In recent years, group self-driving tours have become a popular tour mode among Chinese tourists. However, current research on tourism resource recommendation generally overlooks the distinctive tourism styles and group preferences of self-driving tourists. To address the diversity in tourism styles and group preferences among self-driving tourists, this paper proposes a tourism resource recommendation method tailored for self-driving tours, based on tourism style modeling and group decision-making. First, by integrating tourist and tourism resource information, a knowledge graph of the self-driving tour environment is constructed, enhancing the accuracy of identifying similar tourists. Next, seven fundamental tourism types are incorporated along with factors specific to self-driving tours to better model the tourism styles of tourists, thereby improving recommendation accuracy. Furthermore, to accommodate the varied preferences within a self-driving group, a non-compensatory group decision-making strategy is employed to ensure the recommendation results meet the needs of all group members. Finally, case studies validate the effectiveness of the proposed method. This study can provide a new perspective for resource recommendation in self-driving tours.

Linux utilizes interfaces as communication protocols across different subsystems while ensuring manageability. These interfaces standardize interactions between various subsystems; however, the absence of complete calling contexts can result in the mishandling of data from other entities, i.e., interaction data, thus incurring vulnerabilities. Even worse, the effectiveness of static bug detectors could be severely hindered due to the lack of interface specifications. Previous solutions, seeking to automate the inference of interface specifications, are tailored to a subset of the interaction data behavior and, hence are deficient in generalizability.

With the arrival of the big data era, tens of billions of single-table big data generation, the increasing data scale shows how to efficiently and quickly big data migration has become a key challenge. Current traditional big data migration methods have the problem of slow migration speed, and the risk of abnormal interruptions. This paper proposes a method for breakpoint resumption migration of secondary sharding for single-table data at the billion-scale level. Dividing primary shards into secondary shards, and employing a dynamic load balancing strategy to migrate to multiple databases enhance the independence and controllability of data migration, consequently reducing overall migration time. Additionally, in case of any anomalies during the migration process, simply rolling back and re-migrating the affected data shards ensure minimal impact on other shards or the overall migration process. To evaluate the efficacy of this method, our study conducted experiments on migrating Oracle database to other databases. The findings confirmed that the proposed approach supports migration to diverse databases, and as the number of nodes increases, migration efficiency grows exponentially. Additionally, comparative experiments were conducted between the data migration tool developed by this method and both DataX and the DaMeng Transfer Service (DTS), with equivalent data volumes. The experimental results indicate that this method performs comparably well to the leading DTS in terms of migration efficiency. However, this method’s advantage lies in its support for distributed deployment, resulting in the highest overall migration efficiency. Furthermore, unlike other migration tools, it also supports migration to multiple database platforms.

Time-varying linear equations (TVLEs) play a fundamental role in the engineering field and are of great practical value. Existing methods for the TVLE still have issues with long computation time and insufficient noise resistance. Zeroing neural network (ZNN) with parallel distribution and interference tolerance traits can mitigate these deficiencies and thus are good candidates for the TVLE. Therefore, a new predefined-time adaptive ZNN (PTAZNN) model is proposed for addressing the TVLE in this article. Unlike previous ZNN models with time-varying parameters, the PTAZNN model adopts a novel error-based adaptive parameter, which makes the convergence process more rapid and avoids unnecessary waste of computational resources caused by large parameters. Moreover, the stability, convergence, and robustness of the PTAZNN model are rigorously analyzed. Two numerical examples reflect that the PTAZNN model possesses shorter convergence time and better robustness compared with several variable-parameter ZNN models. In addition, the PTAZNN model is applied to solve the inverse kinematic solution of UR5 robot on the simulation platform CoppeliaSim, and the results further indicate the feasibility of this model intuitively.

