nils-herrmann/clean_scirepeval huggingface dataset:
Designing Attractive Gamification Features for Collaborative Storytelling Websites

Gamification design is considered as the predictor of collaborative storytelling websites' success. Although aforementioned studies have mentioned a broad range of factors that may influence gamification, they neither depicted the actual design features nor relative attractiveness among them. This study aims to identify attractive gamification features for collaborative storytelling websites. We first constructed a hierarchical system structure of gamification design of collaborative storytelling websites and conducted a focus group interview with eighteen frequent users to identify 35gamification features. After that, this study determined the relative attractiveness of these gamification features by administrating an online survey to 6333 collaborative storytelling websites users. The results indicated that the top 10 most attractive gamification features could account for more than 50% of attractiveness among these 35 gamification features. The feature of unpredictable time pressure is important to website users, yet not revealed in previous relevant studies. Implications of the findings were discussed.
Improving multi-label classification using scene cues

Multi-label classification is one of the most challenging tasks in the computer vision community, owing to different composition and interaction (e.g. partial visibility or occlusion) between objects in multi-label images. Intuitively, some objects usually co-occur with some specific scenes, e.g. the sofa often appears in a living room. Therefore, the scene of a given image may provides informative cues for identifying those embedded objects. In this paper, we propose a novel scene-aware deep framework for addressing the challenging multi-label classification task. In particular, we incorporate two sub-networks that are pre-trained for different tasks (i.e. object classification and scene classification) into a unified framework, so that informative scene-aware cues can be leveraged for benefiting multi-label object classification. In addition, we also present a novel one vs. all multiple-cross-entropy (MCE) loss for optimizing the proposed scene-aware deep framework by independently penalizing the classification error for each label. The proposed method can be learned in an end-to-end manner and extensive experimental results on Pascal VOC 2007 and MS COCO demonstrate that our approach is able to make a noticeable improvement for the multi-label classification task.
Measuring the web crawler ethics

Web crawlers are highly automated and seldom regulated manually. The diversity of crawler activities often leads to ethical problems such as spam and service attacks. In this research, quantitative models are proposed to measure the web crawler ethics based on their behaviors on web servers. We investigate and define rules to measure crawler ethics, referring to the extent to which web crawlers respect the regulations set forth in robots.txt configuration files. We propose a vector space model to represent crawler behavior and measure the ethics of web crawlers based on the behavior vectors. The results show that ethicality scores vary significantly among crawlers. Most commercial web crawlers' behaviors are ethical. However, many commercial crawlers still consistently violate or misinterpret certain robots.txt rules. We also measure the ethics of big search engine crawlers in terms of return on investment. The results show that Google has a higher score than other search engines for a US website but has a lower score than Baidu for Chinese websites.
Glyph-based Visualization: Foundations, Design Guidelines, Techniques and Applications

This state of the art report focuses on glyph-based visualization, a common form of visual design where a data set is depicted by a collection of visual objects referred to as glyphs. Its major strength is that patterns of multivariate data involving more than two attribute dimensions can often be more readily perceived in the context of a spatial relationship, whereas many techniques for spatial data such as direct volume rendering find difficult to depict with multivariate or multi-field data, and many techniques for non-spatial data such as parallel coordinates are less able to convey spatial relationships encoded in the data. This report fills several major gaps in the literature, drawing the link between the fundamental concepts in semiotics and the broad spectrum of glyph-based visualization, reviewing existing design guidelines and implementation techniques, and surveying the use of glyph-based visualization in many applications.
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation

Recent work has shown that optical flow estimation can be formulated as a supervised learning task and can be successfully solved with convolutional networks. Training of the so-called FlowNet was enabled by a large synthetically generated dataset. The present paper extends the concept of optical flow estimation via convolutional networks to disparity and scene flow estimation. To this end, we propose three synthetic stereo video datasets with sufficient realism, variation, and size to successfully train large networks. Our datasets are the first large-scale datasets to enable training and evaluation of scene flow methods. Besides the datasets, we present a convolutional network for real-time disparity estimation that provides state-of-the-art results. By combining a flow and disparity estimation network and training it jointly, we demonstrate the first scene flow estimation with a convolutional network.
Practicing Oblivious Access on Cloud Storage: the Gap, the Fallacy, and the New Way Forward

