Capability-based distributed layout and its simulation based analyses

The capability-based distributed layout approach was first proposed by Baykasoğlu (Int J Prod Res 41, 2597–2618, 2003) for job shops which are working under highly volatile manufacturing environments in order to avoid high reconfiguration costs. It was argued that the capability-based distributed layout can also be a valid (or better) option for “classical functional layouts” which are generally operating under “high variety” and “low-stable demand”. In this paper first the capability-based distributed layout approach and related issues are reviewed and discussed afterwards the performance of “Capability Based Distributed Layout: (CB-DL)” is tested via extensive simulation experiments. After the simulation experiments, it is observed that capability-based distributed layout has a big potential and can also be considered as an alternative to classical process types of layouts.     

E-commerce success criteria: determining which criteria count most

We explore in this paper how performance of e-commerce websites in terms of various criteria influences customers’ intention to shop again in the same website. Our approach is based on an interesting use of statistical regression in the hotel literature that attempted to classify different cues in hotels as critical, satisfier, dissatisfier, etc. We use online ratings for 484 e-commerce websites for this study. Our study shows that “satisfaction with claims” is the single most important criterion valued as critical by online customers. “Comparative prices” and “Refunds/returns” are desirable criteria. “Management accessibility”, “Payment process” and “Privacy experience” are satisfiers while “on-time delivery” is a dissatisfier.     

Process mining: a two-step approach to balance between underfitting and overfitting

Process mining includes the automated discovery of processes from event logs. Based on observed events (e.g., activities being executed or messages being exchanged) a process model is constructed. One of the essential problems in process mining is that one cannot assume to have seen all possible behavior. At best, one has seen a representative subset. Therefore, classical synthesis techniques are not suitable as they aim at finding a model that is able to exactly reproduce the log. Existing process mining techniques try to avoid such “overfitting” by generalizing the model to allow for more behavior. This generalization is often driven by the representation language and very crude assumptions about completeness. As a result, parts of the model are “overfitting” (allow only for what has actually been observed) while other parts may be “underfitting” (allow for much more behavior without strong support for it). None of the existing techniques enables the user to control the balance between “overfitting” and “underfitting”. To address this, we propose a two-step approach. First, using a configurable approach, a transition system is constructed. Then, using the “theory of regions”, the model is synthesized. The approach has been implemented in the context of ProM and overcomes many of the limitations of traditional approaches.     

“Those found responsible have been sacked”: some observations on the usefulness of error

Erik Hollnagel’s body of work in the past three decades has molded much of the current research approach to system safety, particularly notions of “error”. Hollnagel regards “error” as a dead-end and avoids using the term. This position is consistent with Rasmussen’s claim that there is no scientifically stable category of human performance that can be described as “error”. While this systems view is undoubtedly correct, “error” persists. Organizations, especially formal business, political, and regulatory structures, use “error” as if it were a stable category of human performance. They apply the term to performances associated with undesired outcomes, tabulate occurrences of “error”, and justify control and sanctions through “error”. Although a compelling argument can be made for Hollnagel’s view, it is clear that notions of “error” are socially and organizationally productive. The persistence of “error” in management and regulatory circles reflects its value as a means for social control.     

Facial expression recognition of a speaker using front-view face judgment, vowel judgment, and thermal image processing

For facial expression recognition, we selected three images: (i) just before speaking, (ii) speaking the first vowel, and (iii) speaking the last vowel in an utterance. In this study, as a pre-processing module, we added a judgment function to distinguish a front-view face for facial expression recognition. A frame of the front-view face in a dynamic image is selected by estimating the face direction. The judgment function measures four feature parameters using thermal image processing, and selects the thermal images that have all the values of the feature parameters within limited ranges which were decided on the basis of training thermal images of front-view faces. As an initial investigation, we adopted the utterance of the Japanese name “Taro,” which is semantically neutral. The mean judgment accuracy of the front-view face was 99.5% for six subjects who changed their face direction freely. Using the proposed method, the facial expressions of six subjects were distinguishable with 84.0% accuracy when they exhibited one of the intentional facial expressions of “angry,” “happy,” “neutral,” “sad,” and “surprised.” We expect the proposed method to be applicable for recognizing facial expressions in daily conversation.     

