nils-herrmann/clean_scirepeval huggingface dataset:
Task-Structure Analysis for Knowledge Modeling

n recent years there has been increasing interest in describing complicated information processing systems in terms of the knowledge they have, rather than by the details of their implementation. This requires a means of modeling the knowledge in a system. Several different approaches to knowledge modeling have been developed by researchers working in Artificial Intelligence (AI). Most of these approaches share the view that knowledge must be modeled with respect to a goal or task. In this article , we outline our modeling approach in terms of the notion of a task-structure, which recursively links a task to alternative methods and to their subtasks. Our emphasis is on the notion of modeling domain knowledge using tasks and methods as mediating concepts. We begin by tracing the development of a number of different knowledge-modeling approaches. These approaches share many features, but their differences make it difficult to compare systems that have been modeled using different approaches. We present these approaches and describe their similarities and differences. We then give a detailed description , based on the task structure, of our knowledge-modeling approach and illustrate it with task structures for diagnosis and design. Finally, we show how the task structure can be used to compare and unify the other approaches. A knowledge-based system (KBS) has explicit representations of knowledge as well as inference processes that operate on these representations to achieve a goal. An inference process consists of a number of inference steps, each step creating additional knowledge. The process of applying inference steps is repeated until the information needed to fulfill the requirements of the problem-solving goal or task is generated. Typically, both domain knowledge and possible inference steps have to be modeled and represented in some form. In one sense, knowledge is of general utility-the same piece can be utilized in different contexts and problems; so, unlike traditional procedural approaches, knowledge should not be tied to one task or goal. On the other hand, it is difficult to know what knowledge to put in a system without having an idea of the tasks the KBS will confront. In spite of claims of generality, all KBSs are designed with some task or class of tasks in mind. Similarly, they are designed to be operational across some range of domains. Thus, a clear understanding of the relationship between tasks, knowledge and inferences required to perform the task is needed before knowledge in …
Collaborative Departure Queue Management An Example of Airport Collaborative Decision Making in the United States

The management of airport surface operations to provide shared situational awareness and to control taxi times through the use of ‘virtual queues’ has become an important component of Air Traffic Management (ATM) research and development in both Europe and the United States. Airport Collaborative Decision Making (CDM) has been implemented at a number of airports in Europe, and multiple departure metering concepts have now been tested in the US National Airspace System (NAS). This paper provides a review and comparison of the different airport surface departure management concepts, and describes one such concept in detail that has been evaluated operationally in the field in the US, the Collaborative Departure Queue Management (CDQM) concept. CDQM has been developed and evaluated by the Federal Aviation Administration (FAA) under the Surface Trajectory Based Operations (STBO) project. This paper provides a description of the operational field evaluation of CDQM that was conducted in Memphis, Tennessee, during 2009 and 2010. An analysis of the effectiveness, accuracy and benefit of CDQM in managing departure operations during the field evaluation is presented. CDQM was found to provide reduced taxi times, and resultant reduced fuel usage and emissions, while maintaining full use of departure capacity. Keywords-airport surface traffic management; departure queue management; scheduling algorithms; collaborative decision making; equitable rationing of capacity
A General Account of Argumentation with Preferences

This paper builds on the recent ASPIC formalism, to develop a general framework for argumentation with preferences. We motivate a revised definition of conflict free sets of arguments, adapt ASPIC to accommodate a broader range of instantiating logics, and show that under some assumptions, the resulting framework satisfies key properties and rationality postulates. We then show that the generalised framework accommodates Tarskian logic instantiations extended with preferences, and then study instantiations of the framework by classical logic approaches to argumentation. We conclude by arguing that ASPIC’s modelling of defeasible inference rules further testifies to the generality of the framework, and then examine and counter recent critiques of Dung’s framework and its extensions to accommodate preferences.
Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke

The paper presents a novel method and system for personalised (individualised) modelling of spatio/spectro-temporal data (SSTD) and prediction of events. A novel evolving spiking neural network reservoir system (eSNNr) is proposed for the purpose. The system consists of: spike-time encoding module of continuous value input information into spike trains; a recurrent 3D SNNr; eSNN as an evolving output classifier. Such system is generated for every new individual, using existing data of similar individuals. Subject to proper training and parameter optimisation, the system is capable of accurate spatiotemporal pattern recognition (STPR) and of early prediction of individual events. The method and the system are generic, applicable to various SSTD and classification and prediction problems. As a case study, the method is applied for early prediction of occurrence of stroke on an individual basis. Preliminary experiments demonstrated a significant improvement in accuracy and time of event prediction when using the proposed method when compared with standard machine learning methods, such as MLR, SVM, MLP. Future development and applications are discussed.
Introducing IoT and Wearable Technologies into Task-Based Language Learning for Young Children

