A modified AHP algorithm for network selection

This paper addresses the concept of ranking networks to the multiple criteria of customers to find the best and alternative networks. The use of modified AHP algorithm has been shown to provide better network ranking for reasonable customer objectives than the traditional AHP method. Both the traditional method and the proposed method produced results subjective to the customer requirements. However, the proposed method is more intuitive to the customers through direct capture of their exact requirements rather than an interpretation of their requirements through pair-wise comparison alone. Also, the proposed method is less time-consuming and results are of higher quality

Automated legal sensemaking: the centrality of relevance and intentionality

Introduction: In a perfect world, discovery would ideally be conducted by the senior litigator who is responsible for developing and fully understanding all nuances of their client’s legal strategy. Of course today we must deal with the explosion of electronically stored information (ESI) that never is less than tens-of-thousands of documents in small cases and now increasingly involves multi-million-document populations for internal corporate investigations and litigations. Therefore scalable processes and technologies are required as a substitute for the authority’s judgment. The approaches taken have typically either substituted large teams of surrogate human reviewers using vastly simplified issue coding reference materials or employed increasingly sophisticated computational resources with little focus on quality metrics to insure retrieval consistent with the legal goal. What is required is a system (people, process, and technology) that replicates and automates the senior litigator’s human judgment. In this paper we utilize 15 years of sensemaking research to establish the minimum acceptable basis for conducting a document review that meets the needs of a legal proceeding. There is no substitute for a rigorous characterization of the explicit and tacit goals of the senior litigator. Once a process has been established for capturing the authority’s relevance criteria, we argue that literal translation of requirements into technical specifications does not properly account for the activities or states-of-affairs of interest. Having only a data warehouse of written records, it is also necessary to discover the intentions of actors involved in textual communications. We present quantitative results for a process and technology approach that automates effective legal sensemaking

Is One Hyperparameter Optimizer Enough?

Hyperparameter tuning is the black art of automatically finding a good combination of control parameters for a data miner. While widely applied in empirical Software Engineering, there has not been much discussion on which hyperparameter tuner is best for software analytics. To address this gap in the literature, this paper applied a range of hyperparameter optimizers (grid search, random search, differential evolution, and Bayesian optimization) to defect prediction problem. Surprisingly, no hyperparameter optimizer was observed to be `best' and, for one of the two evaluation measures studied here (F-measure), hyperparameter optimization, in 50\% cases, was no better than using default configurations. We conclude that hyperparameter optimization is more nuanced than previously believed. While such optimization can certainly lead to large improvements in the performance of classifiers used in software analytics, it remains to be seen which specific optimizers should be applied to a new dataset.Comment: 7 pages, 2 columns, accepted for SWAN1

Requirements Prioritization Based on Benefit and Cost Prediction: A Method Classification Framework

In early phases of the software development process, requirements prioritization necessarily relies on the specified requirements and on predictions of benefit and cost of individual requirements. This paper induces a conceptual model of requirements prioritization based on benefit and cost. For this purpose, it uses Grounded Theory. We provide a detailed account of the procedures and rationale of (i) how we obtained our results and (ii) how we used them to form the basis for a framework for classifying requirements prioritization methods

SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model

To realize human-like robot intelligence, a large-scale cognitive architecture is required for robots to understand the environment through a variety of sensors with which they are equipped. In this paper, we propose a novel framework named Serket that enables the construction of a large-scale generative model and its inference easily by connecting sub-modules to allow the robots to acquire various capabilities through interaction with their environments and others. We consider that large-scale cognitive models can be constructed by connecting smaller fundamental models hierarchically while maintaining their programmatic independence. Moreover, connected modules are dependent on each other, and parameters are required to be optimized as a whole. Conventionally, the equations for parameter estimation have to be derived and implemented depending on the models. However, it becomes harder to derive and implement those of a larger scale model. To solve these problems, in this paper, we propose a method for parameter estimation by communicating the minimal parameters between various modules while maintaining their programmatic independence. Therefore, Serket makes it easy to construct large-scale models and estimate their parameters via the connection of modules. Experimental results demonstrated that the model can be constructed by connecting modules, the parameters can be optimized as a whole, and they are comparable with the original models that we have proposed

Towards Design Principles for Data-Driven Decision Making: An Action Design Research Project in the Maritime Industry

Data-driven decision making (DDD) refers to organizational decision-making practices that emphasize the use of data and statistical analysis instead of relying on human judgment only. Various empirical studies provide evidence for the value of DDD, both on individual decision maker level and the organizational level. Yet, the path from data to value is not always an easy one and various organizational and psychological factors mediate and moderate the translation of data-driven insights into better decisions and, subsequently, effective business actions. The current body of academic literature on DDD lacks prescriptive knowledge on how to successfully employ DDD in complex organizational settings. Against this background, this paper reports on an action design research study aimed at designing and implementing IT artifacts for DDD at one of the largest ship engine manufacturers in the world. Our main contribution is a set of design principles highlighting, besides decision quality, the importance of model comprehensibility, domain knowledge, and actionability of results