We introduce a compression technique for small polygons. The main application is to embed compressed polygons in emergency alert messages that have strict length restrictions, as in the case of Wireless Emergency Alert messages. We transform polygon coordinates to sets of integers and are able to compress them to between 10.4% and 25.6% of original length reducing original polygon lengths from 43-331 characters to 9-61 characters. The compression technique introduced in this work takes advantage of a strongly skewed polygon coordinate distribution.

As mobile context-aware services gain mainstream popularity, there is increased interest in developing techniques that can detect anomalous activities for applications such as user authentication, adaptive assist technologies and remote elder-care monitoring. Existing approaches have limited applicability as they regularly poll power-hungry sensors (e.g., accelerometer, GPS) reducing the availability of devices to perform anomaly detection. This paper present SALCS (Soft Authentication with Low-Cost Signatures), an approach for anomaly detection on a user's routine comprised of a collection of anomaly detection techniques utilizing soft-sensor data (e.g., call-logs, messages) and radio channel information (e.g., GSM cell IDs), all of which are available as part of a phone's routine usage. Using these information sources we model aspects of a person's routine, such as movement, messaging and conversation patterns. We present extensive evaluations of the individual anomaly detection techniques, compare the collection SALCS to an existing power-hungry approach showing SALCS has a 7.6% higher detection rate and gives 5x better coverage throughout the day.

People engage in routine behaviors. Automatic routine discovery goes beyond low-level activity recognition such as sitting or standing and analyzes human behaviors at a higher level (e.g., commuting to work). With recent developments in ubiquitous sensor technologies, it becomes easier to acquire a massive amount of sensor data. One main line of research is to mine human routines from sensor data using parametric topic models such as latent Dirichlet allocation. The main shortcoming of parametric models is that it assumes a fixed, pre-specified parameter regardless of the data. Choosing an appropriate parameter usually requires an inefficient trial-and-error model selection process. Furthermore, it is even more difficult to find optimal parameter values in advance for personalized applications. In this paper, we present a novel nonparametric framework for human routine discovery that can infer high-level routines without knowing the number of latent topics beforehand. Our approach is evaluated on public datasets in two routine domains: a 34-daily-activity dataset and a transportation mode dataset. Experimental results show that our nonparametric framework can automatically learn the appropriate model parameters from sensor data without any form of model selection procedure and can outperform traditional parametric approaches for human routine discovery tasks.

Understanding human activities is important for user-centric and context-aware applications. Previous studies showed promising results using various machine learning algorithms. However, most existing methods can only recognize the activities that were previously seen in the training data. In this paper, we present a new zero-shot learning framework for human activity recognition that can recognize an unseen new activity even when there are no training samples of that activity in the dataset. We propose a semantic attribute sequence model that takes into account both the hierarchical and sequential nature of activity data. Evaluation on datasets in two activity domains show that the proposed zero-shot learning approach achieves 70-75% precision and recall recognizing unseen new activities, and outperforms supervised learning with limited labeled data for the new classes.

We study the problem of how to recognize a new human activity when we have never seen any training example of that activity before. Recognizing human activities is an essential element for user-centric and context-aware applications. Previous studies showed promising results using various machine learning algorithms. However, most existing methods can only recognize the activities that were previously seen in the training data. A previously unseen activity class cannot be recognized if there were no training samples in the dataset. Even if all of the activities can be enumerated in advance, labeled samples are often time consuming and expensive to get, as they require huge effort from human annotators or experts. In this paper, we present NuActiv, an activity recognition system that can recognize a human activity even when there are no training data for that activity class. Firstly, we designed a new representation of activities using semantic attributes, where each attribute is a human readable term that describes a basic element or an inherent characteristic of an activity. Secondly, based on this representation, a two-layer zero-shot learning algorithm is developed for activity recognition. Finally, to reinforce recognition accuracy using minimal user feedback, we developed an active learning algorithm for activity recognition. Our approach is evaluated on two datasets, including a 10-exercise-activity dataset we collected, and a public dataset of 34 daily life activities. Experimental results show that using semantic attribute-based learning, NuActiv can generalize knowledge to recognize unseen new activities. Our approach achieved up to 79% accuracy in unseen activity recognition.