In the last few years, in an attempt to further motivate students to learn a foreign language, there has been an increasing interest in task-based teaching techniques, which emphasize communication and the practical use of language, thus moving away from the repetitive grammar-translation methods. Within this approach, the significance of situating foreign language learners in scenarios where they can meaningfully learn has become a major priority for many educators. This approach is particularly relevant in the context of teaching foreign languages to young children, who need to be introduced to a new language by means of very concrete vocabulary, which is facilitated by the use of objects that they can handle and see. In this study, we investigate the benefits of using wearable and Internet-of-Things (IoT) technologies in streamlining the creation of such realistic task-based language learning scenarios. We show that the use of these technologies will prove beneficial by freeing the instructors of having to keep records of the tasks performed by each student during the class session. Instead, instructors can focus their efforts on creating a friendly environment and encouraging students to participate. Our study sets up a basis for showing the great benefits of using wearable and IoT technologies in streamlining 1) the creation of realistic scenarios in which young foreign language learners can feel comfortable engaging in chat and becoming better prepared for social interaction in a foreign language, and 2) the acquisition and processing of performance metrics.
A Taxonomy of Data Quality Challenges in Empirical Software Engineering

Reliable empirical models such as those used in software effort estimation or defect prediction are inherently dependent on the data from which they are built. As demands for process and product improvement continue to grow, the quality of the data used in measurement and prediction systems warrants increasingly close scrutiny. In this paper we propose a taxonomy of data quality challenges in empirical software engineering, based on an extensive review of prior research. We consider current assessment techniques for each quality issue and proposed mechanisms to address these issues, where available. Our taxonomy classifies data quality issues into three broad areas: first, characteristics of data that mean they are not fit for modeling, second, data set characteristics that lead to concerns about the suitability of applying a given model to another data set, and third, factors that prevent or limit data accessibility and trust. We identify this latter area as of particular need in terms of further research.
Internet of Thing based healthcare monitoring system

Advances in the information and communication technologies have led to the emergence of Internet of Thing (IoT). IoT allows many physical devices to capture transmit data, through the internet, providing more data interoperability methods. Nowadays IoT plays an important role not only in communication, but also in monitoring, recording, storage and display. Hence the latest trend in Healthcare communication method using IoT is adapted. Monitored on a continual basis, aggregated and effectively analyzed-such information can bring about a massive positive transformation in the field of healthcare. Our matter of concern in this project is to focus on the development and implementation of an effective healthcare monitoring system based on IoT. The proposed system monitors the vital health parameters and transmits the data through a wireless communication, which is further transferred to a network via a Wi-Fi module. The data can be accessed anytime promoting the reception of the current status of the patient. In case any abnormal behavior or any vital signs are recognized, the caretaker, as well as the doctors are notified immediately through a message service or an audio signaling device (buzzer). In order to design an efficient remote monitoring system, security plays an important part. Cloud computing and password protected Wi-Fi module handles authentication, privacy and security of patient details by allowing restricted access to the database. Hence the system provides quality healthcare to all. This paper is a review of Healthcare Monitoring system using IoT.
A 79-GHz LTCC microstrip half-grid array antenna using a laminated waveguide feed

A microstrip half-grid array antenna (HGA) based on low temperature co-fired ceramic (LTCC) technology is presented in this paper. The antenna is designed for the 77-81 GHz radar frequency band and uses a high permittivity material (εr = 7.3). The traditional single-grid array antenna (SGA) uses two radiating elements in the H-plane. For applications using digital beam forming, the focusing of an SGA in the scanning plane (H-plane) limits the field of view (FoV) of the radar system and the width of the SGA enlarges the minimal spacing between the adjacent channels. To overcome this, an array antenna using only half of the grid as radiating element was designed. As feeding network, a laminated waveguide with a vertically arranged power divider was adopted. For comparison, both an SGA and an HGA were fabricated. The measured results show: using an HGA, an HPBW increment in the H-plane can be achieved and their beam patterns in the E-plane remain similar. This compact LTCC antenna is suitable for radar application with a large FoV requirement.
A FDR Sensor for Measuring Complex Soil Dielectric Permittivity in the 10–500 MHz Frequency Range

