標(biāo)題: Titlebook: Behavior Computing; Modeling, Analysis, Longbing Cao,Philip S. Yu Book 2012 Springer-Verlag London 2012 Behavior Impact Analysis.Behavior [打印本頁(yè)] 作者: whiplash 時(shí)間: 2025-3-21 18:15
書目名稱Behavior Computing影響因子(影響力)
書目名稱Behavior Computing影響因子(影響力)學(xué)科排名
書目名稱Behavior Computing網(wǎng)絡(luò)公開度
書目名稱Behavior Computing網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Behavior Computing被引頻次
書目名稱Behavior Computing被引頻次學(xué)科排名
書目名稱Behavior Computing年度引用
書目名稱Behavior Computing年度引用學(xué)科排名
書目名稱Behavior Computing讀者反饋
書目名稱Behavior Computing讀者反饋學(xué)科排名
作者: 擔(dān)心 時(shí)間: 2025-3-22 00:14 作者: 恭維 時(shí)間: 2025-3-22 04:02
Behaviour Representation and Management Making Use of the Narrative Knowledge Representation Language, NKRL (Narrative Knowledge Representation Language), to deal with the most common types of human “behaviours”. All possible kinds of multimedia “narratives”, fictional or non-fictional, can be seen in fact as streams of elementary events that concern the behaviours, in the most general meaning of 作者: 痛苦一下 時(shí)間: 2025-3-22 06:22 作者: champaign 時(shí)間: 2025-3-22 10:42
P-SERS: Personalized Social Event Recommender Systemlmed by hundreds of social events. In this work, we propose P-SERS, a Personalized Social Event Recommender System, which consists of three phases: (1)?Candidate selection, (2)?Social measurement and (3)?Recommendation. Among these, potential candidate events are selected based on user preference an作者: 鐵砧 時(shí)間: 2025-3-22 14:09 作者: Inoperable 時(shí)間: 2025-3-22 18:13 作者: 易碎 時(shí)間: 2025-3-22 21:19 作者: flourish 時(shí)間: 2025-3-23 02:44 作者: 率直 時(shí)間: 2025-3-23 07:24
An Introduction to Prognostic Searchicit feedback is usually not available to public or even research communities at large for reasons like being a potential threat to privacy of web users. This makes it difficult to experiment and evaluate web search related research and especially web search personalization algorithms. Given these p作者: 復(fù)習(xí) 時(shí)間: 2025-3-23 12:37
Clustering Clues of Trajectories for Discovering Frequent Movement Behaviorser. In addition to spatial and temporal biases, we observe that trajectories contain ., i.e., the time durations when no data points are available to describe movements of users, which bring many challenge issues in clustering trajectories. We claim that a movement behavior would leave some . in its作者: 植物茂盛 時(shí)間: 2025-3-23 15:49
Linking Behavioral Patterns to Personal Attributes Through Data Re-Miningvior pattern analysis. This study presents such a methodology, that can be converted into a decision support system, by the appropriate integration of existing tools for association mining and graph visualization. The methodology enables the linking of behavioral patterns to personal attributes, thr作者: perpetual 時(shí)間: 2025-3-23 21:08
Mining Causality from Non-categorical Numerical Dataa, most of the times causality is difficult to detect and measure. In fact, considering two time series, although it is possible to measure the correlation between both associated variables, correlation metrics don’t show the cause-effect direction and then, . and . variables are not identified by t作者: 開始發(fā)作 時(shí)間: 2025-3-24 01:32
A Fast Algorithm for Mining High Utility Itemsetsequent itemset may not be the itemset with high value. High utility itemset mining considers both of the profits and purchased quantities for the items, which is to find the itemsets with high utility for the business. The previous approaches for mining high utility itemsets first apply frequent ite作者: 沙文主義 時(shí)間: 2025-3-24 06:09
Individual Movement Behaviour in Secure Physical Environments: Modeling and Detection of Suspicious entially suspicious actions, data about the movement of users can be captured through the use of RFID tags and sensors, and patterns of suspicious behaviour detected in the captured data. This chapter presents four types of suspicious behavioural patterns, namely temporal, repetitive, displacement a作者: 鼓掌 時(shí)間: 2025-3-24 09:21 作者: CLOWN 時(shí)間: 2025-3-24 13:11 作者: 值得 時(shí)間: 2025-3-24 16:56 作者: 他姓手中拿著 時(shí)間: 2025-3-24 22:25 作者: forager 時(shí)間: 2025-3-24 23:16
https://doi.org/10.1007/978-94-011-7633-0ever, research on its application to incorporate personalization in generalized software packages is rare. In this paper, we use a semi-Markov model to dynamically display personalized information in the form of high-utility software functions (states) of a software package to a user. We develop a d作者: 原諒 時(shí)間: 2025-3-25 06:31
