Trace-based schedulability analysis to enhance passive side-channel attack resilience of embedded software

Side channel attacks (SCAs) are a practical threat to the security of cryptographic implementations. A well known countermeasure against them is to alter the temporal location of instructions among different executions of the code. In this work we provide an algorithm to generate valid schedules of block cipher implementations. The proposed algorithm relies on a trace-theory based analysis and efficiently generates any valid schedule of the implementation under exam, selecting the ones with higher diversity among them. The algorithm was implemented as a pass in the backend of the LLVM compiler suite, and the results of the automated instruction scheduling are provided to validate its effectiveness as an SCA countermeasure employing the whole ISO standard block cipher suite.     

Flexible business strategies to enhance resilience in manufacturing supply chains: An empirical study

Resilience and flexibility in manufacturing and supply chains are widely discussed at times of the present crises situation due to COVID-19 pandemic. It is a question of ongoing debate among practitioners about the role of flexibility and complexity in building resilience for firms and their supply chains. In this research, we examine the flexible business strategies at the demand, supply, and process side of supply chains contributing to resilience. Five of the major flexible business strategies were acknowledged in this research and the single items for constructing them were posited. In this study, we observe the correlations among the constructs based on survey-based research in electronics manufacturing firms, followed by a dimensionality reduction of constructs using factor analysis. The data collected were subject to several initial tests for ensuring validity, reliability, and adequacy using relevant statistical indicators. The measurement model was converted into a structural model and path coefficients were determined. From the path analysis, the latent variables contributing to flexibility in supply chains were found to be independent estimators of resilience. Adding to it, we observe that the single items for measuring the flexibility of supply, process, product, and pricing strategies were evidenced to be strongly correlated. The results are useful to managers for taking decisions related to flexibility implementation, towards enhancing resilience in supply chains.     

Using interceptors to enhance CORBA

The integration of distributed computing and the object model leads to distributed object computing, in which objects rather than processes are distributed across multiple computers. A well-established standard for distributed object computing is the Common Object Request Broker Architecture (CORBA). Previously, you would have had to create-and enable the application to use-the components that provide such additional capabilities. Using these components requires specialized knowledge and understanding of problems outside the application domain. With the advent of interceptors-non-application components that can alter application behavior-you can enhance CORBA applications at runtime with components whose operation is transparent to both the application and the CORBA framework, modifying application behavior without modifying the application or the CORBA framework     

加强位置隐私保护的策略

为了使移动用户在充分体验基于位置的服务带来的便利的同时,其隐私也能够得到保护,提出了一种新的体系架构来加强位置掩护.通过采用缓存和周期查询处理策略来提供更强大的隐私保护,同时给出了LRU缓存块替代算法来提高缓存池响应移动对象请求的有效性.该种策略使位置掩护设施能够以时间和空间属性的方式来掩盖用户的真实位置.通过缜密的系统仿真,结果表明了该策略的正确性和有效性.     

从计算机辅助翻译到协同翻译

由于机器翻译系统的译文质量仍难以达到实用化要求,计算机辅助翻译技术逐渐成为研究热点,并且取得了很好的实际效果,大大提高了翻译产业的生产率。随着辅助翻译规模的不断扩大,多名在空间上分散的用户被组织起来共同完成一项翻译任务已成为普遍现象,这种新的翻译模式称为协同翻译。该文对计算机辅助翻译和协同翻译技术进行综述,首先从辅助译文生成、译后编辑和系统反馈学习等方面介绍了计算机辅助翻译技术的常用方法和研究进展,随后讨论了计算机辅助翻译与协同翻译之间的联系和区别,分析了协同翻译技术所面临的主要问题,并介绍了现有研究的解决方法。最后对协同翻译的未来发展方向进行了展望。     

基于心理测量学的协同过滤相似度方法

基于邻居(neighborhood-based)的协同过滤是一项很受欢迎的用于推荐系统的技术.它可以分为基于用户(user-based)和基于项目(item-based)协同过滤.它通过用户或项目之间的相似性预测用户对于未评分项目的偏好.然而,传统的相似度方法易受数据稀疏影响.为了解决这个问题,提出了基于心理测量学(psychometrics-based)的相似度方法.实验结果表明,提出的相似方法更适合基于邻居协同过滤,它们可以提高推荐准确性和覆盖度(coverage).     

