nils-herrmann/clean_scirepeval huggingface dataset:
Semantic Documents Relatedness using Concept Graph Representation

We deal with the problem of document representation for the task of measuring semantic relatedness between documents. A document is represented as a compact concept graph where nodes represent concepts extracted from the document through references to entities in a knowledge base such as DBpedia. Edges represent the semantic and structural relationships among the concepts. Several methods are presented to measure the strength of those relationships. Concepts are weighted through the concept graph using closeness centrality measure which reflects their relevance to the aspects of the document. A novel similarity measure between two concept graphs is presented. The similarity measure first represents concepts as continuous vectors by means of neural networks. Second, the continuous vectors are used to accumulate pairwise similarity between pairs of concepts while considering their assigned weights. We evaluate our method on a standard benchmark for document similarity. Our method outperforms state-of-the-art methods including ESA (Explicit Semantic Annotation) while our concept graphs are much smaller than the concept vectors generated by ESA. Moreover, we show that by combining our concept graph with ESA, we obtain an even further improvement.
Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection

This paper presents a completely data-driven and machine-learning-based approach, in two stages, to first characterize and then forecast hourly water demand in the short term with applications of two different data sources: urban water demand (SCADA data) and individual customer water consumption (AMR data). In the first case, reliable forecasting can be used to optimize operations, particularly the pumping schedule, in order to reduce energy-related costs, while in the second case, the comparison between forecast and actual values may support the online detection of anomalies, such as smart meter faults, fraud or possible cyber-physical attacks. Results are presented for a real case: the water distribution network in Milan.
Education and Creativity Education and Creativity

J. P. Guilford (1950) asked in his inaugural address to the American Psychological Association why schools were not producing more creative persons. He also asked, “Why is there so little apparent correlation between education and creative productiveness” (p. 444)? This article presents a review of past and current research on the relation of education to creativity in students of preschool age through age 16 in U.S. public schools. Several models of creative thinking are presented (e.g., Guilford, 1985; Renzulli, 1992; Runco & Chand, 1995), as well as techniques for developing creativity (e.g., Davis, 1982; Sternberg & Williams, 1996). Some research presented indicates a relation between creativity and learning (e.g., Karnes et al., 1961; Torrance, 1981). Implications for research and practice
Wheat grass juice may improve hematological toxicity related to chemotherapy in breast cancer patients: a pilot study.

Myelotoxicity induced by chemotherapy may become life-threatening. Neutropenia may be prevented by granulocyte colony-stimulating factors (GCSF), and epoetin may prevent anemia, but both cause substantial side effects and increased costs. According to non-established data, wheat grass juice (WGJ) may prevent myelotoxicity when applied with chemotherapy. In this prospective matched control study, 60 patients with breast carcinoma on chemotherapy were enrolled and assigned to an intervention or control arm. Those in the intervention arm (A) were given 60 cc of WGJ orally daily during the first three cycles of chemotherapy, while those in the control arm (B) received only regular supportive therapy. Premature termination of treatment, dose reduction, and starting GCSF or epoetin were considered as "censoring events." Response rate to chemotherapy was calculated in patients with evaluable disease. Analysis of the results showed that five censoring events occurred in Arm A and 15 in Arm B (P = 0.01). Of the 15 events in Arm B, 11 were related to hematological events. No reduction in response rate was observed in patients who could be assessed for response. Side effects related to WGJ were minimal, including worsening of nausea in six patients, causing cessation of WGJ intake. In conclusion, it was found that WGJ taken during FAC chemotherapy may reduce myelotoxicity, dose reductions, and need for GCSF support, without diminishing efficacy of chemotherapy. These preliminary results need confirmation in a phase III study.
Design and Practice of an Elevator Control System Based on PLC

This paper describes the development of two nine-storey elevators control system for a residential building. The control system adopts PLC as controller, and uses a parallel connection dispatching rule based on "minimum waiting time" to run two elevators in parallel mode. The paper gives the basic structure, control principle and realization method of the PLC control system in detail. It also presents the ladder diagram of the key aspects of the system. The system has simple peripheral circuit and the operation result showed that it enhanced the reliability and pe.rformance of the elevators.
Modular and Adaptive Wheelchair Automation

