nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Mood Classification for Music

Sound of their breath fades with the light. I think about the loveless fascination, Under the milky way tonight. Lower the curtain down in memphis, Lower the curtain down all right. I got no time for private consultation, Under the milky way tonight. Wish I knew what you were looking for. Might have known what you would find. And it's something quite peculiar, Something thats shimmering and white. It leads you here despite your destination, Under the milky way tonight (chorus) Preface This Master's Thesis concludes my studies in Human Aspects of Information Technology (HAIT) at Tilburg University. It describes the development, implementation, and analysis of an automatic mood classifier for music. I would like to thank those who have contributed to and supported the contents of the thesis. Special thanks goes to my supervisor Menno van Zaanen for his dedication and support during the entire process of getting started up to the final results. Moreover, I would like to express my appreciation to Fredrik Mjelle for providing the user-tagged instances exported out of the MOODY database, which was used as the dataset for the experiments. Furthermore, I would like to thank Toine Bogers for pointing me out useful website links regarding music mood classification and sending me papers with citations and references. I would also like to thank Michael Voong for sending me his papers on music mood classification research, Jaap van den Herik for his support and structuring of my writing and thinking. I would like to recognise Eric Postma and Marieke van Erp for their time assessing the thesis as members of the examination committee. Finally, I would like to express my gratitude to my family for their enduring support.  This research presents the outcomes of research into using the lingual part of music for building an automatic mood classification system. Using a database consisting of extracted lyrics and user-tagged mood attachments, we built a classifier based on machine learning techniques. By testing the classification system on various mood frameworks (or dimensions) we examined to what extent it is possible to attach mood tags automatically to songs based on lyrics only. Furthermore, we examined to what extent the linguistic part of music revealed adequate information for assigning a mood category and which aspects of mood can be classified best. Our results show that the use of term frequencies and tf*idf values provide a valuable source of …
Building Consistent Transactions with Inconsistent Replication

Application programmers increasingly prefer distributed storage systems with strong consistency and distributed transactions (e.g., Google's Spanner) for their strong guarantees and ease of use. Unfortunately, existing transactional storage systems are expensive to use -- in part because they require costly replication protocols, like Paxos, for fault tolerance. In this paper, we present a new approach that makes transactional storage systems more affordable: we eliminate consistency from the replication protocol while still providing distributed transactions with strong consistency to applications.
 We present TAPIR -- the Transactional Application Protocol for Inconsistent Replication -- the first transaction protocol to use a novel replication protocol, called inconsistent replication, that provides fault tolerance without consistency. By enforcing strong consistency only in the transaction protocol, TAPIR can commit transactions in a single round-trip and order distributed transactions without centralized coordination. We demonstrate the use of TAPIR in a transactional key-value store, TAPIR-KV. Compared to conventional systems, TAPIR-KV provides better latency and throughput.
Transfer representation learning for medical image analysis

There are two major challenges to overcome when developing a classifier to perform automatic disease diagnosis. First, the amount of labeled medical data is typically very limited, and a classifier cannot be effectively trained to attain high disease-detection accuracy. Second, medical domain knowledge is required to identify representative features in data for detecting a target disease. Most computer scientists and statisticians do not have such domain knowledge. In this work, we show that employing transfer learning can remedy both problems. We use Otitis Media (OM) to conduct our case study. Instead of using domain knowledge to extract features from labeled OM images, we construct features based on a dataset entirely OM-irrelevant. More specifically, we first learn a codebook in an unsupervised way from 15 million images collected from ImageNet. The codebook gives us what the encoders consider being the fundamental elements of those 15 million images. We then encode OM images using the codebook and obtain a weighting vector for each OM image. Using the resulting weighting vectors as the feature vectors of the OM images, we employ a traditional supervised learning algorithm to train an OM classifier. The achieved detection accuracy is 88.5% (89.63% in sensitivity and 86.9% in specificity), markedly higher than all previous attempts, which relied on domain experts to help extract features.
Biomass for Renewable Energy and Fuels

