nils-herrmann/clean_scirepeval huggingface dataset:
Heuristics for motion-based control in games

Gesturing and motion control have become common as interaction methods for video games since the advent of the Nintendo Wii game console. Despite the growing number of motion-based control platforms for video games, no set of shared design heuristics for motion control across the platforms has been published. Our approach in this paper combines analysis of player experiences across platforms. We work towards a collection of design heuristics for motion-based control by studying game reviews in two motion-based control platforms, Xbox 360 Kinect and PlayStation 3 Move. In this paper we present an analysis of player problems within 256 game reviews, on which we ground a set of heuristics for motion-controlled games.
A framework to explore innovation at SAP through bibliometric analysis of patent applications

Easily accessible patent databases and advances in technology have enabled the exploration of organizational innovation through the analysis of patent records. However, the textual content of patents presents obstacles to gleaning useful information. In this study, we develop an expert system framework that utilizes text and data mining procedures for analyzing innovation through textual patent data. Specifically, we use patent titles representing the innovation activity at one company (SAP) and perform a bibliometric analysis using our proposed framework. Enterprise software, of which SAP is a pioneering developer, must serve a wide assortment of functions for companies in many different industries. In addition, SAP’s sole focus is on enterprise software and it is a market leader in the category with substantial patent activity over the last decade. Using our framework to analyze SAP’s patent activity provides a demonstration of how our bibliometric analysis can summarize and identify trends in innovation in a large software company. Our results illustrate that SAP has a breadth of innovative activity spread over the three-tier software engineering architecture and a lack of topical repetition indicative of limited depth. SAP’s innovation is also seen to emphasize data management and quickly integrate emerging technologies. Results of an analysis on any company following our framework could be used for a variety of purposes, including: to examine the scope and scale of innovation of an organization, to examine the influence of technological trends on businesses, or to gain insight into corporate strategy that could be used to aid planning, investment, and purchasing decisions.
Evolving Construction Heuristics for the Symmetric Travelling Salesman Problem

The symmetric travelling salesman problem is a real world combinatorial optimization problem and a well researched domain. When solving combinatorial optimization problems such as the travelling salesman problem a low-level construction heuristic is usually used to create an initial solution, rather than randomly creating a solution, which is further optimized using techniques such as tabu search, simulated annealing and genetic algorithms, amongst others. These heuristics are usually manually derived by humans and this is a time consuming process requiring many man hours. The research presented in this paper forms part of a larger initiative aimed at automating the process of deriving construction heuristics for combinatorial optimization problems.
 The study investigates genetic programming to induce low-level construction heuristics for the symmetric travelling salesman problem. While this has been examined for other combinatorial optimization problems, to the authors' knowledge this is the first attempt at evolving low-level construction heuristics for the travelling salesman problem. In this study a generational genetic programming algorithm randomly creates an initial population of low-level construction heuristics which is iteratively refined over a set number of generations by the processes of fitness evaluation, selection of parents and application of genetic operators.
 The approach is tested on 23 problem instances, of varying problem characteristics, from the TSPLIB and VLSI benchmark sets. The evolved heuristics were found to perform better than the human derived heuristic, namely, the nearest neighbourhood heuristic, generally used to create initial solutions for the travelling salesman problem.
nisotropic geometrical-spreading correction or wide-azimuth P-wave reflections

Compensation for geometrical spreading along a raypath is one of the key steps in AVO amplitude-variation-with-offset analysis, in particular, for wide-azimuth surveys. Here, we propose an efficient methodology to correct long-spread, wide-azimuth reflection data for geometrical spreading in stratified azimuthally anisotropic media. The P-wave geometrical-spreading factor is expressed through the reflection traveltime described by a nonhyperbolic moveout equation that has the same form as in VTI transversely isotropic with a vertical symmetry axis media. The adapted VTI equation is parameterized by the normal-moveout NMO ellipse and the azimuthally varying anellipticity parameter . To estimate the moveout parameters, we apply a 3D nonhyperbolic semblance algorithm of Vasconcelos and Tsvankin that operates simultaneously with traces at all offsets and
Addiction and Depression , Anxiety and Stress

The purpose of this study is to examine the relationships between internet addiction and depression, anxiety, and stress. Participants were 300 university students who were enrolled in mid-size state University, in Turkey. In this study, the Online Cognition Scale and the Depression Anxiety Stress Scale were used. In correlation analysis, internet addiction was found positively related to depression, anxiety, and stress. According to path analysis results, depression, anxiety, and stress were predicted positively by internet addiction. This research shows that internet addiction has a direct impact on depression, anxiety, and stress.
Multi-armed bandits in metric spaces