For the widely deployed on-chip shared buffer, efficient buffer management is the key to absorbing bursts and avoiding packet loss during transient congestion. However, as the buffer-per-port-per-Gbps in production data centers decreases, it becomes more challenging to provide efficient buffer management to meet the requirements of heterogeneous traffic. We observe that typical shared buffer management policies have two steps: first, they identify short flows arriving at ports and then allocate more buffer room for these ports. Unfortunately, the lack of isolation between long and short flows leads to increased queue buildup and even packet loss of short flows. To address this limitation, we propose D2T, which uses different queue length thresholds for long and short flows. Specifically, we first design a compact data structure to distinguish between long and short flows. Then when two kinds of flows coexist at the same port, the threshold of long flows will decrease to absorb the bursty short flows. What’s more, we introduce D2T${}^{*}$which combines D2T with advanced DRL techniques to move toward mastering buffer management for further improving performance across various scenarios. We implement D2T at a P4-programmable switch and large-scale simulations. The results demonstrate that D2T reduces both average and tail flow completion times (FCT) of short flows by up to 29% and 62% compared with the state-of-the-art policies, respectively.

This paper presents Siro, a new program transformation framework that translates between different versions of Intermediate Representations (IR), aiming to better address the issue of IR version incompatibility on IR-based software, such as static analyzers. We introduce a generic algorithm skeleton for Siro based on the divide-and-conquer principle. To minimize labor-intensive tasks of the implementation process, we further employ program synthesis to automatically generate translators for IR instructions within vast search spaces. Siro is instantiated on LLVM IR and has effectively helped to produce ten well-functioning IR translators for different version pairs, each taking less than three hours. From a practical perspective, we utilize these translators to assist static analyzers and fuzzers in reporting bugs and achieving accuracy of 91% and 95%, respectively. Remarkably, Siro has already been deployed in real-world scenarios and makes existing static analyzers available to safeguard the Linux kernel by uncovering 80 new vulnerabilities.

Teaching early warning is of great significance for avoiding teaching risks and continuously improving teaching quality. However, none of existing approaches assess the degree of course goals attainment and teacher's teaching quality from the perspective of cognitive diagnosis. This poses a challenge in providing accurate teaching early warning. This paper proposed an early warning approach for teachers based on cognitive diagnosis and long short-term memory (LSTM). First, this approach accurately evaluates students' cognitive status on knowledge concepts using a cognitive diagnosis model to assess their knowledge understanding degree and knowledge application ability. Second, the cognitive status on knowledge concepts is utilized to assess students' attainment degree of course goals and teachers' teaching quality. Third, the teachers' teaching quality is predicted in the future by using the LSTM network to mine students' learning process data, Finally, an accurate teaching early warning is provided to teachers based on a four-level early warning evaluation rule. In experiments, the real datasets are used and the results reveal that the proposed approach can accurately diagnose students' cognitive status and effectively predict teachers' teaching quality. This approach can provide an accurate teaching early warning service for teachers.

Despite the benefits of using third-party libraries (TPLs), the misuse of TPL functions raises quality and security concerns. Using traditional static analysis to detect bugs caused by TPL function is non-trivial. One promising solution would be to automatically generate and persist the summaries of TPL functions offline and then reuse these summaries in compositional static analysis online. However, when dealing with millions of lines of TPL code, the summaries designed by existing studies suffer from an unresolved paradox. That is, a highly precise form of summary leads to an unaffordable space and time overhead, while an imprecise one seriously hurts its precision or recall.