barrels of oil equivalent (boe) The total energy content of a non-petroleum-based product or fuel in GJ divided by 5.904 6.115 GJ/boe. biodiesel The methyl or ethyl esters of transesterified triglycerides (lipids, fats, cooking greases) from biomass. biofuel A solid, gaseous, or liquid fuel produced from biomass. biogas A medium-energy-content gaseous fuel, generally containing 40 to 80 volume percent methane, produced from biomass by methane fermentation (anaerobic digestion). biomass All non-fossil-based living or dead organisms and organic materials that have an intrinsic chemical energy content. biorefinery A processing plant for converting waste and virgin biomass feedstocks to energy, fuels, and other products. gasohol A blend of 10 volume percent ethanol and 90 volume percent gasoline. independent power producer (IPP) A nonutility generator of electricity, usually produced in a small capacity plant or industrial facility. integrated biomass production conversion system (IBPCS) A system in which all operations concerned with the production of virgin biomass feedstocks and their conversion to energy, fuels, or chemicals are integrated. landfill gas (LFG) Amedium-energy-content fuel gas high in methane and carbon dioxide produced by landfills that contain municipal solid wastes and other waste biomass. methyl t-butyl ether (MTBE) An organic compound used as an oxygenate and octane-enhancing additive in motor gasolines. oxygenated gasoline Gasolines that contain soluble oxygen-containing organic compounds such as fuel ethanol and MTBE. quad One quad is 10 (1 quadrillion) Btu. refuse-derived fuel (RDF) The combustible portion of municipal solid wastes. tonnes of oil equivalent (toe) The total energy content of a non-petroleum-based product or fuel in GJ divided by 43.395 44.945 GJ/toe.
The origin and evolution of religious prosociality.

We examine empirical evidence for religious prosociality, the hypothesis that religions facilitate costly behaviors that benefit other people. Although sociological surveys reveal an association between self-reports of religiosity and prosociality, experiments measuring religiosity and actual prosocial behavior suggest that this association emerges primarily in contexts where reputational concerns are heightened. Experimentally induced religious thoughts reduce rates of cheating and increase altruistic behavior among anonymous strangers. Experiments demonstrate an association between apparent profession of religious devotion and greater trust. Cross-cultural evidence suggests an association between the cultural presence of morally concerned deities and large group size in humans. We synthesize converging evidence from various fields for religious prosociality, address its specific boundary conditions, and point to unresolved questions and novel predictions.
Solving the accuracy-diversity dilemma via directed random walks

Random walks have been successfully used to measure user or object similarities in collaborative filtering (CF) recommender systems, which is of high accuracy but low diversity. A key challenge of a CF system is that the reliably accurate results are obtained with the help of peers' recommendation, but the most useful individual recommendations are hard to be found among diverse niche objects. In this paper we investigate the direction effect of the random walk on user similarity measurements and find that the user similarity, calculated by directed random walks, is reverse to the initial node's degree. Since the ratio of small-degree users to large-degree users is very large in real data sets, the large-degree users' selections are recommended extensively by traditional CF algorithms. By tuning the user similarity direction from neighbors to the target user, we introduce a new algorithm specifically to address the challenge of diversity of CF and show how it can be used to solve the accuracy-diversity dilemma. Without relying on any context-specific information, we are able to obtain accurate and diverse recommendations, which outperforms the state-of-the-art CF methods. This work suggests that the random-walk direction is an important factor to improve the personalized recommendation performance.
One-Shot Visual Imitation Learning via Meta-Learning

In order for a robot to be a generalist that can perform a wide range of jobs, it must be able to acquire a wide variety of skills quickly and efficiently in complex unstructured environments. High-capacity models such as deep neural networks can enable a robot to represent complex skills, but learning each skill from scratch then becomes infeasible. In this work, we present a meta-imitation learning method that enables a robot to learn how to learn more efficiently, allowing it to acquire new skills from just a single demonstration. Unlike prior methods for one-shot imitation, our method can scale to raw pixel inputs and requires data from significantly fewer prior tasks for effective learning of new skills. Our experiments on both simulated and real robot platforms demonstrate the ability to learn new tasks, end-to-end, from a single visual demonstration.
Image registration by local histogram matching

We previously presented an image registration method, referred to hierarchical attribute matching mechanism for elastic registration (HAMMER), which demonstrated relatively high accuracy in inter-subject registration of MR brain images. However, the HAMMER algorithm requires the pre-segmentation of brain tissues, since the attribute vectors used to hierarchically match the corresponding pairs of points are defined from the segmented image. In many applications, the segmentation of tissues might be difficult, unreliable or even impossible to complete, which potentially limits the use of the HAMMER algorithm in more generalized applications. To overcome this limitation, we have used local spatial intensity histograms to design a new type of attribute vector for each point in an intensity image. The histogram-based attribute vector is rotationally invariant, and importantly it also captures spatial information by integrating a number of local intensity histograms from multi-resolution images of original intensity image. The new attribute vectors are able to determine the corresponding points across individual images. Therefore, by hierarchically matching new attribute vectors, the proposed method can perform as successfully as the previous HAMMER algorithm did in registering MR brain images, while providing more generalized applications in registering images of various organs. Experimental results show good performance of the proposed method in registering MR brain images, DTI brain images, CT pelvis images, and MR mouse images. 2006 Pattern Recognition Society.
A Memory-Augmented Neural Model for Automated Grading