Charting the behavioural state of a person using a backpropagation neural network

This paper describes the application of a backpropagation artificial neural network (ANN) for charting the behavioural state of previously unseen persons. In a simulated theft scenario participants stole or did not steal some money and were interviewed about the location of the money. A video of each interview was presented to an automatic system, which collected vectors containing nonverbal behaviour data. Each vector represented a participant’s nonverbal behaviour related to “deception” or “truth” for a short period of time. These vectors were used for training and testing a backpropagation ANN which was subsequently used for charting the behavioural state of previously unseen participants. Although behaviour related to “deception” or “truth” is charted the same strategy can be used to chart different psychological states over time and can be tuned to particular situations, environments and applications. We thank those who kindly volunteered to participate in the study.     

Matters of (Meta-) Modeling

With the recent trend to model driven engineering a common understanding of basic notions such as “model” and “metamodel” becomes a pivotal issue. Even though these notions have been in widespread use for quite a while, there is still little consensus about when exactly it is appropriate to use them. The aim of this article is to start establishing a consensus about generally acceptable terminology. Its main contributions are the distinction between two fundamentally different kinds of model roles, i.e. “token model” versus “type model” (The terms “type” and “token” have been introduced by C.S. Peirce, 1839–1914.), a formal notion of “metaness”, and the consideration of “generalization” as yet another basic relationship between models. In particular, the recognition of the fundamental difference between the above mentioned two kinds of model roles is crucial in order to enable communication among the model driven engineering community that is free of both unnoticed misunderstandings and unnecessary disagreement.     

As Hangs the Flexible Line: Equilibrium of Masonry Arches

In 1675, English scientist Robert Hooke discovered “the true... ...manner of arches for building,” which he summarized with a single phrase: “As hangs the flexible line, so but inverted will stand the rigid arch.” In the centuries that followed, Hooke’s simple idea has been used to understand and design numerous important works. Recent research at MIT on the interactive analysis of structural forces provides new graphical tools for the understanding of arch behavior, which are useful for relating the forces and geometry of masonry structures. The key mathematical principle is the use of graphical analysis to determine possible equilibrium states.     

An active styling platform for designing and developing product families

In recent years, fierce market competition has increased the need for incorporating manufacturing flexibility into product styling. The product styling platform approach proposed in this paper offers an insight into how this challenge can be dealt with by transforming an existing product fashion into an intangible styling module with the view to refreshing its appeal. This paper addresses the important issues of how the approach can be turned into a robust applicational tool to facilitate the design of product families to satisfy diverse customer needs. The paper investigates the nature and quantification of modular styling. By capturing and analysing design experts’ opinions on product styling, the evolution of a fashion can be quantified in the form of a schema of diffusion styles. The study has also led to the development of the ideas of “Prompt Time Market Segmentation Grid” and the “Active Style Diagnosis”. The concepts are applied in an “Active Product Styling Platform” with scalable and expandable capabilities to enhance the effectiveness of conventional product styling practices. This approach can also help the formation of product style families and facilitate the development of products particularly in a mass customisation environment.     

A Standard Model—Theoretic Approach to Operational Semmantics of Recursiv Programs

In this paper we try to introduce a new approach to operational semantics of recursive programs by using ideas in the“priority method”which is a fundamental tool in Recursion Theory.In lieu of modelling partial functions by introducing undefined values in a traditional approach,we shall define a priority derivation tree for every term,and by respecting thr rule“attacking the subtem of the highest priority first”we define transition relations,computation sequences etc.directly based on a standard interpretation whic includes no undefined value in its domain,Finally,we prove that our new approach generates the same opeational semantics as the traditional one.It is also pointed out that we can use our strategy oto refute a claim of Loeckx and Sieber that the opperational semantics of recursive programs cannot be built based on predicate logic.     

User-Sensitive Inclusive Design

Although “User-Centred”, “Participatory”, and other similar design approaches have proved to be very valuable for mainstream design, their principles are more difficult to apply successfully when the user group contains, or is composed of, older and/or disabled users. In the field of design for older and disabled people, the “Universal Design”, “Inclusive Design” and “Design for All” movements have encouraged designers to extend their design briefs to include older and disabled people. The downside of these approaches is that they can tend to encourage designers to follow a traditional design path to produce a prototype design, and only then investigate how to modify their interfaces and systems to cope with older and/or disabled users. This can lead to an inefficient design process and sometimes an inappropriate design, which may be “accessible” to people with disabilities, but in practice unusable. This paper reviews the concept that the authors have called “User-Sensitive Inclusive Design”, which suggests a different approach to designing for marginalised groups of people. Rather than suggesting that designers rely on standards and guidelines, it is suggested that designers need to develop a real empathy with their user groups. A number of ways to achieve this are recommended, including the use of ethnography and techniques derived from professional theatre both for requirements gathering and for improving designers’ empathy for marginalised groups of users, such as older and disabled people.     

