Impact of operator experience on information feedback and reusability

Keeping a record of operator experience remains a challenge to operation management and a major source of inefficiency in information management. The objective is to develop a framework that enables an explicit presentation of experience based on information use. A purposive sampling method is used to select four small and medium-sized enterprises as case studies. The unit of analysis is the production process in the machine shop. Data collection is by structured interview, observation and documentation. A comparative case analysis is applied. The findings suggest experience is an accumulation of tacit information feedback, which can be made explicit in information use interoperatability matrix. The matrix is conditioned upon information use typology, which is strategic in waste reduction. The limitations include difficulty of participant anonymity where the organisation nominates a participant. Areas for further research include application of the concepts to knowledge management and shop floor resource management.

Detecting Anomalies in Graphs with Numeric Labels

This paper presents Yagada, an algorithm to search labelled graphs for anomalies using both structural data and numeric attributes. Yagada is explained using several security-related examples and validated with experiments on a physical Access Control database. Quantitative analysis shows that in the upper range of anomaly thresholds, Yagada detects twice as many anomalies as the best-performing numeric discretization algorithm. Qualitative evaluation shows that the detected anomalies are meaningful, representing a com- bination of structural irregularities and numerical outliers.

Unveiling Fairness for the Consumer:The Law, Economics and Justice of Expanded Arbitration

In recent years, the US Supreme Court has rather controversially extended the ambit of the Federal Arbitration Act to extend arbitration’s reach into, inter alia¸ consumer matters, with the consequence that consumers are often (and unbeknownst to them) denied remedies which would otherwise be available. Such denied remedies include recourse to class action proceedings, effective denial of punitive damages, access to discovery and the ability to resolve the matter in a convenient forum.

The court’s extension of arbitration’s ambit is controversial. Attempts to overturn this extension have been made in Congress, but to no avail. In contrast to American law, European consumer law looks at pre-dispute agreements to arbitrate directed at consumers with extreme suspicion, and does so on the grounds of fairness. In contrast, some argue that pre-dispute agreements in consumer (and employment) matters are consumer welfare enhancing: they decrease the costs of doing business, which is then passed on to the consumer. This Article examines these latter claims from both an economic and normative perspective.

The economic analysis of these arguments shows that their assumptions do not hold. Rather than being productive of consumer surplus, the use of arbitration is likely to have the opposite effect. The industries from which the recent Supreme Court cases originated not only do not exhibit the industrial structure assumed by the proponents of expanded arbitration, but are also industries which exhibit features that facilitate consumer welfare reducing collusion.

The normative analysis addresses the fairness concerns. It is explicitly based upon John Rawls’ notion of “justice as fairness,” which can provide a lens to evaluate social institutions. This Rawlsian analysis considers the use of extended arbitration in consumer matters in the light of the earlier economic results. It suggests that the asymmetries present in the contractual allocation of rights serve as prima facie evidence that such arbitration–induced exclusions are prima facie unjust/unfair. However, as asymmetry is only a prima facie test, a generalized criticism of the arbitration exclusions (of the sort found in Congress and underlying the European regime) is overbroad.

Two-part Segmentation of Text Documents

We consider the problem of segmenting text documents that have a
two-part structure such as a problem part and a solution part. Documents
of this genre include incident reports that typically involve
description of events relating to a problem followed by those pertaining
to the solution that was tried. Segmenting such documents
into the component two parts would render them usable in knowledge
reuse frameworks such as Case-Based Reasoning. This segmentation
problem presents a hard case for traditional text segmentation
due to the lexical inter-relatedness of the segments. We develop
a two-part segmentation technique that can harness a corpus
of similar documents to model the behavior of the two segments
and their inter-relatedness using language models and translation
models respectively. In particular, we use separate language models
for the problem and solution segment types, whereas the interrelatedness
between segment types is modeled using an IBM Model
1 translation model. We model documents as being generated starting
from the problem part that comprises of words sampled from
the problem language model, followed by the solution part whose
words are sampled either from the solution language model or from
a translation model conditioned on the words already chosen in the
problem part. We show, through an extensive set of experiments on
real-world data, that our approach outperforms the state-of-the-art
text segmentation algorithms in the accuracy of segmentation, and
that such improved accuracy translates well to improved usability
in Case-based Reasoning systems. We also analyze the robustness
of our technique to varying amounts and types of noise and empirically
illustrate that our technique is quite noise tolerant, and
degrades gracefully with increasing amounts of noise