nils-herrmann/clean_scirepeval huggingface dataset:
An Automatic Text Summarization Approach using Content-Based and Graph-Based Characteristics

The continuing growth of World Wide Web and on-line text collections makes a large volume of information available to users. Automatic text summarization allows users to quickly understand documents. In this paper, we propose an automated technique for single document summarization which combines content-based and graph-based approaches and introduce the Hopfield network algorithm as a technique for ranking text segments. A series of experiments are performed using the DUC collection and a Thai-document collection. The results show the superiority of the proposed technique over reference systems, in addition the Hopfield network algorithm on undirected graph is shown to be the best text segment ranking algorithm in the study
OmniKinect: real-time dense volumetric data acquisition and applications

Real-time three-dimensional acquisition of real-world scenes has many important applications in computer graphics, computer vision and human-computer interaction. Inexpensive depth sensors such as the Microsoft Kinect allow to leverage the development of such applications. However, this technology is still relatively recent, and no detailed studies on its scalability to dense and view-independent acquisition have been reported. This paper addresses the question of what can be done with a larger number of Kinects used simultaneously. We describe an interference-reducing physical setup, a calibration procedure and an extension to the KinectFusion algorithm, which allows to produce high quality volumetric reconstructions from multiple Kinects whilst overcoming systematic errors in the depth measurements. We also report on enhancing image based visual hull rendering by depth measurements, and compare the results to KinectFusion. Our system provides practical insight into achievable spatial and radial range and into bandwidth requirements for depth data acquisition. Finally, we present a number of practical applications of our system.
Two Section Wideband 90° Hybrid Coupler Using Parallel-Coupled Three-Line

Two section wideband hybrid coupler with short-circuited coupled lines in the middle branch is demonstrated. The coupled lines are designed with a parallel-coupled 3-line, which has tight coupling and symmetric transmission phase over the center frequency. Without defected ground planes and a multilayer process, the proposed two-section coupler has the widest fractional bandwidth (FBW) than other two or even three section hybrid couplers modified from branch-line couplers. The designed wideband coupler has 55% FBW at the center frequency of 1.9 GHz. The bandwidth is limited by 1-dB power imbalance and the worst return loss, isolation, and phase imbalance within the bandwidth are 20.1 dB, 20.8 dB, and 3.2°, respectively.
Initial provisioning and spare parts inventory network optimisation in a multi maintenance base environment

Aviation spare parts provisioning is a highly complex problem. Traditionally, provisioning has been carried out using a conventional Poisson-based approach where inventory quantities are calculated separately for each part number and demands from different operations bases are consolidated into one single location. In an environment with multiple operations bases, however, such simplifications can lead to situations in which spares -- although available at another airport -- first have to be shipped to the location where the demand actually arose, leading to flight delays and cancellations. In this paper we demonstrate how simulation-based optimisation can help with the multi-location inventory problem by quantifying synergy potential between locations and how total service lifecycle cost can be further reduced without increasing risk right away from the Initial Provisioning (IP) stage onwards by taking into account advanced logistics policies such as pro-active re-balancing of spares between stocking locations.
Genetic Programming for Region Detection, Feature Extraction, Feature Construction and Classification in Image Data

Image analysis is a key area in the computer vision domain that has many applications. Genetic Programming (GP) has been successfully applied to this area extensively, with promising results. Highlevel features extracted from methods such as Speeded Up Robust Features (SURF) and Histogram of Oriented Gradients (HoG) are commonly used for object detection with machine learning techniques. However, GP techniques are not often used with these methods, despite being applied extensively to image analysis problems. Combining the training process of GP with the powerful features extracted by SURF or HoG has the potential to improve the performance by generating high-level, domaintailored features. This paper proposes a new GP method that automatically detects di↵erent regions of an image, extracts HoG features from those regions, and simultaneously evolves a classifier for image classification. By extending an existing GP region selection approach to incorporate the HoG algorithm, we present a novel way of using high-level features with GP for image classification. The ability of GP to explore a large search space in an e cient manner allows all stages of the new method to be optimised simultaneously, unlike in existing approaches. The new approach is applied across a range of datasets, with promising results when compared to a variety of well-known machine learning techniques. Some high-performing GP individuals are analysed to give insight into how GP can e↵ectively be used with high-level features for image classification.
Improved Interrupted Sampling Repeater Jamming based on DRFM

