BACKGROUND: Understanding the relationships between genes, drugs, and disease states is at the core of pharmacogenomics. Two leading approaches for identifying these relationships in medical literature are: human expert led manual curation efforts, and modern data mining based automated approaches. The former generates small amounts of high-quality data, and the latter offers large volumes of mixed quality data. The algorithmically extracted relationships are often accompanied by supporting evidence, such as, confidence scores, source articles, and surrounding contexts (excerpts) from the articles, that can be used as data quality indicators. Tools that can leverage these quality indicators to help the user gain access to larger and high-quality data are needed. APPROACH: We introduce GeneDive, a web application for pharmacogenomics researchers and precision medicine practitioners that makes gene, disease, and drug interactions data easily accessible and usable. GeneDive is designed to meet three key objectives: (1) provide functionality to manage information-overload problem and facilitate easy assimilation of supporting evidence, (2) support longitudinal and exploratory research investigations, and (3) offer integration of user-provided interactions data without requiring data sharing. RESULTS: GeneDive offers multiple search modalities, visualizations, and other features that guide the user efficiently to the information of their interest. To facilitate exploratory research, GeneDive makes the supporting evidence and context for each interaction readily available and allows the data quality threshold to be controlled by the user as per their risk tolerance level. The interactive search-visualization loop enables relationship discoveries between diseases, genes, and drugs that might not be explicitly described in literature but are emergent from the source medical corpus and deductive reasoning. The ability to utilize user's data either in combination with the GeneDive native datasets or in isolation promotes richer data-driven exploration and discovery. These functionalities along with GeneDive's applicability for precision medicine, bringing the knowledge contained in biomedical literature to bear on particular clinical situations and improving patient care, are illustrated through detailed use cases. CONCLUSION: GeneDive is a comprehensive, broad-use biological interactions browser. The GeneDive application and information about its underlying system architecture are available at http://www.genedive.net. GeneDive Docker image is also available for download at this URL, allowing users to (1) import their own interaction data securely and privately; and (2) generate and test hypotheses across their own and other datasets.

SUMMARY: Limited efficacy and intolerable safety limit therapeutic development and identification of potential liabilities earlier in development could significantly improve this process. Computational approaches which aggregate data from multiple sources and consider the drug's pathways effects could add to identification of these liabilities earlier. Such computational methods must be accessible to a variety of users beyond computational scientists, especially regulators and industry scientists, in order to impact the therapeutic development process. We have previously developed and published PathFX, an algorithm for identifying drug networks and phenotypes for understanding drug associations to safety and efficacy. Here we present a streamlined and easy-to-use PathFX web application that allows users to search for drug networks and associated phenotypes. We have also added visualization, and phenotype clustering to improve functionality and interpretability of PathFXweb. AVAILABILITY AND IMPLEMENTATION: https://www.pathfxweb.net/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This Research Full Paper builds on our previous research in Software Engineering (SE) Teamwork Assessment and Prediction project (SETAP) where we used Random Forest (RF) classifier to predict with over 70% accuracy the student learning effectiveness in software engineering teamwork based on 115 objective and quantitative Team Activity Measures (TAM). These TAM measures have been obtained from monitoring and measuring activities of 74 student teams during the creation of their final class project in a joint software engineering classes which ran concurrently at three universities (San Francisco State University, Fulda University and Florida Atlantic University) over the period of four years and, together with team outcomes, have been collected in publicly available SETAP database. In this paper, we provide much deeper analysis of how and why RF made its decisions, namely we address the explainability of our RF classification. We also provide in-depth analysis of differences in grading and behavior of local and global student teams (composed of students from multiple schools). We then use these insights to provide concrete and practical guidance to educators teaching SE teamwork in local and global classroom setting.