Collaborative Writing Is Hard to Support: A Field Study of Collaborative Writing

This paper documents the results of a field test of Instant Update, a collaborative writing tool by a geographically dispersed department at the MITRE Corporation. Thirty-six participants were given Instant Update software and free training and support in exchange for their cooperation with data collection. These participants spent a great deal of time writing and communicating with each other about their written work. They began the pilot test with enthusiasm, using Instant Update for many types of documents and anticipating many benefits. After three months of experience they rated the actual benefits they received from the software lower than they had expected. Their usage became much more refined. They continued to use it for some types of documents, but stopped for others. The collaborative writing software was used successfully to create joint documents that have a consistent format and are produced on a regular basis (such as weekly activity reports) and for documents that provide access to shared information (such as a vacation calendar). It was used less successfully for the production of documents that require a complex work flow and have a changing group of contributors. We concluded that although collaborative writing is difficult to support and the currently available collaborative writing tools need to be improved before they can meet the needs of most co-writers, they are adequate for some types of tasks. Documents that are well defined and are created by experienced users who can cooperate well can benefit from the currently available tools.     

增强普适性的VPN系统设计与分析

作为较普及的VPN系统,SSL VPN比IPSec VPN更具有维护成本低、组网灵活、操作简便等特点。因此在比较了传统VPN系统的优缺点之后,对基于SSL VPN的产品OpenVPN进行研究,通过结合Ethernet over SSL、Mail Callback、Relay Sever技术,增强了整个VPN系统的普适性,使得系统能够满足更灵活的环境配置需求,并在客户端配置Client Firewall,以降低由远程登陆用户给企业内部网络带来的安全隐患。最后对改进的系统建立了GSPN模型进行性能分析,通过与实际测试结果的比较,可以看出改进的系统同样具备优秀的性能。     

Using artificial neural networks to enhance CART

Accuracy is a critical factor in predictive modeling. A predictive model such as a decision tree must be accurate to draw conclusions about the system being modeled. This research aims at analyzing and improving the performance of classification and regression trees (CART), a decision tree algorithm, by evaluating and deriving a new methodology based on the performance of real-world data sets that were studied. This paper introduces a new approach to tree induction to improve the efficiency of the CART algorithm by combining the existing functionality of CART with the addition of artificial neural networks (ANNs). Trained ANNs are utilized by the tree induction algorithm by generating new, synthetic data, which have been shown to improve the overall accuracy of the decision tree model when actual training samples are limited. In this paper, traditional decision trees developed by the standard CART methodology are compared with the enhanced decision trees that utilize the ANN’s synthetic data generation, or CART+. This research demonstrates the improved accuracies that can be obtained with CART+, which can ultimately improve the knowledge that can be extracted by researchers about a system being modeled.     

运用DH-EKE增强WTLS握手协议的安全性

WTLS握手协议不满足前向安全性,非匿名验证模式下不满足用户匿名性,完全匿名模式下易遭受中间人攻击.DH-EKE协议具有认证的密钥协商功能,将改进的DH-EKE集成到WTLS握手协议中,只需使用可记忆的用户口令,不需使用鉴权证书及数字签名.该方案适用于完全匿名的验证模式,可抵御中间人攻击和字典式攻击,且在服务器中不直接存储口令,攻击者即使攻破服务器获得口令文件也无法冒充用户,能够在WTLS握手协议中实现简单身份认证和安全密钥交换.     

Planning effective HCI to enhance access to educational applications

Information and communications technologies (ICT) are widely believed to offer new options for Web-mediated courseware design. Multimedia and online courseware development accentuates a belief that highly graphical (or visual) delivery media will meet the individualised instructional requirements of diverse student cohorts. While most electronic courseware may allow the user to proceed at their own pace, two assumptions are commonly made by courseware designers. Firstly, to facilitate learning, all users are assumed capable of assimilating the graphical content with their current experiential knowledge. There is little or no consideration of different cognitive styles. Understanding learner attributes is essential to increasing accessibility to computerised information. Secondly, learning is assumed rather than demonstrated. To deal with this issue, data analysis techniques can be used to differentiate between what an individual knows from what they do not. This paper presents two research projects that demonstrate the importance of awareness for the human-dimension of human-computer interaction (HCI) in designing effective online experiential learning for special education.