Mechanical details as well as electrical models of FDR (frequency domain reflectometry) sensors for the measurement of the complex dielectric permittivity of porous materials are presented. The sensors are formed from two stainless steel parallel waveguides of various lengths. Using the data from VNA (vector network analyzer) with the connected FDR sensor and selected models of the applied sensor it was possible obtain the frequency spectrum of dielectric permittivity from 10 to 500 MHz of reference liquids and soil samples of various moisture and salinity. The performance of the analyzed sensors were compared with TDR (time domain reflectometry) ones of similar mechanical construction.
Gradient boosting machines, a tutorial

Gradient boosting machines are a family of powerful machine-learning techniques that have shown considerable success in a wide range of practical applications. They are highly customizable to the particular needs of the application, like being learned with respect to different loss functions. This article gives a tutorial introduction into the methodology of gradient boosting methods with a strong focus on machine learning aspects of modeling. A theoretical information is complemented with descriptive examples and illustrations which cover all the stages of the gradient boosting model design. Considerations on handling the model complexity are discussed. Three practical examples of gradient boosting applications are presented and comprehensively analyzed.
Automatic mapping of NES games with mappy

Game maps are useful for human players, general-game-playing agents, and data-driven procedural content generation. These maps are generally made by hand-assembling manually-created screen-shots of game levels. Besides being tedious and error-prone, this approach requires additional effort for each new game and level to be mapped. The results can still be hard for humans or computational systems to make use of, privileging visual appearance over semantic information. We describe a software system, Mappy, that produces a good approximation of a linked map of rooms given a Nintendo Entertainment System game program and a sequence of button inputs exploring its world. In addition to visual maps, Mappy outputs grids of tiles (and how they change over time), positions of non-tile objects, clusters of similar rooms that might in fact be the same room, and a set of links between these rooms. We believe this is a necessary step towards developing larger corpora of high-quality semantically-annotated maps for PCG via machine learning and other applications.
Learned helplessness in social situations.

Helplessness in social situations was conceptualized as the perceived inability to surmount rejection, as revealed by causal attributions for rejection. Although current research on children's social adjustment emphasizes differences in social skills between popular and unpopular children or behavioral intervention as an aid for withdrawn children, the present study explores responses to rejection across popularity levels. The results show that individual differences in attributions for rejection are related to disruption of goal-directed behavior following rejection. As predicted, the most severe disruption of attempts to gain social approval (withdrawal and perseveration) was associated with the tendency to emphasize personal incompetence as the cause of rejection, regardless of popularity level. The findings suggest that cognitive mediators of overt social behavior and ability to solve problems when faced with difficulties need to be considered in the study of children's social relations.
Music Similarity Measures: What's the use?

Electronic Music Distribution (EMD) is in demand of robust, automatically extracted music descriptors. We introduce a timbral similarity measures for comparing music titles. This measure is based on a Gaussian model of cepstrum coefficients. We describe the timbre extractor and the corresponding timbral similarity relation. We describe experiments in assessing the quality of the similarity relation, and show that the measure is able to yield interesting similarity relations, in particular when used in conjunction with other similarity relations. We illustrate the use of the descriptor in several EMD applications developed in the context of the Cuidado European project.
Lifetime prevalence of mental disorders in U.S. adolescents: results from the National Comorbidity Survey Replication--Adolescent Supplement (NCS-A).

To present estimates of the lifetime prevalence of DSM-IV mental disorders with and without severe impairment, their comorbidity across broad classes of disorder, and their sociodemographic correlates.



The National Comorbidity Survey-Adolescent Supplement NCS-A is a nationally representative face-to-face survey of 10,123 adolescents aged 13 to 18 years in the continental United States. DSM-IV mental disorders were assessed using a modified version of the fully structured World Health Organization Composite International Diagnostic Interview.



Anxiety disorders were the most common condition (31.9%), followed by behavior disorders (19.1%), mood disorders (14.3%), and substance use disorders (11.4%), with approximately 40% of participants with one class of disorder also meeting criteria for another class of lifetime disorder. The overall prevalence of disorders with severe impairment and/or distress was 22.2% (11.2% with mood disorders, 8.3% with anxiety disorders, and 9.6% behavior disorders). The median age of onset for disorder classes was earliest for anxiety (6 years), followed by 11 years for behavior, 13 years for mood, and 15 years for substance use disorders.