The overall goal of our Software Engineering Teamwork Assessment and Prediction (SETAP) project is to develop effective machine-learning-based methods for assessment and early prediction of student learning effectiveness in software engineering teamwork. Specifically, we use the Random Forest (RF) machine learning (ML) method to predict the effectiveness of software engineering teamwork learning based on data collected during student team project development. These data include over 100 objective and quantitative Team Activity Measures (TAM) obtained from monitoring and measuring activities of student teams during the creation of their final class project in our joint software engineering classes which ran concurrently at San Francisco State University (SFSU), Fulda University (Fulda) and Florida Atlantic University (FAU). In this paper we provide the first RF analysis results done at SFSU on our full data set covering four years of our joint SE classes. These data include 74 student teams with over 380 students, totaling over 30000 discrete data points. These data are grouped into 11 time intervals, each measuring important phases of project development during the class (e.g. early requirement gathering and design, development, testing and delivery). We briefly elaborate on the methods of data collection and describe the data itself. We then show prediction results of the RF analysis applied to this full data set. Results show that we are able to detect student teams who are bound to fail or need attention in early class time with good (about 70%) accuracy. Moreover, the variable importance analysis shows that the features (TAM measures) with high predictive power make intuitive sense, such as late delivery/late responses, time used to help each other, and surprisingly statistics on commit messages to the code repository, etc. In summary, we believe we demonstrate the viability of using ML on objective and quantitative team activity measures to predict student learning of software engineering teamwork, and point to easy-to-measure factors that can be used to guide educators and software engineering managers to implement early intervention for teams bound to fail. Details about the project and the complete ML training database are downloadable from the project web site.

Effective teaching of teamwork skills in local and globally distributed Software Engineering (SE) teams is recognized as an important part of the education of current and future software engineers. Effective methods for assessment and early prediction of learning effectiveness in SE teamwork are not only a critical part of teaching but also of value in industrial training and project management. This paper presents a novel analytical approach to the assessment and, most importantly, the prediction of learning outcomes in SE teamwork based on data from our joint software engineering class concurrently taught at San Francisco State University (SFSU), Florida Atlantic University (FAU) and Fulda University, Germany (Fulda). Our approach focuses on assessment and prediction of SE teamwork in terms of ability of student teams to apply best SE processes and develop SE products. It differs from existing work in the following aspects: a) it develops and uses only objective and quantitative measures of team activity from multiple sources, such as statistics of student time use, software engineering tool use, and instructor observations; b) it leverages powerful machine learning (ML) techniques applied to team activity measurements to identify quantitative and objective factors which can assess and predict learning of software engineering teamwork skills at the team level. In this paper we provide the following contributions: a) we present in detail for the first time the full team activity measurement data set we developed, consisting of over 40 objective and quantitative measures extracted from student teams working on class projects; b) we present a ML framework which applies the Random Forest (RF) algorithm to the team activity measurements and team outcomes, focusing on predicting teams that are likely to fail; c) we describe in detail our now fully implemented and operational data processing pipeline, consisting of data collection methods from multiple sources, ML training database creation, and ML analysis subsystems; and finally d) we present very preliminary results of ML analysis results based on the data from our joint software engineering classes in Fall 2012, and Spring 2013, with the data from 17 student teams. While our ML training database is currently small, it continuously grows. Our preliminary results, verified with two independent accuracy measures, show that RF is able to predict SE Process and SE Product team performance in intuitively explainable manner.

Machine learning-based prediction of protein functions plays a key role in bioinformatics and pharmaceutical research, facilitating swift discovery of new drugs in high-throughput settings. This paper presents an adaptation of Random Forest to the structure-based protein function prediction. Our system represents protein's 3D physicochemical structural information in microenvironment descriptors whose spatial resolution is much finer than other sequence-based protein descriptors. We prepare our datasets for seven active sites from five protein function classes by using multiple public data banks and train Random Forest classifiers to identify these seven function models in proteins. This paper presents two experiment studies: 1) a 5-fold stratified cross-validation for comparing Random Forest with Naive Bayes and Support Vector Machine and 2) systematic comparison of Random Forest's two variable importance measures. Promising results of these studies demonstrate a potential for Random Forest to improve the accuracy of the current protein function assays.

One of the challenges in effective software engineering (SE) education is the lack of objective assessment methods of how well student teams learn the critically needed teamwork practices, defined as the ability: (i) to learn and effectively apply SE processes in a teamwork setting, and (ii) to work as a team to develop satisfactory software (SW) products. In addition, there are no effective methods for predicting learning effectiveness in order to enable early intervention in the classroom. Most of the current approaches to assess achievement of SE teamwork skills rely solely on qualitative and subjective data taken as surveys at the end of the class and analyzed only with very rudimentary data analysis. In this paper we present a novel approach to address the assessment and prediction of student learning of teamwork effectiveness in software engineering education based on: a) extracting only objective and quantitative student team activity data during their team class project; b) pairing these data with related independent observations and grading of student team effectiveness in SE process and SE product components in order to create “training database” and c) applying a machine learning (ML) approach, namely random forest classification (RF), to the above training database in order to create ML models, ranked factors and rules that can both explain (e.g. assess) as well as provide prediction of the student teamwork effectiveness. These student team activity data are being collected in joint and already established (since 2006) SE classes at San Francisco State University (SFSU), Florida Atlantic University (FAU) and Fulda University, Germany (Fulda), from approximately 80 students each year, working in about 15 teams, both local and global (with students from multiple schools).