We present in this paper a novel framework for the design of a modular and adaptive partial-automation wheelchair. Our design in particular aims to address hurdles to the adoption of partial-automation wheelchairs within general society. In this experimental work, a single assistance module (assisted doorway traversal) is evaluated, with arbitration between multiple goals (from multiple detected doors) and multiple control signals (from an autonomous path planner, and the human user). The experimental work provides the foundation and proofof-concept for the technical components of our proposed modular and adaptive wheelchair robot. The system is evaluated within multiple environmental scenarios and shows good performance.
Stereo vision and laser odometry for autonomous helicopters in GPS-denied indoor environments

This paper presents our solution for enabling a quadrotor helicopter to autonomously navigate unstructured and unknown indoor environments. We compare two sensor suites, specifically a laser rangefinder and a stereo camera. Laser and camera sensors are both well-suited for recovering the helicopter’s relative motion and velocity. Because they use different cues from the environment, each sensor has its own set of advantages and limitations that are complimentary to the other sensor. Our eventual goal is to integrate both sensors on-board a single helicopter platform, leading to the development of an autonomous helicopter system that is robust to generic indoor environmental conditions. In this paper, we present results in this direction, describing the key components for autonomous navigation using either of the two sensors separately.
FINANCIAL MARKET CONTAGION IN THE ASIAN CRISIS

This paper tests for evidence of contagion between the financial markets of Thailand, Malaysia, Indonesia, Korea, and the Philippines. We find that correlations in currency and sovereign spreads increase significantly during the crisis period, whereas the equity market correlations offer mixed evidence. We construct a set of dummy variables using daily news to capture the impact of own-country and cross-border news on the markets. We show that after controlling for owncountry news and other fundamentals, there is evidence of cross-border contagion in the currency and equity markets.
Continual State Representation Learning for Reinforcement Learning using Generative Replay

We consider the problem of building a state representation model in a continual fashion. As the environment changes, the aim is to efficiently compress the sensory state’s information without losing past knowledge. The learned features are then fed to a Reinforcement Learning algorithm to learn a policy. We propose to use Variational Auto-Encoders for state representation, and Generative Replay, i.e. the use of generated samples, to maintain past knowledge. We also provide a general and statistically sound method for automatic environment change detection. Our method provides efficient state representation as well as forward transfer, and avoids catastrophic forgetting. The resulting model is capable of incrementally learning information without using past data and with a bounded system size.
Comparison of Image Steganography Techniques

Steganography is an important area of research in recent years involving a number of applications. It is the science of embedding information into the cover image viz., text, video,and image (payload) without causing statistically significant modification to the cover image. The modern secure image steganography presents a challenging task of transferring the embedded information to the destination without being detected.This paper deals with hiding text in an image file using Least Significant Bit (LSB) based Steganography, Discrete Cosine Transform (DCT) based Steganography and Discrete Wavelet Transform (DWT) based steganography.The LSB algorithm is implemented in spatial domain in which the payload bits are embedded into the least significant bits of cover image to derive the stegoimage whereas DCT & DWT algorithm are implemented in frequency domain in which the stego-image is transformed from spatial domain to the frequency domain and the payload bits are embedded into the frequency components of the cover image.The performance and comparison of these three techniques is evaluated on the basis of the parameters MSE, PSNR, NC, processing time, Capacity& Robustness.
Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework

Computational thinking (CT) draws on concepts and practices that are fundamental to computing and computer science. It includes epistemic and representational practices, such as problem representation, abstraction, decomposition, simulation, verification, and prediction. However, these practices are also central to the development of expertise in scientific and mathematical disciplines. Recently, arguments have been made in favour of integrating CT and programming into the K-12 STEM curricula. In this paper, we first present a theoretical investigation of key issues that need to be considered for integrating CT into K-12 science topics by identifying the synergies between CT and scientific expertise using a particular genre of computation: agent-based computation. We then present a critical review of the literature in educational computing, and propose a set of guidelines for designing learning environments on science topics that can jointly foster the development of computational thinking with scientific expertise. This is followed by the description of a learning environment that supports CT through modeling and simulation to help middle school students learn physics and biology. We demonstrate the effectiveness of our system by discussing the results of a small study conducted in a middle school science classroom. Finally, we discuss the implications of our work for future research on developing CT-based science learning environments.
Emergent elasticity in the neural code for space.

