nils-herrmann/clean_scirepeval huggingface dataset:
Automatic game tuning for strategic diversity

Finding the ideal game parameters is a common problem solved by game designers by manually tweaking game parameters. The aim is to ensure the desired gameplay outcomes for a specific game, a tedious process which could be alleviated through the use of Artificial Intelligence: using automatic game tuning. This paper presents an example of this process and introduces the concept of simulation based fitness evaluation focused on strategic diversity. A simple but effective Random Mutation Hill Climber algorithm is used to evolve a Zelda inspired game, by ensuring that agents using distinct heuristics are capable of achieving similar degrees of fitness. Two versions of the same game are presented to human players and their gameplay data is analyzed to identify whether they indeed find slightly more varied paths to the goal in the game evolved to be the more strategically diverse. Although the evolutionary process yields promising results, the human trials are unable to conclude a statistically significant difference between the two variants.
Evaluation of a Dry EEG System for Application of Passive Brain-Computer Interfaces in Autonomous Driving

We tested the applicability and signal quality of a 16 channel dry electroencephalography (EEG) system in a laboratory environment and in a car under controlled, realistic conditions. The aim of our investigation was an estimation how well a passive Brain-Computer Interface (pBCI) can work in an autonomous driving scenario. The evaluation considered speed and accuracy of self-applicability by an untrained person, quality of recorded EEG data, shifts of electrode positions on the head after driving-related movements, usability, and complexity of the system as such and wearing comfort over time. An experiment was conducted inside and outside of a stationary vehicle with running engine, air-conditioning, and muted radio. Signal quality was sufficient for standard EEG analysis in the time and frequency domain as well as for the use in pBCIs. While the influence of vehicle-induced interferences to data quality was insignificant, driving-related movements led to strong shifts in electrode positions. In general, the EEG system used allowed for a fast self-applicability of cap and electrodes. The assessed usability of the system was still acceptable while the wearing comfort decreased strongly over time due to friction and pressure to the head. From these results we conclude that the evaluated system should provide the essential requirements for an application in an autonomous driving context. Nevertheless, further refinement is suggested to reduce shifts of the system due to body movements and increase the headset's usability and wearing comfort.
A Tutorial on Deep Learning for Music Information Retrieval

Following their success in Computer Vision and other areas, deep learning techniques have recently become widely adopted in Music Information Retrieval (MIR) research. However, the majority of works aim to adopt and assess methods that have been shown to be effective in other domains, while there is still a great need for more original research focusing on music primarily and utilising musical knowledge and insight. The goal of this paper is to boost the interest of beginners by providing a comprehensive tutorial and reducing the barriers to entry into deep learning for MIR. We lay out the basic principles and review prominent works in this hard to navigate field. We then outline the network structures that have been successful in MIR problems and facilitate the selection of building blocks for the problems at hand. Finally, guidelines for new tasks and some advanced topics in deep learning are discussed to stimulate new research in this fascinating field.
Generating Natural Language Questions to Support Learning On-Line

When instructors prepare learning materials for students, they frequently develop accompanying questions to guide learning. Natural language processing technology can be used to automatically generate such questions but techniques used have not fully leveraged semantic information contained in the learning materials or the full context in which the question generation task occurs. We introduce a sophisticated template-based approach that incorporates semantic role labels into a system that automatically generates natural language questions to support online learning. While we have not yet incorporated the full learning context into our approach, our preliminary evaluation and evaluation methodology indicate our approach is a promising one for supporting learning.
Generalized Transfer Subspace Learning Through Low-Rank Constraint

It is expensive to obtain labeled real-world visual data for use in training of supervised algorithms. Therefore, it is valuable to leverage existing databases of labeled data. However, the data in the source databases is often obtained under conditions that differ from those in the new task. Transfer learning provides techniques for transferring learned knowledge from a source domain to a target domain by finding a mapping between them. In this paper, we discuss a method for projecting both source and target data to a generalized subspace where each target sample can be represented by some combination of source samples. By employing a low-rank constraint during this transfer, the structure of source and target domains are preserved. This approach has three benefits. First, good alignment between the domains is ensured through the use of only relevant data in some subspace of the source domain in reconstructing the data in the target domain. Second, the discriminative power of the source domain is naturally passed on to the target domain. Third, noisy information will be filtered out during knowledge transfer. Extensive experiments on synthetic data, and important computer vision problems such as face recognition application and visual domain adaptation for object recognition demonstrate the superiority of the proposed approach over the existing, well-established methods.
Radar Signal Processing

