Showing papers in "Tsinghua Science & Technology in 2014"

TL;DR: This paper presents a comprehensive survey of the recent advances in activity recognition with smartphones' sensors, starting with the basic concepts such as sensors, activity types, etc and reviewing the core data mining techniques behind the main stream activity recognition algorithms.

TL;DR: The preprocessing operations and the state of the art methods of MRI-based brain tumor segmentation are introduced, the evaluation and validation of the results are discussed, and an objective assessment is presented.

TL;DR: In this paper, an attack tree based threat model is presented to illustrate the energy-theft behaviors in AMI and summarize the current AMI energytheft detection schemes into three categories, i.e., classification-based, state estimation-based and game theory-based ones.

TL;DR: This work is dedicated to Jianer Chen, one of the strongest problem solvers in the history of parameterized algorithmics, on the occasion of his 60th birthday.

TL;DR: Dache is proposed, a data-aware cache framework for big-data applications that is implemented by extending Hadoop and demonstrates that Dache significantly improves the completion time of MapReduce jobs.

TL;DR: A security level based protection policy is proposed for simplifying the security rule management for vCNSMS, a collaborative network security prototype system used in a multi-tenant data center.

TL;DR: This paper discusses the problems of mining sensor data in CPS and introduces IntruMine, a framework to discover intruders from untrustworthy sensor data that analyzes the trustworthiness of sensor data, detects the intruders' locations, and verifies the detections based on a graph model of the relationships between sensors and intruders.

TL;DR: Experimental results show that the DR-Cloud model can cooperate with cloud service providers with various parameters effectively, while its data scheduling strategies can achieve their optimization objectives efficiently and are widely applicable.

TL;DR: In this article, the authors classified the parameterized and kernelization complexity of the feedback vertex set problem on undirected graphs, and showed that the problem admits a polynomial kernel when parameterized by the vertex-deletion distance to a pseudo forest.

TL;DR: This paper introduces China Unicom's big data platform, which is building an industry ecosystem based on Mobile Internet Big Data, and considers that a telecom operator centric ecosystem can be formed that is critical to reach prosperity in the modern communications business.

TL;DR: In this paper, the authors used the Elaboration Likelihood Model (ELM) and social presence theory to examine the microblogging reposting mechanism and found that users are able to perceive social presence when interacting with micro blogging messages.

TL;DR: In this article, the authors present an integrated simulation environment to provide a unified platform for the investigation of smart grid applications involving power grid monitoring, communication, and control, which allows the network simulator to operate independently, importing its results to the power system simulation.

TL;DR: The experimental results show that employing the proposed personalized recommendation algorithm based on the preference-feature would significantly improve the accuracy of evaluation predictions compared to two previous approaches.

TL;DR: In this article, a tiny pressure sensor based on the piezoresistive effect was used to measure the intracranial pressure (ICP) and the test results yield sensitivities of 1.033×10-2 mV/kPaforthe childhood type detection and 1.257×10−2 mv/kPa for the adult detection with sensor chip sizes of 0.40×0.40 mm2 and 0.50×0.50 mm2, respectively.

TL;DR: In this paper, a dynamic version of the DOMINATING SET problem is introduced and proved to be fixed-parameter tractable (FPT) in settings where problem instances evolve, and the main purpose of this paper is to exposit two very different but very general, motivational schemes in the art of parameterization.

TL;DR: A comprehensive review of recent advances in methods for eQTL analysis in population-based studies, which presents traditional pairwise association methods and newly developed statistical learning methods including Lasso-based models.

TL;DR: For each type of problem, typical examples together with recent results are outlined, the main techniques are analyzed, and some suggestions for future research in this field are provided.

TL;DR: A new targeted fully homomorphic encryption scheme based on the discrete logarithm problem is presented and inspired by ElGamal and BGN cryptography is obtained by selecting a new group and adding an extra component to the ciphertext.

TL;DR: A critical review and comparison of eight popular methods used for detecting gene-gene interactions among genetic loci, i.e., BOOST, TEAM, epiForest, EDCF, SNPHarvester, EpiMODE, MECPM, and MIC, which are used for investigating multi-SNPs interactions in genome-wide association studies.

TL;DR: This paper proposes a novel power capping module in the Hadoop scheduler to mitigate power peaks and shows that the proposed solution can effectively smooth the power consumption curve and mitigate temporary power peaks for Hadoops clusters.

TL;DR: A class of Generalized Low-Density Parity-Check (GLDPC) codes is designed for data transmission over a Partial-Band Jamming (PBJ) environment and employs A Posteriori Probability (APP) fast BCH transform for decoding the BCH check nodes at each decoding iteration.

TL;DR: A common feature of p-Kemeny AGGregation and p-One-Sided Crossing Minimization is analyzed to provide new insights and findings of interest to both the graph drawing community and the social choice community.

TL;DR: Experimental results show that the graph mining algorithm components in BPGM are efficient and have better performance than big cloud-based parallel data miner and BC-BSP.

TL;DR: A series of observations in two real mobile social networks are presented and a method based on a dynamic factor-graph model for modeling and predicting users' activities is proposed, showing that the proposed ACTPred model can achieve better performance than baseline methods.

TL;DR: This paper introduces a general framework to accelerate the learning-based view-based 3-D object matching in large scale data and demonstrates the effectiveness of the proposed framework.

TL;DR: A privacy quantification model is proposed, which is based on Bayes conditional privacy, to specify a general adversarial model for most existing privacy metrics, and it is shown that this model permits interpretation and comparison of various popular LBS privacy metrics under a common perspective.

TL;DR: A worst-input mutation approach for testing Web service vulnerability based on SOAP messages, which uses the farthest neighbor concept to guide generation of the test suite and results indicate that the proposed approach is both practical and effective.

TL;DR: In this article, the effects of several sputter parameters (DC power, Ar pressure, deposition time, and substrate temperature) on thin-film coverage for TSV applications are investigated.

TL;DR: An electricity services based dependability analysis model of PGCN is proposed, which includes methods of analyzing its dependability and procedures of designing the dependable strategies and the deterministic analysis method based on matrix analysis and stochastic analysis model based on stochastically Petri nets are discussed.

TL;DR: A new algorithm to establish the data association between a camera and a 2-D LIght Detection And Ranging sensor (LIDAR) is proposed and the line-point correspondence is employed to construct geometric constraint on the homography matrix, enabling checkerboard to be not essential and any object with straight boundary can be an effective target.