Upon encountering a novel environment, an animal must construct a consistent environmental map, as well as an internal estimate of its position within that map, by combining information from two distinct sources: self-motion cues and sensory landmark cues. How do known aspects of neural circuit dynamics and synaptic plasticity conspire to accomplish this feat? Here we show analytically how a neural attractor model that combines path integration of self-motion cues with Hebbian plasticity in synaptic weights from landmark cells can self-organize a consistent map of space as the animal explores an environment. Intriguingly, the emergence of this map can be understood as an elastic relaxation process between landmark cells mediated by the attractor network. Moreover, our model makes several experimentally testable predictions, including (i) systematic path-dependent shifts in the firing fields of grid cells toward the most recently encountered landmark, even in a fully learned environment; (ii) systematic deformations in the firing fields of grid cells in irregular environments, akin to elastic deformations of solids forced into irregular containers; and (iii) the creation of topological defects in grid cell firing patterns through specific environmental manipulations. Taken together, our results conceptually link known aspects of neurons and synapses to an emergent solution of a fundamental computational problem in navigation, while providing a unified account of disparate experimental observations.
The Motivational Pull of Video Games : A Self-Determination Theory Approach

Four studies apply self-determination theory (SDT; Ryan & Deci, 2000) in investigating motivation for computer game play, and the effects of game play on wellbeing. Studies 1–3 examine individuals playing 1, 2 and 4 games, respectively and show that perceived in-game autonomy and competence are associated with game enjoyment, preferences, and changes in well-being preto post-play. Competence and autonomy perceptions are also related to the intuitive nature of game controls, and the sense of presence or immersion in participants’ game play experiences. Study 4 surveys an on-line community with experience in multiplayer games. Results show that SDT’s theorized needs for autonomy, competence, and relatedness independently predict enjoyment and future game play. The SDT model is also compared with Yee’s (2005) motivation taxonomy of game play motivations. Results are discussed in terms of the relatively unexplored landscape of human motivation within virtual worlds.
Evaluating effectiveness of shallow and deep networks to intrusion detection system

Network intrusion detection system (NIDS) is a tool used to detect and classify the network breaches dynamically in information and communication technologies (ICT) systems in both academia and industries. Adopting a new and existing machine learning classifiers to NIDS has been a significant area in security research due to the fact that the enhancement in detection rate and accuracy is of important in large volume of security audit data including diverse and dynamic characteristics of attacks. This paper evaluates the effectiveness of various shallow and deep networks to NIDS. The shallow and deep networks are trained and evaluated on the KDDCup ‘99’ and NSL-KDD data sets in both binary and multi-class classification settings. The deep networks are performed well in comparison to the shallow networks in most of the experiment configurations. The main reason to this might be a deep network passes information through several layers to learn the underlying hidden patterns of normal and attack network connection records and finally aggregates these learned features of each layer together to effectively distinguish the normal and various attacks of network connection records. Additionally, deep networks have not only performed well in detecting and classifying the known attacks additionally in unknown attacks too. To achieve an acceptable detection rate, we used various configurations of network settings and its parameters in deep networks. All the various configurations of deep network are run up to 1000 epochs in training with a learning rate in the range [0.01-0.5] to effectively capture the time varying patterns of normal and various attacks.
Art for reward's sake: Visual art recruits the ventral striatum

A recent study showed that people evaluate products more positively when they are physically associated with art images than similar non-art images. Neuroimaging studies of visual art have investigated artistic style and esthetic preference but not brain responses attributable specifically to the artistic status of images. Here we tested the hypothesis that the artistic status of images engages reward circuitry, using event-related functional magnetic resonance imaging (fMRI) during viewing of art and non-art images matched for content. Subjects made animacy judgments in response to each image. Relative to non-art images, art images activated, on both subject- and item-wise analyses, reward-related regions: the ventral striatum, hypothalamus and orbitofrontal cortex. Neither response times nor ratings of familiarity or esthetic preference for art images correlated significantly with activity that was selective for art images, suggesting that these variables were not responsible for the art-selective activations. Investigation of effective connectivity, using time-varying, wavelet-based, correlation-purged Granger causality analyses, further showed that the ventral striatum was driven by visual cortical regions when viewing art images but not non-art images, and was not driven by regions that correlated with esthetic preference for either art or non-art images. These findings are consistent with our hypothesis, leading us to propose that the appeal of visual art involves activation of reward circuitry based on artistic status alone and independently of its hedonic value.
The evolution of institutions for collective action

