nils-herrmann/clean_scirepeval huggingface dataset:
Work Extrinsic and Intrinsic Motivation Scale : Its Value for Organizational Psychology Research

The Work Extrinsic and Intrinsic Motivation Scale (WEIMS) is an 18-item measure of work motivation theoretically grounded in self-determination theory (Deci & Ryan, 2000). The purpose of the present research was twofold. First, the applicability of the WEIMS in different work environments was evaluated. Second, its factorial structure and psychometric properties were assessed. Two samples of workers (military: N ! 465; civilians: N ! 192) voluntarily completed questionnaires. Using the WEIMS’s 3 indexes (work self-determination index, work self-determined and nonself-determined motivation, respectively), results of regression analyses were supportive of its ability to predict positive and negative criteria in the workplace. Results also showed the adequacy of both its construct validity and internal consistency. Its factorial structure was also invariant across samples. Finally, its quasi-simplex pattern and relationships with psychological correlates further supported the self-determination continuum. Overall, these findings provide evidence for the applicability as well as the reliability and validity of the WEIMS in organisational settings. Results are discussed in regard to the applicability of self-determination theory to the workplace.
Biological effects due to weak magnetic field on plants.

Throughout the evolution process, Earth's magnetic field (MF, about 50 microT) was a natural component of the environment for living organisms. Biological objects, flying on planned long-term interplanetary missions, would experience much weaker magnetic fields, since galactic MF is known to be 0.1-1 nT. However, the role of weak magnetic fields and their influence on functioning of biological organisms are still insufficiently understood, and is actively studied. Numerous experiments with seedlings of different plant species placed in weak magnetic field have shown that the growth of their primary roots is inhibited during early germination stages in comparison with control. The proliferative activity and cell reproduction in meristem of plant roots are reduced in weak magnetic field. Cell reproductive cycle slows down due to the expansion of G1 phase in many plant species (and of G2 phase in flax and lentil roots), while other phases of cell cycle remain relatively stable. In plant cells exposed to weak magnetic field, the functional activity of genome at early pre-replicate period is shown to decrease. Weak magnetic field causes intensification of protein synthesis and disintegration in plant roots. At ultrastructural level, changes in distribution of condensed chromatin and nucleolus compactization in nuclei, noticeable accumulation of lipid bodies, development of a lytic compartment (vacuoles, cytosegresomes and paramural bodies), and reduction of phytoferritin in plastids in meristem cells were observed in pea roots exposed to weak magnetic field. Mitochondria were found to be very sensitive to weak magnetic field: their size and relative volume in cells increase, matrix becomes electron-transparent, and cristae reduce. Cytochemical studies indicate that cells of plant roots exposed to weak magnetic field show Ca2+ over-saturation in all organelles and in cytoplasm unlike the control ones. The data presented suggest that prolonged exposures of plants to weak magnetic field may cause different biological effects at the cellular, tissue and organ levels. They may be functionally related to systems that regulate plant metabolism including the intracellular Ca2+ homeostasis. However, our understanding of very complex fundamental mechanisms and sites of interactions between weak magnetic fields and biological systems is still incomplete and still deserve strong research efforts.
Biomedical Named Entity Recognition Using Neural Networks

We investigate the task of Named Entity Recognition (NER) in the domain of biomedical text. There is little published work employing modern neural network techniques in this domain, probably due to the small sizes of human-labeled data sets, as non-trivial neural models would have great difficulty avoiding overfitting. In this work we follow a semi-supervised learning approach: We first train state-of-the art (deep) neural networks on a large corpus of noisy machine-labeled data, then “transfer” and fine-tune the learned model on two higher-quality humanlabeled data sets. This approach yields higher performance than the current best published systems for the class DISEASE. It trails but is not far from the currently best systems for the class CHEM.
Deep learning code fragments for code clone detection

