Working Notes from the 1992 AAAI Workshop on Automating Software Design. Theme: Domain Specific Software Design

The goal of this workshop is to identify different architectural approaches to building domain-specific software design systems and to explore issues unique to domain-specific (vs. general-purpose) software design. Some general issues that cut across the particular software design domain include: (1) knowledge representation, acquisition, and maintenance; (2) specialized software design techniques; and (3) user interaction and user interface

Aerated blast furnace slag filters for enhanced nitrogen and phosphorus removal from small wastewater treatment plants

Rock filters (RF) are a promising alternative technology for natural wastewater treatment for upgrading WSP effluent. However, the application of RF in the removal of eutrophic nutrients, nitrogen and phosphorus, is very limited. Accordingly, the overall objective of this study was to develop a lowcost RF system for the purpose of enhanced nutrient removal from WSP effluents, which would be able to produce effluents which comply with the requirements of the EU Urban Waste Water Treatment Directive (UWWTD) (911271lEEC) and suitable for small communities. Therefore, a combination system comprising a primary facultative pond and an aerated rock filter (ARF) system-either vertically or horizontally loaded-was investigated at the University of Leeds' experimental station at Esholt Wastewater Treatment Works, Bradford, UK. Blast furnace slag (BFS) and limestone were selected for use in the ARF system owing to their high potential for P removal and their low cost. This study involved three major qperiments: (1) a comparison of aerated vertical-flow and horizontal-flow limestone filters for nitrogen removal; (2) a comparison of aerated limestone + blast furnace slag (BFS) filter and aerated BFS filters for nitrogen and phosphorus removal; and (3) a comparison of vertical-flow and horizontal-flow BFS filters for nitrogen and phosphorus removal. The vertical upward-flow ARF system was found to be superior to the horizontal-flow ARF system in terms of nitrogen removal, mostly thiough bacterial nitrification processes in both the aerated limestone and BFS filter studies. The BFS filter medium (whieh is low-cost) showed a much higher potential in removing phosphortls from pond effluent than the limestone medium. As a result, the combination of a vertical upward-flow ARF system and an economical and effective P-removal filter medium, such as BFS, was found to be an ideal optionfor the total nutrient removal of both nitrogen and phosphorus from wastewater. In parallel with these experiments, studies on the aerated BFS filter effective life and major in-filter phosphorus removal pathways were carried out. From the standard batch experiments of Pmax adsorption capacity of BFS, as well as six-month data collection of daily average P-removal, it was found that the effective life of the aerated BFS filter was 6.5 years. Scanning electron microscopy and X-ray diffraction spectrometric analyses on the surface of BFS, particulates and sediment samples revealed that the apparent mechanisms of P-removal in the filter are adsorption on the amorphous oxide phase of the BFS surface and precipitation within the filter

Finding Eyewitness Tweets During Crises

Disaster response agencies have started to incorporate social media as a source of fast-breaking information to understand the needs of people affected by the many crises that occur around the world. These agencies look for tweets from within the region affected by the crisis to get the latest updates of the status of the affected region. However only 1% of all tweets are geotagged with explicit location information. First responders lose valuable information because they cannot assess the origin of many of the tweets they collect. In this work we seek to identify non-geotagged tweets that originate from within the crisis region. Towards this, we address three questions: (1) is there a difference between the language of tweets originating within a crisis region and tweets originating outside the region, (2) what are the linguistic patterns that can be used to differentiate within-region and outside-region tweets, and (3) for non-geotagged tweets, can we automatically identify those originating within the crisis region in real-time

CSI: A Hybrid Deep Model for Fake News Detection

The topic of fake news has drawn attention both from the public and the academic communities. Such misinformation has the potential of affecting public opinion, providing an opportunity for malicious parties to manipulate the outcomes of public events such as elections. Because such high stakes are at play, automatically detecting fake news is an important, yet challenging problem that is not yet well understood. Nevertheless, there are three generally agreed upon characteristics of fake news: the text of an article, the user response it receives, and the source users promoting it. Existing work has largely focused on tailoring solutions to one particular characteristic which has limited their success and generality. In this work, we propose a model that combines all three characteristics for a more accurate and automated prediction. Specifically, we incorporate the behavior of both parties, users and articles, and the group behavior of users who propagate fake news. Motivated by the three characteristics, we propose a model called CSI which is composed of three modules: Capture, Score, and Integrate. The first module is based on the response and text; it uses a Recurrent Neural Network to capture the temporal pattern of user activity on a given article. The second module learns the source characteristic based on the behavior of users, and the two are integrated with the third module to classify an article as fake or not. Experimental analysis on real-world data demonstrates that CSI achieves higher accuracy than existing models, and extracts meaningful latent representations of both users and articles.Comment: In Proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM) 201

Automating the Design Optimization of Vehicle Structures

structures, parametric design and finite element analysis are common tools for simulating structural behavior of systems under static and quasi-static loading. While these tools provide significant benefits over physical experimentation, cumbersome to set up and this time-consuming setup may need to be repeated many times while iterating on a design. The designers would therefore benefit from automating this design and analysis process so that they can explore the design space more efficiently or obtain higher performance design alternatives. To take full advantage of the benefits of this automation, it important to make the process as quick and easy as possible. Otherwise, the cost of setting up the automated analysis may exceed the benefits obtained during design exploration and iteration. This research introduces a template-based approach to the automation of structural design and analysis that simplifies the setup process for certain classes of design problems. The platform for this automation is the process integration and design optimization tool, modeFRONTIER. Through several case studies in the area of vehicle structure analysis and design, it will be demonstrated how templates can significantly reduce the time and effort needed to frame complex structural design problems

Data mining: a tool for detecting cyclical disturbances in supply networks.

Disturbances in supply chains may be either exogenous or endogenous. The ability automatically to detect, diagnose, and distinguish between the causes of disturbances is of prime importance to decision makers in order to avoid uncertainty. The spectral principal component analysis (SPCA) technique has been utilized to distinguish between real and rogue disturbances in a steel supply network. The data set used was collected from four different business units in the network and consists of 43 variables; each is described by 72 data points. The present paper will utilize the same data set to test an alternative approach to SPCA in detecting the disturbances. The new approach employs statistical data pre-processing, clustering, and classification learning techniques to analyse the supply network data. In particular, the incremental k-means clustering and the RULES-6 classification rule-learning algorithms, developed by the present authors’ team, have been applied to identify important patterns in the data set. Results show that the proposed approach has the capability automatically to detect and characterize network-wide cyclical disturbances and generate hypotheses about their root cause