Interrupted Sampling Repeater Jamming (ISRJ) can be implemented easily based on Digital Radio Frequency Memory (DRFM) technique. An improved method is presented in this paper which has all advantages of traditional ISRJ. Furthermore, the improved ISRJ obtains a train of false targets that precede the true one. The radar radio signal is downshifted and sampled by receive-transmit time-sharing antenna and then stored to digital memory. Its retransmitting signal reserves the coherent property so that a large number of false targets will be achieved after matched filter of the pulse compression radar. The principal and quantitative relation between the false targets and the true one is deduced and analyzed. Simulation illustrates that the improved ISRJ method obtains several false targets ahead of the true one and the amplitude of the main false target decrease compared with the traditional method. The amplitude decrease can be remedied by enhancing the power of transmitting antenna. Imaging results with improved ISRJ method to real SAR raw are shown at last.
Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs

The Softmax function is used in the final layer of nearly all existing sequence-tosequence models for language generation. However, it is usually the slowest layer to compute which limits the vocabulary size to a subset of most frequent types; and it has a large memory footprint. We propose a general technique for replacing the softmax layer with a continuous embedding layer. Our primary innovations are a novel probabilistic loss, and a training and inference procedure in which we generate a probability distribution over pre-trained word embeddings, instead of a multinomial distribution over the vocabulary obtained via softmax. We evaluate this new class of sequence-to-sequence models with continuous outputs on the task of neural machine translation. We show that our models train up to 2.5x faster than the state-of-the-art models while achieving comparable translation quality. These models are capable of handling very large vocabularies without compromising on translation quality or speed. They also produce more meaningful errors than the softmax-based models, as these errors typically lie in a subspace of the vector space of the reference translations1.
Counseling in fetal medicine: evidence-based answers to clinical questions on morbidly adherent placenta.

Although the incidence of morbidly adherent placenta (MAP) has risen progressively in the last two decades, there remains uncertainty about the diagnosis and management of this condition. The aim of this review is to provide up-to-date and evidence-based answers to common clinical questions regarding the diagnosis and management of MAP. Different risk factors have been associated with MAP; however, previous Cesarean section and placenta previa are the most frequently associated. Ultrasound is the primary method for diagnosing MAP and has a good overall diagnostic accuracy for its detection. When considering the different ultrasound signs of MAP, color Doppler seems to provide the best diagnostic performance. Magnetic resonance imaging has the same accuracy in diagnosing MAP as does ultrasound examination; its use should be considered when a resective procedure, such as hysterectomy, is planned as it can provide detailed information about the topography of placental invasion and predict difficulties that may arise in surgery. The optimal gestational age for delivery in pregnancies with MAP is yet to be established; planning surgery between 34 and 36 weeks of gestation provides the best balance between fetal maturity and the risk of unexpected episodes of heavy bleeding, which are more likely to occur with delivery after this timepoint, especially in severe cases of MAP. The optimal surgical approach to MAP depends on multiple factors, including availability of an experienced team, specific surgical skills and hospital resources.
Modeling and Optimizing Automotive Electric/Electronic (E/E) Architectures: Towards Making Clafer Accessible to Practitioners

Modern automotive electric/electronic (E/E) architectures are growing to the point where architects can no longer manually predict the effects of their design decisions. Thus, in addition to applying an architecture reference model to decompose their architectures, they also require tools for synthesizing and evaluating candidate architectures during the design process. Clafer is a modeling language, which has been used to model variable multilayer, multi-perspective automotive system architectures according to an architecture reference model. Clafer tools allow architects to synthesize optimal candidates and evaluate effects of their design decisions. However, since Clafer is a general-purpose structural modeling language, it does not help the architects in building models conforming to the given architecture reference model. In this paper, we present an E/E architecture domain-specific language (DSL) built on top of Clafer, which embodies the reference model and which guides the architects in correctly applying it. We evaluate the DSL and its implementation by modeling two existing automotive systems, which were originally modeled in plain Clafer. The evaluation showed that by using the DSL, an evaluator obtained correct models by construction because the DSL helped prevent typical errors that are easy to make in plain Clafer. The evaluator was also able to synthesize and evaluate candidate architectures as with plain Clafer.
Facial volume restoration of the aging face with poly-l-lactic acid.

The purpose of this article is to discuss current techniques used with poly-l-lactic acid to safely and effectively address changes observed in the aging face. Several important points deserve mention. First, this unique agent is not a filler but a stimulator of the host's own collagen, which then acts to volumize tissue in a gradual, progressive, and predictable manner. The technical differences between the use of biostimulatory agents and replacement fillers are simple and straightforward, but are critically important to the safe and successful use of these products and will be reviewed in detail. Second, in addition to gains in technical insights that have improved our understanding of how to use the product to best advantage, where to use the product to best advantage in facial filling has also improved with ever-evolving insights into the changes observed in the aging face. Finally, it is important to recognize that a patient's final outcome, and the amount of product and work it will take to get there, is a reflection of the quality of tissues with which they start. This is, of course, an issue of patient selection and not product selection.
BreakSense: Combining Physiological and Location Sensing to Promote Mobility during Work-Breaks