With the massive popularization of e-learning, collaborative learning via learning teams has become indispensable to enhancing the learning efficiency and learning quality of overall learners. The team leader usually plays a key role in collaborative learning. However, the existing research ignores the key characteristics of learners and constraints relevant to e-learners when identifying appropriate team leaders and compatible members. A novel collaborative optimization approach to learning team formation is proposed based on a refined learner model and the environments—classes, agents, roles, groups, and objects (E-CARGO) model. With the proposed approach, a learner is modeled by combining 5-D characteristics (i.e., cognitive ability, leadership, sociability, learning style, and personality) and three types of constraints (e.g., conflicts, genders, and the number of members), and an assessment mechanism is designed to measure the comprehensive abilities of learners for identifying an ideal team leader and selecting the team members for a team. By innovatively introducing the role-based collaboration theory and E-CARGO model, the leader-centered learning team formation problem is formalized as a collaborative optimization problem. The mathematical model and the constraint relations are established for this problem, which is solved based on the IBM CPLEX package. Finally, a case study and experiments demonstrate that the proposed approach is efficient and feasible, in favor of improving the satisfaction degree of learners.

Presently, numerical algorithms for solving quaternion least-squares problems have been intensively studied and utilized in various disciplines. However, they are unsuitable for solving the corresponding time-variant problems, and thus few studies have explored the solution to the time-variant inequality-constrained quaternion matrix least-squares problem (TVIQLS). To do so, this article designs a fixed-time noise-tolerance zeroing neural network (FTNTZNN) model to determine the solution of the TVIQLS in a complex environment by exploiting the integral structure and the improved activation function (AF). The FTNTZNN model is immune to the effects of initial values and external noise, which is much superior to the conventional zeroing neural network (CZNN) models. Besides, detailed theoretical derivations about the global stability, the fixed-time (FXT) convergence, and the robustness of the FTNTZNN model are provided. Simulation results indicate that the FTNTZNN model has a shorter convergence time and superior robustness compared to other zeroing neural network (ZNN) models activated by ordinary AFs. At last, the construction method of the FTNTZNN model is successfully applied to the synchronization of Lorenz chaotic systems (LCSs), which shows the practical application value of the FTNTZNN model.

Time-varying complex-valued tensor inverse (TVCTI) is a public problem worthy of being studied, while numerical solutions for the TVCTI are not effective enough. This work aims to find the accurate solution to the TVCTI using zeroing neural network (ZNN), which is an effective tool in terms of solving time-varying problems and is improved in this article to solve the TVCTI problem for the first time. Based on the design idea of ZNN, an error-adaptive dynamic parameter and a new enhanced segmented signum exponential activation function (ESS-EAF) are first designed and applied to the ZNN. Then a dynamic-varying parameter-enhanced ZNN (DVPEZNN) model is proposed to solve the TVCTI problem. The convergence and robustness of the DVPEZNN model are theoretically analyzed and discussed. In order to highlight better convergence and robustness of the DVPEZNN model, it is compared with four varying-parameter ZNN models in the illustrative example. The results show that the DVPEZNN model has better convergence and robustness than the other four ZNN models in different situations. In addition, the state solution sequence generated by the DVPEZNN model in the process of solving the TVCTI cooperates with the chaotic system and deoxyribonucleic acid (DNA) coding rules to obtain the chaotic-ZNN-DNA (CZD) image encryption algorithm, which can encrypt and decrypt images with good performance.

Value-flow analysis is a fundamental technique in program analysis, benefiting various clients, such as memory corruption detection and taint analysis. However, existing efforts suffer from the low potential speedup that leads to a deficiency in scalability. In this work, we present a parallel algorithm Octopus to collect path conditions for realizable paths efficiently. Octopus builds on the realizability decomposition to collect the intraprocedural path conditions of different functions simultaneously on-demand and obtain realizable path conditions by concatenation, which achieves a high potential speedup in parallelization. We implement Octopus as a tool and evaluate it over 15 real-world programs. The experiment shows that Octopus significantly outperforms the state-of-the-art algorithms. Particularly, it detects NULL-pointer-dereference bugs for the project llvm with 6.3 MLoC within 6.9 minutes under the 40-thread setting. We also state and prove several theorems to demonstrate the soundness, completeness, and high potential speedup of Octopus . Our empirical and theoretical results demonstrate the great potential of Octopus in supporting various program analysis clients. The implementation has officially deployed at Ant Group, scaling the nightly code scan for massive FinTech applications.

