Test data compression based on geometric shapes

The increasing complexity of systems-on-a-chip with the accompanied increase in their test data size has made the need for test data reduction imperative. In this paper, we introduce a novel and efficient test-independent compression technique based on geometric shapes. The technique exploits reordering of test vectors to minimize the number of shapes needed to encode the test data. The test set is partitioned into blocks and then each block is encoded separately. The encoder has the choice of encoding either the 0’s or the 1’s in a block. In addition, it encodes a block that contains only 0’s (or 1’s) and x’s with only 3 bits. Furthermore, if the cost of encoding a block using geometric shapes is higher than the original cost of the block, the block is stored as is without encoding. The effectiveness of the technique in achieving high compression ratio is demonstrated on the largest full-scanned versions of ISCAS89 benchmark circuits. The proposed technique achieves significantly higher compression in comparison to other test-independent compression techniques reported in the literature.     

Analysis of a contention-based opportunistic spectrum access under general channel activity model

We investigate a distributed contention-based spectrum access scheme in cognitive radio networks where ON/OFF periods of the channel by primary users follow discrete phase (PH) type distributions. The main motivation for ON/OFF having PH distributions is that the channel activity has a more general behavior depending on the primary users’ traffic. In the past most other researchers assumed that ON/OFF periods of a channel follow a geometric distribution for the purpose of mathematical tractability even though this assumption is restrictive.We propose a distributed medium access control (MAC) scheme for the secondary users (SUs) which is characterized by a constant contention window size and a method to decide whether for each SU to participate in competition or not depending on the queueing delay of a head-of-line (HoL) packet. In order to investigate the performance of our proposed MAC protocol, we construct a two-dimensional Markov chain which incorporates both the proposed MAC scheme and the general channel activity. The resulting one-step transition probability matrix of the Markov chain has a very special structure. With the help of the censored Markov chain method, we provide a computationally efficient method to obtain the stationary distribution of the Markov chain. We then obtain the system capacity, which is defined as the maximum number of SUs that can be accommodated with a quality of service (QoS) guarantee on the packet dropping probability and the packet delay. Numerical examples show that the system capacity considerably depends on the distributions of ON/OFF periods and our proposed MAC scheme achieves a higher capacity than the existing one.     

A genetic algorithm approach to determine the sample size for attribute control charts

Determining the sample size for control charts (CCs) is generally an important problem in the literature. In this paper, Kaya and Engin’s [?. Kaya, O. Engin, A new approach to define sample size at attributes control chart in multistage processes: an application in engine piston manufacturing process, Journal of Materials Processing Technology 183 (2007) 38-48] model based on minimum cost and maximum acceptance probability to determine the sample size for attribute control charts (ACCs), and solved by genetic algorithms (GAs) with linear binary representation structure, is handled to solve it by a linear real-valued representation. A new chromosome structure is also suggested to increase the efficiency of GAs. The performance of GAs depends on mutation and crossover operators, and their ratios. To determine the most appropriate operators, five different mutation and crossover operators are used and they are compared with each other. An application in a motor engine factory is illustrated. u-Control charts are constructed with respect to the sample size determined by GA in the model. The piston production stages in this factory are monitorized using the obtained control charts.     

A logical duality for underspecified probabilistic systems

This paper establishes a Stone-type duality between specifications and infLMPs. An infLMP is a probabilistic process whose transitions satisfy super-additivity instead of additivity. Interestingly, its simple structure can encode a mix of probabilistic and non-deterministic behavior, which, as we show, is strongly related to another well-known such model: probabilistic automata. Our duality puts in relation the category of infLMPs and a category of abstract representations of them based on properties only. We exhibit a Galois connection between these categories and show that we have an adjunct pair of functors when restricted to LMPs only. Our duality also shows that an infLMP can be considered as a demonic representative of a system’s information. Moreover, it carries forward a view where states are less important, and events, or properties, become the main characters, as it should be in probability theory. Along the way, we show that bisimulation and simulation are naturally interpreted in this setting, and we exhibit the interesting relationship between infLMPs and the usual probabilistic modal logics. This paper is an extended version of a Concur ’09 paper [13]; in particular, the comparison of infLMPs with probabilistic automata and the Galois connection are new.     

