SUPPORT OF MANAGERIAL DECISION MAKING BY TRANSDUCTIVE LEARNING

Transductive inference has been introduced as a novelparadigm towards building predictive classi¯cation modelsfrom empirical data. Such models are routinely employedto support decision making in, e.g., marketing, risk manage-ment and manufacturing. To that end, the characteristics ofthe new philosophy are reviewed and their implications fortypical decision problems are examined. The paper\u27s objec-tive is to explore the potential of transductive learning forcorporate planning. The analysis reveals two main factorsthat govern the applicability of transduction in business set-tings, decision scope and urgency. In a similar fashion, twomajor drivers for its e®ectiveness are identi¯ed and empir-ical experiments are undertaken to con¯rm their in°uence.The results evidence that transductive classi¯ers are wellsuperior to their inductive counterparts if their speci¯c ap-plication requirements are ful¯lled

Attribute Sentiment Scoring with Online Text Reviews: Accounting for Language Structure and Missing Attributes

The authors address two significant challenges in using online text reviews to obtain fine-grained attribute level sentiment ratings. First, they develop a deep learning convolutional-LSTM hybrid model to account for language structure, in contrast to methods that rely on word frequency. The convolutional layer accounts for the spatial structure (adjacent word groups or phrases) and LSTM accounts for the sequential structure of language (sentiment distributed and modified across non-adjacent phrases). Second, they address the problem of missing attributes in text in construct-ing attribute sentiment scores—as reviewers write only about a subset of attributes and remain silent on others. They develop a model-based imputation strategy using a structural model of heterogeneous rating behavior. Using Yelp restaurant review data, they show superior accuracy in converting text to numerical attribute sentiment scores with their model. The structural model finds three reviewer segments with different motivations: status seeking, altruism/want voice, and need to vent/praise. Interestingly, our results show that reviewers write to inform and vent/praise, but not based on attribute importance. Our heterogeneous model-based imputation performs better than other common imputations; and importantly leads to managerially significant corrections in restaurant attribute ratings

On contemporary misdefinition of power and the importance of definitional fidelity

This paper's conceptual treatment documents a recent tendency in the literature to abandon the traditional definition of an important social construct: power. Naturally, such flexibility or looseness of conceptualization contains detrimental implications for operationalization and theory. When very different measures or manipulations are derived from incongruent conceptual definitions for a given nominal construct, it can produce uncertainty about the variable really being measured, from which a theoretical void follows. So, in this case, it is possible that many published studies and empirical findings ostensibly applying to social power, in fact, may not be. Thus, an entire literature stream appears to be misleading, even vitiated. Along with empirical grounding, remedial information is provided here to address the concern

Attribute Sentiment Scoring with Online Text Reviews: Accounting for Language Structure and Missing Attributes

The authors address two significant challenges in using online text reviews to obtain fine-grained attribute level sentiment ratings. First, they develop a deep learning convolutional-LSTM hybrid model to account for language structure, in contrast to methods that rely on word frequency. The convolutional layer accounts for the spatial structure (adjacent word groups or phrases) and LSTM accounts for the sequential structure of language (sentiment distributed and modified across non-adjacent phrases). Second, they address the problem of missing attributes in text in construct-ing attribute sentiment scores—as reviewers write only about a subset of attributes and remain silent on others. They develop a model-based imputation strategy using a structural model of heterogeneous rating behavior. Using Yelp restaurant review data, they show superior accuracy in converting text to numerical attribute sentiment scores with their model. The structural model finds three reviewer segments with different motivations: status seeking, altruism/want voice, and need to vent/praise. Interestingly, our results show that reviewers write to inform and vent/praise, but not based on attribute importance. Our heterogeneous model-based imputation performs better than other common imputations; and importantly leads to managerially significant corrections in restaurant attribute ratings

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

Enhancing Automated Network Management

Network management benefits from automated tools. With the recent advent of software-defined principles, automated tools have been proposed from both industry and academia to fulfill function components in the network management control loop. While automation aims to accommodate the ever increasing network diversity and dynamics with improved reliability and management efficiency, it also brings new concerns as it’s becoming more difficult to understand the control of the network and operators cannot rely on traditional troubleshooting tools. Meanwhile, how to effectively integrate new automation tools with existing legacy networks remains a question. This dissertationpresents efficient methods to address key functionalities within the control loop in the adaption of automated network management.Identifying the network-wide forwarding behaviors of a packet is essential for many network management tasks, including policy enforcement, rule verification, and fault localization. We start by presenting AP Classifier. AP Classifier was developed based on the concept of atomic predicates which can be used to characterize the forwarding behaviors of packets. There is an increasing trend that enterprises outsource their Network Function (NF) processing to a cloud to lower cost and ease management. To avoid threats to the enterprise’s private information, we propose SICS based on AP Classifier, a secure and dynamic NF outsourcing framework. Stateful NFs have become essential parts of modern networks, increasing the complexity in network management. A major step in network automation is to automatically translate high level network intents into low level configurations. To ensure those configurations and the states generated by automation match intents, we present Epinoia, a network intent checker for stateful networks. While the concept of auto-translation sounds promising, operators may not know what intents should be. To close the control loop, we present AutoInfer to automatically infer intents of running networks, which helps operators understand the network runtime states

Reverse classification accuracy: predicting segmentation performance in the absence of ground truth

When integrating computational tools such as au- tomatic segmentation into clinical practice, it is of utmost importance to be able to assess the level of accuracy on new data, and in particular, to detect when an automatic method fails. However, this is difficult to achieve due to absence of ground truth. Segmentation accuracy on clinical data might be different from what is found through cross-validation because validation data is often used during incremental method development, which can lead to overfitting and unrealistic performance expectations. Before deployment, performance is quantified using different metrics, for which the predicted segmentation is compared to a reference segmentation, often obtained manually by an expert. But little is known about the real performance after deployment when a reference is unavailable. In this paper, we introduce the concept of reverse classification accuracy (RCA) as a framework for predicting the performance of a segmentation method on new data. In RCA we take the predicted segmentation from a new image to train a reverse classifier which is evaluated on a set of reference images with available ground truth. The hypothesis is that if the predicted segmentation is of good quality, then the reverse classifier will perform well on at least some of the reference images. We validate our approach on multi-organ segmentation with different classifiers and segmentation methods. Our results indicate that it is indeed possible to predict the quality of individual segmentations, in the absence of ground truth. Thus, RCA is ideal for integration into automatic processing pipelines in clinical routine and as part of large-scale image analysis studies