nils-herrmann/clean_scirepeval huggingface dataset:
The 2014 General Video Game Playing Competition

This paper presents the framework, rules, games, controllers, and results of the first General Video Game Playing Competition, held at the IEEE Conference on Computational Intelligence and Games in 2014. The competition proposes the challenge of creating controllers for general video game play, where a single agent must be able to play many different games, some of them unknown to the participants at the time of submitting their entries. This test can be seen as an approximation of general artificial intelligence, as the amount of game-dependent heuristics needs to be severely limited. The games employed are stochastic real-time scenarios (where the time budget to provide the next action is measured in milliseconds) with different winning conditions, scoring mechanisms, sprite types, and available actions for the player. It is a responsibility of the agents to discover the mechanics of each game, the requirements to obtain a high score and the requisites to finally achieve victory. This paper describes all controllers submitted to the competition, with an in-depth description of four of them by their authors, including the winner and the runner-up entries of the contest. The paper also analyzes the performance of the different approaches submitted, and finally proposes future tracks for the competition.
A double-blind, placebo-controlled study of risperidone addition in serotonin reuptake inhibitor-refractory obsessive-compulsive disorder.

To date, only 1 controlled study has found a drug (haloperidol) to be efficacious in augmenting response in patients with obsessive-compulsive disorder (OCD) refractory to serotonin reuptake inhibitor (SRI) monotherapy; patients with comorbid chronic tic disorders showed a preferential response. This report describes the first controlled study of risperidone addition in patients with OCD refractory to treatment with SRI alone.



Seventy adult patients with a primary DSM-IV diagnosis of OCD received 12 weeks of treatment with an SRI. Thirty-six patients were refractory to the SRI and were randomized in a double-blind manner to 6 weeks of risperidone (n = 20) or placebo (n = 16) addition. Behavioral ratings, including the Yale-Brown Obsessive Compulsive Scale, were obtained at baseline and throughout the trial. Placebo-treated patients subsequently received an identical open-label trial of risperidone addition.



For study completers, 9 (50%) of 18 risperidone-treated patients were responders (mean daily dose, 2.2 +/-0.7 mg/d) compared with 0 of 15 in the placebo addition group (P<. 005). Seven (50%) of 14 patients who received open-label risperidone addition responded. Risperidone addition was superior to placebo in reducing OCD (P<.001), depressive (P<.001), and anxiety (P =.003) symptoms. There was no difference in response between OCD patients with and without comorbid diagnoses of chronic tic disorder or schizotypal personalty disorder. Other than mild, transient sedation, risperidone was well tolerated.



These results suggest that OCD patients with and without comorbid chronic tic disorders or schizotypal personality disorder may respond to the addition of low-dose risperidone to ongoing SRI therapy.
The What and How of Smart Personal Assistants: Principles and Application Domains for IS Research

Digitization brings new possibilities to ease our daily life activities by the means of assistive technology. Amazon Alexa, Microsoft Cortana, Samsung Bixby, to name only a few, heralded the age of smart personal assistants (SPAs), personified agents that combine artificial intelligence, machine learning, natural language processing and various actuation mechanisms to sense and influence the environment. However, SPA research seems to be highly fragmented among different disciplines, such as computer science, human-computer-interaction and information systems, which leads to ‘reinventing the wheel approaches’ and thus impede progress and conceptual clarity. In this paper, we present an exhaustive, integrative literature review to build a solid basis for future research. We have identified five functional principles and three research domains which appear promising for future research, especially in the information systems field. Hence, we contribute by providing a consolidated, integrated view on prior research and lay the foundation for an SPA classification scheme.
NLP and Machine Learning Techniques for Detecting Insulting Comments on Social Networking Platforms

In the era of social media and networking, the usage of bad words and aggressive words has been increased significantly. The young population is playing a major role in it. Cyberbullying affects more than half of the young population using social media. Insults in social media websites create negative interactions within the network. These remarks build up a culture of disrespect in cyberspace. Algorithms and tools used to understand and mitigate it are mostly inactive. Also, current implementations on insult detection using machine learning and natural language processing have very low recall rates. In short, the paper involves determining ways to identify bullying in text by analyzing and experimenting with different methods to find the feasible way of classifying such comments. We proposed a efficient algorithm to identify the bullying test and aggressive comments and analyses these comments to check the validity. NLP and Machine learning is used for analyzing the social comment and identified the aggressive effect of an individual or a group. An effective classifier acts as the core component in a final prototype system that can detect cyberbullying on social media.
Using Language Models for Information Retrieval