Efficiently maintaining and managing personal relationships have become imperative in our personal and professional lives. In this paper we describe the design of a mobile personal relationship manager (PRM), a prototype implementation on Nokia S60 smartphones, and results from initial user studies. The PRM system automatically extracts and manages contacts and their network from user's various communication activities, and provides various functions for managing personal communications efficiently. Our contributions are to make it easy to enable the users to manage personal relationships and communications, and the user studies that validate the design of our prototype.

Numerous applications require accurate personal navigation for environments where neither GPS signals nor infrastructure beacons, such as WiFi, are available. Inertial navigation using low-cost sensors suffers from the noisy readings which leads to drifting errors over time. In this paper, we introduce a novel inertial navigation approach ProbIN using Bayesian probabilistic framework. ProbIN models the inertial navigation problem as a noise channel problem where we want to recover the actual motion/displacement of the user from the noisy sensor readings. Building on the top of dead reckoning, ProbIN learns a statistical model to map the noisy sensor readings to user's displacements instead of using the double integral of the acceleration. ProbIN also builds a statistical model to estimate the a priori probability of a user's trajectory pattern. Combining the mapping model and the trajectory model in a Bayesian framework, ProbIN searches for a trajectory that has the highest probability given the sensor input. Our experiments show that ProbIN significantly reduces the error of inertial navigation using low-cost MEMS sensors in mobile phones.

Carnegie Mellon's West Coast Campus offers an MS in Software Engineering, with technical and development management tracks, targeted at working software professionals in Silicon Valley. We believe the program to be unique in that it is entirely team-based and project-centered. Students learn by doing as they are coached just in time by faculty in the context of authentic projects, and they are evaluated based on what they produce. Student satisfaction is high: 92% believe that the program has given them a competitive advantage with respect to their professional peers, and their promotion and salary histories bear out this belief.

The development of multiagent systems (MAS) is not a trivial task. In addition, with the advances in Internet technologies, MAS are undergoing a transition from closed to open architectures composed of a huge number of autonomous agents, which operate and move across different environments. In fact, openness introduces additional complexity to the system modeling, design and implementation. It also impacts on most quality attributes of MAS, including scalability, interoperability, reliability and adaptability. This paper brings together researchers and practitioners to discuss the current state and future direction of research in software engineering for open MAS. A particular interest is to understand those issues in the agent technology that make it difficult and/or improve the production of large open systems.

The increasing complexity of enterprise applications, the expanding number of networked machines, and the rapid deployment of Internet-based business applications (E-commerce), emphasize the importance and value of application management. One of the main problems in current application management products is the amount of time and effort needed to install and customize them. Application auto-discovery is a key technology for solving this problem. In this paper, we present a generic approach to application auto-discovery along with some examples. Our approach is to create a model-based discovery engine that is driven by an application template model. While the application template model captures the variation from one application to another, the auto-discovery engine uses sophisticated mechanisms such as scoping to execute an invariant auto-discovery process.

Organizations building highly complex business and technical systems must architect families of systems and implement these with large-scale component reuse. Without carefully architecting the systems, components, organizations and processes for reuse, reuse will not succeed. The talk explains our experience with component-based software reuse and a systematic reuse process using UML, and object-oriented business and system modeling.

We have integrated the feature modeling of Feature-Oriented Domain Analysis (FODA) into the processes and work products of the Reuse-Driven Software Engineering Business (RSEB). The RSEB is a use case driven systematic reuse process: architecture and reusable subsystems are first described by use cases and then transformed into object models that are traceable to these use cases. Variability in the RSEB is captured by structuring use case and object models using explicit variation points and variants. Traditional domain engineering steps have been distributed into the steps of the architectural and component system development methods of the RSEB. But the RSEB prescribes no explicit models of the essential features that characterize the different versions. Building on our experience in applying FODA and RSEB to the telecom domain, we have added explicit domain engineering steps and an explicit feature model to the RSEB to support domain engineering and component reuse. These additions provide an effective reuse oriented model as a 'catalog' capability to link use cases, variation points, reusable components and configured applications.