A neural networks approach to minority game

The minority game (MG) comes from the so-called “El Farol bar” problem by W.B. Arthur. The underlying idea is competition for limited resources and it can be applied to different fields such as: stock markets, alternative roads between two locations and in general problems in which the players in the “minority” win. Players in this game use a window of the global history for making their decisions, we propose a neural networks approach with learning algorithms in order to determine players strategies. We use three different algorithms to generate the sequence of minority decisions and consider the prediction power of a neural network that uses the Hebbian algorithm. The case of sequences randomly generated is also studied. Research supported by Local Project 2004–2006 (EX 40%) Università di Foggia. A. Sfrecola is a researcher financially supported by Dipartimento di Scienze Economiche, Matematiche e Statistiche, Università degli Studi di Foggia, Foggia, Italy.     

Habits make smartphone use more pervasive

Examining several sources of data on smartphone use, this paper presents evidence for the popular conjecture that mobile devices are “habit-forming.” The form of habits we identified is called a checking habit: brief, repetitive inspection of dynamic content quickly accessible on the device. We describe findings on kinds and frequencies of checking behaviors in three studies. We found that checking habits occasionally spur users to do other things with the device and may increase usage overall. Data from a controlled field experiment show that checking behaviors emerge and are reinforced by informational “rewards” that are very quickly accessible. Qualitative data suggest that although repetitive habitual use is frequent, it is experienced more as an annoyance than an addiction. We conclude that supporting habit-formation is an opportunity for making smartphones more “personal” and “pervasive.”     

Self-organization based on nonlinear nonequilibrium. Dynamics of autonomous agents

This paper discusses a study on the mechanism of self-organization. A global order is organized by the simple and locally coordinated actions of autonomous agents using only local information, so complex or globally coordinated actions which use global communication and high-level strategies are not necessary. The fundamental factors for establishing a global order using self-organization are a “dissipative structure,” an “autocatalysis mechanism,” and “intentional fluctuations.” If an environment where there are agents has a dissipative structure and those agents have some sort of autocatalysis and intentional fluctuation mechanisms within themselves, it is possible to form a global order for them using only their simple and locally coordinated actions. “The blind-hunger dilemma” is used as an example to simulate the self-organization and coordinated actions of agents. In this simulation environment, there are many ant-like agents which must get energy. However, there is only one small energy supply base, so either an efficient method or the coordinated actions of agents is needed. As a result, the agents using our approach could move and get energy more efficiently than agents using conventional coordination mechanisms involving global communication and high-level strategies. Real World Computing Partnership This work was presented, in part, at the Second International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1997     

Dynamic Bayesian approach for detecting cheats in multi-player online games

Massively multi-player games hold a huge market in the digital entertainment industry. Companies invest heavily in game developments since a successful online game can attract millions of users, and this translates to a huge investment payoff. However, multi-player online games are also subjected to various forms of “hacks” and “cheats”. Hackers can alter the graphic rendering to reveal information otherwise be hidden in a normal game, or cheaters can use software robots to play the game automatically and thus gain an unfair advantage. To overcome these problems, some popular online games release software patches constantly to block “known” hacks or incorporate anti-cheating software to detect “known” cheats. This not only creates deployment difficulty but new cheats will still be able to breach the normal game logic until software patches or updates of the anti-cheating software are available. Moreover, the anti-cheating software themselves are also vulnerable to hacks. In this paper, we propose a “scalable” and “efficient” method to detect whether a player is cheating or not. The methodology is based on the dynamic Bayesian network approach. The detection framework relies solely on the game states and runs in the game server only. Therefore, it is invulnerable to hacks and it is a much more deployable solution. To demonstrate the effectiveness of the proposed method, we have implemented a prototype multi-player game system to detect whether a player is using any “aiming robot” for cheating or not. Experiments show that the proposed method can effectively detect cheaters on a first-person shooter game with extremely low false positive rate. We believe the proposed methodology and the prototype system provide a first step toward a systematic study of cheating detection and security research in the area of online multi-player games.     