Learning early warning is of great significance for coping with students' learning risks. The existing research fails in modeling the fluctuation of students' learning states and providing the multi-level early warning for students at different levels. To address them, a new approach of learning early warning is proposed to predict at-risk students in e-learning environment by combining cognitive diagnosis with learning behaviors analysis. In this approach, the students' learning process is modeled from four dimensions, i.e., learning quality, learning engagement, latent learning state, and historical learning performance. The convolutional neural network and long short-term memory network are used to explore the students' latent learning features. Then, the Adaboost algorithm is applied to predict students' learning performance. Based on the predicted performance, the evaluation rules are designed to provide multi-level learning early warning for students. Finally, the experiments demonstrate that the proposed method could predict at-risk students efficiently and accurately.

This paper presents Squid, a new conjunctive query synthesis algorithm for searching code with target patterns. Given positive and negative examples along with a natural language description, Squid analyzes the relations derived from the examples by a Datalog-based program analyzer and synthesizes a conjunctive query expressing the search intent. The synthesized query can be further used to search for desired grammatical constructs in the editor. To achieve high efficiency, we prune the huge search space by removing unnecessary relations and enumerating query candidates via refinement. We also introduce two quantitative metrics for query prioritization to select the queries from multiple candidates, yielding desired queries for code search. We have evaluated Squid on over thirty code search tasks. It is shown that Squid successfully synthesizes the conjunctive queries for all the tasks, taking only 2.56 seconds on average.

A FinTech system is a cluster of FinTech applications that intensively interact with databases containing a large quantity of user data. To ensure data consistency, it is a common practice to specify data constraints to validate data at runtime. However, data constraints often evolve according to changes in business requirements. Meanwhile, the developers can hardly keep up with the latest requirements during the development cycle. Such an information barrier increases the communication burden and prevents FinTech applications from being updated in time, impeding the development cycle significantly. In this paper, we present a comprehensive empirical study on data constraints in FinTech systems, investigating how they evolve and affect the development process. Our results show that developers find it hard to update their code timely because no mapping from data constraint changes to code is provided. Inspired by the findings from code updates respecting data constraint changes, we propose DCLINK, a traceability link analysis for linking each data constraint change to target methods demanding the code update in the FinTech application. We extensively evaluate DCLINK upon real-world change cases in Ant Group. The results show that DCLINK can effectively and efficiently localize the target methods.

As a category of the recurrent neural network (RNN), zeroing neural network (ZNN) can effectively handle time-variant optimization issues. Compared with the fixed-parameter ZNN that needs to be adjusted frequently to achieve good performance, the conventional variable-parameter ZNN (VPZNN) does not require frequent adjustment, but its variable parameter will tend to infinity as time grows. Besides, the existing noise-tolerant ZNN model is not good enough to deal with time-varying noise. Therefore, a new-type segmented VPZNN (SVPZNN) for handling the dynamic quadratic minimization issue (DQMI) is presented in this work. Unlike the previous ZNNs, the SVPZNN includes an integral term and a nonlinear activation function, in addition to two specially constructed time-varying piecewise parameters. This structure keeps the time-varying parameters stable and makes the model have strong noise tolerance capability. Besides, theoretical analysis on SVPZNN is proposed to determine the upper bound of convergence time in the absence or presence of noise interference. Numerical simulations verify that SVPZNN has shorter convergence time and better robustness than existing ZNN models when handling DQMI.

The prediction of examinee performance via cognitive diagnosis models might provide an important decision-making support for personalized learning instruction in an e-learning system. Aiming at the uncertainty of learners' skill proficiency caused by the complexity of skills, and the large-scale volume of score profiles, a collaborative prediction approach of examinee performance is proposed based on a new fuzzy cloud cognitive diagnosis model. In this approach, the normal cloud models are used to measure the uncertainty of the skill proficiency from three aspects (i.e., expectation, variation degree, and variation frequency), and an e-learner’s skill proficiency is characterized with a fuzzy interval number. Based on a collaborative parameter estimation method, the predicted scores on every test item could be obtained for learners. Finally, the experiments demonstrate that this approach provides good accuracy and less execution time for predicting examinee performance than other approaches.