One, and two, and three police persons spring out of the shadows Down the corner comes one more And we scream into that city night: " three plus one makes four! " Well, they seem to think we're disturbing the peace But we won't let them make us sad 'Cause kids like you and me baby, we were born to add Born To Add, Sesame Street (sung to the tune of Bruce Springsteen's Born to Run) to Ursula Preface In October 1996, I got a position as a research assistant working on the Twenty-One project. The project aimed at providing a software architecture that supports a multilingual community of people working on local Agenda 21 initiatives in exchanging ideas and publishing their work. Local Agenda 21 initiatives are projects of local governments, aiming at sustainable processes in environmental , human, and economic terms. The projects cover themes like combating poverty, protecting the atmosphere, human health, freshwater resources, waste management, education, etc. Documentation on local Agenda 21 initiatives are usually written in the language of the local government, very much unlike documentation on research in e.g. information retrieval for which English is the language of international communication. Automatic cross-language retrieval systems are therefore a helpful tool in the international cooperation between local governments. Looking back, I regret not being more involved in the non-technical aspects of the Twenty-One project. To make up for this loss, many of the examples in this thesis are taken from the project's domain. Working on the Twenty-One project convinced me that solutions to cross-language information retrieval should explicitly combine translation models and retrieval models into one unifying framework. Working in a language technology group, the use of language models seemed a natural choice. A choice that simplifies things considerably for that matter. The use of language models for information retrieval practically reduces ranking to simply adding the occurrences of terms: complex weighting algorithms are no longer needed. " Born to add " is therefore the motto of this thesis. By adding out loud, it hopefully annoys-no offence, and with all due respect-some of the well-established information retrieval approaches, like Bruce Stringbean and The Sesame Street Band annoys the Sesame Street police. Acknowledgements The research presented in this thesis is funded in part by the European Union projects Twenty-One, Pop-Eye and Olive, and the Telematics Institute project Druid. I am most grateful to Wessel Kraaij of TNO-TPD …
School-based practice: acquiring the knowledge and skills.

The purpose of this descriptive study is to investigate the perceptions of occupational therapists working in school-based settings regarding their level of preparation for practice. The Survey of School-Based Practice was mailed to 1,102 occupational therapists working in school-based practice (SBP) throughout the United States. The mailing was based on a random sampling of 20% of the School System Special Interest Section of the American Occupational Therapy Association. Results were based on 450 returned completed surveys representing a response rate of 41%. The results were analyzed according to the respondents years of experience in SBP, entry (less than 3 years), transitioning from another practice setting with less than 1 year in SBP, and experienced with 3 or more years experience in SBP. The results indicated the majority of occupational therapists perceive themselves to be poorly prepared for this practice area based on entry-level education alone. Statistically significant differences were seen between continuing education needs and level of experience in SBP. Continuing education and mentoring were most often listed by therapists as preparing them for SBP Implications for the profession are discussed.
The Concepts of Loneliness and Social Isolation Loneliness

Loneliness is nowadays considered to be one of the main problems in society. The negative experience of a discrepancy between the desired and the achieved personal network of relationships is common and affects both younger and older adults. This chapter first addresses well-established aspects and new developments in the main concepts of loneliness and social isolation, the measuring instruments and the prevalence of loneliness. The chapter continues by an overview of theoretical ideas regarding loneliness, focusing on individuallevel and societal predisposing characteristics as well as on genetic/evolutionary perspectives on the onset and continuation of loneliness. The main part of the chapter is dedicated to empirical evidence from many sources and disciplines including psychology, sociology and epidemiological sciences. The prevention of loneliness, coping and interventions are addressed in the final part of the chapter. Other chapters in this volume address topics related to loneliness, namely social rejection, the neuroscience of social disconnection, social networks, and relationships and health (see Leary & Acosta, this volume; Cacioppo, this volume; Felmlee & Sinclair, this volume; and Holt-Lunstad, this volume).
Interacting with Financial Data using Natural Language