To understand the gap between theory and practice for oblivious cloud storage, we experimentally evaluate four representative Oblivious RAM (ORAM) designs on Amazon S3. We replay realistic application traces to these ORAMs in order to understand whether they can meet the demands of various real applications using cloud storage as a backend. We find that metrics traditionally used in the ORAM literature, e.g., bandwidth overhead, fail to capture the practical needs of those applications. With a new understanding of the desirable properties, relevant metrics, and observations about the cloud services and their applications, we propose CURIOUS, a new modular partition-based ORAM framework, and show experimentally that it is thus far the most promising approach.
Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity

Empirical scoring functions based on either molecular force fields or cheminformatics descriptors are widely used, in conjunction with molecular docking, during the early stages of drug discovery to predict potency and binding affinity of a drug-like molecule to a given target. These models require expert-level knowledge of physical chemistry and biology to be encoded as hand-tuned parameters or features rather than allowing the underlying model to select features in a data-driven procedure. Here, we develop a general 3-dimensional spatial convolution operation for learning atomic-level chemical interactions directly from atomic coordinates and demonstrate its application to structure-based bioactivity prediction. The atomic convolutional neural network is trained to predict the experimentally determined binding affinity of a protein-ligand complex by direct calculation of the energy associated with the complex, protein, and ligand given the crystal structure of the binding pose. Non-covalent interactions present in the complex that are absent in the protein-ligand sub-structures are identified and the model learns the interaction strength associated with these features. We test our model by predicting the binding free energy of a subset of protein-ligand complexes found in the PDBBind dataset and compare with state-of-the-art cheminformatics and machine learning-based approaches. We find that all methods achieve experimental accuracy (less than 1 kcal/mol mean absolute error) and that atomic convolutional networks either outperform or perform competitively with the cheminformatics based methods. Unlike all previous protein-ligand prediction systems, atomic convolutional networks are end-to-end and fully-differentiable. They represent a new data-driven, physics-based deep learning model paradigm that offers a strong foundation for future improvements in structure-based bioactivity prediction.
Morphology and Meaning in the English Mental Lexicon

The authors investigated the lexical entry for morphologically complex words in English, Six experiments, using a cross-modal repetition priming task, asked whether the lexical entry for derivationally suffixed and prefixed words is morphologically structured and how this relates to the semantic and phonological transparency of the surface relationship between stem and affix. There was clear evidence for morphological decomposition of semantically transparent forms. This was independent of phonological transparency, suggesting that morphemic representations are phonologically abstract. Semantically opaque forms, in contrast, behave like monomorphemic words. Overall, suffixed and prefixed derived words and their stems prime each other through shared morphemes in the lexical entry, except for pairs of suffixed forms, which show a cohort-based interference effect.
5D Data Modelling: Full Integration of 2D/3D Space, Time and Scale Dimensions

This paper proposes an approach for data modelling in five dimensions. Apart from three dimensions for geometrical representation and a fourth dimension for time, we identify scale as fifth dimensional characteristic. Considering scale as an extra dimension of geographic information, fully integrated with the other dimensions, is new. Through a formal definition of geographic data in a conceptual 5D continuum, the data can be handled by one integrated approach assuring consistency across scale and time dimensions. Because the approach is new and challenging, we choose to step-wise studying several combinations of the five dimensions, ultimately resulting in the optimal 5D model. We also propose to apply mathematical theories on multidimensional modelling to well established principles of multidimensional modelling in the geo-information domain. The result is a conceptual full partition of the 3Dspace+time+scale space (i.e. no overlaps, no gaps) realised in a 5D data model implemented in a Database
Combining Answer Set Programming with Description Logics for the Semantic Web