In 1985, the National Academy of Sciences sponsored a conference in Annapolis, Maryland, to discuss common property resource management. This conference was a watershed in the development of the theoretical underpinning of institutional design for successful common pool resource (CPR) management. Since then, an international network of over 2,000 researchers has developed, and the International Association for the Study of Common Property (IASCP), formed in 1989, has held two successful international conferences. Dominating the intellectual evolution of the field has been the work of Elinor Ostrom, co-director of the Workshop in Political Theory and Policy Analysis at Indiana University. Her book, Governing the Commons, presents a lucid exposition of the current state of institutional analysis of common property problems. Part of the Cam-bridge series on Political Economy of Institutions and Decisions, the book addresses how common pool resources may be managed successfully without falling prey to the "tragedy of the commons." Common pool resources are characterized by subtractability (i.e., withdrawal by one user reduces the amount of the resource left for other users) and joint use by a group of appropriators. Thus, a common village grazing field has forage for a limited number of beasts, and all the villagers are entitled to pasture their animals on the field. Community rules of access and management are required to sustain the field from season to season. Problems in managing CPRs arise when the rational individual determines that he will still have access to the resource even if he does not fully contribute to its maintenance (the "free rider" problem). An extensive literature discusses the effect of free riders, concluding that common pool resources will inevitably fall into ruin. One of two solutions is usually offered to avoid this problem: centralized governmental regulation or privatization. Noting the numerous occasions in which common pool resources are managed successfully with neither centralized governmental control nor privatization, Ostrom argues for a third approach to resolving the problem of the commons: the design of durable cooperative institutions that are organized and governed by the resource users. In Governing the Commons she examines small-scale common-pool resources. Resource user groups examined range in size from 50-15,000 people who rely substantially on the common pool resource for their economic well-being. She has further
Collaborative Recurrent Neural Networks for Dynamic Recommender Systems

Modern technologies enable us to record sequences of online user activity at an unprecedented scale. Although such activity logs are abundantly available, most approaches to recommender systems are based on the rating-prediction paradigm, ignoring temporal and contextual aspects of user behavior revealed by temporal, recurrent patterns. In contrast to explicit ratings, such activity logs can be collected in a non-intrusive way and can offer richer insights into the dynamics of user preferences, which could potentially lead more accurate user models. In this work we advocate studying this ubiquitous form of data and, by combining ideas from latent factor models for collaborative filtering and language modeling, propose a novel, flexible and expressive collaborative sequence model based on recurrent neural networks. The model is designed to capture a user’s contextual state as a personalized hidden vector by summarizing cues from a data-driven, thus variable, number of past time steps, and represents items by a real-valued embedding. We found that, by exploiting the inherent structure in the data, our formulation leads to an efficient and practical method. Furthermore, we demonstrate the versatility of our model by applying it to two different tasks: music recommendation and mobility prediction, and we show empirically that our model consistently outperforms static and non-collaborative methods.
Fusing Multi-Stream Deep Networks for Video Classification

This paper studies deep network architectures to address the problem of video classification. A multi-stream framework is proposed to fully utilize the rich multimodal information in videos. Specifically, we first train three Convolutional Neural Networks to model spatial, short-term motion and audio clues respectively. Long Short Term Memory networks are then adopted to explore long-term temporal dynamics. With the outputs of the individual streams, we propose a simple and effective fusion method to generate the final predictions, where the optimal fusion weights are learned adaptively for each class, and the learning process is regularized by automatically estimated class relationships. Our contributions are two-fold. First, the proposed multi-stream framework is able to exploit multimodal features that are more comprehensive than those previously attempted. Second, we demonstrate that the adaptive fusion method using the class relationship as a regularizer outperforms traditional alternatives that estimate the weights in a “free” fashion. Our framework produces significantly better results than the state of the arts on two popular benchmarks, 92.2% on UCF-101 (without using audio) and 84.9% on Columbia Consumer Videos.
Input Combination Strategies for Multi-Source Transformer Decoder

In multi-source sequence-to-sequence tasks, the attention mechanism can be modeled in several ways. This topic has been thoroughly studied on recurrent architectures. In this paper, we extend the previous work to the encoder-decoder attention in the Transformer architecture. We propose four different input combination strategies for the encoderdecoder attention: serial, parallel, flat, and hierarchical. We evaluate our methods on tasks of multimodal translation and translation with multiple source languages. The experiments show that the models are able to use multiple sources and improve over single source baselines.