How different information types affect viewer’s attention on internet advertising

The effect of internet advertising has been a controversial issue, especially on the topic of how to effectively draw more attention from internet users. According to traditional attention theory, we know people pay lesser attention on other objects if the main browsing contents occupy more of the viewer’s mental resources. Therefore, we know different information types of webpage should have different influences on users’ attention. On the other hand, an effect called ‘banner blindness’ makes viewers naturally overlook the advertising based on their previous experience. It is therefore becoming more and more difficult to increase viewer’s attention on advertisement simply by adding salient features on the advertisements. In light of this new challenge in Internet advertising, verifying the different influences of the information types on advertising attention is the main goal of this study.Great amounts of previous studies relevant to internet advertising focused on the advertisement itself, like the form, color, size and location. However, this study put focus on how the information types and the webpage structure influence the viewer’s attention on banner advertising. This research tested the effect on user attention of four common information types on Internet webpages: (1) text-based webpage; (2) text-picture mixed webpage; (3) picture-based webpage; and (4) video-based webpage. This study hopes to provide valuable information for matching advertising with viewing tasks that will stimulate the most user attention.     

Trace monoids with idempotent generators and measure-only quantum automata

In this paper, we analyze a model of 1-way quantum automaton where only measurements are allowed ( MON -1qfa). The automaton works on a compatibility alphabet (S, E)(\Sigma, E) of observables and its probabilistic behavior is a formal series on the free partially commutative monoid FI(S, E)\hbox{FI}(\Sigma, E) with idempotent generators. We prove some properties of this class of formal series and we apply the results to analyze the class LMO(S, E){\bf LMO}(\Sigma, E) of languages recognized by MON -1qfa’s with isolated cut point. In particular, we prove that LMO(S, E){\bf LMO}(\Sigma, E) is a boolean algebra of recognizable languages with finite variation, and that LMO(S, E){\bf LMO}(\Sigma, E) is properly contained in the recognizable languages, with the exception of the trivial case of complete commutativity.     

Solving the design of distributed layout problem using forecast windows: A hybrid algorithm approach

In today’s competitive environment, manufacturing facilities have to be more responsive to the frequent changes in product mix and demand by realigning their organizational structure for minimizing material handling cost. However, manufacturing firms are reluctant to modify the layout as it leads to operation disruption and excess rearrangement cost. In this paper, we present an alternative approach for designing a multi-period layout (i.e., distributed layout) that maintains a tradeoff between re-layout cost and cost of excess material handling. Obtaining an optimal solution to distributed layout problem is generally a difficult task, owing to larger size of quadratic assignment problem. In order to overcome the aforementioned drawback, a meta-heuristic, named ‘CSO-DLP’ (Clonal Symbiotic Operated-Distributed Layout Planning) is developed for designing a distributed layout that jointly determines the arrangement of department and flow allocation among them. It inherits its trait from Symbiotic algorithm and Clonal algorithm. In addition to these; the concept of ‘forecast window’ is used, which evaluates the layout for varying number of periods at a given time. The proposed meta-heuristic is applied on a benchmark dataset and the effect of system parameters, such as rearrangement cost, department disintegration, and duplication are investigated and benchmarked in this paper.     

Predictive student model supported by fuzzy-causal knowledge and inference

In this article we explore the paradigm of student-centered education. The aim is to enhance the learning of students by the self-adaptation of a Web-based educational system (WBES). The adaptive system’s behavior is achieved as a result of the decisions made by a student model (SM). The decision reveals the lecture option most suitable to teach a concept according to the student’s profile. Thus, the lecture content is authored from different view points (e.g. learning theory, type of media, complexity level, and user-system interaction degree). The purpose is to tailor several educational options to teach a given concept. Thereby, the SM elicits psychological attributes of the student to describe subjective traits, such as: cognitive, personality, and learning preferences. It also depicts pedagogical properties of the available lecture’s options. Moreover, the SM dynamically builds a cognitive map (CM) to set fuzzy-causal relationships among the lecture’s option properties and the student’s attributes. Based on a fuzzy-causal engine, the SM predicts the bias that a lecture’s option exerts on the student’s apprenticeship. The conceptual, theoretical, and formal grounds of the approach were tested by a computer implementation of the SM and an experiment. As a result of a field trial, we found that: the average learning acquired by an experimental group of volunteers that used this approach was 17% higher than the average apprenticeship of another equivalent control group, whose lectures were randomly chosen. Thus we conclude that: learning is better stimulated when the delivered lectures account a student’s profile than when they ignore it.     

An assessment of organizational size and sense and response capability on the early adoption of disruptive technology

This study used the results from a survey of 73 senior executives, representing their Fortune 1000 or INC 500 firm, to better understand the relationship between an organization’s size and its technological-sensing and response capability on the early adoption of disruptive technology. Results indicated that an organization’s size, measured by number of employees and annual sales, was shown to be an impediment to an organization’s response capability despite its increased capability to sense the technology earlier than smaller firms.     