These findings provide the first prevalence data on a broad range of mental disorders in a nationally representative sample of U.S. adolescents. Approximately one in every four to five youth in the U.S. meets criteria for a mental disorder with severe impairment across their lifetime. The likelihood that common mental disorders in adults first emerge in childhood and adolescence highlights the need for a transition from the common focus on treatment of U.S. youth to that of prevention and early intervention.
Towards Automatic Identification of Fake News : Headline-Article Stance Detection with LSTM Attention Models

As participants in Fake News Challenge 1 (FNC-1), we approach the problem of fake news via stance detection. Given an article as ”ground truth”, we attempt to classify whether a headline discusses, agrees, disagrees, or is unrelated to a given article. In this paper, we first leverage an SVM trained on TF-IDF cosine similarity features to discern whether a headline-article pairing is related or unrelated. If we classify the pairing as the former, we then employ various neural network architectures built on top of Long-Short-Term-Memory Models (LSTMs) to label the pairing as agree, disagree, or discuss. Ultimately, our best performing neural network architecture proved to be a pair of Bidirectional Conditionally Encoded LSTMs with Bidirectional Global Attention. Using our linear SVM for the unrelated/related subproblem and our best neural network for the agree/disagree/discuss subproblem, we scored .8658 according to the FNC-1’s performance metric.
Review of shape representation and description techniques

More and more images have been generated in digital form around the world. There is a growing interest in 1nding images in large collections or from remote databases. In order to 1nd an image, the image has to be described or represented by certain features. Shape is an important visual feature of an image. Searching for images using shape features has attracted much attention. There are many shape representation and description techniques in the literature. In this paper, we classify and review these important techniques. We examine implementation procedures for each technique and discuss its advantages and disadvantages. Some recent research results are also included and discussed in this paper. Finally, we identify some promising techniques for image retrieval according to standard principles. ? 2003 Pattern Recognition Society.
The Elements of Fashion Style

The outfits people wear contain latent fashion concepts capturing styles, seasons, events, and environments. Fashion theorists have proposed that these concepts are shaped by design elements such as color, material, and silhouette. A dress may be "bohemian" because of its pattern, material, trim, or some combination of them: it is not always clear how low-level elements translate to high-level styles. In this paper, we use polylingual topic modeling to learn latent fashion concepts jointly in two languages capturing these elements and styles. Using this latent topic formation we can translate between these two languages through topic space, exposing the elements of fashion style. We train the polylingual topic model (PLTM) on a set of more than half a million outfits collected from Polyvore, a popular fashion-based social net- work. We present novel, data-driven fashion applications that allow users to express their needs in natural language just as they would to a real stylist and produce tailored item recommendations for these style needs.
Investigating Spectrum Sharing between 5G Millimeter Wave Networks and Fixed Satellite Systems

In this paper we study coexistence of 5G small cells with fixed satellite systems (FSSs) in a scenario where both systems operate co-channel in the large spectrum bandwidth available around 28 GHz. Such studies are of great importance to inform the research community,industry and regulators which are currently investigating spectrum requirements and technology options for 5G systems. Focusing on the FSS uplink scenario, we use realistic FSS parameters and radiation pattern, combined with very recent channel from the literature, we analyzed the impact of interference resulting from FSS radiation on the achievable capacity and throughput of 5G small cells considering various multiple antenna configurations at the base stations (BSs) and different deployments of the mobile transmitters when no cooperation is allowed between the BSs. Starting from the lower bound, represented by an omnidirectional configuration of the transmitters, we extend our work to the analysis of large antenna arrays that will be used in the new generation of mobile cellular systems. Our results indicate that by exploiting a large number of antennas at the BSs and properly setting the protection distance between FSS and cellular BS, co-channel deployment of 5G small cells with FSS earth stations is possible, in the sense that adequate user data rates could be provided to the majority of mobile users.
Opposition-based krill herd algorithm with Cauchy mutation and position clamping

Krill herd (KH) has been proven to be an efficient algorithm for function optimization. For some complex functions, this algorithmmay have problems with convergence or being trapped in local minima. To cope with these issues, this paper presents an improved KH-based algorithm, called Opposition Krill Herd (OKH). The proposed approach utilizes opposition-based learning (OBL), position clamping (PC) and method while both PC and heavy-tailed CM help KH escape from local optima. Simulations are implemented on an array of benchmark functions and two engineering optimization problems. The results show that OKH has a good performance on majority of the considered functions and two engineering cases. The influence of each individual strategy (OBL, CM and PC) on KH is verified through 25 benchmarks. The results show that the KH with OBL, CM and PC operators, has the best performance among different variants of OKH. & 2015 Elsevier B.V. All rights reserved.