Teaching software engineering (SE) is now critical part of all major curricula in computer science programs. The ultimate goals of such programs include development of teamwork practices and techniques important for software (SW) development and application of modern SE practices and processes. One of the most challenging parts in these activities is the assessment process whose goal is to evaluate student's achievement of those learning objectives, namely adherence to the software engineering process and their ability to develop adequate SW product. The challenges of the assessment process include the following: a) Fairness of the assessment process - reflection of individual performance and contribution as well as of ability of students to work in collaborative environment and support teamwork; b) Effectiveness, efficiency and ease of implementation; c) Fast and timely access to the relevant feedback about performance and collaboration of the students; d) Automation of the process and availability of the recorded data for further analysis. To address these questions we have designed a tool Elassys for assessment and analysis of teamwork and individual student performance in software engineering projects.

In this paper we present assessment and comparison of local and global software (SW) engineering practices based on our software engineering (SE) class jointly taught for the last there years between San Francisco State University (SFSU) and the University of Applied Sciences, Fulda University, Germany. We define global SE practices as those used when the team members are for the most part distributed in terms of location and time, and hence do not meet regularly in person. While global SE practices have become a significant mode of SW development, surprisingly little formal measurements and comparisons have been done to understand it and compare it with traditional (local) SE practices. Moreover, the challenge remains as to how to effectively prepare students and employees for this environment. The key contribution of this paper is an attempt to objectively compare differences between local and global SW engineering practices from the developers' perspective, using measurements from our SFSU/Fulda SW engineering class as a simulation of a real-world environment. We also propose practical SE teamwork assessment methods based on our comparison measurements. In analyzing the differences between local and global SE practices we measure and analyze the following five main factors: quality of final delivery (including development of milestone documentation), progress impediment factors, expended effort, level of collaborative activity and teamwork problems. One of the surprising findings is that in today environment the differences between local and global SE practices are blurring, since local groups have to employ many practices of global groups, e.g. are teams of diverse cultures who seldom meet each other in person. We also confirm that global groups spend significantly more effort in producing comparable deliverables.

Changes and globalization of software (SW) development urgently require new ways of teaching and training students in practical and global SW engineering (SE). In this paper we present the innovative methods and experiences from several years of teaching practical and global SW engineering at the Computer Science Departments of San Francisco State University (SFSU), USA in conjunction with the University of Applied Sciences, Fulda University, Germany. We also provide analysis and evaluation of class effectiveness based on several assessment methods including extensive student questionnaires, with our recommendations for teaching such SE classes. Our approach was to combine and synchronize class teaching about SW engineering methods and processes with actual SW development work in a setting designed to simulate a small SW company. Students were divided in "local" groups of 4-6 members, each group forming small SW "companies" in charge of developing a complete working WWW application as a final class project. Several smaller groups of students at SFSU were paired with groups of students at Fulda University, whom they never met face to face, to form "global" groups, thus simulating global SW engineering in a realistic setting. Students developed their final project incorporating five well-defined milestones typical for full SW development lifecycle. Instructors spent considerable time supervising and interacting with student "companies" in the role of company customers, CTO and VPs of engineering, marketing and sales. All student groups (including global ones) were able to produce impressive final project applications and provided very positive feedback for this class.

The detection and recognition of events is a challenging problem in video databases. It involves cross-linking and combining information available in multiple modalities such as audio, video and associated text metadata. CueVideo is a system designed for the discovery and recognition of specific events called topics of discussion through advanced video summarization and cross-modal indexing. It supports search for relevant video content through several modes of video summarization including storyboards, moving storyboards and time-scale modified audio summarization. It also enables the recognition and indexing of topical events through cross-model search of audio and video content based on text and image queries respectively.