一种增强的Linux系统安全审计机制

提出了一个增强Linux系统安全审计功能的机制。该机制以可装载内核模块技术为基础,提供系统层和应用层两个层次的审计功能。系统审计为每个安全相关的系统调用生成审计记录。应用层审计改变了传统的完全依赖于应用程序在用户空间写日志文件的方式,由应用程序和内核共同产生审计记录。最后给出用该审计机制进行入侵检测的例子。     

Social and attitudinal determinants of viral marketing dynamics

At this time of uncertainty, viral marketing is emerging as one of the most intriguing communication strategies, due to low cost and the results it obtains. However, the success of this kind of practice depends on a range of factors including what we explore and refer to in the present research as the individual’s “viral dynamics”. We thus propose a causal model in which viral dynamics is determined by the individual’s social capital and prior attitudes. Based on a survey of young adults, the authors test the effects of structural and relational capital as well as attitudes on viral dynamics. The results evidence that the individual’s connectedness in the email network does not impact viral dynamics, whereas the individual’s integration and relationship with the network and the attitudes towards viral messages prove critical to the individual involved in the receiving-forwarding process.     

具有公平关切的零售商对双渠道供应链决策影响分析

针对市场需求信息不对称的双渠道供应链,考虑零售商具有公平关切心理,研究零售商的公平关切行为对供应链各成员决策及效用的影响.通过定义制造商判断市场需求规模的阈值,以获取零售商订单中所传递的市场需求规模信息,并得到零售商的最优订货量和制造商的最优直销量.研究发现,阈值随零售商公平关切程度的增加呈现递减趋势,且最终趋于常数. 数值实验结果表明,零售商的公平关切行为有利于增加自身的效用,但会损害制造商的利润.     

The effect of nonhuman’s versus human’s external regulation on children’s speech use, manifested self-regulation, and satisfaction during learning tasks

Because of several analytical and methodological critiques on the findings and contexts of children’s private speech (PS), self-regulation learning (SRL), and thinking aloud (TA), the present study was conducted to shed new light on the effect of the nonhuman’s/computer’s versus human’s/teacher’s intervention (C-Condition versus T-Condition) on young children’s speech use, SRL, and satisfaction during learning tasks. Four developmental measurements with novel criteria were used to measure: (1) speech analysis, (3) SRL as a function of task level selection, (3) SRL as a function of task precision, and (4) a friendly-chat questionnaire to measure children’s satisfaction. Two types of intervention (enacted versus verbal encouragement) were applied through computer-based learning environment and investigated by forty preschool children divided by their teachers between the two conditions equivalently. It was hypothesized that children who acted alone (C-Condition) were more PS productive, manifested higher SRL, task performance, and satisfaction. The results confirmed the hypothesis with no significant differential effect of the gender on performance, showed that the injudicious use of encouragement hindered the children’s regulation behavior, and proved that PS and TA elicitation were fully different. However, the results were not confirmed Vygotsky’s view and simultaneously not fully inline with Piaget’s view of self-regulation development.     

非等间隔GM(1, 1, ????) 幂次时间项模型及其应用

GM(1, 1, ??^??) 幂次时间项模型是灰色GM(1, 1) 模型的推广. 在灰色GM(1, 1) 模型和等间隔GM(1, 1, ??^??) 幂次时间项模型的基础上提出非等间隔GM(1, 1, ??^??) 幂次时间项模型, 并对模型进行求解. 讨论了GM(1, 1, ????) 幂次时间项模型的曲线形状、发展系数以及幂指数间的关系, 研究了非等间隔GM(1, 1, ??^??) 幂次时间项模型的参数空间. 将平均相对误差看成幂指数的函数, 根据序列形状判断幂指数的范围, 并利用粒子群算法求解幂指数. 实际应用验证了所提出模型的有效性.

     

The impact of structural complexity on the understandability of UML statechart diagrams

The effectiveness of current software development strategies, such as Model-Driven Development (MDD), depends largely on the quality of their primary artefacts, i.e. software models. As the standard modelling language for software systems is the Unified Modelling Language (UML), quality assurance of UML models is a major research field in Computer Science. Understandability, i.e. a model’s ability to be easily understood, is one model quality property that is currently heavily under investigation. In particular, researchers are searching for the factors that determine an UML model’s understandability and are looking for ways to manipulate these factors. This paper presents an empirical study investigating the effect that structural complexity has on the understandability of one particular type of UML model, i.e. the statechart diagram. Based on data collected in a family of three experiments, we have identified three dimensions of structural complexity that affect understandability: (i) the size and control flow complexity of the statechart in terms of features such as the number of states, events, guards and state transitions; (ii) the actions that are performed when entering or leaving a state; (iii) the sequence of actions that is performed while staying within a state. Based on these structural complexity dimensions we have built an understandability prediction model using a regression technique that is specifically recommended for data obtained through a repeated measures design. Our test results show that each of the underlying structural complexity dimensions has a significant impact on the understandability of a statechart diagram.     

