A Fast Diagnosis Method of Escalator Reversal Faults Based on Dynamic Information and Multiattribute Decision-Making

There are many reasons for escalator reversal failure, and the reasons are distributed in different locations. It is difficult to locate the specific location of the fault in the actual fault troubleshooting. At the same time, the information related to the failure is not used in the troubleshooting, so there is a problem of inefficient troubleshooting. To this end, this paper proposes a multiattribute decision-making method that integrates dynamic information and gives the optimal troubleshooting order to improve the efficiency of the troubleshooting. First of all, according to the structure of the escalator components, the escalator reversal fault tree is established. Secondly, a static decision matrix is established by comprehensively considering the failure probability, search cost, and influence degree of the bottom event of the fault tree. Finally, the influence matrix of information on each attribute is given by the dynamic information obtained in troubleshooting, the static decision fusion influence matrix determines the dynamic decision matrix, the dynamic decision matrix is weighted and normalized, and the Technique for Order Preference by Similarity to Ideal Solution is used to determine the optimal troubleshooting order. Taking the reversal failure of a certain type of escalators as an example, the method of multiattribute decision-making of fusion dynamic information is used to shorten the troubleshooting time, improve the efficiency of troubleshooting, and verify the effectiveness of this method.

How Many 'Clicks' Does it Take? Finding Price Information on New Zealand Lawyers' Websites

This article examines price transparency in New Zealand's civil legal services market and compares the civil legal services market characteristics to those of other jurisdictions. The current law does not incentivise providers within the legal services market to communicate price information to consumers searching for a provider. The researcher utilised a web-sweep method to assess how New Zealand law firms that provide dispute resolution services and employment advocates share information through their websites. The web-sweep covered the websites of 96 New Zealand law firms and 30 New Zealand employment advocates. The author assessed the ease with which prospective consumers could navigate and understand price-related information. The results demonstrated that in a majority of instances, price information is unclear and uncertain. It is therefore not comparable between providers. Consumers in New Zealand face a high search cost when looking for prices and deciding on a legal service provider. They are unable to make a meaningful price comparison between providers of dispute resolution services before engaging them. Stronger regulation of providers (lawyers and employment advocates) to require the display of pricing information would lower search costs for consumers and increase competition.

A Method for Gradient Differentiable Network Architecture Search by Selecting and Clustering Candidate Operations

The current evolution of deep learning requires further optimization in terms of accuracy and time. From the perspective of new requirements, AutoML is an area that could provide possible solutions. AutoML has a neural architecture search (NAS) field. DARTS is a widely used approach in NAS and is based on gradient descent; however, it has some drawbacks. In this study, we attempted to overcome some of the drawbacks of DARTS by improving the accuracy and decreasing the search cost. The DARTS algorithm uses a mixed operation that combines all operations in the search space. The architecture parameter of each operation comprising a mixed operation is trained using gradient descent, and the operation with the largest architecture parameter is selected. The use of a mixed operation causes a problem called vote dispersion: similar operations share architecture parameters during gradient descent; thus, there are cases where the most important operation is disregarded. In this selection process, vote dispersion causes DARTS performance to degrade. To cope with this problem, we propose a new algorithm based on DARTS called DG-DARTS. Two search stages are introduced, and the clustering of operations is applied in DG-DARTS. In summary, DG-DARTS achieves an error rate of 2.51% on the CIFAR10 dataset, and its search cost is 0.2 GPU days because the search space of the second stage is reduced by half. The speed-up factor of DG-DARTS to DARTS is 6.82, which indicates that the search cost of DG-DARTS is only 13% that of DARTS.

Is Genericness Still Adequately Defined? Internet Search Firms and the Economic Rationale for Trademarks

Landes and Posner’s highly cited economics of trademark law based on search cost reduction has influenced economists, legislators, and courts for decades. Their account, however, predates consumer use of the Internet for search and did not anticipate the rise of firms such as Google to economy-wide power in search. Consequently, trademark law intended to help consumers find a preferred brand now also protects the means they typically use. An outdated view of trademarks as a natural and equitable right––very scrutable to STS––has led to Internet search firms owning reflexive “marks for finding other marks,” a structural advantage they have exploited through new dynamic and microtargeted forms of advertising into technoscientific rentiership. This paper revises Landes and Posner’s model to fit the case of an economy containing dominant firms with significant economy-wide search cost reduction power, adding (1) differentiation of technological elements of the original formal model and (2) analysis of the distribution and function of marks such as GOOGLE in consumer decision-making. The updated theory shows that marks granted to search firms are equivalent to generic marks in economic effect and constitute a new but unrecognized class that are functionally “super-generic.”

Fast and Low-cost Search for Efficient Cloud Configurations for HPC Workloads

The wide variety of virtual machine types, network configurations, number of instances, among others configuration tweaks, in cloud computing, makes the finding of the best configuration a hard problem. Trying to reduce costs and resource underutilization while achieving acceptable performance can be a hard task even for specialists. Thus, many approaches to find these optimal or almost optimal configurations for a given program were proposed in the literature. Observing the performance of an application in the cloud takes time and money. Therefore, most of the approaches aim not only to find good solutions but also to reduce the search cost. One of those approaches relies on Bayesian Optimization, which analyzes fewer configurations, reducing the search cost while still finding good solutions. Another approach found in the literature is the use of a technique named Paramount Iteration, which enables users to reason about cloud configurations' cost and performance without executing the application to its completion (early-stopping) this approach reduces the cost of each observation. In this work, we show that both techniques can be used together to do fewer and lower-cost observations. We demonstrate that such an approach can recommend solutions that are 1.68x better on average than Random Searching and with a 6x cheaper search.