The need for automated grading tools for essay writing and open-ended assignments has received increasing attention due to the unprecedented scale of Massive Online Courses (MOOCs) and the fact that more and more students are relying on computers to complete and submit their school work. In this paper, we propose an efficient memory networks-powered automated grading model. The idea of our model stems from the philosophy that with enough graded samples for each score in the rubric, such samples can be used to grade future work that is found to be similar. For each possible score in the rubric, a student response graded with the same score is collected. These selected responses represent the grading criteria specified in the rubric and are stored in the memory component. Our model learns to predict a score for an ungraded response by computing the relevance between the ungraded response and each selected response in memory. The evaluation was conducted on the Kaggle Automated Student Assessment Prize (ASAP) dataset. The results show that our model achieves state-of-the-art performance in 7 out of 8 essay sets.
Estimating Depth From Monocular Images as Classification Using Deep Fully Convolutional Residual Networks

Depth estimation from single monocular images is a key component in scene understanding. Most existing algorithms formulate depth estimation as a regression problem due to the continuous property of depths. However, the depth value of input data can hardly be regressed exactly to the ground-truth value. In this paper, we propose to formulate depth estimation as a pixelwise classification task. Specifically, we first discretize the continuous ground-truth depths into several bins and label the bins according to their depth ranges. Then, we solve the depth estimation problem as classification by training a fully convolutional deep residual network. Compared with estimating the exact depth of a single point, it is easier to estimate its depth range. More importantly, by performing depth classification instead of regression, we can easily obtain the confidence of a depth prediction in the form of probability distribution. With this confidence, we can apply an information gain loss to make use of the predictions that are close to ground-truth during training, as well as fully-connected conditional random fields for post-processing to further improve the performance. We test our proposed method on both indoor and outdoor benchmark RGB-Depth datasets and achieve state-of-the-art performance.
Causal Effects in Nonexperimental Studies : Reevaluating the Evaluation of Training Programs

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Illusory conjunctions in the perception of objects

In perceiving objects we may synthesize conjunctions of separable features by directing attention serially to each item in turn (A. Treisman and G. Gelade, Cognitive Psychology, 1980, 12, 97136). This feature-integration theory predicts that when attention is diverted or overloaded, features may be wrongly recombined, giving rise to “illusory conjunctions.” The present paper confirms that illusory conjunctions are frequently experienced among unattended stimuli varying in color and shape, and that they occur also with size and solidity (outlined versus filled-in shapes). They are shown both in verbal recall and in simultaneous and successive matching tasks, making it unlikely that they depend on verbal labeling or on memory failure. They occur as often between stimuli differing on many features as between more similar stimuli, and spatial separation has little effect on their frequency. Each feature seems to be coded as an independent entity and to migrate, when attention is diverted, with few constraints from the other features of its source or destination.
Reflective Informatics: Conceptual Dimensions for Designing Technologies of Reflection

Despite demonstrated interest in designing for reflection, relatively little work provides a detailed explication of what exactly is meant by reflection or how to design around it. This paper fills that gap by reviewing and engaging with conceptual and theoretical models of reflection, organized by the disciplinary and epistemological perspectives each embodies. Synthesizing across this theoretical background, the paper identifies three dimensions of reflection: breakdown, inquiry, and transformation. Together, these dimensions serve as the foundation for reflective informatics, a conceptual approach that helps bring clarity and guidance to the discussion of designing for reflection. The paper distinguishes reflective informatics by demonstrating how it both differs from and complements existing related work. Finally, the paper provides a critically reflexive consideration of its own latent assumptions, especially about the value of reflection, and how they might impact work on designing for reflection.
An approach to position sensorless drive for brushless DC motors

A brushless DC motor without a position sensor is described. Variable speed is achieved by adjusting the average motor voltage, just like chopper control of DC motors. The position-sensorless drive proposed is based on detection of the conducting interval of free wheeling diodes connected in antiparallel with power transistors. This approach makes it possible to detect the rotor position over a wide speed range, especially at a lower speed. Experimental results obtained from a prototype brushless DC motor with a rating of 300 W are shown to confirm the validity of the sensorless drive from 45 to 2300 r/min. The starting procedure of the motor is also discussed because it is impossible to detect the rotor position at a standstill.<<ETX>>
DisSent: Sentence Representation Learning from Explicit Discourse Relations