In a multi-armed bandit problem, an online algorithm chooses from a set of strategies in a sequence of $n$ trials so as to maximize the total payoff of the chosen strategies. While the performance of bandit algorithms with a small finite strategy set is quite well understood, bandit problems with large strategy sets are still a topic of very active investigation, motivated by practical applications such as online auctions and web advertisement. The goal of such research is to identify broad and natural classes of strategy sets and payoff functions which enable the design of efficient solutions.
 In this work we study a very general setting for the multi-armed bandit problem in which the strategies form a metric space, and the payoff function satisfies a Lipschitz condition with respect to the metric. We refer to this problem as the "Lipschitz MAB problem". We present a complete solution for the multi-armed problem in this setting. That is, for every metric space (L,X) we define an isometry invariant Max Min COV(X) which bounds from below the performance of Lipschitz MAB algorithms for $X$, and we present an algorithm which comes arbitrarily close to meeting this bound. Furthermore, our technique gives even better results for benign payoff functions.
Identifying novel salt-tolerant genes from Dunaliella salina using a Haematococcus pluvialis expression system

Dunaliella salina (Dunal) Teod, a unicellular eukaryotic green alga, is a highly salt-tolerant organism. To identify novel genes with potential roles in salinity tolerance, a salt stress-induced D. salina cDNA library was screened based on the expression in Haematococcus pluvialis, an alga also from Volvocales but one that is hypersensitive to salt. Five novel salt-tolerant clones were obtained from the library. Among them, Ds-26-16 and Ds-A3-3 contained the same open reading frame (ORF) and encoded a 6.1 kDa protein. Transgenic tobacco overexpressing Ds-26-16 and Ds-A3-3 exhibited increased leaf area, stem height, root length, total chlorophyll, and glucose content, but decreased proline content, peroxidase activity, and ascorbate content, and enhanced transcript level of Na+/H+ antiporter salt overly sensitive 1 gene (NtSOS1) expression, compared to those in the control plants under salt condition, indicating that Ds-26-16 enhanced the salt tolerance of tobacco plants. The transcript of Ds-26-16 in D. salina was upregulated in response to salt stress. The expression of Ds-26-16 in Escherichia coli showed that the ORF contained the functional region and changed the protein(s) expression profile. A mass spectrometry assay suggested that the most abundant and smallest protein that changed is possibly a DNA-binding protein or Cold shock-like protein. Subcellular localization analysis revealed that Ds-26-16 was located in the nuclei of onion epidermal cells or nucleoid of E. coli cells. In addition, the possible use of shoots regenerated from leaf discs to quantify the salt tolerance of the transgene at the initial stage of tobacco transformation was also discussed.
The formation and design of the 'Acute Admission Database'- a database including a prospective, observational cohort of 6279 patients triaged in the emergency department in a larger Danish hospital

Management and care of the acutely ill patient has improved over the last years due to introduction of systematic assessment and accelerated treatment protocols. We have, however, sparse knowledge of the association between patient status at admission to hospital and patient outcome. A likely explanation is the difficulty in retrieving all relevant information from one database. The objective of this article was 1) to describe the formation and design of the 'Acute Admission Database', and 2) to characterize the cohort included.



All adult patients triaged at the Emergency Department at Hillerød Hospital and admitted either to the observationary unit or to a general ward in-hospital were prospectively included during a period of 22 weeks. The triage system used was a Danish adaptation of the Swedish triage system, ADAPT. Data from 3 different data sources was merged using a unique identifier, the Central Personal Registry number; 1) Data from patient admission; time and date, vital signs, presenting complaint and triage category, 2) Blood sample results taken at admission, including a venous acid-base status, and 3) Outcome measures, e.g. length of stay, admission to Intensive Care Unit, and mortality within 7 and 28 days after admission.



In primary triage, patients were categorized as red (4.4%), orange (25.2%), yellow (38.7%) and green (31.7%). Abnormal vital signs were present at admission in 25% of the patients, most often temperature (10.5%), saturation of peripheral oxygen (9.2%), Glasgow Coma Score (6.6%) and respiratory rate (4.8%). A venous acid-base status was obtained in 43% of all patients. The majority (78%) had a pH within the normal range (7.35-7.45), 15% had acidosis (pH < 7.35) and 7% had alkalosis (pH > 7.45). Median length of stay was 2 days (range 1-123). The proportion of patients admitted to Intensive Care Unit was 1.6% (95% CI 1.2-2.0), 1.8% (95% CI 1.5-2.2) died within 7 days, and 4.2% (95% CI 3.7-4.7) died within 28 days after admission.



