Embodying Product and Process Flexibility to Cope with Challenging Project Deliveries

Four factors make it challenging to manage semiconductor fabrication facility (“fabs”) projects: technical complexity of the product design, need to compress the project duration, need to reduce upfront costs, and unexpected project changes. The strategies employed by practitioners to cope with these challenges form an intricate puzzle. We empirically develop a framework that provides a structure for helping to solve this puzzle, which comprises two principles: investing upfront in a flexible product design and structuring a flexible process. Empirical findings reveal that project teams make commitments early on by overdesigning but also postpone critical decisions by differentiating the scope of their work. Project teams employ other strategies such as increasing communication, using modular architectures, engaging in four-dimensional computer-based modeling, and fabricating components and subsystems off-site. Our analysis yields understanding on the purposes and performance tradeoffs of these strategies, and on how they embody the two principles. Project managers may find the framework useful when deciding which strategies best suit other equally challenging projects.     

Toward Spoken Human–Computer Tutorial Dialogues

Oral discourse is the primary form of human–human communication, hence, computer interfaces that communicate via unstructured spoken dialogues will presumably provide a more efficient, meaningful, and naturalistic interaction experience. Within the context of learning environments, there are theoretical positions supporting a speech facilitation hypothesis that predicts that spoken tutorial dialogues will increase learning more than typed dialogues. We evaluated this hypothesis in an experiment where 24 participants learned computer literacy via a spoken and a typed conversation with AutoTutor, an intelligent tutoring system with conversational dialogues. The results indicated that (a) enhanced content coverage was achieved in the spoken condition; (b) learning gains for both modalities were on par and greater than a no-instruction control; (c) although speech recognition errors were unrelated to learning gains, they were linked to participants' evaluations of the tutor; (d) participants adjusted their conversational styles when speaking compared to typing; (e) semantic and statistical natural language understanding approaches to comprehending learners' responses were more resilient to speech recognition errors than syntactic and symbolic-based approaches; and (f) simulated speech recognition errors had differential impacts on the fidelity of different semantic algorithms. We discuss the impact of our findings on the speech facilitation hypothesis and on human–computer interfaces that support spoken dialogues.     

AUTOMATIC DETECTION OF LEARNER'S AFFECT FROM GROSS BODY LANGUAGE

We explored the reliability of detecting learners' affect by monitoring their gross body language (body position and arousal) during interactions with an intelligent tutoring system called AutoTutor. Training and validation data on affective states were collected in a learning session with AutoTutor, after which the learners' affective states (i.e., emotions) were rated by the learner, a peer, and two trained judges. An automated body pressure measurement system was used to capture the pressure exerted by the learner on the seat and back of a chair during the tutoring session. We extracted two sets of features from the pressure maps. The first set focused on the average pressure exerted, along with the magnitude and direction of changes in the pressure during emotional experiences. The second set of features monitored the spatial and temporal properties of naturally occurring pockets of pressure. We constructed five data sets that temporally integrated the affective judgments with the two sets of pressure features. The first four datasets corresponded to judgments of the learner, a peer, and two trained judges, whereas the final data set integrated judgments of the two trained judges. Machine-learning experiments yielded affect detection accuracies of 73%, 72%, 70%, 83%, and 74%, respectively (chance = 50%) in detecting boredom, confusion, delight, flow, and frustration, from neutral. Accuracies involving discriminations between two, three, four, and five affective states (excluding neutral) were 71%, 55%, 46%, and 40% with chance rates being 50%, 33%, 25%, and 20%, respectively.     

基于形态学变换等技术的川南石刻图像预处理方法研究

川南石刻图像自动识别系统中的预处理是十分关键的一步,预处理效果的好坏直接关系到后面特征提取与识别的质量.论文详细讨论了图像预处理的方法与过程,并在此基础上提出基于数学形态学的川南石刻图像分割、灰度、二值化、增强、边缘提取等预处理方法,最后在仿真实验中证明了该方法的有效性.     

Data privacy preservation algorithm with k-anonymity

World Wide Web - With growing concern of data privacy violations, privacy preservation processes become more intense. The k-anonymity method, a widely applied technique, transforms the data such...     

Determination of citrus jams genuineness by flavonoid analysis

An easy technique that enables the analysis of flavonoids from citrus jams is described. The flavonoids from commercial sour orange, sweet orange, mandarin, lemon, bitter lemon, grapefruit and lime jams were analysed by HPLC. Each citrus jam has a characteristic flavonoid pattern that can be used in genuineness determinations. Commercial jams produced with mixtures of orange, lemon and grapefruit can easily be characterized by HPLC. No differences between the flavonoid profiles of natural fruits and those of the corresponding jams were observed, showing that the manufacturing process does not affect the flavonoid profile and, therefore, that this profile can be used in genuineness studies.