■ This article recounts the development of radar signal processing at Lincoln Laboratory. The Laboratory’s significant efforts in this field were initially driven by the need to provide detected and processed signals for air and ballistic missile defense systems. The first processing work was on the Semi-Automatic Ground Environment (SAGE) air-defense system, which led to algorithms and techniques for detection of aircraft in the presence of clutter. This work was quickly followed by processing efforts in ballistic missile defense, first in surface-acoustic-wave technology, in concurrence with the initiation of radar measurements at the Kwajalein Missile Range, and then by exploitation of the newly evolving technology of digital signal processing, which led to important contributions for ballistic missile defense and Federal Aviation Administration applications. More recently, the Laboratory has pursued the computationally challenging application of adaptive processing for the suppression of jamming and clutter signals. This article discusses several important programs in these areas.
Preserving Semantic Relations for Zero-Shot Learning

Zero-shot learning has gained popularity due to its potential to scale recognition models without requiring additional training data. This is usually achieved by associating categories with their semantic information like attributes. However, we believe that the potential offered by this paradigm is not yet fully exploited. In this work, we propose to utilize the structure of the space spanned by the attributes using a set of relations. We devise objective functions to preserve these relations in the embedding space, thereby inducing semanticity to the embedding space. Through extensive experimental evaluation on five benchmark datasets, we demonstrate that inducing semanticity to the embedding space is beneficial for zero-shot learning. The proposed approach outperforms the state-of-the-art on the standard zero-shot setting as well as the more realistic generalized zero-shot setting. We also demonstrate how the proposed approach can be useful for making approximate semantic inferences about an image belonging to a category for which attribute information is not available.
Evaluating Critical Security Issues of the IoT World: Present and Future Challenges

Social Internet of Things (SIoT) is a new paradigm where Internet of Things (IoT) merges with social networks, allowing people and devices to interact, and facilitating information sharing. However, security and privacy issues are a great challenge for IoT but they are also enabling factors to create a “trust ecosystem.” In fact, the intrinsic vulnerabilities of IoT devices, with limited resources and heterogeneous technologies, together with the lack of specifically designed IoT standards, represent a fertile ground for the expansion of specific cyber threats. In this paper, we try to bring order on the IoT security panorama providing a taxonomic analysis from the perspective of the three main key layers of the IoT system model: 1) perception; 2) transportation; and 3) application levels. As a result of the analysis, we will highlight the most critical issues with the aim of guiding future research directions.
Learning models for visual 3D localization with implicit mapping

We propose a formulation of visual localization that does not require construction of explicit maps in the form of point clouds or voxels. The goal is to learn an implicit representation of the environment at a higher, more abstract level, for instance that of objects. To study this approach we consider procedurally generated Minecraft worlds, for which we can generate visually rich images along with camera pose coordinates. We first show that Generative Query Networks (GQNs) enhanced with a novel attention mechanism can capture the visual structure of 3D scenes in Minecraft, as evidenced by their samples. We then apply the models to the localization problem, investigating both generative and discriminative approaches, and compare the different ways in which they each capture task uncertainty. Our results show that models with implicit mapping are able to capture the underlying 3D structure of visually complex scenes, and use this to accurately localize new observations, paving the way towards future applications in sequential localization.
The Effect of Brand Image and Product Knowledge on Purchase Intention Moderated by Price Discount

Most of previous studies either take product knowle dge as a moderator, or involve varying degree of product knowledge on the consumers’ perceived evalu ation as an influential factor. It is rare to come across research that discusses how both brand image and product knowledge affect purchase intention. Thus, this research has chosen both an intrinsic an d extrinsic product cue ─brand image and product knowledge─as independent variables while using price discount as a moderator and conducted research on purchase intention.
A Corpus for Research on Deliberation and Debate

Deliberative, argumentative discourse is an important component of opinion formation, belief revision, and knowledge discovery; it is a cornerstone of modern civil society. Argumentation is productively studied in branches ranging from theoretical artificial intelligence to political rhetoric, but empirical analysis has suffered from a lack of freely available, unscripted argumentative dialogs. This paper presents the Internet Argument Corpus (IAC), a set of 390, 704 posts in 11, 800 discussions extracted from the online debate site 4forums.com. A 2866 thread/130, 206 post extract of the corpus has been manually sided for topic of discussion, and subsets of this topic-labeled extract have been annotated for several dialogic and argumentative markers: degrees of agreement with a previous post, cordiality, audiencedirection, combativeness, assertiveness, emotionality of argumentation, and sarcasm. As an application of this resource, the paper closes with a discussion of the relationship between discourse marker pragmatics, agreement, emotionality, and sarcasm in the IAC corpus.
Physiological effects in aromatherapy