Facing with the massive learning resources, the learners are often confronted with information disorientation and overload problems. To help learners select the appropriate MOOCs quickly, a hybrid recommendation approach of personalized MOOC resources is proposed by exploiting the user context to capture the learners' explicit and implicit features. An improved hybrid similarity calculation method is presented to identify the neighboring users for reducing the calculation errors, and an improved course modeling method is used to extract semantic information for enhancing the accuracy of course modeling with low labor cost. Based on this approach, a real system is developed. The experiments demonstrate that this approach provides the higher recommendation accuracy and ideal execution performance compared with the traditional approaches for supporting personalized learning efficiently.

It is a fundamental function of a personalized elearning system to recommend suitable exercises to learners for improving their learning efficiencies and qualities. These exercises relevant to the current learning progress and the skill proficiency of learners could be selected by analyzing their score profiles in the past exams. Aiming at the limitations of existing research, a new exercise recommendation approach is proposed based on cognitive diagnosis and Neutrosophic set. In it, the learners' cognitive status is measured from multiple perspectives comprehensively by introducing the Neutrosophic set theory. The similarity between the learners is calculated with a Neutrosophic set method. The learner's performance on the new exercises could be predicted by collaborative filtering algorithm, and the exercises suitable to learners are recommended to them according to their preferences. The experiments show that the accuracy of proposed approach is higher than the existing approaches.

Containers, such as lists and maps, are fundamental data structures in modern programming languages. However, improper choice of container types may lead to significant performance issues. This paper presents Cres, an approach that automatically synthesizes container replacements to improve runtime performance. The synthesis algorithm works with static analysis techniques to identify how containers are utilized in the program, and attempts to select a method with lower time complexity for each container method call. Our approach can preserve program behavior and seize the opportunity of reducing execution time effectively for general inputs. We implement Cres and evaluate it on 12 real-world Java projects. It is shown that Cres synthesizes container replacements for the projects with 384.2 KLoC in 14 minutes and discovers six categories of container replacements, which can achieve an average performance improvement of 8.1%.

Satisfiability Modulo Theories (SMT) solvers serve as the core engine of many techniques, such as symbolic execution. Therefore, ensuring the robustness and correctness of SMT solvers is critical. While fuzzing is an efficient and effective method for validating the quality of SMT solvers, we observe that prior fuzzing work only focused on generating various first-order formulas as the inputs but neglected the algorithmic configuration space of an SMT solver, which leads to under-reporting many deeply-hidden bugs. In this paper, we present Falcon, a fuzzing technique that explores both the formula space and the configuration space. Combining the two spaces significantly enlarges the search space and makes it challenging to detect bugs efficiently. We solve this problem by utilizing the correlations between the two spaces to reduce the search space, and introducing an adaptive mutation strategy to boost the search efficiency. During six months of extensive testing, Falcon finds 518 confirmed bugs in CVC4 and Z3, two state-of-the-art SMT solvers, 469 of which have already been fixed. Compared to two state-of-the-art fuzzers, Falcon detects 38 and 44 more bugs and improves the coverage by a large margin in 24 hours of testing.

