nils-herrmann/clean_scirepeval huggingface dataset:
Model of Learning Computational Thinking

There is a high demand for qualified information and communication technology (ICT) practitioners in the European labour market, but the problem at many universities is a high dropout rate among ICT students, especially during the first study year. The solution might be to focus more on improving students’ computational thinking (CT) before starting university studies. Therefore, research is needed to find the best methods for learning CT already at comprehensive school level to raise the interest in and awareness of studying computer science. Doing so requires a clear understanding of CT and a model to improve it at comprehensive schools. Through the analysis of the articles found in EBSCO Discovery Search tool, this study gives an overview of the definition of CT and presents three models of CT. The models are analysed to find out their similarities and differences in order to gather together the core elements of CT and form a revised model of learning CT in comprehensive school ICT lessons or integrating CT in other subjects.
Surgical management of pygopagus conjoined twins with spinal bifida

Conjoined twins are a rare complication of 9 monozygotic twins and are associated with high perinatal mortality. Pygopagus are one of the rare types of conjoined twins with only a handful of cases reported in the literature. We present the case of one-and-half month-old male pygopagus conjoined twins, who were joined together dorsally in lower lumbar and sacral region and had spina bifida and shared a single thecal sac with combined weight of 6.14 kg. Spinal cord was separated at the level of the conus followed by duraplasty. They had uneventful recovery with normal 15 months follow-up. Separation of conjoined twins is recommended in where this is feasible with the anticipated survival of both or one infant.
A Probabilistic Framework for Location Inference from Social Media

We study the extent to which we can infer users’ geographical locations from social media. Location inference from social media can benet many applications, such as disaster management, targeted advertising, and news content tailoring. In recent years, a number of algorithms have been proposed for identifying user locations on social media platforms such as Twier and Facebook from message contents, friend networks, and interactions between users. In this paper, we propose a novel probabilistic model based on factor graphs for location inference that oers several unique advantages for this task. First, the model generalizes previous methods by incorporating content, network, and deep features learned from social context. e model is also exible enough to support both supervised learning and semi-supervised learning. Second, we explore several learning algorithms for the proposed model, and present a Two-chain Metropolis-Hastings (MH+) algorithm, which improves the inference accuracy. ird, we validate the proposed model on three dierent genres of data – Twier, Weibo, and Facebook – and demonstrate that the proposed model can substantially improve the inference accuracy (+3.3-18.5% by F1-score) over that of several state-of-the-art methods.
Optimizing Expensive Queries in Complex Event Processing

Pattern queries are widely used in complex event processing (CEP) systems. Existing pattern matching techniques, however, can provide only limited performance for expensive queries in real-world applications, which may involve Kleene closure patterns, flexible event selection strategies, and events with imprecise timestamps. To support these expensive queries with high performance, we begin our study by analyzing the complexity of pattern queries, with a focus on the fundamental understanding of which features make pattern queries more expressive and at the same time more computationally expensive. This analysis allows us to identify performance bottlenecks in processing those expensive queries, and provides key insights for us to develop a series of optimizations to mitigate those bottlenecks. Microbenchmark results show superior performance of our system for expensive pattern queries while most state-of-the-art systems suffer from poor performance. A thorough case study on Hadoop cluster monitoring further demonstrates the efficiency and effectiveness of our proposed techniques.
Intuitive Theories as Grammars for Causal Inference

This chapter considers a set of questions at the interface of the study of intuitive theories, causal knowledge, and problems of inductive inference. By an intuitive theory, we mean a cognitive structure that in some important ways is analogous to a scientific theory. It is becoming broadly recognized that intuitive theories play essential roles in organizing our most basic knowledge of the world, particularly for causal structures in physical, biological, psychological or social domains (Atran, 1995; Carey, 1985a; Kelley, 1973; McCloskey, 1983; Murphy & Medin, 1985; Nichols & Stich, 2003). A principal function of intuitive theories in these domains is to support the learning of new causal knowledge: generating and constraining people’s hypotheses about possible causal relations, highlighting variables, actions and observations likely to be informative about those hypotheses, and guiding people’s interpretation of the data they observe (Ahn & Kalish, 2000; Pazzani, 1987; Pazzani, Dyer & Flowers, 1986; Waldmann, 1996). Leading accounts of cognitive development argue for the importance of intuitive theories in children’s mental lives and frame the major transitions of cognitive development as instances of theory change (Carey, 1985a; Gopnik & Meltzoff, 1997; Inagaki & Hatano 2002; Wellman & Gelman, 1992). Here we attempt to lay out some prospects for understanding the structure, function, and acquisition of intuitive theories from a rational computational perspective. From this viewpoint, theory-like representations are not just a convenient way of summarizing certain aspects of human knowledge. They provide crucial foundations for successful learning and reasoning, and we want to understand how they do so. With this goal in mind, we focus on
IMPROVING SOFTWARE QUALITY THROUGH DESIGN PATTERNS : A CASE STUDY OF ADAPTIVE GAMES AND AUTO DYNAMIC DIFFICULTY

