The article presents a study of rather simple local search heuristics for the single machine total weighted tardiness problem (SMTWTP), namely hillclimbing and Variable Neighborhood Search. In particular, we revisit these approaches for the SMTWTP as there appears to be a lack of appropriate/challenging benchmark instances in this case. The obtained results are impressive indeed. Only few instances remain unsolved, and even those are approximated within 1% of the optimal/best known solutions. Our experiments support the claim that metaheuristics for the SMTWTP are very likely to lead to good results, and that, before refining search strategies, more work must be done with regard to the proposition of benchmark data. Some recommendations for the construction of such data sets are derived from our investigations.

In this paper, we propose a first-order ontology for generalized stratified order structure. We then classify the models of the theory using model-theoretic techniques. An ontology mapping from this ontology to the core theory of Process Specification Language is also discussed.

An important problem in the world of art historians is determining the type of paper on which a photograph is printed.  One way to determine the paper type is to capture a highly magnified image of the paper, then to compare this image to a database of known paper images.  Traditionally, this process is carried out by a human and is generally time-intensive.  Here we propose an automated solution to this problem, using wavelet decomposition techniques from image processing, as well as metric learning from the machine learning area.  We show, on a collection of real-world images of photographic paper, that the use of machine learning techniques produces a much better solution than image processing alone.

The Tokyo Metropolitan Government is the largest municipality in Japan and conducts building change identification work. Recently, Tokyo terminated its traditional visual identification work that has been in use for 20 years and shifted to a new automated system. This paper is intended to introduce the Fixed Assets Change Judgment (FACJ) system and its core tool, RealScape. RealScape automatically detects the changes in the height and color of buildings based on three-dimensional (3D) analysis of aerial photographs. It employs a unique pixel-by-pixel stereo processing method and enables the foot-level precision for each building. RealScape detects building changes more accurately than visual judgment operations by humans and reduces the labor costs to one third of the traditional approach and the required judgment duration to about two weeks per 100km2.

Realistic rendering is the process of generating a 2D image from an abstract description of a 3D scene, aiming at achieving the quality of a photo. The quality of the generated image depends on the accuracy with which the employed render method simulates the behaviour of the light particles through the scene. According to the current practice, it is up to the user to choose optimal settings of input parameters for these methods in terms of time-efficiency, as well as image quality. This is an iterative trial and error process, even for expert users. This paper describes a novel approach based on techniques from the field of data mining and genetic computing to assist the user in the selection of render parameters. Experimental results are presented which show the benefits of this approach.

We present an implementation of a plan adaptation system, BioPlanner, built for biological pathway prediction across species. BioPlanner formulates a pathway discovery problem as a Hierarchical Task Network (HTN) planning problem and solves it by adapting a plan solution of another well-studied pathway. BioPlanner provides the following functionalities: It automatically builds HTN planning models for a biological pathway domain from the semantic web biological knowledge bases (KBs). It retrieves plan cases from the biological KBs. It generates hypothetical pathways using plan adaptation strategies with the aid of biological domain knowledge. It evaluates the hypothetical plan candidates, ranks them, and recommends the most likely hypotheses to users. It employs an information gathering multi-agent system to capture knowledge from heterogeneous sources to help the hypothetical plan generation process. We utilize BioPlanner to predict Signaling Transduction pathways for Mus musculus, Gallus gallus, and Drosophila melanogaster from Homo sapiens.

Spam filtering, as a key problem in electronic communication, has drawn significant attention due to increasingly huge amounts of junk email on the Internet. Content-based filtering is one reliable method in combating with spammers' changing tactics. Naive Bayes (NB) is one of the earliest content-based machine learning methods both in theory and practice in combating with spammers, which is easy to implement while can achieve considerable accuracy. In this paper, the traditional online Bayesian classifier are enhanced  by two ways. First, from theory's point of view, we devise a self-adaptive mechanism to gradually weaken the assumption of independence required by original NB in the online training process, and as a result of that our NSNB is no longer ``naive''. Second, we propose other engineering ways to make the classifier more robust and accuracy. The experiment results show that our NSNB does give state-of-the-art classification performance on online spam filtering on large benchmark data sets while it is extremely fast and takes up little memory in comparison with other statistical methods.

The goal of the Pedagogical Discourse project is to develop instructional tools that will help students and instructors use discussion boards more effectively, with an emphasis on automatically assessing discussion activities and building tools for promoting student discussion participation and learning. In this paper, we present a two related participation and learning scaffolding tools that exploit natural language processing and information retrieval techniques. The PedaBot tool is designed to aid student knowledge acquisition and promote reflection about course topics by connecting related discussions from a knowledge base of past discussions to the current discussion thread. The MentorMatch tool aims at promoting student participation using student mentors, i.e., course peers with a relatively good understanding of a particular topic. The system identifies students who often provide answers on a given topic and encourages classmates to invite mentors to participate in related discussions. Both tools have been integrated into a live discussion board that is used by an undergraduate computer science course. This paper describes our approaches to applying information retrieval and natural language processing techniques in the development of the tools and presents initial results from instrumentation and survey.

Many city governments are under pressure to optimize the utilization of their resources to respond to fire, rescue and medical emergencies. In this paper we describe a simulation-based optimization software called SOFER that learns from a history of emergency requests to optimize the placement of resources and response policies. We describe a two-level random-restart hill climbing approach that yields policies which perform better than the current practice, satisfy the usability constraints, and are sensitive to optimization metrics and population changes. Some of the policies learned by the system give insight into response practices that would otherwise be counterintuitive.

In this paper, an event-centric commodity trading simulation powered by the multiagent framework is presented. The purpose of this simulation platform is for training novice traders. The simulation is progressed by announcing news events that affect various aspects of the commodity supply chain. Upon receiving these events, market agents that play the roles of producers, consumers, and speculators would adjust their views on the market and act accordingly. Their actions would be based on their roles and also their private information, and collectively they shape the market dynamics. This simulation has been effectively deployed for several training sessions. We will present the underlying technologies that are employed and discuss the practical significance of such platform.