Blood Ties and the Immunitary Bioeconomy,lmed by hundreds of social events. In this work, we propose P-SERS, a Personalized Social Event Recommender System, which consists of three phases: (1)?Candidate selection, (2)?Social measurement and (3)?Recommendation. Among these, potential candidate events are selected based on user preference an作者: Conquest 時(shí)間: 2025-3-25 11:26 作者: 結(jié)束 時(shí)間: 2025-3-25 11:56 作者: antiquated 時(shí)間: 2025-3-25 19:40
Blood Ties and the Immunitary Bioeconomy,ificial intelligence and data mining, the similarity of symbolic data has been estimated by techniques ranging from feature-matching and correlation analysis to .. One set of techniques that has received very little attention are those based upon cognitive models of similarity and concept formation.作者: Creatinine-Test 時(shí)間: 2025-3-26 00:04 作者: JAMB 時(shí)間: 2025-3-26 04:08 作者: 歸功于 時(shí)間: 2025-3-26 05:47
Jacinta E. Cooper,Edward N. Janoffer. In addition to spatial and temporal biases, we observe that trajectories contain ., i.e., the time durations when no data points are available to describe movements of users, which bring many challenge issues in clustering trajectories. We claim that a movement behavior would leave some . in its作者: 哺乳動(dòng)物 時(shí)間: 2025-3-26 10:36 作者: 易于交談 時(shí)間: 2025-3-26 13:59 作者: 切割 時(shí)間: 2025-3-26 19:10
Influenza Virus Pathogenesis and Vaccinesequent itemset may not be the itemset with high value. High utility itemset mining considers both of the profits and purchased quantities for the items, which is to find the itemsets with high utility for the business. The previous approaches for mining high utility itemsets first apply frequent ite作者: 察覺 時(shí)間: 2025-3-26 21:02
Ann-Mari Svennerholm,Firdausi Qadrientially suspicious actions, data about the movement of users can be captured through the use of RFID tags and sensors, and patterns of suspicious behaviour detected in the captured data. This chapter presents four types of suspicious behavioural patterns, namely temporal, repetitive, displacement a作者: Admire 時(shí)間: 2025-3-27 04:31
Ramandeep Kaur,Urvashi,Amit Kumaric ones for digital libraries. In this work we consider a behavioral modeling approach to discover unauthorized copying of a large amount of documents from a digital library. Supposing the regular user has interest in semantically related documents, we treat referencing to semantically unrelated doc作者: Dorsal-Kyphosis 時(shí)間: 2025-3-27 07:42
Longbing Cao,Philip S. YuIncludes six case studies on behavior applications.Presents new techniques for capturing behavior characteristics in social media.First dedicated source of references for the theory and applications o作者: Urgency 時(shí)間: 2025-3-27 11:26
http://image.papertrans.cn/b/image/182759.jpg作者: 波動(dòng) 時(shí)間: 2025-3-27 14:40 作者: 歡樂東方 時(shí)間: 2025-3-27 21:23
978-1-4471-6206-3Springer-Verlag London 2012作者: insurgent 時(shí)間: 2025-3-28 00:21 作者: 宏偉 時(shí)間: 2025-3-28 05:26
https://doi.org/10.1007/978-3-642-76021-1ial individuals across sites. We evaluate our approaches on several of the popular social media sites. Among other interesting findings, we discover that influential individuals on one site are more likely to be influential on other sites as well. We also find that influential users are more likely 作者: abolish 時(shí)間: 2025-3-28 09:25
Blood Ties and the Immunitary Bioeconomy,opularity of the target item respectively. P-SERS evaluates each candidate social event by these social measures and produces a recommendation list. In addition, explanations and the grouping function are provided to improve the recommendation. Finally, we examine P-SERS by recommending group buying作者: 徹底明白 時(shí)間: 2025-3-28 11:22
https://doi.org/10.1057/978-1-137-55247-1ning models, and gives extensive discussions about these models; and the fourth, Conclusions, makes some suggestions for the academic database providers efficiently. Based on the theories and models of reinforcement learning behavior, this research takes the freshmen and senior students from univers作者: erythema 時(shí)間: 2025-3-28 16:53
Blood Ties and the Immunitary Bioeconomy,ing the feedback data is repeatable and customizable. In this chapter, we describe a simple yet effective approach for creating simulated feedback. We evaluated our system using clickthrough data of a set of real world users and achieved 65% accuracy in generating click-through data of those users.作者: Paleontology 時(shí)間: 2025-3-28 22:45 作者: 品牌 時(shí)間: 2025-3-28 23:38 作者: Oafishness 時(shí)間: 2025-3-29 03:32 作者: 龍蝦 時(shí)間: 2025-3-29 08:55