Status code mappings reveal state shifts of a program, mapping one status code to another. Due to careless programming or the lack of the system-wide knowledge of a whole program, developers can make incorrect mappings. Such errors are widely spread across modern software, some of which have even become critical vulnerabilities. Unfortunately, existing solutions merely focus on single status code values, while never considering the relationships, that is, mappings, among them. Therefore, it is imperative to propose an effective method to detect status code mapping errors.In this paper, we propose Transcode to detect potential status code mapping errors. It firstly conducts value flow analysis to efficiently and precisely collect candidate status code values, that is, the integer values, which are checked by following conditional comparisons. Then, it aggregates the correlated status codes according to whether they are propagated with the same variable. Finally, Transcode extracts mappings based on control dependencies and reports the mapping error if one status code is mapped to two others of the same kind. We have implemented Transcode as a prototype system, and evaluated it with 5 real-world software projects, each of which possesses in the order of a million lines of code. The experimental results show that Transcode is capable of handling large-scale systems in both a precise and efficient manner. Furthermore, it has discovered 59 new errors in the tested projects, among which 13 have been fixed by the community. We also deploy Transcode in WeChat, a widely-used instant messaging service, and have succeeded in finding real mapping errors in the industrial settings.

Ensuring the equality of SMT solvers is critical due to its broad spectrum of applications in academia and industry, such as symbolic execution and program verification. Existing approaches to testing SMT solvers are either too costly or find difficulties generalizing to different solvers and theories, due to the test oracle problem. To complement existing approaches and overcome their weaknesses, this paper introduces skeletal approximation enumeration (SAE), a novel lightweight and general testing technique for all first-order theories. To demonstrate its practical utility, we have applied the SAE technique to test Z3 and CVC4, two comprehensively tested, state-of-the-art SMT solvers. By the time of writing, our approach had found 71 confirmed bugs in Z3 and CVC4,55 of which had already been fixed.

Recently, growing cloud services (CSs) have been leased by organizations for high-performance computation and massive data storage of data-intensive applications (DiAs). To improve the resource utilization of leased CSs, it has become a challenging task to optimize infrastructure as a service CS composition for DiAs (ICSCDs) from the user side. This paper proposes a resource utilization-aware collaborative optimization approach. Targeting the collaboration features of tasks in a DiA, the environments-classes, agents, roles, groups, and objects model is used to formalize the ICSCD problem from the perspective of role-based collaboration. Aiming at the dynamic characteristics of the cloud environment, an integrated method is presented to evaluate the qualification of CSs via the interval numbers with multiple parameters. Based on the exact qualification values, the ICSCD can be optimized for improving the resource utilization of the CSs. A solution using the IBM ILOG CPLEX optimization package is put forward to solve the problem. The experimental results demonstrate that the approach can provide high precision, performance, stability, resource utilization, and low usage cost for the resource utilization-aware ICSCD from the user side.

Multi-path TCP (MPTCP) is increasingly popular with the widespread usage of multihomed devices. MPTCP allows data streams to be delivered across multiple simultaneous connections, providing higher bandwidth aggregation and throughput in comparison with single-path TCP. However, due to the path heterogeneity and packet losses, the occurrence of Out-of-Order (OFO) packets is inevitable for MPTCP. Although many approaches have been proposed to mitigate OFO, most of them focused on compensating path delay differences but not considered the impact of packet loss. In this paper, we take the first step towards analyzing the impact of packet loss on OFO, and propose Loss-Aware Throughput Estimation scheduler, LATE. LATE comprehensively considers each subflow's path characteristics and protocol parameters including Round Trip Time (RTT), congestion window (cwnd), and loss rate, to predict the data amount that can be sent over each subflow at a given time and determine wisely which segments should be allocated to which subflows. Experimental results show that LATE achieves a gain of 5.13% in mean goodput with long-lasting flows while reducing the completion time of short flows by about 26.68% compared to the state-of-the-art scheduler for MPTCP.