Auto dynamic difficulty (ADD) is the technique of automatically changing the level of difficulty of a video game in real time to match player expertise. Recreating an ADD system on a game-by-game basis is both expensive and time consuming, ultimately limiting its usefulness. Thus, we leverage the benefits of software design patterns to construct an ADD framework. In this paper, we discuss a number of desirable software quality attributes that can be achieved through the usage of these design patterns, based on a case study of two video games.
Dermoscopy of squamous cell carcinoma and keratoacanthoma.

To characterize dermoscopic criteria of squamous cell carcinoma (SCC) and keratoacanthoma and to compare them with other lesions.  Observer-masked study of consecutive lesions performed from March 1 through December 31, 2011.  Primary care skin cancer practice in Brisbane,  A total of 186 patients with 206 lesions.  Sensitivity, specificity, predictive values, and odds ratios.  In a retrospective analysis of 60 invasive SCC and 43 keratoacanthoma cases, keratin, surface scale, blood spots, white structureless zones, white circles, and coiled vessels were commonly found in both types of lesions. We reevaluated the significance of these criteria in 206 raised, nonpigmented lesions (32 SCCs, 29 keratoacanthomas, and 145 other lesions). Central keratin was more common in keratoacanthoma than in SCC (51.2% vs 30.0%, P = .03). Keratin had the highest sensitivity for keratoacanthoma and SCC (79%), and white circles had the highest specificity (87%). When keratoacanthoma and SCC were contrasted with basal cell carcinoma, the positive predictive values of keratin and white circles were 92% and 89%, respectively. When SCC and keratoacanthoma were contrasted with actinic keratosis and Bowen disease, the positive predictive value of keratin was 50% and that of white circles was 92%. In a multivariate model, white circles, keratin, and blood spots were independent predictors of SCC and keratoacanthoma. White circles had the highest odds ratio in favor of SCC and keratoacanthoma. The interobserver agreement for white circles was good (0.55; 95% CI, 0.44-0.65).  White circles, keratin, and blood spots are useful clues to differentiate SCC and keratoacanthoma from other raised nonpigmented skin lesions by dermoscopy. The significance of these criteria depends on the clinical context.
The Application of Wireless Food Ordering System

This research work aims to design and develop a wireless food ordering system in the restaurant. The project presents in-depth on the technical operation of the Wireless Ordering System (WOS) including systems architecture, function, limitations and recommendations. It is believed that with the increasing use of handheld device e.g PDAs in restaurants, pervasive application will become an important tool for restaurants to improve the management aspect by utilizing PDAs to coordinate food ordering could increase efficiency for restaurants and caterers by saving time, reducing human errors and by providing higher quality customer service. With the combination of simple design and readily available emerging communications technologies, it can be concluded that this system is an attractive solution for the hospitality industry.
Catechol-functionalized chitosan/pluronic hydrogels for tissue adhesives and hemostatic materials.

Bioinspired from adhesion behaviors of mussels, injectable and thermosensitive chitosan/Pluronic composite hydrogels were synthesized for tissue adhesives and hemostatic materials. Chitosan conjugated with multiple catechol groups in the backbone was cross-linked with terminally thiolated Pluronic F-127 triblock copolymer to produce temperature-sensitive and adhesive sol-gel transition hydrogels. A blend mixture of the catechol-conjugated chitosan and the thiolated Pluronic F-127 was a viscous solution state at room temperature but became a cross-linked gel state with instantaneous solidification at the body temperature and physiological pH. The adhesive chitosan/Pluronic injectable hydrogels with remnant catechol groups showed strong adhesiveness to soft tissues and mucous layers and also demonstrated superior hemostatic properties. These chitosan/Pluronic hydrogels are expected to be usefully exploited for injectable drug delivery depots, tissue engineering hydrogels, tissue adhesives, and antibleeding materials.
Toll-like receptor signalling

One of the mechanisms by which the innate immune system senses the invasion of pathogenic microorganisms is through the Toll-like receptors (TLRs), which recognize specific molecular patterns that are present in microbial components. Stimulation of different TLRs induces distinct patterns of gene expression, which not only leads to the activation of innate immunity but also instructs the development of antigen-specific acquired immunity. Here, we review the rapid progress that has recently improved our understanding of the molecular mechanisms that mediate TLR signalling.
A fail-operational truck platooning architecture