Towards the integration of rules and ontologies in the Semantic Web, we propose a combination of logic programming under the answer set semantics with the description logics SHIF(D) and SHOIN (D), which underly the Web ontology languages OWL Lite and OWL DL, respectively. This combination allows for building rules on top of ontologies but also, to a limited extent, building ontologies on top of rules. We introduce description logic programs (dl-programs), which consist of a description logic knowledge base L and a finite set of description logic rules (dl-rules) P . Such rules are similar to usual rules in logic programs with negation as failure, but may also contain queries to L, possibly default-negated, in their bodies. We define Herbrand models for dl-programs, and show that satisfiable positive dl-programs have a unique least Herbrand model. More generally, consistent stratified dl-programs can be associated with a unique minimal Herbrand model that is characterized through iterative least Herbrand models. We then generalize the (unique) minimal Herbrand model semantics for positive and stratified dl-programs to a strong answer set semantics for all dl-programs, which is based on a reduction to the least model semantics of positive dl-programs. We also define a weak answer set semantics based on a reduction to the answer sets of ordinary logic programs. Strong answer sets are weak answer sets, and both properly generalize answer sets of ordinary normal logic programs. We then give fixpoint characterizations for the (unique) minimal Herbrand model semantics of positive and stratified dl-programs, and show how to compute these models by finite fixpoint iterations. Furthermore, we give a precise picture of the complexity of deciding strong and weak answer set existence for a dl-program. 1Institut für Informationssysteme, Technische Universität Wien, Favoritenstraße 9-11, A-1040 Vienna, Austria;  {eiter, lukasiewicz, roman, tompits}@kr.tuwien.ac.at. 2Dipartimento di Informatica e Sistemistica, Università di Roma “La Sapienza”, Via Salaria 113, I-00198 Rome, Italy; e-mail: lukasiewicz@dis.uniroma1.it.  This work has been partially supported by the Austrian Science Fund project Z29N04 and a Marie Curie Individual Fellowship of the European Community programme “Human Potential” under contract number HPMF-CT-2001-001286 (disclaimer: The authors are solely responsible for information communicated and the European Commission is not responsible for any views or results expressed). We would like to thank Ian Horrocks and Ulrike Sattler for providing valuable information on complexityrelated issues during the preparation of this paper.
Toward Streaming Synapse Detection with Compositional ConvNets

Connectomics is an emerging field in neuroscience that aims to reconstruct the 3−dimensional morphology of neurons from electron microscopy (EM) images. Recent studies have successfully demonstrated the use of convolutional neural networks (ConvNets) for segmenting cell membranes to individuate neurons. However, there has been comparatively little success in high-throughput identification of the intercellular synaptic connections required for deriving connectivity graphs. In this study, we take a compositional approach to segmenting synapses, modeling them explicitly as an intercellular cleft co-located with an asymmetric vesicle density along a cell membrane. Instead of requiring a deep network to learn all natural combinations of this compositionality, we train lighter networks to model the simpler marginal distributions of membranes, clefts and vesicles from just 100 electron microscopy samples. These feature maps are then combined with simple rules-based heuristics derived from prior biological knowledge. Our approach to synapse detection is both more accurate than previous state-of-the-art (7% higher recall and 5% higher F1-score) and yields a 20-fold speed-up compared to the previous fastest implementations. We demonstrate by reconstructing the first complete, directed connectome from the largest available anisotropic microscopy dataset (245 GB) of mouse somatosensory cortex (S1) in just 9.7 hours on a single shared-memory CPU system. We believe that this work marks an important step toward the goal of a microscope-pace streaming connectomics pipeline.
Face recognition: a convolutional neural-network approach

We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.
The Emergence of Educational Technology

This paper traces the emergence of Information Technology (IT) for educational purposes. It begins with a discourse on the relationship between culture and educational technology. A key premise is that culture is an important influence in education. Similarly, the evolution of educational technology is very much intertwined with culture. This paper traces educational development from the traditional times to modern times. Educational technology was initially viewed as technology in education, a sole focus on IT tools. Subsequently, a holistic perspective of the educational system was adopted, termed the technology of education. In this larger view, educational technology is informed by research from learning theories and other educational research. The paper elaborates on these theoretical underpinnings with some examples of educational technology before concluding with implications for practitioners and researchers.
Deep Learning-Based Recommendation: Current Issues and Challenges

Due to the revolutionary advances of deep learning achieved in the field of image processing, speech recognition and natural language processing, the deep learning gains much attention. The recommendation task is influenced by the deep learning trend which shows its significant effectiveness and the high-quality of recommendations. The deep learning based recommender models provide a better detention of user preferences, item features and users-items interactions history. In this paper, we provide a recent literature review of researches dealing with deep learning based recommendation approaches which are preceded by a presentation of the main lines of the recommendation approaches and the deep learning techniques. We propose also classification criteria of the different deep learning integration model. Then we finish by presenting the recommendation approach adopted by the most popular video recommendation platform YouTube which is based essentially on deep learning advances. Recommender system; deep learning; neural network; YouTube recommendation
Pose Guided Person Image Generation