Hongratanaworakit, T. Physiological effects in aromatherapy Songklanakarin  2004, 26(1) : 117-125 The effects of aromas on humans are divided into physiological and psychological effects. The physiological effect acts directly on the physical organism, the psychological effect acts via the sense of smell or olfactory system, which in turn may cause a physiological effect. This paper reviews on the physiological effects which are used for the evaluation of the effects of aromas. Physiological parameters, i.e. heart rate blood pressure, electrodermal activity, electroencephalogram, slow potential brain waves (contingent negative variation), and eye blink rate or pupil functions, are used as indices for the measurement of the aroma effects
A plant-based diet for the prevention and treatment of type 2 diabetes

The prevalence of type 2 diabetes is rising worldwide, especially in older adults. Diet and lifestyle, particularly plant-based diets, are effective tools for type 2 diabetes prevention and management. Plant-based diets are eating patterns that emphasize legumes, whole grains, vegetables, fruits, nuts, and seeds and discourage most or all animal products. Cohort studies strongly support the role of plant-based diets, and food and nutrient components of plant-based diets, in reducing the risk of type 2 diabetes. Evidence from observational and interventional studies demonstrates the benefits of plant-based diets in treating type 2 diabetes and reducing key diabetes-related macrovascular and microvascular complications. Optimal macronutrient ratios for preventing and treating type 2 diabetes are controversial; the focus should instead be on eating patterns and actual foods. However, the evidence does suggest that the type and source of carbohydrate (unrefined versus refined), fats (monounsaturated and polyunsaturated versus saturated and trans), and protein (plant versus animal) play a major role in the prevention and management of type 2 diabetes. Multiple potential mechanisms underlie the benefits of a plant-based diet in ameliorating insulin resistance, including promotion of a healthy body weight, increases in fiber and phytonutrients, food-microbiome interactions, and decreases in saturated fat, advanced glycation endproducts, nitrosamines, and heme iron.
Feature Selection as Causal Inference: Experiments with Text Classification

PEOPLE TEXT Subject Document Treatment Word Outcome Class label CAUSALITY AND PROPENSITY SCORE MATCHING Classifiers learn associations between features and classes. For a variety of reasons, these associations can be noisy and misleading. This work uses causal inference methods to learn more accurate feature associations. One goal of presenting these ideas is to generate new ideas for how to incorporate these techniques into NLP methods. LEARNING BETTER WORD-­‐CLASS ASSOCIATIONS
Learning from Failures in the Sharing Economy

Fueled by the popularity of firms such as Airbnb and Uber, the sharing economy, otherwise referred to as the collaborative economy or peer economy, has recently gained increasing attention among practitioners and academics. In the sharing economy,3 new ventures develop and deploy digital platforms to enable peer-to-peer sharing of goods, services and information. The underlying proposition of sharing economy firms is that they can add value by allowing owners of resources to make their idle personal assets (e.g., rooms or homes) available to those who need them (e.g., travelers).4 As such, sharing economy firms are direct alternatives to established businesses (e.g., hotels). The resource optimization offered by these firms has become possible through recent technological advances in search, rating and matching algorithms, the spread of mobile consumer devices and the explosive growth in the use of
LSTM Hypertagging

Hypertagging, or supertagging for surface realization, is the process of assigning lexical categories to nodes in an input semantic graph. Previous work has shown that hypertagging significantly increases realization speed and quality by reducing the search space of the realizer. Building on recent work using LSTMs to improve accuracy on supertagging for parsing, we develop an LSTM hypertagging method for OpenCCG, an open source NLP toolkit for CCG. Our results show significant improvements in both hypertagging accuracy and downstream realization performance.
A systematic method to create search strategies for emerging technologies based on the Web of Science: illustrated for ‘Big Data’

