Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Sociology
Title: Telephone psychotherapy and telephone care management for primary care patients starting antidepressant treatment: a randomized controlled trial
Abstract: CONTEXT Both antidepressant medication and structured psychotherapy have been proven efficacious, but less than one third of people with depressive disorders receive effective levels of either treatment. OBJECTIVE To compare usual primary care for depression with 2 intervention programs: telephone care management and telephone care management plus telephone psychotherapy. DESIGN Three-group randomized controlled trial with allocation concealment and blinded outcome assessment conducted between November 2000 and May 2002. SETTING AND PARTICIPANTS A total of 600 patients beginning antidepressant treatment for depression were systematically sampled from 7 group-model primary care clinics; patients already receiving psychotherapy were excluded. INTERVENTIONS Usual primary care; usual care plus a telephone care management program including at least 3 outreach calls, feedback to the treating physician, and care coordination; usual care plus care management integrated with a structured 8-session cognitive-behavioral psychotherapy program delivered by telephone. MAIN OUTCOME MEASURES Blinded telephone interviews at 6 weeks, 3 months, and 6 months assessed depression severity (Hopkins Symptom Checklist Depression Scale and the Patient Health Questionnaire), patient-rated improvement, and satisfaction with treatment. Computerized administrative data examined use of antidepressant medication and outpatient visits. RESULTS Treatment participation rates were 97% for telephone care management and 93% for telephone care management plus psychotherapy. Compared with usual care, the telephone psychotherapy intervention led to lower mean Hopkins Symptom Checklist Depression Scale depression scores (P =.02), a higher proportion of patients reporting that depression was "much improved" (80% vs 55%, P<.001), and a higher proportion of patients "very satisfied" with depression treatment (59% vs 29%, P<.001). The telephone care management program had smaller effects on patient-rated improvement (66% vs 55%, P =.04) and satisfaction (47% vs 29%, P =.001); effects on mean depression scores were not statistically significant. CONCLUSIONS For primary care patients beginning antidepressant treatment, a telephone program integrating care management and structured cognitive-behavioral psychotherapy can significantly improve satisfaction and clinical outcomes. These findings suggest a new public health model of psychotherapy for depression including active outreach and vigorous efforts to improve access to and motivation for treatment.
Extracted statement — Depression outcomes (sources): Symptom Checklist
Research field: Software Engineering
Title: Mining Twitter Feeds for Software User Requirements
Abstract: Twitter enables large populations of end-users of software to publicly share their experiences and concerns about software systems in the form of micro-blogs. Such data can be collected and classified to help software developers infer users' needs, detect bugs in their code, and plan for future releases of their systems. However, automatically capturing, classifying, and presenting useful tweets is not a trivial task. Challenges stem from the scale of the data available, its unique format, diverse nature, and high percentage of irrelevant information and spam. Motivated by these challenges, this paper reports on a three-fold study that is aimed at leveraging Twitter as a main source of software user requirements. The main objective is to enable a responsive, interactive, and adaptive data-driven requirements engineering process. Our analysis is conducted using 4,000 tweets collected from the Twitter feeds of 10 software systems sampled from a broad range of application domains. The results reveal that around 50% of collected tweets contain useful technical information. The results also show that text classifiers such as Support Vector Machines and Naive Bayes can be very effective in capturing and categorizing technically informative tweets. Additionally, the paper describes and evaluates multiple summarization strategies for generating meaningful summaries of informative software-relevant tweets.
Extracted statement — Machine learning algorithms: Naive bayes
Research field: Software Engineering
Title: Automatic Classification of Non-Functional Requirements from Augmented App User Reviews
Abstract: Context: The leading App distribution platforms, Apple App Store, Google Play, and Windows Phone Store, have over 4 million Apps. Research shows that user reviews contain abundant useful information which may help developers to improve their Apps. Extracting and considering Non-Functional Requirements (NFRs), which describe a set of quality attributes wanted for an App and are hidden in user reviews, can help developers to deliver a product which meets users' expectations. Objective: Developers need to be aware of the NFRs from massive user reviews during software maintenance and evolution. Automatic user reviews classification based on an NFR standard provides a feasible way to achieve this goal. Method: In this paper, user reviews were automatically classified into four types of NFRs (reliability, usability, portability, and performance), Functional Requirements (FRs), and Others. We combined four classification techniques BoW, TF-IDF, CHI2, and AUR-BoW (proposed in this work) with three machine learning algorithms Naive Bayes, J48, and Bagging to classify user reviews. We conducted experiments to compare the F-measures of the classification results through all the combinations of the techniques and algorithms. Results: We found that the combination of AUR-BoW with Bagging achieves the best result (a precision of 71.4%, a recall of 72.3%, and an F-measure of 71.8%) among all the combinations. Conclusion: Our finding shows that augmented user reviews can lead to better classification results, and the machine learning algorithm Bagging is more suitable for NFRs classification from user reviews than Naïve Bayes and J48.