No abstract available.

Our work on systematic domain-specific reuse has led to the notion of domain-specific kits, comprised of compatible, domain-specific components, frameworks and glue languages, and supported by a variety of tools. A key element of a successful kit is a flexible, open architecture. We discuss guidelines, methods and technologies that lead to more effective kits. >

The authors explore object-oriented reuse from several perspectives, covering process, methods, technology, architecture, tools, patterns, frameworks and experience. The theme is: objects are neither necessary nor sufficient for effective reuse, but are extremely important if done right.>

article Free Access Share on Reuse (panel): truth or fiction Authors: Paul McCollough View Profile , Bob Atkinson View Profile , Adele Goldberg View Profile , Martin Griss View Profile , John Morrison View Profile Authors Info & Claims ACM SIGPLAN NoticesVolume 27Issue 10Oct. 1992 pp 41–44https://doi.org/10.1145/141937.141941Published:31 October 1992Publication History 2citation231DownloadsMetricsTotal Citations2Total Downloads231Last 12 Months9Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

article Free Access Share on The economics of software reuse Authors: Martin Griss Hewlett-Packard Laboratories Hewlett-Packard LaboratoriesView Profile , Sam S. Adams Knowledge Systems Corporation Knowledge Systems CorporationView Profile , Howard Baetjer Agorics Project, George Mason University Agorics Project, George Mason UniversityView Profile , Brad J. Cox Information Age Consulting Information Age ConsultingView Profile , Adele Goldberg ParcPlace Systems ParcPlace SystemsView Profile Authors Info & Claims ACM SIGPLAN NoticesVolume 26Issue 11Nov. 1991 pp 264–270https://doi.org/10.1145/118014.117974Online:01 November 1991Publication History 4citation651DownloadsMetricsTotal Citations4Total Downloads651Last 12 Months26Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Abstract The programming language LISP is usually implemented via an interpreter, and a compiler is added later as a LISP program. However, all such production compilers known to the authors produce explicit instructions for the given computer being used. This paper describes the development of a portable LISP compiler in the sense that only Standard LISP functions are used in its definition and the output is a sequence of abstract machine codes, easily mapped to instruction sequences on current computers. The resulting code is quite efficient, demonstrating once again the maxim that most compiler optimization is largely machine independent.

PICTUREBALM is a portable, interactive, LISP-based language system for graphics applications programming. PICTUREBALM's design and initial experimental implementation is described from the point of view of both the user and the language system implementor. The approach of extending a LISP-based language by adding graphical operations was chosen because many of the recognized requirements for graphics programming languages are standard features of LISP-like systems. Future work is proposed.

In this paper, the use of an efficient sparse minor expansion method to directly compute the subresultants needed for the greatest common denominator (GCD) of two polynomials is described. The sparse minor expansion method (applied either to Sylvester's or Bezout's matrix) naturally computes the coefficients of the subresultants in the order corresponding to a polynomial remainder sequence (PRS), avoiding wasteful recomputation as much as possible. It is suggested that this is an efficient method to compute the resultant and GCD of sparse polynomials.

article Free Access Share on The Algebraic Solution of Sparse Linear Systems via Minor Expansion Author: Martin L. Griss 201 North Physics Building, University of Utah, Salt Lake City, UT 201 North Physics Building, University of Utah, Salt Lake City, UTView Profile Authors Info & Claims ACM Transactions on Mathematical SoftwareVolume 2Issue 1March 1976 pp 31–49https://doi.org/10.1145/355666.355668Published:01 March 1976Publication History 11citation375DownloadsMetricsTotal Citations11Total Downloads375Last 12 Months35Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF