查看更多>>摘要：By regarding a Solid-state drive(SSD) as a black box and observing its external behavior instead of peeping into its internal details,we investigate how the factors of I/O granularity and I/O queue depth influence the throughput of an SSD through a series of experiments and relate to the internal parallelism of an SSD,and then propose the concept of Combination equivalence class (CEC) as the set of combination pairs of these two factors.A novel buffer allocation scheme for hash join over SSDs is invented by taking both factors into account.Extensive experiments demonstrate the effectiveness of our scheme.
查看更多>>摘要：We propose a robust fuzzy time series forecasting method based on multi-partition approach and outlier detection for forecasting market prices.The multi-partition approach employs a specific partition criterion for each dimension of the time series.We use a Gaussian kernel version fuzzy C-means clustering to construct the fuzzy logic relationships and detect the outliers by calculating the grade of membership.We apply an additional model,which is trained on the set of outliers by Levenberg-Marquardt algorithm,for forecasting the outliers in testing set.The experiment results show that the proposed method improves the robustness and the average forecasting accuracy rate.
查看更多>>摘要：Collaborative filtering recommender systems (CFRSs) are known to be highly vulnerable to profile injection attacks,in which malicious users insert fake profiles into the rating database in order to bias the systems' output,since their openness,and attack detection is still a challenging problem in CFRSs.In order to provide more accurate recommendations,many schemas have been proposed to detect such shilling attacks.However,almost all of them are proposed to detect one or several specific attack types,and few of them can handle hybrid attack types,which usually happen in practice.With this problem in mind,we propose a novel L2,1-norm regularized matrix completion incorporating prior information (LRMCPI) model to detect shilling attacks by combining matrix completion and L2,1-norm.The proposed LRMCPI formalizes the attack detection problem as a missing value estimation problem,and it is appropriate because the user-item rating matrix is approximately low-rank and attack profiles could be considered as structural noise.The proposed LRMCPI model not only can better recover the rating matrix using correct rating value but also can detect the positions where the attackers are injected.We evaluate our model on three well-known data sets with different density and the experimental results show that our model outperforms baseline algorithms in both single and hybrid attack types.
查看更多>>摘要：A thermopile-based microwave power sensor and a double-channel microwave power sensor are compared in order to research the measurement accuracy of microwave power.The relationship of the displacement of MEMS cantilever beam with the measured microwave power is researched,and the reason that the microwave power consumed by the MEMS cantilever cannot be ignored at low power level is explained.The ratio of microwave power consumed by the MEMS cantilever with the microwave frequency is obtained,and the measured results show that the percentage is 51.96％@8GHz,52.31％@10GHz and 55.11％@12GHz on the average,respectively.There is an important reference value to achieve the accurate microwave power measurement of the double-channel microwave power sensors.
查看更多>>摘要：We investigated the transmission characteristics of Cu/CNT composite Through-silicon via (TSV)interconnects.The equivalent lumped-element circuit model was established,with the effective conductivity employed for impedance extraction.The impacts of CNT filling ratio,temperature,and other geometrical parameters on the performance were examined.The sensitivity analysis of Cu/CNT composite TSVs was carried out.The electrical performance of Cu/CNT composite TSVs were optimized by utilizing low-permittivity dielectrics or even air-gap.
查看更多>>摘要：Defect distribution prediction is a meaningful topic because software defects are the fundamental cause of many attacks and data loss.Building accurate prediction models can help developers find bugs and prioritize their testing efforts.Previous researches focus on exploring different machine learning algorithms based on the features that encode the characteristics of programs.The problem of data redundancy exists in software defect data set,which has great influence on prediction effect.We propose a defect distribution prediction model (Deep belief network prediction model,DBNPM),a system for detecting whether a program module contains defects.The key insight of DBNPM is Deep belief network (DBN) technology,which is an effective deep learning technique in image processing and natural language processing,whose features are similar to defects in source program.Experiment results show that DBNPM can efficiently extract and process the data characteristics of source program and the performance is better than Support vector machine (SVM),Locally linear embedding SVM (LLE-SVM),and Neighborhood preserving embedding SVM (NPE-SVM).
查看更多>>摘要：By converting the problem of preimage distribution of a perfect nonlinear function with three binary outputs to that with two binary outputs,this paper presents the preimage distributions of perfect nonlinear functions with three binary outputs.This paper also characterizes the fundamental characterization of the preimage distribution of vectorial plateaued functions with single amplitude and its component functions being all unbalanced,and gives the preimage distributions of such functions with two or three binary outputs.
查看更多>>摘要：Non-malleable extractor is an important tool for studying the problem of privacy amplification in classical and quantum cryptography with an active adversary.The randomness of the weakly-random source X before privacy amplification always depends on the information adversary has,called side information.We study properties of such extractors in the presence of classical and quantum side information,and show that any non-malleable extractor is essentially secure in the case where the adversary has classical side information.We also prove that non-malleable extractors are quantum-proof with uniform seed,or only require the seed to be weakly random.
查看更多>>摘要：Security-sensitive operations in Android applications (apps for short) can either be benign or malicious.In this work,we introduce an approach of static program analysis that extracts "second-step behavior features",i.e.,what was triggered by the security-sensitive operation,to assist app analysis in differentiating between malicious and benign operations.Firstly,we summarized the characteristics of malicious operations,such as spontaneity,independence,stealthiness and continuity,which can be used to classify the malicious operations and benign ones.Secondly,according to these characteristics,Second step behavior features (SSBFs for short) have been presented,including structural features and semantic features.Thirdly,an analysis prototype named SSdroid has been implemented to automatically extract SSBFs of security-sensitive operations.Finally,experiments on 9285 operations from both benign and malicious apps show that SSBFs are effective and usefulness.Our evaluation results suggest that the second-step behavior can greatly assist in Android malware detection.
查看更多>>摘要：The Boolean Satisfiability (SAT) problem is the key problem in computer theory and application.Field-programmable gate array (FPGA) has been addressed frequently to accelerate the SAT solving process in the last few years owing to its parallelism and flexibility.We have proposed a novel SAT solver based on an improved local search algorithm on the reconfigurable hardware platform.The new software preprocessing procedure and hardware architecture are involved to solve large-scale SAT problems instances.As compared with the past solvers,the proposed solver has the following advantages: the preprocessing technology can strongly improve the efficiency of solver;the strategy of strengthening the variable selection can avoid the same variable flipped continuously and repeatedly.It reduces the possibility of search falling into local minima.The experimental results indicate that the solver can solve problems of up to 32K variables/128K clauses without off-chip memory banks,and has better performance than previous works.
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