Extracted statement — Machine learning algorithms: Naive bayes
Research field: Software Engineering
Title: From scenario modeling to scenario programming for reactive systems with dynamic topology
Abstract: Software-intensive systems often consist of cooperating reactive components. In mobile and reconfigurable systems, their topology changes at run-time, which influences how the components must cooperate. The Scenario Modeling Language (SML) offers a formal approach for specifying the reactive behavior such systems that aligns with how humans conceive and communicate behavioral requirements. Simulation and formal checks can find specification flaws early. We present a framework for the Scenario-based Programming (SBP) that reflects the concepts of SML in Java and makes the scenario modeling approach available for programming. SBP code can also be generated from SML and extended with platform-specific code, thus streamlining the transition from design to implementation. As an example serves a car-to-x communication system. Demo video and artifact: http://scenariotools.org/esecfse-2017-tool-demo/
Extracted statement — model: Scenario Modeling
Research field: Software Engineering
Title: A Scenario-based MDE Process for Developing Reactive Systems: A Cleaning Robot Example
Abstract: This paper presents the SCENARIOTOOLS solution for developing a cleaning robot system, an instance of the rover problem of the MDE Tools Challenge 2017. We present an MDE process that consists of (1) the modeling of the system behavior as a scenario-based assume-guarantee specification with SML (Scenario Modeling Language), (2) the formal realizabilitychecking and verification of the specification, (3) the generation of SBP (Scenario-Based Programming) Java code from the SML specification, and, finally, (4) adding platform-specific code to connect specification-level events with platform-level sensorand actuator-events. The resulting code can be executed on a RaspberryPi-based robot. The approach is suited for developing reactive systems with multiple cooperating components. Its strength is that the scenario-based modeling corresponds closely to how humans conceive and communicate behavioral requirements. SML in particular supports the modeling of environment assumptions and dynamic component structures. The formal checks ensure that the system satisfies its specification.
Extracted statement — model: Scenario Modeling
Research field: Software Engineering
Title: From scenario modeling to scenario programming for reactive systems with dynamic topology
Abstract: Software-intensive systems often consist of cooperating reactive components. In mobile and reconfigurable systems, their topology changes at run-time, which influences how the components must cooperate. The Scenario Modeling Language (SML) offers a formal approach for specifying the reactive behavior such systems that aligns with how humans conceive and communicate behavioral requirements. Simulation and formal checks can find specification flaws early. We present a framework for the Scenario-based Programming (SBP) that reflects the concepts of SML in Java and makes the scenario modeling approach available for programming. SBP code can also be generated from SML and extended with platform-specific code, thus streamlining the transition from design to implementation. As an example serves a car-to-x communication system. Demo video and artifact: http://scenariotools.org/esecfse-2017-tool-demo/
Extracted statement — programming language: Scenario Modeling Language
Research field: Software Engineering
Title: A Scenario-based MDE Process for Developing Reactive Systems: A Cleaning Robot Example
Abstract: This paper presents the SCENARIOTOOLS solution for developing a cleaning robot system, an instance of the rover problem of the MDE Tools Challenge 2017. We present an MDE process that consists of (1) the modeling of the system behavior as a scenario-based assume-guarantee specification with SML (Scenario Modeling Language), (2) the formal realizabilitychecking and verification of the specification, (3) the generation of SBP (Scenario-Based Programming) Java code from the SML specification, and, finally, (4) adding platform-specific code to connect specification-level events with platform-level sensorand actuator-events. The resulting code can be executed on a RaspberryPi-based robot. The approach is suited for developing reactive systems with multiple cooperating components. Its strength is that the scenario-based modeling corresponds closely to how humans conceive and communicate behavioral requirements. SML in particular supports the modeling of environment assumptions and dynamic component structures. The formal checks ensure that the system satisfies its specification.
Extracted statement — programming language: Scenario Modeling Language
Research field: Software Engineering
Title: Measuring human values in software engineering
Abstract: Background: Human values, such as prestige, social justice, and financial success, influence software production decision-making processes. While their subjectivity makes some values difficult to measure, their impact on software motivates our research. Aim: To contribute to the scientific understanding and the empirical investigation of human values in Software Engineering (SE). Approach: Drawing from social psychology, we consider values as mental representations to be investigated on three levels: at a system (L1), personal (L2), and instantiation level (L3). Method: We design and develop a selection of tools for the investigation of values at each level, and focus on the design, development, and use of the Values Q-Sort. Results: From our study with 12 software practitioners, it is possible to extract three values `prototypes' indicative of an emergent typology of values considerations in SE. Conclusions: The Values Q-Sort generates quantitative values prototypes indicating values relations (L1) as well as rich personal narratives (L2) that reflect specific software practices (L3). It thus offers a systematic, empirical approach to capturing values in SE.
Extracted statement — Subjects: software practitioners
Research field: Software Engineering
Title: Mining Twitter Feeds for Software User Requirements
Abstract: Twitter enables large populations of end-users of software to publicly share their experiences and concerns about software systems in the form of micro-blogs. Such data can be collected and classified to help software developers infer users' needs, detect bugs in their code, and plan for future releases of their systems. However, automatically capturing, classifying, and presenting useful tweets is not a trivial task. Challenges stem from the scale of the data available, its unique format, diverse nature, and high percentage of irrelevant information and spam. Motivated by these challenges, this paper reports on a three-fold study that is aimed at leveraging Twitter as a main source of software user requirements. The main objective is to enable a responsive, interactive, and adaptive data-driven requirements engineering process. Our analysis is conducted using 4,000 tweets collected from the Twitter feeds of 10 software systems sampled from a broad range of application domains. The results reveal that around 50% of collected tweets contain useful technical information. The results also show that text classifiers such as Support Vector Machines and Naive Bayes can be very effective in capturing and categorizing technically informative tweets. Additionally, the paper describes and evaluates multiple summarization strategies for generating meaningful summaries of informative software-relevant tweets.
Extracted statement — Machine learning algorithms: Support vector machines
Research field: Software Engineering
Title: Software Architecture Optimization Methods: A Systematic Literature Review
Abstract: Due to significant industrial demands toward software systems with increasing complexity and challenging quality requirements, software architecture design has become an important development activity and the research domain is rapidly evolving. In the last decades, software architecture optimization methods, which aim to automate the search for an optimal architecture design with respect to a (set of) quality attribute(s), have proliferated. However, the reported results are fragmented over different research communities, multiple system domains, and multiple quality attributes. To integrate the existing research results, we have performed a systematic literature review and analyzed the results of 188 research papers from the different research communities. Based on this survey, a taxonomy has been created which is used to classify the existing research. Furthermore, the systematic analysis of the research literature provided in this review aims to help the research community in consolidating the existing research efforts and deriving a research agenda for future developments.
Extracted statement — Has method: Systematic Literature Review
Research field: Systems and Integrative Physiology
Title: Pilot Study to Estimate "Difficult" Area in e-Learning Material by Physiological Measurements
Abstract: To improve designs of e-learning materials, it is necessary to know which word or figure a learner felt "difficult" in the materials. In this pilot study, we measured electroencephalography (EEG) and eye gaze data of learners and analyzed to estimate which area they had difficulty to learn. The developed system realized simultaneous measurements of physiological data and subjective evaluations during learning. Using this system, we observed specific EEG activity in difficult pages. Integrating of eye gaze and EEG measurements raised a possibility to determine where a learner felt "difficult" in a page of learning materials. From these results, we could suggest that the multimodal measurements of EEG and eye gaze would lead to effective improvement of learning materials. For future study, more data collection using various materials and learners with different backgrounds is necessary. This study could lead to establishing a method to improve e-learning materials based on learners' mental states.