This paper proposes the novel Pose Guided Person Generation Network (PG) that allows to synthesize person images in arbitrary poses, based on an image of that person and a novel pose. Our generation framework PG utilizes the pose information explicitly and consists of two key stages: pose integration and image refinement. In the first stage the condition image and the target pose are fed into a U-Net-like network to generate an initial but coarse image of the person with the target pose. The second stage then refines the initial and blurry result by training a U-Net-like generator in an adversarial way. Extensive experimental results on both 128×64 re-identification images and 256×256 fashion photos show that our model generates high-quality person images with convincing details.
Learning Concept Graphs from Online Educational Data

This paper addresses an open challenge in educational data mining, i.e., the problem of automatically mapping online courses from different providers (universities, MOOCs, etc.) onto a universal space of concepts, and predicting latent prerequisite dependencies (directed links) among both concepts and courses. We propose a novel approach for inference within and across course-level and concept-level directed graphs. In the training phase, our system projects partially observed course-level prerequisite links onto directed concept-level links; in the testing phase, the induced concept-level links are used to infer the unknown courselevel prerequisite links. Whereas courses may be specific to one institution, concepts are shared across different providers. The bi-directional mappings enable our system to perform interlingua-style transfer learning, e.g. treating the concept graph as the interlingua and transferring the prerequisite relations across universities via the interlingua. Experiments on our newly collected datasets of courses from MIT, Caltech, Princeton and CMU show promising results.
Learning how to explain neural networks: PatternNet and PatternAttribution

DeConvNet, Guided BackProp, LRP, were invented to better understand deep neural networks. We show that these methods do not produce the theoretically correct explanation for a linear model. Yet they are used on multi-layer networks with millions of parameters. This is a cause for concern since linear models are simple neural networks. We argue that explanation methods for neural nets should work reliably in the limit of simplicity, the linear models. Based on our analysis of linear models we propose a generalization that yields two explanation techniques (PatternNet and PatternAttribution) that are theoretically sound for linear models and produce improved explanations for deep networks.
ClusCite: effective citation recommendation by information network-based clustering

Citation recommendation is an interesting but challenging research problem. Most existing studies assume that all papers adopt the same criterion and follow the same behavioral pattern in deciding relevance and authority of a paper. However, in reality, papers have distinct citation behavioral patterns when looking for different references, depending on paper content, authors and target venues. In this study, we investigate the problem in the context of heterogeneous bibliographic networks and propose a novel cluster-based citation recommendation framework, called ClusCite, which explores the principle that citations tend to be softly clustered into interest groups based on multiple types of relationships in the network. Therefore, we predict each query's citations based on related interest groups, each having its own model for paper authority and relevance. Specifically, we learn group memberships for objects and the significance of relevance features for each interest group, while also propagating relative authority between objects, by solving a joint optimization problem. Experiments on both DBLP and PubMed datasets demonstrate the power of the proposed approach, with 17.68% improvement in Recall@50 and 9.57% growth in MRR over the best performing baseline.
DeepUNet: A Deep Fully Convolutional Network for Pixel-Level Sea-Land Segmentation

Semantic segmentation is a fundamental research in optical remote sensing image processing. Because of the complex maritime environment, the sea-land segmentation is a challenging task. Although the neural network has achieved excellent performance in semantic segmentation in the last years, there were a few of works using CNN for sea-land segmentation and the results could be further improved. This paper proposes a novel deep convolution neural network named DeepUNet. Like the U-Net, its structure has a contracting path and an expansive path to get high-resolution optical output. But differently, the DeepUNet uses DownBlocks instead of convolution layers in the contracting path and uses UpBlock in the expansive path. The two novel blocks bring two new connections that are U-connection and Plus connection. They are promoted to get more precise segmentation results. To verify the network architecture, we construct a new challenging sea-land dataset and compare the DeepUNet on it with the U-Net, SegNet, and SeNet. Experimental results show that DeepUNet can improve 1–2% accuracy performance compared with other architectures, especially in high-resolution optical remote sensing imagery.