Complex-valued time-dependent matrix inversion (TDMI) is extensively exploited in practical industrial and engineering fields. Many current neural models are presented to find the inverse of a matrix in an ideal noise-free environment. However, the outer interferences are normally believed to be ubiquitous and avoidable in practice. If these neural models are applied to complex-valued TDMI in a noise environment, they need to take a lot of precious time to deal with outer noise disturbances in advance. Thus, a noise-suppression model is urgent to be proposed to address this problem. In this article, a complex-valued noise-tolerant zeroing neural network (CVNTZNN) on the basis of an integral-type design formula is established and investigated for finding complex-valued TDMI under a wide variety of noises. Furthermore, both convergence and robustness of the CVNTZNN model are carefully analyzed and rigorously proved. For comparison and verification purposes, the existing zeroing neural network (ZNN) and gradient neural network (GNN) have been presented to address the same problem under the same conditions. Numerical simulation consequences demonstrate the effectiveness and excellence of the proposed CVNTZNN model for complex-valued TDMI under various kinds of noises, by comparing the existing ZNN and GNN models.

Many complex software systems use error codes to differentiate error states. Therefore, it is crucial to ensure those error codes are used correctly. Misuses of error codes can lead to hardly sensible but fatal system failures. These errors are especially difficult to debug, since the failure points are usually far away from the root causes. Existing static analysis approaches to detecting error handling bugs mainly focus on how an error code is propagated or used in a program. However, they do not consider whether an error code is correctly chosen for propagation or usage within different program contexts, and thus miss to detect many error code misuse bugs. In this work, we conduct an empirical study on error code misuses in a mature commercial system. We collect error code issues from the commit history and conclude three main causes of them. To further resolve this problem, we propose a static approach that can automatically detect error code misuses. Our approach takes error code definition and error domain assignment as the input, and uses a novel static analysis method to detect the occurrence of the three categories of error code misuses in the source code.

The multi-valued evaluations of quality of service (QoS), the complicated constraints between cloud services (CSs) and the collaborative resource assignments add many difficulties to the problem of CS composition for data-intensive applications (DiA) in a hybrid cloud (CSCD-HC). Solving the CSCD-HC problem has become a challenging task due to the uncertain QoS, the diverse hardware configurations and the flexible pricing about CSs. This paper proposes a collaborative optimization approach for CSCD-HC. This approach models a DiA as a role-based collaboration (RBC) system and employs the environments-classes, agents, roles, groups, and objects (E-CARGO) model to formalize the CSCD-HC problem with complicated constraints. To deal with the multi-valued QoS evaluations, this paper exploits the cloud model theory to analyze the performance of CSs, and presents a new method utilizing the Mahalanobis distance to improve the similarity calculation of QoS cloud models. Based on it, the qualification of candidate CSs can be precisely measured for supporting CS composition. A solution via the IBM ILOG CPLEX optimization package is put forward to solve the CSCD-HC problem. The experimental results demonstrate that the proposed approach is effective and feasible for optimizing CSCD-HC.

With the growing cloud services (CSs) rented by an organization, it has become a challenging problem to optimize the CS composition for data-intensive applications (DiAs) from the user side, with consideration of improving the resource utilization of rented CSs. This paper proposes a resource utilization-aware approach to optimizing the CS composition for DiAs (CSCD). From the perspective of role-based collaboration, this approach utilizes the environments - classes, agents, roles, groups, and objects (E-CARGO) model to formalize the CSCD problem. The qualification of a CS for one task is assessed and the compatibility between new tasks and the running task is identified. A solution using IBM ILOG CPLEX package is put forward to optimize the CSCD problem. The experimental results demonstrate that the proposed approach is effective and feasible for optimizing the resource utilization-aware CSCD problem from the user side.

For detecting malicious codes, a classification method of support vector machine (SVM) based on rough set theory (RST) is proposed. The original sample data is preprocessed with the knowledge reduction algorithm of RST, and the redundant features and conflicting samples are eliminated from the working sample dataset to reduce space dimension of sample data. Then the preprocessed sample data is used as training sample data of SVM. By utilizing SVM, the generalizing ability of detection system is still good even the sample dataset size is small. Experiment results show that the proposed detection system needs few priori knowledge and can improve the training speed and precision of classification.