P-SERS: Personalized Social Event Recommender Systemopularity of the target item respectively. P-SERS evaluates each candidate social event by these social measures and produces a recommendation list. In addition, explanations and the grouping function are provided to improve the recommendation. Finally, we examine P-SERS by recommending group buying作者: 落葉劑 時(shí)間: 2025-3-29 13:28 作者: 言行自由 時(shí)間: 2025-3-29 16:03 作者: 大約冬季 時(shí)間: 2025-3-29 22:11
A Fast Algorithm for Mining High Utility Itemsetsaper, we present an efficient algorithm for mining high utility itemsets. Our algorithm is based on a tree structure in which a part of utilities for the items are recorded. A mechanism is proposed to reduce the mining space and make our algorithm can directly generate high utility itemsets from the作者: heirloom 時(shí)間: 2025-3-30 01:52
https://doi.org/10.1057/978-1-137-55247-1with 656 respondents. We find that the demographic (indirect) questions can be almost as successful as risk-related (direct) questions in predicting risk preference classes of respondents. Using a decision-tree based classification model, we discuss how one can generate actionable business rules based on the findings.作者: 有說(shuō)服力 時(shí)間: 2025-3-30 06:17
Per Brandtzaeg,Finn-Eirik Johansenough the re-mining of colored association graphs that represent item associations. The methodology is described and mathematically formalized, and is demonstrated in a case study related with retail industry.作者: Lucubrate 時(shí)間: 2025-3-30 08:54 作者: Ingredient 時(shí)間: 2025-3-30 12:36
Book 2012ior computing, or behavior informatics, consists of methodologies, techniques and practical tools for examining and interpreting behaviours in these various worlds. Behavior computing contributes to the in-depth understanding, discovery, applications and management of behavior intelligence.With cont作者: OATH 時(shí)間: 2025-3-30 18:50
cated source of references for the theory and applications o‘Behavior‘ is an increasingly important concept in the scientific, societal, economic, cultural, political, military, living and virtual worlds. Behavior computing, or behavior informatics, consists of methodologies, techniques and practica作者: Provenance 時(shí)間: 2025-3-30 23:13 作者: 冥界三河 時(shí)間: 2025-3-31 02:10
Semi-Markovian Representation of User Behavior in Software Packageso dynamically display personalized information in the form of high-utility software functions (states) of a software package to a user. We develop a demo package of ActiveX Servers and Controls as a test-bed.作者: 血友病 時(shí)間: 2025-3-31 07:21 作者: hemoglobin 時(shí)間: 2025-3-31 11:42
Jacinta E. Cooper,Edward N. Janoffsimilar trajectories into groups to capture the movement behaviors of the user. We validate our ideas and evaluate the proposed CCT framework by experiments using both synthetic and real datasets. Experimental results show that CCT is more effective in discovering trajectory patterns than the state-of-the-art techniques in trajectory clustering.作者: 不法行為 時(shí)間: 2025-3-31 14:43 作者: 丑惡 時(shí)間: 2025-3-31 17:46
Modeling and Analysis of Social Activity Processmodel checking. More specifically, we construct behavior models from sub-models of actor, action, environment and relationship, followed by the translation from concrete properties to formal temporal logic formulae, finally obtain analyzing results with model checker SPIN. Online shopping process is illustrated to explain this whole framework.作者: 宮殿般 時(shí)間: 2025-3-31 21:59
Clustering Clues of Trajectories for Discovering Frequent Movement Behaviorssimilar trajectories into groups to capture the movement behaviors of the user. We validate our ideas and evaluate the proposed CCT framework by experiments using both synthetic and real datasets. Experimental results show that CCT is more effective in discovering trajectory patterns than the state-of-the-art techniques in trajectory clustering.作者: HOWL 時(shí)間: 2025-4-1 04:32
A Behavioral Modeling Approach to Prevent Unauthorized Large-Scale Documents Copying from Digital Lion application of Markov chains. We also present the results of experiments conducted within development of a prototype digital library protection system. Finally, examples of a normal profile and an automatically detected anomalous session derived from the real data logs of a digital library illustrate the suggested approach to the problem.作者: 全等 時(shí)間: 2025-4-1 09:17
Scoring and Predicting Risk Preferenceswith 656 respondents. We find that the demographic (indirect) questions can be almost as successful as risk-related (direct) questions in predicting risk preference classes of respondents. Using a decision-tree based classification model, we discuss how one can generate actionable business rules based on the findings.