Truck platooning for which multiple trucks follow at a short distance is considered a near-term truck automation opportunity, with the potential to reduce fuel consumption. Short following distances and increasing automation make it hard for a driver to be the backup if the system fails. The EcoTwin consortium successfully demonstrated a two truck platooning system with trucks following at 20 meters distance at the public road, in which the driver is the backup. The ambition of the consortium is to increase the truck automation and to reduce the following distance, which requires a new fail-operational truck platooning architecture. This paper presents a level 2+ platooning system architecture, which is fail-operational for a single failure, and the corresponding process to obtain it. First insights in the existing two truck platooning system are obtained by analyzing its key aspects, being utilization, latency, reliability, and safety. Using these insights, candidate level 2+ platooning system architectures are defined from which the most suitable truck platooning architecture is selected. Future work is the design and implementation of a prototype, based on the presented level 2+ platooning system architecture.
Eutrophication and Harmful Algal Blooms: A Scientific Consensus.

In January 2003, the US Environmental Protection Agency sponsored a "roundtable discussion" to develop a consensus on the relationship between eutrophication and harmful algal blooms (HABs), specifically targeting those relationships for which management actions may be appropriate. Academic, federal, and state agency representatives were in attendance. The following seven statements were unanimously adopted by attendees based on review and analysis of current as well as pertinent previous data: 1) Degraded water quality from increased nutrient pollution promotes the development and persistence of many HABs and is one of the reasons for their expansion in the U.S. and the world; 2) The composition - not just the total quantity - of the nutrient pool impacts HABs; 3) High biomass blooms must have exogenous nutrients to be sustained; 4) Both chronic and episodic nutrient delivery promote HAB development; 5) Recently developed tools and techniques are already improving the detection of some HABs, and emerging technologies are rapidly advancing toward operational status for the prediction of HABs and their toxins; 6) Experimental studies are critical to further the understanding of the role of nutrients in HAB expression, and will strengthen prediction and mitigation of HABs; and 7) Management of nutrient inputs to the watershed can lead to significant reduction in HABs. Supporting evidence and pertinent examples for each consensus statement is provided herein.
Co-scheduling Amdahl applications on cache-partitioned systems

Cache-partitioned architectures allow subsections of the shared last-level cache (LLC) to be exclusively reserved for some applications. This technique dramatically limits interactions between applications that are concurrently executing on a multi-core machine. Consider n applications that execute concurrently, with the objective to minimize the makespan, defined as the maximum completion time of the n applications. Key scheduling questions are: (i) which proportion of cache and (ii) how many processors should be given to each application? In this paper, we provide answers to (i) and (ii) for Amdahl applications. Even though the problem is shown to be NP-complete, we give key elements to determine the subset of applications that should share the LLC (while remaining ones only use their smaller private cache). Building upon these results, we design efficient heuristics for Amdahl applications. Extensive simulations demonstrate the usefulness of co-scheduling when our efficient cache partitioning strategies are deployed.
Alpha or Beta in the Eye of the Beholder : What Drives Hedge Fund Flows ?

Hedge funds are unique among investment vehicles in that they are relatively unconstrained in their use of derivative investments, short-selling, and leverage. This flexibility allows investment managers to span a broad spectrum of distinct risks, such as momentum and option-like investments. Taking a revealed preference approach, we find that Capital Asset Pricing Model (CAPM) alpha explains hedge fund flows better than alphas from more sophisticated models. This result suggests that investors pool together sophisticated model alpha with returns from exposures to traditional and exotic risks. We decompose performance into traditional and exotic risk components and find that while investors chase both components, they place greater relative emphasis on returns associated with exotic risk exposures that can only be obtained through hedge funds. However, we find little evidence of persistence in performance from traditional or exotic risks, which cautions against investors’ practice of seeking out risk exposures following periods of recent success.
Maximum Achievable Power Conversion Efficiency Obtained Through an Optimized Rectenna Structure for RF Energy Harvesting

High-efficiency rectennas for radio frequency (RF) energy harvesting have been studied for decades, but most of the literature straightforwardly applies the rectenna aiming at dedicated RF sources to this situation, even though the level of input power is significantly different. Since previous studies address antenna design collecting more ambient RF power, the improvement of power conversion efficiency (PCE) has emerged in a scattered way, because the theoretical limit of PCE has not yet been characterized, and the optimal rectenna structure approaching such maximum PCE is still uninvestigated. In this paper, we characterize the performance limit of rectennas with input power ranging from −20 to 0 dBm, proposing optimal rectenna design demonstrating the maximum PCE. The maximum achievable PCE is cast into a mathematical programming problem. Solving this optimization model clarifies the effect of design factors, including operational frequencies, rectifier topologies, and parameterization. To achieve the maximum PCE, our investigation shows that the optimal rectenna structure should not only optimize those design factors but also eliminate the matching circuit between an antenna and a rectifier for ultralow-power scenarios. The resultant PCE at 2.45 GHz is 61.4% and 31.8% at −5 and −15 dBm, respectively, closely approaching the theoretical bound.
A fast approach for omnidirectional surveillance with multiple virtual perspective views