Combined word-based index and phonetic indexes have been used to improve the performance of spoken document retrieval systems primarily by addressing the out-of-vocabulary retrieval problem. However, a known problem with phonetic recognition is its limited accuracy in comparison with word level recognition. We propose a novel method for phonetic retrieval in the CueVideo system based on the probabilistic formulation of term weighting using phone confusion data in a Bayesian framework. We evaluate this method of spoken document retrieval against word-based retrieval for the search levels identified in a realistic video-based distributed learning setting. Using our test data, we achieved an average recall of 0.88 with an average precision of 0.69 for retrieval of out-of-vocabulary words on phonetic transcripts with 35% word error rate. For in-vocabulary words, we achieved a 17% improvement in recall over word-based retrieval with a 17% loss in precision for word error rites ranging from 35 to 65%.

No abstract available.

The role of audio in the context of multimedia applications involving video is becoming increasingly important. Many efforts in this area focus on audio data that contains some built-in semantic information structure such as in broadcast news, or focus on classification of audio that contains a single type of sound such as cleaar speech or clear music only. In the CueVideo system, we detect and classify audio that consists of mixed audio, i.e. combinations of speech and music together with other types of background sounds. Segmentation of mixed audio has applications in detection of story boundaries in video, spoken document retrieval systems, audio retrieval systems etc. We modify and combine audio features known to be effective in distinguishing speech from music, and examine their behavior on mixed audio. Our preliminary experimental results show that we can achieve a classification accuracy of over 80% for such mixed audio. Our study also provides us with several helpful insights related to analyzing mixed audio in the context of real applications.

No abstract available.

Article Key to effective video retrieval: effective cataloging and browsing Share on Authors: Dulce Ponceleon IBM Almaden Research Center, 650 Harry Road, San Jose, CA IBM Almaden Research Center, 650 Harry Road, San Jose, CAView Profile , Savitha Srinivasan IBM Almaden Research Center, 650 Harry Road, San Jose, CA IBM Almaden Research Center, 650 Harry Road, San Jose, CAView Profile , Arnon Amir IBM Almaden Research Center, 650 Harry Road, San Jose, CA IBM Almaden Research Center, 650 Harry Road, San Jose, CAView Profile , Dragutin Petkovic IBM Almaden Research Center, 650 Harry Road, San Jose, CA IBM Almaden Research Center, 650 Harry Road, San Jose, CAView Profile , Dan Diklic School of Engineering, Santa Clara University, 500 El Camino Real, Santa Clara, CA School of Engineering, Santa Clara University, 500 El Camino Real, Santa Clara, CAView Profile Authors Info & Claims MULTIMEDIA '98: Proceedings of the sixth ACM international conference on MultimediaSeptember 1998 Pages 99–107https://doi.org/10.1145/290747.290760Published:01 September 1998 40citation1,156DownloadsMetricsTotal Citations40Total Downloads1,156Last 12 Months13Last 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 SiteGet Access

On-line collections of images are growing larger and more common, and tools are needed to efficiently manage, organize, and navigate through them. The authors have developed a prototype system called QBIC which allows complex multi-object and multi-feature queries of large image databases. The queries are based on image content-the colors, textures, shapes, and positions of images and the objects/regions they contain. The system computes numeric features to represent the image properties and uses similarity measures based on these features for image retrieval. The focus of the paper is its user interface which allows a user to graphically pose and refine queries based on multiple visual properties of images and their objects.>

This paper describes algorithms for automatically inspecting the air-bearing surface (ABS) of disk sliders (heads) for certain types of defects that are global or systematic in the sense that, when they occur, they occur on every slider in a row or, in some cases, on every slider in the entire carrier. These defects are: wrong part, missing rail, geometry error, missing taper, extra taper, misplaced taper, missing poletip and misplaced pole-tip. The inspection system is in production use and has resulted in a significant improvement of the quality of shipped sliders. Here, the associated image-analysis algorithms are described in detail.

We describe how the QBIC (Query By Image Content) system handles "multi-*" queries-queries on large image collections involving multifeatures of each image as a whole and of multiple objects within each image. The queries are based on properties of image content-such as colors, textures, shapes, and edges. The system computes a set of features to describe the above properties, uses distance-like measures on the features to provide similarity based retrieval, and has a graphical interface that enable users pose queries visually. In this paper, we present QBIC indexing algorithms that allow these "multi-*" queries to run efficiently.