Extracted statement — Material: a page of learning materials
Research field: Systems and Integrative Physiology
Title: Pilot Study to Estimate "Difficult" Area in e-Learning Material by Physiological Measurements
Abstract: To improve designs of e-learning materials, it is necessary to know which word or figure a learner felt "difficult" in the materials. In this pilot study, we measured electroencephalography (EEG) and eye gaze data of learners and analyzed to estimate which area they had difficulty to learn. The developed system realized simultaneous measurements of physiological data and subjective evaluations during learning. Using this system, we observed specific EEG activity in difficult pages. Integrating of eye gaze and EEG measurements raised a possibility to determine where a learner felt "difficult" in a page of learning materials. From these results, we could suggest that the multimodal measurements of EEG and eye gaze would lead to effective improvement of learning materials. For future study, more data collection using various materials and learners with different backgrounds is necessary. This study could lead to establishing a method to improve e-learning materials based on learners' mental states.
Extracted statement — Material: difficult pages
Research field: Systems and Integrative Physiology
Title: Pilot Study to Estimate "Difficult" Area in e-Learning Material by Physiological Measurements
Abstract: To improve designs of e-learning materials, it is necessary to know which word or figure a learner felt "difficult" in the materials. In this pilot study, we measured electroencephalography (EEG) and eye gaze data of learners and analyzed to estimate which area they had difficulty to learn. The developed system realized simultaneous measurements of physiological data and subjective evaluations during learning. Using this system, we observed specific EEG activity in difficult pages. Integrating of eye gaze and EEG measurements raised a possibility to determine where a learner felt "difficult" in a page of learning materials. From these results, we could suggest that the multimodal measurements of EEG and eye gaze would lead to effective improvement of learning materials. For future study, more data collection using various materials and learners with different backgrounds is necessary. This study could lead to establishing a method to improve e-learning materials based on learners' mental states.
Extracted statement — Material: e-learning materials
Research field: Systems and Integrative Physiology
Title: Pilot Study to Estimate "Difficult" Area in e-Learning Material by Physiological Measurements
Abstract: To improve designs of e-learning materials, it is necessary to know which word or figure a learner felt "difficult" in the materials. In this pilot study, we measured electroencephalography (EEG) and eye gaze data of learners and analyzed to estimate which area they had difficulty to learn. The developed system realized simultaneous measurements of physiological data and subjective evaluations during learning. Using this system, we observed specific EEG activity in difficult pages. Integrating of eye gaze and EEG measurements raised a possibility to determine where a learner felt "difficult" in a page of learning materials. From these results, we could suggest that the multimodal measurements of EEG and eye gaze would lead to effective improvement of learning materials. For future study, more data collection using various materials and learners with different backgrounds is necessary. This study could lead to establishing a method to improve e-learning materials based on learners' mental states.
Extracted statement — Data: learners' mental states
Research field: Systems and Integrative Physiology
Title: Pilot Study to Estimate "Difficult" Area in e-Learning Material by Physiological Measurements
Abstract: To improve designs of e-learning materials, it is necessary to know which word or figure a learner felt "difficult" in the materials. In this pilot study, we measured electroencephalography (EEG) and eye gaze data of learners and analyzed to estimate which area they had difficulty to learn. The developed system realized simultaneous measurements of physiological data and subjective evaluations during learning. Using this system, we observed specific EEG activity in difficult pages. Integrating of eye gaze and EEG measurements raised a possibility to determine where a learner felt "difficult" in a page of learning materials. From these results, we could suggest that the multimodal measurements of EEG and eye gaze would lead to effective improvement of learning materials. For future study, more data collection using various materials and learners with different backgrounds is necessary. This study could lead to establishing a method to improve e-learning materials based on learners' mental states.
Extracted statement — Material: which word or figure a learner
Research field: Systems Biology
Title: MetaboMAPS: Pathway sharing and multi-omics data visualization in metabolic context
Abstract: Metabolic pathways are an important part of systems biology research since they illustrate complex interactions between metabolites, enzymes, and regulators. Pathway maps are drawn to elucidate metabolism or to set data in a metabolic context. We present MetaboMAPS, a web-based platform to visualize numerical data on individual metabolic pathway maps. Metabolic maps can be stored, distributed and downloaded in SVG-format. MetaboMAPS was designed for users without computational background and supports pathway sharing without strict conventions. In addition to existing applications that established standards for well-studied pathways, MetaboMAPS offers a niche for individual, customized pathways beyond common knowledge, supporting ongoing research by creating publication-ready visualizations of experimental data.
Extracted statement — Scope: Pathway sharing