Work breaks can play an important role in the mental and physical well-being of workers and contribute positively to productivity. In this paper we explore the use of activity-, physiological-, and indoor-location sensing to promote mobility during work-breaks. While the popularity of devices and applications to promote physical activity is growing, prior research highlights important constraints when designing for the workplace. With these constraints in mind, we developed BreakSense, a mobile application that uses a Bluetooth beacon infrastructure, a smartphone and a smartwatch to encourage mobility during breaks with a game-like design. We discuss constraints imposed by design for work and the workplace, and highlight challenges associated with the use of noisy sensors and methods to overcome them. We then describe a short deployment of BreakSense within our lab that examined bound vs. unbound augmented breaks and how they affect users' sense of completion and readiness to work.
Robust Submodular Observation Selection

In many applications, one has to actively select among a set of expensive observations before making an informed decision. For example, in environmental monitoring, we want to select locations to measure in order to most effectively predict spatial phenomena. Often, we want to select observations which are robust against a number of possible objective functions. Examples include minimizing the maximum posterior variance in Gaussian Process regression, robust experimental design, and sensor placement for outbreak detection. In this paper, we present the Submodular Saturation algorithm, a simple and efficient algorithm with strong theoretical approximation guarantees for cases where the possible objective functions exhibit submodularity, an intuitive diminishing returns property. Moreover, we prove that better approximation algorithms do not exist unless NP-complete problems admit efficient algorithms. We show how our algorithm can be extended to handle complex cost functions (incorporating non-unit observation cost or communication and path costs). We also show how the algorithm can be used to near-optimally trade off expected-case (e.g., the Mean Square Prediction Error in Gaussian Process regression) and worst-case (e.g., maximum predictive variance) performance. We show that many important machine learning problems fit our robust submodular observation selection formalism, and provide extensive empirical evaluation on several real-world problems. For Gaussian Process regression, our algorithm compares favorably with state-of-the-art heuristics described in the geostatistics literature, while being simpler, faster and providing theoretical guarantees. For robust experimental design, our algorithm performs favorably compared to SDP-based algorithms. ∗ School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA † Google Inc., Pittsburgh, PA, USA.
Release of glucagon-like peptide 1 (GLP-1 [7-36 amide]), gastric inhibitory polypeptide (GIP) and insulin in response to oral glucose after upper and lower intestinal resections.

Glucagon-like peptide 1 (GLP-1[7-36 amide]) is an incretin hormone primarily synthesized in the lower gut (ileum, colon/rectum). Nevertheless, there is an early increment in plasma GLP-1 immediately after ingesting glucose or mixed meals, before nutrients have entered GLP-1 rich intestinal regions. The responsible signalling pathway between the upper and lower gut is not clear. It was the aim of this study to see, whether small intestinal resection or colonectomy changes GLP-1[7-36 amide] release after oral glucose. In eight healthy controls, in seven patients with inactive Crohn's disease (no surgery), in nine patients each after primarily jejunal or ileal small intestinal resections, and in six colonectomized patients not different in age (p = 0.10), body-mass-index (p = 0.24), waist-hip-ratio (p = 0.43), and HbA1c (p = 0.22), oral glucose tolerance tests (75 g) were performed in the fasting state. GLP-1[7-36 amide], insulin C-peptide, GIP and glucagon (specific (RIAs) were measured over 240 min.



Repeated measures ANOVA, t-test (significance: p < 0.05). A clear and early (peak: 15-30 min) GLP-1[7-36 amide] response was observed in all subjects, without any significant difference between gut-resected and control groups (p = 0.95). There were no significant differences in oral glucose tolerance (p = 0.21) or in the suppression of pancreatic glucagon (p = 0.36). Colonectomized patients had a higher insulin (p = 0.011) and C-peptide (p = 0.0023) response in comparison to all other groups. GIP responses also were higher in the colonectomized patients (p = 0.0005). Inactive Crohn's disease and resections of the small intestine as well as proctocolectomy did not change overall GLP-1[7-36 amide] responses and especially not the early increment after oral glucose. This may indicate release of GLP-1[7-36 amide] after oral glucose from the small number of GLP-1[7-36 amide] producing L-cells in the upper gut rather than from the main source in the ileum, colon and rectum. Colonectomized patients are characterized by insulin hypersecretion, which in combination with their normal oral glucose tolerance possibly indicates a reduced insulin sensitivity in this patient group. GIP may play a role in mediating insulin hypersecretion in these patients.
A design science approach to an HCI research project