Despite challenges of data registration, we succeeded in creating a database of adequate size and data quality. Future studies will focus on the association between patient status at admission and patient outcome, e.g. admission to Intensive Care Unit or in-hospital mortality.
Quantum Cellular Automata : The Physics of Computing with Arrays of Quantum Dot Molecules

We discuss the fundamental limits of computing using a new paradigm for quantum computation, cellular automata composed of arrays of Coulombically coupled quantum dot molecules, which we term quantum cellular automata (QCA). Any logical or arithmetic operation can be performed in this scheme. QCA’s provide a valuable concrete example of quantum computation in which a number of fundamental issues come to light. We examine the physics of the computing process in this paradigm. We show to what extent thermodynamic considerations impose limits on the ultimate size of individual QCA arrays. Adiabatic operation of the QCA is examined and the implications for dissipationless computing are explored.
Internet-Based Cognitive Behavior Therapy for Procrastination: Study Protocol for a Randomized Controlled Trial

Procrastination, to voluntarily delay an intended course of action despite expecting to be worse-off for the delay, is a persistent behavior pattern that can cause major psychological suffering. Approximately half of the student population and 15%-20% of the adult population are presumed having substantial difficulties due to chronic and recurrent procrastination in their everyday life. However, preconceptions and a lack of knowledge restrict the availability of adequate care. Cognitive behavior therapy (CBT) is often considered treatment of choice, although no clinical trials have previously been carried out.



The aim of this study will be to test the effects of CBT for procrastination, and to investigate whether it can be delivered via the Internet.



Participants will be recruited through advertisements in newspapers, other media, and the Internet. Only people residing in Sweden with access to the Internet and suffering from procrastination will be included in the study. A randomized controlled trial with a sample size of 150 participants divided into three groups will be utilized. The treatment group will consist of 50 participants receiving a 10-week CBT intervention with weekly therapist contact. A second treatment group with 50 participants receiving the same treatment, but without therapist contact, will also be employed. The intervention being used for the current study is derived from a self-help book for procrastination written by one of the authors (AR). It includes several CBT techniques commonly used for the treatment of procrastination (eg, behavioral activation, behavioral experiments, stimulus control, and psychoeducation on motivation and different work methods). A control group consisting of 50 participants on a wait-list control will be used to evaluate the effects of the CBT intervention. For ethical reasons, the participants in the control group will gain access to the same intervention following the 10-week treatment period, albeit without therapist contact.



The current study is believed to result in three important findings. First, a CBT intervention is assumed to be beneficial for people suffering from problems caused by procrastination. Second, the degree of therapist contact will have a positive effect on treatment outcome as procrastination can be partially explained as a self-regulatory failure. Third, an Internet based CBT intervention is presumed to be an effective way to administer treatment for procrastination, which is considered highly important, as the availability of adequate care is limited. The current study is therefore believed to render significant knowledge on the treatment of procrastination, as well as providing support for the use of Internet based CBT for difficulties due to delayed tasks and commitments.



To our knowledge, the current study is the first clinical trial to examine the effects of CBT for procrastination, and is assumed to render significant knowledge on the treatment of procrastination, as well as investigating whether it can be delivered via the Internet.


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A Layered Approach to People Detection in 3D Range Data

People tracking is a key technology for autonomous systems, intelligent cars and social robots operating in populated environments. What makes the task di fficult is that the appearance of humans in range data can change drastically as a function of body pose, distance to the sensor, self-occlusion and occlusion by other objects. In this paper we propose a novel approach to pedestrian detection in 3D range data based on supervised learning techniques to create a bank of classifiers for different height levels of the human body. In particular, our approach applies AdaBoost to train a strong classifier from geometrical and statistical features of groups of neighboring points at the same height. In a second step, the AdaBoost classifiers mutually enforce their evidence across di fferent heights by voting into a continuous space. Pedestrians are finally found efficiently by mean-shift search for local maxima in the voting space. Experimental results carried out with 3D laser range data illustrate the robustness and e fficiency of our approach even in cluttered urban environments. The learned people detector reaches a classification rate up to 96% from a single 3D scan.
Juniper: A Tree+Table Approach to Multivariate Graph Visualization