Rapid Fault Diagnosis Method of Elevator System Based on Multiattribute Decision Making

Fault tree analysis is often used in elevator fault diagnosis because of its simplicity and reliability. However, the traditional fault tree method has the problems of low efficiency due to ignoring location change of bottom events during troubleshooting. This paper proposes a rapid diagnosis method based on multiattribute decision making to solve the problem. The fault tree of the elevator system is constructed based on expert knowledge and multisource data, and the location-related matrix is constructed according to the complex vertical structure of the elevator. Then, the attributes of bottom events such as the failure probability, search cost, location time cost, and location-related attributes are comprehensively analyzed in this paper. Finally, the TOPSIS method for dynamic attributes is used based on the work above to achieve the optimal troubleshooting sequence of elevator vibration fault. The results show that the proposed method is more efficient when compared to the optimal troubleshooting sequence obtained by the traditional method.

EXPRESS: Prominent Retailer and Intra-brand Competition

Online retail search traffic is often concentrated at a “prominent” retailer for a product. The authors unpack the ramification of this pattern on pricing, profit, and consumer welfare in an intra-brand setting. Prominence denotes a larger number of heterogenous search cost consumers starting their search at the prominent retailer than at any other retailer. This analyses show that search traffic concentration can intensify intra-brand competition, can lower average prices of all retailers, and can improve consumer welfare. Interestingly, the prominent retailer's incremental traffic advantage can increase or reduce its own profit; the authors denote these as the “blessing” and “curse” of prominence respectively. The authors extend their analysis to a setting where consumers consider searching only amongst those retailers of whom they are individually aware of; the prominent retailer is included in all these individual awareness sets. The effects on market average prices and welfare carry over, but only below a critical threshold level of the prominent retailer's first-search traffic advantage. Above this threshold, market average prices rise and welfare decreases, making this the region where search concentration warrant scrutiny from policy makers. The authors close with policy remedies, and managerial implications of search concentration.

Backward Private Searchable Symmetric Encryption with Improved Locality

Searchable symmetric encryption (SSE) enables clients to outsource their encrypted documents into a remote server and allows them to search the outsourced data efficiently without violating the privacy of the documents and search queries. Dynamic SSE schemes (DSSE) include performing update queries, where documents can be added or removed at the expense of leaking more information to the server. Two important privacy notions are addressed in DSSE schemes: forward and backward privacy. The first one prevents associating the newly added documents with previously issued search queries. While the second one ensures that the deleted documents cannot be linked with subsequent search queries. Backward has three formal types of leakage ordered from strong to weak security: Type-I, Type-II, and Type-III. In this paper, we propose a new DSSE scheme that achieves Type-II backward and forward privacy by generating fresh keys for each search query and preventing the server from learning the underlying operation (del or add) included in update query. Our scheme improves I/O performance and search cost. We implement our scheme and compare its efficiency against the most efficient backward privacy DSSE schemes in the literature of the same leakage: MITRA and MITRA*. Results show that our scheme outperforms the previous schemes in terms of efficiency in dynamic environments. In our experiments, the server takes 699ms to search and return (100,000) results.

Why do consumers not switch? An experimental investigation of a search and switch model

Search and switching costs are two market frictions that are well known in the literature for preventing people from switching to a new and cheaper provider. Previous experimental literature has studied these two frictions in isolation. However, field evidence shows that these two frictions frequently occur together. Recently, a theoretical framework has been developed (Wilson in Eur Econ Rev 56(6):1070–1086) which studies the interplay between these two costs. We report on an experiment testing this theory to see if individual behaviour with search and switching costs is in line with the theoretical predictions derived from the optimal choice rule of Wilson. The results show the crucial role of the search strategy: not only, according to Wilson model, the search cost has a greater deterrent impact on search than the switching costs, but also the sub-optimality of the search strategy is the major source of sub-optimality in the switching behaviour.

EXPRESS: Top-down influences on working memory representations of faces: Evidence from dual-target visual search

Variability in appearance across different images of the same unfamiliar face often causes participants to perceive different faces. Because perceptual information is not sufficient to link these encounters, top-down guidance may be critical in the initial stages of face learning. Here we examine the interaction between top-down guidance and perceptual information when forming memory representations of unfamiliar faces. In two experiments, we manipulated the names associated with images of a target face that participants had to find in a search array. In Experiment 1, wrongly labelling two images of the same face with different names resulted in more errors relative to when the faces were labelled correctly. In Experiment 2, we compared this cost of mislabelling to the established ‘dual-target search cost’ where searching for two targets produces more search errors relative to one target. We found search costs when searching for two different faces, but not when searching for mislabelled images of the same face. Together, these results suggest that perceptual and semantic information interact when we in the form face memory representations Mislabelling the identity of perceptually similar faces does not cause dual representations to be created, but rather that it impedes the process of forming a single robust representation.