Usability is an important and determinant factor in human-computer systems acceptance. Usability issues are still identified late in the software development process, during testing and deployment. One of the reasons these issues arise late in the process is that current requirements engineering practice does not incorporate usability perspectives effectively into software requirements specifications. The main strength of usability-focused software requirements is the clear visibility of usability aspects for both developers and testers. The explicit expression of these aspects of human-computer systems can be built for optimal usability and also evaluated effectively to uncover usability issues. This paper presents a design science-oriented research design to test the proposition that incorporating user modelling and usability modelling in software requirements specifications improves design. The proposal and the research design are expected to make a contribution to knowledge by theory testing and to practice with effective techniques to produce usable human computer systems.
TWO PERSPECTIVES ON INFORMATION SYSTEM ADAPTATION : USING INSTITUTIONAL THEORY WITH SENSEMAKING

Institutional theory has proven to be a central analytical perspective for investigating the role of larger social and historical structures of Information System (IS) adaptation. However, it does not explicitly account for how organizational actors make sense of and enact IS in their local context. We address this limitation by showing how sensemaking theory can be combined with institutional theory to understand IS adaptation in organizations. Based on a literature review, we present the main assumptions behind institutional and sensemaking theory when used as analytical lenses for investigating the phenomenon of IS adaptation. Furthermore, we explore a combination of the two theories with a case study in a health care setting where an Electronic Patient Record (EPR) system was introduced and used by a group of doctors. The empirical case provides evidence of how existing institutional structures influenced the doctors’ sensemaking of the EPR system. Additionally, it illustrates how the doctors made sense of the EPR system in practice. The paper outlines that: 1) institutional theory has its explanatory power at the organizational field and organizational/group level of analysis focusing on the role that larger institutional structures play in organizational actors’ sensemaking of IS adaptation, 2) sensemaking theory has its explanatory power at the organizational/group and individual/socio-cognitive level focusing on organizational actors’ cognition and situated actions of IS adaptation, and 3) a combined view of the two theories helps us oscillate between levels of analysis, which facilitates a much richer interpretation of IS adaptation.
Mathematical Foundations for a Compositional Distributional Model of Meaning

We propose a mathematical framework for a unification of the distributional theory of meaning in terms of vector space models, and a compositional theory for grammatical types, for which we rely on the algebra of Pregroups, introduced by Lambek. This mathematical framework enables us to compute the meaning of a well-typed sentence from the meanings of its constituents. Concretely, the type reductions of Pregroups are ‘lifted’ to morphisms in a category, a procedure that transforms meanings of constituents into a meaning of the (well-typed) whole. Importantly, meanings of whole sentences live in a single space, independent of the grammatical structure of the sentence. Hence the inner-product can be used to compare meanings of arbitrary sentences, as it is for comparing the meanings of words in the distributional model. The mathematical structure we employ admits a purely diagrammatic calculus which exposes how the information flows between the words in a sentence in order to make up the meaning of the whole sentence. A variation of our ‘categorical model’ which involves constraining the scalars of the vector spaces to the semiring of Booleans results in a Montague-style Boolean-valued semantics.
Requirements Management in a Life Cycle Perspective – A Position Paper

Requirements Management (RM) is about organising the requirements and additional information gathered during the Requirements Engineering (RE) process, and managing changes of these requirements. Practioners as well as researchers acknowledge that RM is both important and difficult, and that changing requirements is a challenging factor in many development projects. But why, then, is so little research done within RM? This position paper identifies and discusses five research areas where further research within RM is needed.
Greenhouse evaluation of Bacillus subtilis AP-01 and Trichoderma harzianum AP-001 in controlling tobacco diseases

Two biological control agents, Bacillus subtilis AP-01 (Larminar(™)) and Trichoderma harzianum AP-001 (Trisan(™)) alone or/in combination were investigated in controlling three tobacco diseases, including bacterial wilt (Ralstonia solanacearum), damping-off (Pythium aphanidermatum), and frogeye leaf spot (Cercospora nicotiana). Tests were performed in greenhouse by soil sterilization prior to inoculation of the pathogens. Bacterial-wilt and damping off pathogens were drenched first and followed with the biological control agents and for comparison purposes, two chemical fungicides. But for frogeye leaf spot, which is an airborne fungus, a spraying procedure for every treatment including a chemical fungicide was applied instead of drenching. Results showed that neither B. subtilis AP-01 nor T harzianum AP-001 alone could control the bacterial wilt, but when combined, their controlling capabilities were as effective as a chemical treatment. These results were also similar for damping-off disease when used in combination. In addition, the combined B. subtilis AP-01 and T. harzianum AP-001 resulted in a good frogeye leaf spot control, which was not significantly different from the chemical treatment.