Financial and economic data are typically available in the form of tables and comprise mostly of monetary amounts, numeric and other domain-specific fields. They can be very hard to search and they are often made available out of context, or in forms which cannot be integrated with systems where text is required, such as voice-enabled devices. This work presents a novel system that enables both experts in the finance domain and non-expert users to search financial data with both keyword and natural language queries. Our system answers the queries with an automatically generated textual description using Natural Language Generation (NLG). The answers are further enriched with derived information, not explicitly asked in the user query, to provide the context of the answer. The system is designed to be flexible in order to accommodate new use cases without significant development effort, thus allowing fast integration of new datasets.
Dielectric waveguide with planar multi-mode excitation for high data-rate chip-to-chip interconnects

An all-electrical, low-cost, wideband chip-to-chip link on a multi-mode dielectric waveguide is proposed. The signal is coupled from the silicon chip to the fundamental and polarization-orthogonal degenerate Ex11 and Ey11 waveguide modes using planar electric and slot dipole antennas, respectively. This approach doubles the capacity of a single line without sacrificing robustness or adding implementation cost and complexity. Two independent ultra-wideband 30GHz channels, each from 90 GHz to 120 GHz, are demonstrated. The large available bandwidth will be channelized in frequency for optimal overall efficiency with a CMOS transceiver. Various design aspects of the structure are examined and discussed. The proposed waveguide offers a solution for Terabit-per-second (Tbps) electrical wireline links.
A Continuous Relaxation of Beam Search for End-to-End Training of Neural Sequence Models

Beam search is a desirable choice of test-time decoding algorithm for neural sequence models because it potentially avoids search errors made by simpler greedy methods. However, typical cross entropy training procedures for these models do not directly consider the behaviour of the final decoding method. As a result, for cross-entropy trained models, beam decoding can sometimes yield reduced test performance when compared with greedy decoding. In order to train models that can more effectively make use of beam search, we propose a new training procedure that focuses on the final loss metric (e.g. Hamming loss) evaluated on the output of beam search. While well-defined, this “direct loss” objective is itself discontinuous and thus difficult to optimize. Hence, in our approach, we form a sub-differentiable surrogate objective by introducing a novel continuous approximation of the beam search decoding procedure. In experiments, we show that optimizing this new training objective yields substantially better results on two sequence tasks (Named Entity Recognition and CCG Supertagging) when compared with both cross entropy trained greedy decoding and cross entropy trained beam decoding baselines.
Towards a Quality-centric Big Data Architecture for Federated Sensor Services

As the Internet of Things (IoT) paradigm gains popularity, the next few years will likely witness 'servitization' of domain sensing functionalities. We envision a cloud-based eco-system in which high quality data from large numbers of independently-managed sensors is shared or even traded in real-time. Such an eco-system will necessarily have multiple stakeholders such as sensor data providers, domain applications that utilize sensor data (data consumers), and cloud infrastructure providers who may collaborate as well as compete. While there has been considerable research on wireless sensor networks, the challenges involved in building cloud-based platforms for hosting sensor services are largely unexplored. In this paper, we present our vision for data quality (DQ)-centric big data infrastructure for federated sensor service clouds. We first motivate our work by providing real-world examples. We outline the key features that federated sensor service clouds need to possess. This paper proposes a big data architecture in which DQ is pervasive throughout the platform. Our architecture includes a markup language called SDQ-ML for describing sensor services as well as for domain applications to express their sensor feed requirements. The paper explores the advantages and limitations of current big data technologies in building various components of the platform. We also outline our initial ideas towards addressing the limitations.
Potree : Rendering Large Point Clouds in Web

This thesis introduces Potree, a web-based renderer for large point clouds. It allows users to view data sets with billions of points, from sources such as LIDAR or photogrammetry, in real time in standard web browsers. One of the main advantages of point cloud visualization in web browser is that it allows users to share their data sets with clients or the public without the need to install third-party applications and transfer huge amounts of data in advance. The focus on large point clouds, and a variety of measuring tools, also allows users to use Potree to look at, analyze and validate raw point cloud data, without the need for a time-intensive and potentially costly meshing step. The streaming and rendering of billions of points in web browsers, without the need to load large amounts of data in advance, is achieved with a hierarchical structure that stores subsamples of the original data at different resolutions. A low resolution is stored in the root node and with each level, the resolution gradually increases. The structure allows Potree to cull regions of the point cloud that are outside the view frustum, and to render distant regions at a lower level of detail. The result is an open source point cloud viewer, which was able to render point cloud data sets of up to 597 billion points, roughly 1.6 terabytes after compression, in real time in a web browser.
Impact of motivation principles on employee turnover