Improvement of a proxy multi-signature scheme without random oracles

A proxy multi-signature scheme permits two or more original singers to delegate their signing powers to the same proxy signer. Recently, Liu et al. proposed the first proxy multi-signature that be proven secure in the standard model [Liu et al. (2008) [20]], which can be viewed as a two-level hierarchical signature due to Waters. However, because of the direct employment of Waters’ signature, their scheme needs a relatively large number of public parameters and is not tightly reduced to the security assumption. In this paper, inspired by Boneh, Boyen’s technique and Waters’ technique, we propose a new proxy multi-signature scheme without random oracles, whose unforgeability can be tightly reduced to the CDH assumption in bilinear groups. The new scheme can be regarded as an improvement to overcome the weaknesses of Liu et al.’s scheme. Compared with Liu et al.’s scheme, the improvement has three merits, tighter security reduction, shorter system parameters and higher efficiency.     

Recursive definitions and fixed-points on well-founded structures

An expression such as ∀x(P(x)↔?(P)), where P occurs in ?(P), does not always define P. When such an expression implicitly definesP, in the sense of Beth (1953) [1] and Padoa (1900) [13], we call it a recursive definition. In the Least Fixed-Point Logic (LFP), we have theories where interesting relations can be recursively defined (Ebbinghaus, 1995 [4], Libkin, 2004 [12]). We will show that for some sorts of recursive definitions there are explicit definitions on sufficiently strong theories of LFP. It is known that LFP, restricted to finite models, does not have Beth’s Definability Theorem (Gurevich, 1996 [7], Hodkinson, 1993 [8], Dawar, 1995 [3]). Beth’s Definability Theorem states that, if a relation is implicitly defined, then there is an explicit definition for it. We will also give a proof that Beth’s Definability Theorem fails for LFP without this finite model restriction. We will investigate fragments of LFP for which Beth’s Definability Theorem holds, specifically theories whose models are well-founded structures.     

A hybrid of sequential rules and collaborative filtering for product recommendation

Customers’ purchase behavior may vary over time. Traditional collaborative filtering (CF) methods make recommendations to a target customer based on the purchase behavior of customers whose preferences are similar to those of the target customer; however, the methods do not consider how the customers’ purchase behavior may vary over time. In contrast, the sequential rule-based recommendation method analyzes customers’ purchase behavior over time to extract sequential rules in the form: purchase behavior in previous periods ⇒ purchase behavior in the current period. If a target customer’s purchase behavior history is similar to the conditional part of the rule, then his/her purchase behavior in the current period is deemed to be the consequent part of the rule. Although the sequential rule method considers the sequence of customers’ purchase behavior over time, it does not utilize the target customer’s purchase data for the current period. To resolve the above problems, this work proposes a novel hybrid recommendation method that combines the segmentation-based sequential rule method with the segmentation-based KNN-CF method. The proposed method uses customers’ RFM (Recency, Frequency, and Monetary) values to cluster customers into groups with similar RFM values. For each group of customers, sequential rules are extracted from the purchase sequences of that group to make recommendations. Meanwhile, the segmentation-based KNN-CF method provides recommendations based on the target customer’s purchase data for the current period. Then, the results of the two methods are combined to make final recommendations. Experiment results show that the hybrid method outperforms traditional CF methods.     

Robust performance in hybrid-memory cooperative caches

Modern servers require large main memories, which so far have been enabled by increasing DRAM’s density. With DRAM’s scalability nearing its limit, Phase-Change Memory (PCM) is being considered as an alternative technology. PCM is denser, more scalable, and consumes lower idle power than DRAM, while exhibiting byte-addressability and access times in the nanosecond range. Still, PCM is slower than DRAM and has limited endurance. These characteristics prompted the study of hybrid memory systems, combining a small amount of DRAM and a large amount of PCM. In this paper, we leverage hybrid memories to improve the performance of cooperative memory caches in server clusters. Our approach entails a novel policy that exploits popularity information in placing objects across servers and memory technologies. Our results show that (1) DRAM-only and PCM-only memory systems do not perform well in all cases; and (2) when managed properly, hybrid memories always exhibit the best or close-to-best performance, with significant gains in many cases, without increasing energy consumption.     

Smoothing large data sets using discrete thin plate splines

Traditional thin plate splines use radial basis functions and require the solution of a dense linear system of equations whose size is proportional to the number of data points. Instead of radial basis functions we present a method based on the use of polynomials with local support defined on finite element grids. This method is more efficient when dealing with large data sets as the resulting system of equations is sparse and its size depends only on the number of nodes in the finite element grid. Theory is developed for general d-dimensional data sets and model problems are presented in 3D to study the convergence behaviour.