Asymptotic optimization for stochastic models based on a compound poisson process

This paper considers the possibility of using a multidimensional compound Poisson process controlled by a continuous-time Markov chain in mathematical decision-making models. A definition of this process is presented and examples are given that illustrate its use in formalizing the concepts of “uncertainty” and “risk” and constructing risk functions and objective functions for the corresponding optimization problems. Some approaches are proposed to solve these problems, in particular, a direct analytic approach consisting of finding explicit formulas for a risk function and a method of approximate solution based on limit theorems of stochastic process theory.     

Structured large margin machines: sensitive to data distributions

This paper proposes a new large margin classifier—the structured large margin machine (SLMM)—that is sensitive to the structure of the data distribution. The SLMM approach incorporates the merits of “structured” learning models, such as radial basis function networks and Gaussian mixture models, with the advantages of “unstructured” large margin learning schemes, such as support vector machines and maxi-min margin machines. We derive the SLMM model from the concepts of “structured degree” and “homospace”, based on an analysis of existing structured and unstructured learning models. Then, by using Ward’s agglomerative hierarchical clustering on input data (or data mappings in the kernel space) to extract the underlying data structure, we formulate SLMM training as a sequential second order cone programming. Many promising features of the SLMM approach are illustrated, including its accuracy, scalability, extensibility, and noise tolerance. We also demonstrate the theoretical importance of the SLMM model by showing that it generalizes existing approaches, such as SVMs and M⁴s, provides novel insight into learning models, and lays a foundation for conceiving other “structured” classifiers. Editor: Dale Schuurmans. This work was supported by the Hong Kong Research Grant Council under Grants G-T891 and B-Q519.     

Design and analysis of a video-on-demand server

The availability of high-speed networks, fast computers and improved storage technology is stimulating interest in the development of video on-demand services that provide facilities similar to a video cassette player (VCP). In this paper, we present a design of a video-on-demand (VOD) server, capable of supporting a large number of video requests with complete functionality of a remote control (as used in VCPs), for each request. In the proposed design, we have used an interleaved storage method with constrained allocation of video and audio blocks on the disk to provide continuous retrieval. Our storage scheme interleaves a movie with itself (while satisfying the constraints on video and audio block allocation. This approach minimizes the starting delay and the buffer requirement at the user end, while ensuring a jitter-free display for every request. In order to minimize the starting delay and to support more non-concurrent requests, we have proposed the use of multiple disks for the same movie. Since a disk needs to hold only one movie, an array of inexpensive disks can be used, which reduces the overall cost of the proposed system. A scheme supported by our disk storage method to provide all the functions of a remote control such as “fast-forwarding”, “rewinding” (with play “on” or “off”), “pause” and “play” has also been discussed. This scheme handles a user request independent of others and satisfies it without degrading the quality of service to other users. The server design presented in this paper achieves the multiple goals of high disk utilization, global buffer optimization, cost-effectiveness and high-quality service to the users.     

Opinion helpfulness prediction in the presence of “words of few mouths”

This paper identifies a widely existing phenomenon in social media content, which we call the “words of few mouths” phenomenon. This phenomenon challenges the development of recommender systems based on users’ online opinions by presenting additional sources of uncertainty. In the context of predicting the “helpfulness” of a review document based on users’ online votes on other reviews (where a user’s vote on a review is either HELPFUL or UNHELPFUL), the “words of few mouths” phenomenon corresponds to the case where a large fraction of the reviews are each voted only by very few users. Focusing on the “review helpfulness prediction” problem, we illustrate the challenges associated with the “words of few mouths” phenomenon in the training of a review helpfulness predictor. We advocate probabilistic approaches for recommender system development in the presence of “words of few mouths”. More concretely, we propose a probabilistic metric as the training target for conventional machine learning based predictors. Our empirical study using Support Vector Regression (SVR) augmented with the proposed probability metric demonstrates advantages of incorporating probabilistic methods in the training of the predictors. In addition to this “partially probabilistic” approach, we also develop a logistic regression based probabilistic model and correspondingly a learning algorithm for review helpfulness prediction. We demonstrate experimentally the superior performance of the logistic regression method over SVR, the prior art in review helpfulness prediction.