The article focuses on the analysis of motivation principles in human resource management and their further verification by factor analysis. The objective is to identify the main motivation principles and their impacts on employee turnover as well as formulate suggested practices to eliminate the negative impact of employee disaffection and turnover. The identification of motivation principles is based on a content analysis of professional and scientific publications aimed at motivation. The results and conclusions of this study were consequently verified by a quantitative survey, the data of which were statistically processed. As a suitable statistical analysis to assess the data from the survey, a factor analysis was chosen. The data for the factor analysis were collected and analysed based on two quantitative surveys focused on the causes of employee turnover. The results of both analyses proved and verified identical principles of employee management that affect job satisfaction and the decisions of employees to stay or leave their current job positions.
Statistical Learning of Syntax : The Role of Transitional Probability

Previous research has shown that, for learners to fully acquire a miniature phrase structure language, the language must contain cues to the phrases—for example, prosodic grouping or morphological agreement of the words within a phrase (Morgan, Meier, & Newport, 1987, 1989). Research on word segmentation has shown that learners can use transitional probabilities between syllables to segment speech into word-like units (Saffran, Aslin, & Newport, 1996). In the present research, we combine and extend these two sets of findings, asking whether learners can use transitional probabilities between words (or word classes) to segment sentences into phrases, and use this phrasal information to fully acquire the syntax of a miniature language. Adult subjects were exposed to sentences from a miniature language. A pattern in the transitional probabilities between words—high within phrases, low at phrase boundaries—was created by adding syntactic properties that are widespread in natural languages: optional phrases, repeated phrases, moved phrases, different-sized form classes, or all four properties combined. All conditions outperformed controls in learning the language. The best learning occurred with all properties combined, despite the fact that this language was the most complex. These data address the important question of how language learning is successful in the face of the massive complexity of natural languages. In our experiments, learning got better, not worse, when properly structured complexity was added to a language. The results also show that the same type of statistical computation useful in word segmentation might be used as well in learning syntax, suggesting that the range of statistics needed for acquiring various types of structure in natural languages might be suitably small. LANGUAGE LEARNING AND DEVELOPMENT, 3(1), 1–42
Influence Maximization in Online Social Networks

Starting with the earliest studies showing that the spread of new trends, information, and innovations is closely related to the social influence exerted on people by their social networks, the research on social influence theory took off, providing remarkable evidence on social influence induced viral phenomena. Fueled by the extreme popularity of online social networks and social media, computational social influence has emerged as a subfield of data mining whose goal is to analyze and optimize social influence using computational frameworks such as algorithm design and theoretical modeling. One of the fundamental problems in this field is the problem of influence maximization, primarily motivated by the application of viral marketing. The objective is to identify a small set of users in a social network who, when convinced to adopt a product, shall influence others in the network in a manner that leads to a large number of adoptions.
 In this tutorial, we extensively survey the research on social influence propagation and maximization, with a focus on the recent algorithmic and theoretical advances. To this end, we provide detailed reviews of the latest research effort devoted to (i) improving the efficiency and scalability of the influence maximization algorithms; (ii) context-aware modeling of the influence maximization problem to better capture real-world marketing scenarios; (iii) modeling and learning of real-world social influence; (iv) bridging the gap between social advertising and viral marketing.
Much ado about nothing: the misestimation and overinterpretation of violent video game effects in eastern and western nations: comment on Anderson et al. (2010).

The issue of violent video game influences on youth violence and aggression remains intensely debated in the scholarly literature and among the general public. Several recent meta-analyses, examining outcome measures most closely related to serious aggressive acts, found little evidence for a relationship between violent video games and aggression or violence. In a new meta-analysis, C. A. Anderson et al. (2010) questioned these findings. However, their analysis has several methodological issues that limit the interpretability of their results. In their analysis, C. A. Anderson et al. included many studies that do not relate well to serious aggression, an apparently biased sample of unpublished studies, and a "best practices" analysis that appears unreliable and does not consider the impact of unstandardized aggression measures on the inflation of effect size estimates. They also focused on bivariate correlations rather than better controlled estimates of effects. Despite a number of methodological flaws that all appear likely to inflate effect size estimates, the final estimate of r = .15 is still indicative of only weak effects. Contrasts between the claims of C. A. Anderson et al. (2010) and real-world data on youth violence are discussed.