In recent years, video surveillance combined with computer vision algorithms like object detection, tracking or automated behaviour analysis has become an important research topic. However, most of these systems are depending on either fixed or remotely controlled narrow angle cameras. When using the former, the area of coverage is extremely limited, while utilizing the latter leads to high failure rates and troubles in camera calibration. In this paper, a method of extracting multiple perspective views from a single omnidirectional image for realtime environments is proposed. An example application of a ceiling-mounted camera setup is used to show the functional principle. Furthermore a performance improvement strategy is both presented and evaluated.
Integration of SQL and XQuery in IBM DB2

F. Özcan D. Chamberlin K. Kulkarni J.-E. Michels Relational database systems have dominated the database industry for a quarter century. However, the advent of the Web has led to requirements for storage of new kinds of information in which the order of information is important and data structure can vary over time and from one document to another. These evolving requirements have given rise to Extensible Markup Language (XML) as a widely accepted data format and to XQuery as an emerging standard language for querying XML data sources. A set of extensions to the Structured Query Language (SQL) called SQL/XML enables XML data to be stored in relational databases, taking advantage of the mature infrastructure of relational systems and combining the advantages of SQL and XQuery. However, building a bridge between SQL and XQuery is challenging due to the many syntactic and semantic differences between the two languages. This paper describes how IBM DB2t deals with this challenge and provides users with a flexible system for storing and processing both relational and XML data.
The Prevention of Hospital Readmissions in Heart Failure.

Heart failure (HF) is a growing healthcare burden and one of the leading causes of hospitalizations and readmission. Preventing readmissions for HF patients is an increasing priority for clinicians, researchers, and various stakeholders. The following review will discuss the interventions found to reduce readmissions for patients and improve hospital performance on the 30-day readmission process measure. While evidence-based therapies for HF management have proliferated, the consistent implementation of these therapies and development of new strategies to more effectively prevent readmissions remain areas for continued improvement.
Label-free classification of cultured cells through diffraction imaging

Automated classification of biological cells according to their 3D morphology is highly desired in a flow cytometer setting. We have investigated this possibility experimentally and numerically using a diffraction imaging approach. A fast image analysis software based on the gray level co-occurrence matrix (GLCM) algorithm has been developed to extract feature parameters from measured diffraction images. The results of GLCM analysis and subsequent classification demonstrate the potential for rapid classification among six types of cultured cells. Combined with numerical results we show that the method of diffraction imaging flow cytometry has the capacity as a platform for high-throughput and label-free classification of biological cells.
Axiomatizing Causal Reasoning

Causal models defined in terms of a collection of equations, as defined by Pearl, are axiomatized here. Axiomatizations are provided for three successively more general classes of causal models: (1) the class of recursive theories (those without feedback), (2) the class of theories where the solutions to the equations are unique, (3) arbitrary theories (where the equations may not have solutions and, if they do, they are not necessarily unique). It is shown that to reason about causality in the most general third class, we must extend the language used by Galles and Pearl (1997, 1998). In addition, the complexity of the decision procedures is characterized for all the languages and classes of models considered.
Competitive Rumor Spread in Social Networks

We consider a setting in which two firms compete to spread rumors in a social network. Firms seed their rumors simultaneously and rumors propagate according to the linear threshold model. Consumers have (randomly drawn) heterogeneous thresholds for each product. Using the concept of cascade centrality introduced by , we provide a sharp characterization of networks in which games admit purestrategy Nash equilibria (PSNE). We provide tight bounds for the efficiency of these equilibria and for the inequality in firms' equilibrium payoffs. When the network is a tree, the model is particularly tractable.
DeMoN: Depth and Motion Network for Learning Monocular Stereo

In this paper we formulate structure from motion as a learning problem. We train a convolutional network end-to-end to compute depth and camera motion from successive, unconstrained image pairs. The architecture is composed of multiple stacked encoder-decoder networks, the core part being an iterative network that is able to improve its own predictions. The network estimates not only depth and motion, but additionally surface normals, optical flow between the images and confidence of the matching. A crucial component of the approach is a training loss based on spatial relative differences. Compared to traditional two-frame structure from motion methods, results are more accurate and more robust. In contrast to the popular depth-from-single-image networks, DeMoN learns the concept of matching and, thus, better generalizes to structures not seen during training.