者: 極肥胖 時(shí)間: 2025-3-25 16:13
Mining Device-Specific Apps Usage Patterns from Appstore Big Datahat extent do device models influence the behaviors of their users? The answer to this question is critical to almost every stakeholder in the smartphone app ecosystem, including app store operators, developers, end-users, and network providers. To approach this question, we collect a longitudinal d作者: 瘋狂 時(shí)間: 2025-3-25 20:57 作者: depreciate 時(shí)間: 2025-3-26 00:43 作者: conjunctivitis 時(shí)間: 2025-3-26 04:52
Real-Time Scientific Impact Prediction in Twitterentific impact prediction, which is mainly based on longtime accumulated citation networks, metadata and the whole text of papers, is relatively hysteretic and can hardly fit the rapid development of technology. Moreover, Twitter has become one of the most import channels to spread latest technique 作者: 意外 時(shí)間: 2025-3-26 10:07 作者: 美學(xué) 時(shí)間: 2025-3-26 16:01 作者: 誤傳 時(shí)間: 2025-3-26 20:34 作者: gout109 時(shí)間: 2025-3-26 22:44
Multiple Meta Paths Combined for Vertex Embedding in Heterogeneous Networks vertices and relations, so it is difficult to deal directly with data mining. At present, although many state-of-the-art methods of network representation learning have been developed, these methods can only deal with homogeneous networks or lose information when handling heterogeneous networks. In作者: jeopardize 時(shí)間: 2025-3-27 05:07 作者: sed-rate 時(shí)間: 2025-3-27 08:49 作者: 翻布尋找 時(shí)間: 2025-3-27 09:48 作者: habitat 時(shí)間: 2025-3-27 16:59
Research on Urban Street Order Based on Data Mining Technologyamount of problems in urban management are increasing. Under the background of new era, the direction and requirement of the city governance has promoted influenced by the “Internet Plus” strategy and big data strategy. In the construction of information and intelligent construction of urban managem作者: 使更活躍 時(shí)間: 2025-3-27 21:17
A Vertex-Centric Graph Simulation Algorithm for Large Graphscations such as mining potential associations between users in online social networks. In recent years, graph processing frameworks such as Pregel bring in a vertex-centric, Bulk Synchronous Parallel (BSP) programming model for processing massive data graphs and achieve encouraging results. However,作者: Peak-Bone-Mass 時(shí)間: 2025-3-28 01:04 作者: Parley 時(shí)間: 2025-3-28 03:41
Conference proceedings 2018processing and text mining; big data analytics and smart computing; big data applications; the application of big data in machine learning; social networks and recommendation systems; parallel computing and storage of big data; data quality control and data governance; big data system and management..作者: Ascribe 時(shí)間: 2025-3-28 06:41 作者: 使成核 時(shí)間: 2025-3-28 12:25
Intimate Investments in Drag King Culturesone saliency map. Our network design enables end-to-end training. We validate our algorithm on a vehicle image dataset. Experimental results show that our approach is accurate, fast and robust, and it achieves better performance than other methods.作者: 不發(fā)音 時(shí)間: 2025-3-28 17:26 作者: 寄生蟲 時(shí)間: 2025-3-28 22:23 作者: Cardioversion 時(shí)間: 2025-3-28 23:51 作者: 猛擊 時(shí)間: 2025-3-29 05:47
Intimate Investments in Drag King Cultureshe proposed correlation filter, we design an efficient ADMM (alternation direction of multipliers) solver. Extensive experimental results on the OTB-2013 dataset show that the proposed approach performs favorably against state-of-the-art trackers.作者: Dawdle 時(shí)間: 2025-3-29 07:15
The Influence of Online Community Interaction on Individual User Behaviorl media community. We then expound the result from our analysis on data gathered and reach certain conclusions which may be useful to the general public who participate in social media communities and may of value to the regulatory agencies and commercial users of social media as well.作者: AGGER 時(shí)間: 2025-3-29 14:17
An Optimized Artificial Bee Colony Based Parameter Training Method for Belief Rule-Baseraining was implemented in combination with the constraint conditions of the Belief rule-base. By fitting the multi-peak function and the leakage detection experiment of oil pipelines, the experimental error were compared with the traditional and existing parameter training methods to verify its effectiveness.作者: 索賠 時(shí)間: 2025-3-29 15:58
Search of , Community in Large Graphsnd online search algorithms (SingleQuery and MultiQuery) which support efficient search of . community in optimal time. Extensive experiments on four real-world large networks demonstrate the efficiency and effectiveness of our methods.作者: monogamy 時(shí)間: 2025-3-29 23:44 作者: Monotonous 時(shí)間: 2025-3-30 03:14 作者: Generic-Drug 時(shí)間: 2025-3-30 05:27
Multiple Meta Paths Combined for Vertex Embedding in Heterogeneous Networksctural information in the network. We conduct experiments on two real world datasets. The experimental results demonstrate the efficacy and efficiency of the proposed method in heterogeneous network mining tasks. Compare to the previous method, our model can cover a wider range of semantic information and be more flexible and scalable.作者: 平息 時(shí)間: 2025-3-30 12:02 作者: 萬神殿 時(shí)間: 2025-3-30 12:22 作者: headlong 時(shí)間: 2025-3-30 19:37
1865-0929 social networks and recommendation systems; parallel computing and storage of big data; data quality control and data governance; big data system and management..978-981-13-2921-0978-981-13-2922-7Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: 成份 時(shí)間: 2025-3-30 21:31 作者: cylinder 時(shí)間: 2025-3-31 01:13
A Method to Chinese-Vietnamese Bilingual Metallurgy Term Extraction Based on a Pivot Language. The method, under the resource absence of Chinese-Vietnamese bilingual alignment corpus, is validated as an effective solution to the difficult problem for Chinese-Vietnamese bilingual metallurgy term extraction.作者: 惰性氣體 時(shí)間: 2025-3-31 08:05
Preprocessing and Feature Extraction Methods for Microfinance Overdue Dataent, including LR, GBDT, XGBoost and RF. Meanwhile, we not only use AUC but also design a new evaluation index that can be adapted to the business background to evaluate the system’s performance. Experiments results show that, in the case of a surge in business volume and around 1.5% of the overdue 作者: TEM 時(shí)間: 2025-3-31 12:31 作者: 一個(gè)攪動(dòng)不安 時(shí)間: 2025-3-31 15:21 作者: Corral 時(shí)間: 2025-3-31 19:35
Online Matrix Factorization Hashing for Large-Scale Image Retrievalin this paper which combines matrix factorization with the idea of online hashing. This method considers the relationship between the previous data and newly arriving data. In addition, it updates the hashing learning model by the matrix factorization when the new data is arrived. The experimental r作者: lattice 時(shí)間: 2025-3-31 22:29