Analyzing large, multivariate graphs is an important problem in many domains, yet such graphs are challenging to visualize. In this paper, we introduce a novel, scalable, tree-table multivariate graph visualization technique, which makes many tasks related to multivariate graph analysis easier to achieve. The core principle we follow is to selectively query for nodes or subgraphs of interest and visualize these subgraphs as a spanning tree of the graph. The tree is laid out linearly, which enables us to juxtapose the nodes with a table visualization where diverse attributes can be shown. We also use this table as an adjacency matrix, so that the resulting technique is a hybrid node-link/adjacency matrix technique. We implement this concept in Juniper and complement it with a set of interaction techniques that enable analysts to dynamically grow, restructure, and aggregate the tree, as well as change the layout or show paths between nodes. We demonstrate the utility of our tool in usage scenarios for different multivariate networks: a bipartite network of scholars, papers, and citation metrics and a multitype network of story characters, places, books, etc.
The Expectation Maximization Algorithm A short tutorial

Revision history 2009-01-09 Corrected grammar in the paragraph which precedes Equation (17). Changed datestamp format in the revision history. 2008-07-05 Corrected caption for Figure (2). Added conditioning on θn for l in convergence discussion in Section (3.2). Changed email contact info to reduce spam. 2006-10-14 Added explanation and disambiguating parentheses in the development leading to Equation (14). Minor corrections. 2006-06-28 Added Figure (1). Corrected typo above Equation (5). Minor corrections. Added hyperlinks. 2005-08-26 Minor corrections. 2004-07-18 Initial revision.
Modified S-Band Satellite Antenna With Isoflux Pattern and Circularly Polarized Wide Beamwidth

A modified isoflux pattern antenna suitable for S-band satellite communication is introduced and investigated with respect to its beam pattern and axial ratios. For uniform coverage over the Earth's surface, an isoflux pattern antenna is required because of its uniform power density over a wide coverage area. A final optimization for the parameter values is carried out from the parametric studies of the antenna, and a simple analysis of the combination of horizontal dipoles and vertically attached top-hat elements is also carried out. The electrical performances of the proposed antenna are verified in terms of the reflection coefficients, radiation pattern, and axial ratios according to a wide beamwidth rather than at a fixed observation angle such as a 0° main beam direction.
Searchable Attribute-Based Mechanism With Efficient Data Sharing for Secure Cloud Storage

To date, the growth of electronic personal data leads to a trend that data owners prefer to remotely outsource their data to clouds for the enjoyment of the high-quality retrieval and storage service without worrying the burden of local data management and maintenance. However, secure share and search for the outsourced data is a formidable task, which may easily incur the leakage of sensitive personal information. Efficient data sharing and searching with security is of critical importance. This paper, for the first time, proposes a searchable attribute-based proxy reencryption system. When compared with the existing systems only supporting either searchable attribute-based functionality or attribute-based proxy reencryption, our new primitive supports both abilities and provides flexible keyword update service. In particular, the system enables a data owner to efficiently share his data to a specified group of users matching a sharing policy and meanwhile, the data will maintain its searchable property but also the corresponding search keyword(s) can be updated after the data sharing. The new mechanism is applicable to many real-world applications, such as electronic health record systems. It is also proved chosen ciphertext secure in the random oracle model.
Cerebellar and prefrontal cortex contributions to adaptation, strategies, and reinforcement learning.

Traditionally, motor learning has been studied as an implicit learning process, one in which movement errors are used to improve performance in a continuous, gradual manner. The cerebellum figures prominently in this literature given well-established ideas about the role of this system in error-based learning and the production of automatized skills. Recent developments have brought into focus the relevance of multiple learning mechanisms for sensorimotor learning. These include processes involving repetition, reinforcement learning, and strategy utilization. We examine these developments, considering their implications for understanding cerebellar function and how this structure interacts with other neural systems to support motor learning. Converging lines of evidence from behavioral, computational, and neuropsychological studies suggest a fundamental distinction between processes that use error information to improve action execution or action selection. While the cerebellum is clearly linked to the former, its role in the latter remains an open question.
CAGAN: Consistent Adversarial Training Enhanced GANs

Generative adversarial networks (GANs) have shown impressive results, however, the generator and the discriminator are optimized in finite parameter space which means their performance still need to be improved. In this paper, we propose a novel approach of adversarial training between one generator and an exponential number of critics which are sampled from the original discriminative neural network via dropout. As discrepancy between outputs of different sub-networks of a same sample can measure the consistency of these critics, we encourage the critics to be consistent to real samples and inconsistent to generated samples during training, while the generator is trained to generate consistent samples for different critics. Experimental results demonstrate that our method can obtain state-of-the-art Inception scores of 9.17 and 10.02 on supervised CIFAR-10 and unsupervised STL10 image generation tasks, respectively, as well as achieve competitive semi-supervised classification results on several benchmarks. Importantly, we demonstrate that our method can maintain stability in training and alleviate mode collapse.