Exploiting unlabeled data to enhance ensemble diversity

Ensemble learning learns from the training data by generating an ensemble of multiple base learners. It is well-known that to construct a good ensemble with strong generalization ability, the base learners are deemed to be accurate as well as diverse. In this paper, unlabeled data is exploited to facilitate ensemble learning by helping augment the diversity among the base learners. Specifically, a semi-supervised ensemble method named udeed, i.e. Unlabeled Data to Enhance Ensemble Diversity, is proposed. In contrast to existing semi-supervised ensemble methods which utilize unlabeled data by estimating error-prone pseudo-labels on them to enlarge the labeled data to improve base learners’ accuracies, udeed works by maximizing accuracies of base learners on labeled data while maximizing diversity among them on unlabeled data. Extensive experiments on 20 regular-scale and five large-scale data sets are conducted under the setting of either few or abundant labeled data. Experimental results show that udeed can effectively utilize unlabeled data for ensemble learning via diversity augmentation, and is highly competitive to well-established semi-supervised ensemble methods.     

A semantic complement to enhance electronic market

Consumers prefer to purchase bundled and related products to use them together to perform a task or satisfy a need. In this paper, we propose a complementary association for bundling products to enhance promotions, recommendations and selling strategies in marketplaces such as combinatorial auction. We propose an ontology based model and define a Need association to determine complement of product classes. Using this type of association, we develop a mathematical model to relatively measure complementary degree of classes and the latest purchased products to recommend Top-N products. We experiment this approach with a recommender system utilizing complementary products. Experimental results on the dataset of Building Equipment Company show superiority in terms of performance and precision.     

增强普适性的VPN系统设计与分析

作为较普及的VPN系统,SSLVPN比IPSecVPN更具有维护成本低、组网灵活、操作简便等特点。因此本文在比较了传统VPN系统的优缺点之后,对基于SSLVPN的产品OpenVPN进行研究,通过结合Ethernetover SSL、Mail Callback、RelaySever技术,增强了整个VPN系统的普适性,使得系统能够满足更灵活的环境配置需求,并在客户端配置Client Firewall,以降低由远程登陆用户给企业内部网络带来的安全隐患。最后对改进的系统建立了GSPN模型进行性能分析,通过与实际测试结果的比较,可以看出改进的系统同样具备优秀的性能.     

Using traffic asymmetry to enhance TCP performance

A wealth of recent work has gone into optimizing the performance of Transmission Control Protocol (TCP) on the downlink channel of wireless networks such as for example, honing its congestion awareness mechanism so that it is minimally affected by random wireless losses, and optimizing achieved fairness of the end-to-end TCP rates. Other work has gone into balancing the allocation of a shared resource between the downlink and uplink in order to optimize TCP performance. We build on such previous research by proposing a cross-layer algorithm for resource allocation in OFDMA systems aiming not only to achieve optimal throughput for competing TCP flows but also to allocate resources appropriately between the downlink and uplink. This is important due to the increasing number of Internet applications where the mobile terminal is the TCP sender (social networking, peer-to-peer, etc.). Therefore, our scheme makes use of the asymmetry in the traffic and by defining the boundary between downlink and uplink capacity dynamically, enhance the TCP performance. Through numerical investigations we show the performance of the proposed scheme in terms of achieved fairness to the receivers and efficient allocation of downlink to uplink ratios based on the TCP traffic.     

多元化教学提升学生计算机应用能力

计算机基础课程在高校基础教育中非常重要。传统的授课模式大多停留在教师多媒体演示和学生上机自主实践的模式,学生被动学习,很难达到能力培养的目标。为了让学生动起来,让课堂活跃起来,让教学效果好起来,提出了多元化教学模式,将271模式、分层分组、情景教学、任务驱动等教学法相结合,灵活应用于实际的教学当中。实践证明,多元化教学模式在提升学生计算机应用能力方面效果明显。     

A Probabilistic Approach to Collaborative Multi-Robot Localization

This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The technique has been implemented and tested using two mobile robots equipped with cameras and laser range-finders for detecting other robots. The results, obtained with the real robots and in series of simulation runs, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization. A further experiment demonstrates that under certain conditions, successful localization is only possible if teams of heterogeneous robots collaborate during localization.