A highly integrated VME-based board (based on parallel pipeline projection engine, PPPE, architecture) containing custom VLSI application-specific ICs (ASICs) that implements the forward and inverse Radon transforms in real time has been designed and fabricated. The board contains DSP microcomputers (AT&T DSP32C) that provide the necessary modularity and programming power to support custom Radon-transform ASICs. Parallel and pipelined processing occurs at both the IC and the board level. The ASIC that executes the highly computational- and I/O-intensive forward and inverse Radon transform algorithms has been designed and fabricated in a 1.6-micron scalable CMOS process. It operates at the required 10-MHz video rate, and consists of over 120000 transistors.>

The architectural and implementation issues of a prototype of a low-level image segmentation architecture (LISA) are described. LISA performs real-time gray-level image segmentation. A decision-theoretic pattern recognition approach divided into feature extraction and decision analysis parts is used. The feature extraction part is based on extracting local and global descriptions of the image pixels. In the decision analysis part, the pixels are classified into one of the user-selected classes based on the extracted features. One of the main properties of the approach is that the feature extraction and decision analysis are performed in real time. The status of the hardware and software development of the LISA prototype for a variety of industrial inspection applications is described. The LISA architecture is being implemented using state-of-the-art VLSI technology.>

The author summarizes important parameters and procedures for verifying the measurement and recognition (classification) accuracy of machine vision algorithms/systems. He asserts that current practice in this area is very inadequate, and proposes some measures to improve this situation. A survey of published machine vision research shows that the verification of algorithms/systems performance is very seldom done in an adequate manner, with claims made on the basis of 2-3 processed images.>

The authors describe applications and give evaluations of rule-based methods for image analysis. They used rule-based programming methods for two image-analysis applications: segmenting and interpreting images of printed circuit boards, and segmenting satellite images of ice floes. Such problems are often divided into low-, mid-, and high-level processing, and they consider rule-based, or declarative, programming at the mid and high levels. They used Knowledge Tool, an OPS-like language that allows an excellent mix of procedural and declarative code. This ability to mix procedural and declarative code was very useful, but the authors nevertheless found limitations using the rule-based programming model. They describe these and list characteristics of image problems in which declarative coding can be applied.>

The authors present two methods for very-high-precision estimation of straight-line parameters from the Hough transform and compare them with the standard method of taking the absolute peak in the Hough array and with least-squares fitting using both extensive simulation and a number of tests with real target images. Both methods use preprocessing and interpolation in the Hough array, and are based on compensating for effects that cause a spreading of the peak in Hough space. By interpolation, the authors achieve accuracy better than the accumulator cell size. A complete set of simulations show that the two methods produce similar results, which are much better than taking the absolute peak in Hough space. They also compare well with least-square fitting, which was considered optimal in the case of zero mean noise. Results of experiments with real images are reported, confirming that the Hough transform can yield very accurate results, almost as good as least-squares fitting for zero mean noise.>

A brief overview is given of current state of the art in robust multiagent decision making in a faulty environment (distributed consensus); and also a family of novel algorithms is presented to achieve consensus that are deterministic, simple, require single-bit messages, and can be implemented in hardware. For such systems to work properly, the issues of reaching common decision (consensus) in the presence of faults have to be addressed. The authors offer a structural solution to this problem. The approach is illustrated on a hypothetical example of a number of autonomous robots in manufacturing that all have to agree on a common decision determined by the values of some central controllers.>

Machine vision algorithms and a supporting architecture that were integrated in a fully automated prototype system for disk head inspection are presented. Some specific methods are elaborated on, including the computation of the Hough transform and multicode masks in pipeline architectures, object segmentation in textured backgrounds, and matching of extracted defects with inspection specifications. Extensive experimental results are given.>

An important step in automatic visual inspection is verifying whether a part is good or bad, by comparing a list of inspection specifications to a list of extracted and measured defects. Our goal is to provide a general, flexible, and efficient solution to this problem. We present a solution following a rule-based approach for the case of specs for visual inspection of disk heads. However, due to the generality of our approach (within the realm of visual inspection), it is easily extendible to verification of specs in other visual inspection applications. While flexibility comes naturally with the rule-based approach, efficiency is still an issue. Therefore, we implemented two techniques to increase the efficiency of our system: one at the rule level, and one at the rule-matching level. We describe our implementation and show experimental results from applying our approach in an experimental system for automatic visual disk head inspection.