Sentence vectors represent an appealing approach to meaning: learn an embedding that encompasses the meaning of a sentence in a single vector, that can be used for a variety of semantic tasks. Existing models for learning sentence embeddings either require extensive computational resources to train on large corpora, or are trained on costly, manually curated datasets of sentence relations. We observe that humans naturally annotate the relations between their sentences with discourse markers like “but” and “because”. These words are deeply linked to the meanings of the sentences they connect. Using this natural signal, we automatically collect a classification dataset from unannotated text. We evaluate our sentence embeddings on a variety of transfer tasks, including discourse-related tasks using Penn Discourse Treebank. We demonstrate that training a model to predict discourse markers yields high quality sentence embeddings.
Capturing and Rendering with Incident Light Fields

This paper presents a process for capturing spatially and directionally varying illumination from a real-world scene and using this lighting to illuminate computer-generated objects. We use two devices for capturing such illumination. In the first we photograph an array of mirrored spheres in high dynamic range to capture the spatially varying illumination. In the second, we obtain higher resolution data by capturing images with an high dynamic range omnidirectional camera as it traverses across a plane. For both methods we apply the light field technique to extrapolate the incident illumination to a volume. We render computer-generated objects as illuminated by this captured illumination using a custom shader within an existing global illumination rendering system. To demonstrate our technique we capture several spatially-varying lighting environments with spotlights, shadows, and dappled lighting and use them to illuminate synthetic scenes. We also show comparisons to real objects under the same illumination.
Real-time anomaly detection over VMware performance data using storm

Anomaly detection is the identification of items or observations which deviate from an expected pattern in a dataset. This paper proposes a novel real time anomaly detection framework for dynamic resource scheduling of a VMware-based cloud data center. The framework monitors VMware performance stream data (e.g. CPU load, memory usage, etc.). Hence, the framework continuously needs to collect data and make decision without any delay. We have used Apache Storm, distributed framework for handling performance stream data and making prediction without any delay. Storm is chosen over a traditional distributed framework (e.g., Hadoop and MapReduce, Mahout) that is good for batch processing. An incremental clustering algorithm to model benign characteristics is incorporated in our storm-based framework. During continuous incoming test stream, if the model finds data deviated from its benign behavior, it considers that as an anomaly. We have shown effectiveness of our framework by providing real-time complex analytic functionality over stream data.
Artifact-free High Dynamic Range imaging

The contrast in real world scenes is often beyond what consumer cameras can capture. For these situations, High Dynamic Range (HDR) images can be generated by taking multiple exposures of the same scene. When fusing information from different images, however, the slightest change in the scene can generate artifacts which dramatically limit the potential of this solution. We present a technique capable of dealing with a large amount of movement in the scene: we find, in all the available exposures, patches consistent with a reference image previously selected from the stack. We generate the HDR image by averaging the radiance estimates of all such regions and we compensate for camera calibration errors by removing potential seams. We show that our method works even in cases when many moving objects cover large regions of the scene.
First-Orderized ResearchCyc : Expressivity and Efficiency in a Common-Sense Ontology

Cyc is the largest existing common-sense knowledge base. It s ontology makes heavy use of higher-order logic constructs such as a context system, first class predicates, etc. Many of these higher-order constructs are believed to be key to Cyc’s ability to represent common-sense knowledge and reason with it efficiently. In this paper, we present a translati on of a large part (around 90%) of the Cyc ontology into FirstOrder Logic. We discuss our methodology, and the tradeoffs between expressivity and efficiency in representation and reasoning. We also present the results of experiments using VAMPIRE, SPASS, and the E Theorem Prover on the firstorderized Cyc KB. Our results indicate that, while the use of higher-order logic is not essential to the representabilit y of common-sense knowledge, it greatly improves the efficiency of reasoning.
Design of Leakage Inductance in Resonant DC-DC Converter for Electric Vehicle Charger

In this paper, the electric charger with single phase PWM converter and a bidirectional half-bridge converter is proposed. A transformer leakage inductance is used for making resonance at the resonant converter, which can make switched current to be sinusoidal without extra inductive component. Both operating principle and design guideline are described in detail. The feasibility for the proposed design is verified through hardware implementation and the experimental results. Detailed results have been obtained to show several advantages such as the compact design and reduced switching losses for different values and conditions of leakage inductance and resonance cases.