Bibliometric and “tech mining” studies depend on a crucial foundation—the search strategy used to retrieve relevant research publication records. Database searches for emerging technologies can be problematic in many respects, for example the rapid evolution of terminology, the use of common phraseology, or the extent of “legacy technology” terminology. Searching on such legacy terms may or may not pick up R&D pertaining to the emerging technology of interest. A challenge is to assess the relevance of legacy terminology in building an effective search model. Common-usage phraseology additionally confounds certain domains in which broader managerial, public interest, or other considerations are prominent. In contrast, searching for highly technical topics is relatively straightforward. In setting forth to analyze “Big Data,” we confront all three challenges—emerging terminology, common usage phrasing, and intersecting legacy technologies. In response, we have devised a systematic methodology to help identify research relating to Big Data. This methodology uses complementary search approaches, starting with a Boolean search model and subsequently employs contingency term sets to further refine the selection. The four search approaches considered are: (1) core lexical query, (2) expanded lexical query, (3) specialized journal search, and (4) cited reference analysis. Of special note here is the use of a “Hit-Ratio” that helps distinguish Big Data elements from less relevant legacy technology terms. We believe that such a systematic search development positions us to do meaningful analyses of Big Data research patterns, connections, and trajectories. Moreover, we suggest that such a systematic search approach can help formulate more replicable searches with high recall and satisfactory precision for other emerging technology studies.
The environmental factor in migration dynamics – a review of African case studies

Claims that climate change will shape the future of global migration are continuously being made in academia as well as popular and policy circles. This paper questions the empirical basis for such claims, drawing on a critical review of 13 case studies of environmentally induced migration in the Sahel and the wider migration and development literature. It highlights some of the conceptual and methodological flaws that recur in many of these studies. First, their terminology is often confused, with concepts such as environment and climate, change and variability being conflated. Second, some do not acknowledge the extreme climate variability and unstable environments that are the norm for many Sahelian people; in this context, mobility can be a successful coping mechanism, potentially reducing environmental stress. Third, the paper criticises the use of static push-pull frameworks which suggest that migrants are being ‘pushed out’ of marginal and degraded environments, neglecting the intertwined environmental, political, economic and cultural factors. Fourth, the paper highlights flaws in the sampling and questionnaires used, particularly in some of the more recent studies. In conclusion, the paper calls for more open research that explores the complex inter-relationship between environmental factors and mobility rather than starting from the assumption of a simplistic causal relationship.
An Investigation of Convolution Attention Based Models for Multilingual Speech Synthesis of Indian Languages

In this paper we investigate multi-speaker, multi-lingual speech synthesis for 4 Indic languages (Hindi, Marathi, Gujarathi, Bengali) as well as English in a fully convolutional attention based model. We show how factored embeddings can allow cross lingual transfer, and investigate methods to adapt the model in a low resource scenario for the case of Marathi and Gujarati. We also show results on how effectively the model scales to a new language and how much data is required to train the system on a new language.
Face Recognition Based on Fitting a 3D Morphable Model

This paper presents a method for face recognition across variations in pose, ranging from frontal to profile views, and across a wide range of illuminations, including cast shadows and specular reflections. To account for these variations, the algorithm simulates the process of image formation in 3D space, using computer graphics, and it estimates 3D shape and texture of faces from single images. The estimate is achieved by fitting a statistical, morphable model of 3D faces to images. The model is learned from a set of textured 3D scans of heads. We describe the construction of the morphable model, an algorithm to fit the model to images, and a framework for face identification. In this framework, faces are represented by model parameters for 3D shape and texture. We present results obtained with 4,488 images from the publicly available CMU-PIE database and 1,940 images from the FERET database.
Aspects of root infinitives *

This paper discusses the phenomenon of root infinitives (RIs) in child language, focussing on a distributional restriction on the verbs that occur in this construction, viz. event-denoting verbs, as well as on a related aspect of interpretation, viz. that RIs receive modal interpretations. The modality of the construction is traced to the infinitival morphology, while the eventivity restriction is derived from the modal meaning. In contrast, the English bare form, which is often taken to instantiate the RI-phenomenon, does not seem to be subject to the eventivity constraint, nor do we find a modal reference effect. This confirms the analysis, which traces these to the infinitival morphology itself, which is absent in English. The approach not only provides a precise characterization of the distribution of the RI-phenomenon within and across languages; it also explains differences between the English bare form phenomenon and the RI-construction in languages with genuine infinitives by reference to the morphosyntax of the languages involved. The fact that children appear to be sensitive to these distinctions in the target systems at such an early age supports the general thesis of Early Morphosyntactic Convergence, which the authors argue is a pervasive property of the acquisition process.  Syntax; Acquisition; Root infinitives; Eventivity; Modality