Code clone detection is an important problem for software maintenance and evolution. Many approaches consider either structure or identifiers, but none of the existing detection techniques model both sources of information. These techniques also depend on generic, handcrafted features to represent code fragments. We introduce learning-based detection techniques where everything for representing terms and fragments in source code is mined from the repository. Our code analysis supports a framework, which relies on deep learning, for automatically linking patterns mined at the lexical level with patterns mined at the syntactic level. We evaluated our novel learning-based approach for code clone detection with respect to feasibility from the point of view of software maintainers. We sampled and manually evaluated 398 file- and 480 method-level pairs across eight real-world Java systems; 93% of the file- and method-level samples were evaluated to be true positives. Among the true positives, we found pairs mapping to all four clone types. We compared our approach to a traditional structure-oriented technique and found that our learning-based approach detected clones that were either undetected or suboptimally reported by the prominent tool Deckard. Our results affirm that our learning-based approach is suitable for clone detection and a tenable technique for researchers.
EgoSet: Exploiting Word Ego-networks and User-generated Ontology for Multifaceted Set Expansion

A key challenge of entity set expansion is that multifaceted input seeds can lead to significant incoherence in the result set. In this paper, we present a novel solution to handling multifaceted seeds by combining existing user-generated ontologies with a novel word-similarity metric based on skip-grams. By blending the two resources we are able to produce sparse word ego-networks that are centered on the seed terms and are able to capture semantic equivalence among words. We demonstrate that the resulting networks possess internally-coherent clusters, which can be exploited to provide non-overlapping expansions, in order to reflect different semantic classes of the seeds. Empirical evaluation against state-of-the-art baselines shows that our solution, EgoSet, is able to not only capture multiple facets in the input query, but also generate expansions for each facet with higher precision.
Practical Automated Machine Learning for the AutoML Challenge 2018

Despite great successes in many fields, machine learning typically requires substantial human resources to determine a good machine learning pipeline (including various types of preprocessing, and the choice of classifiers and hyperparameters). AutoML aims to free human practitioners and researchers from these menial tasks. The current state-of-the-art in AutoML has been evaluated in the AutoML challenge 2018. Here, we describe our winning entry to this challenge, dubbed PoSH Auto-sklearn, which combines an automatically preselected portfolio, ensemble building and Bayesian optimization with successive halving. Finally, we share insights in the importance of different parts of our approach.
Examining the relations among narcissism, impulsivity, and self-defeating behaviors.

A recent meta-analysis (S. Vazire & D. C. Funder, 2006) suggested that narcissism and impulsivity are related and that impulsivity partially accounts for the relation between narcissism and self-defeating behaviors (SDB). This research examines these hypotheses in two studies and tests a competing hypothesis that Extraversion and Agreeableness account for this relation. In Study 1, we examined the relations among narcissism, impulsivity, and aggression. Both narcissism and impulsivity predicted aggression, but impulsivity did not mediate the narcissism-aggression relation. In Study 2, narcissism was related to a measure of SDB and manifested divergent relations with a range of impulsivity traits from three measures. None of the impulsivity models accounted for the narcissism-SDB relation, although there were unique mediating paths for traits related to sensation and fun seeking. The domains of Extraversion and low Agreeableness successfully mediated the entire narcissism-SDB relation. We address the discrepancy between the current and meta-analytic findings.
Sequential Deep Learning for Human Action Recognition

We propose in this paper a fully automated deep model, which learns to classify human actions without using any prior knowledge. The first step of our scheme, based on the extension of Convolutional Neural Networks to 3D, automatically learns spatio-temporal features. A Recurrent Neural Network is then trained to classify each sequence considering the temporal evolution of the learned features for each timestep. Experimental results on the KTH dataset show that the proposed approach outperforms existing deep models, and gives comparable results with the best related works.
Inverse Depth Parametrization for Monocular SLAM

We present a new parametrization for point features within monocular simultaneous localization and mapping (SLAM) that permits efficient and accurate representation of uncertainty during undelayed initialization and beyond, all within the standard extended Kalman filter (EKF). The key concept is direct parametrization of the inverse depth of features relative to the camera locations from which they were first viewed, which produces measurement equations with a high degree of linearity. Importantly, our parametrization can cope with features over a huge range of depths, even those that are so far from the camera that they present little parallax during motion---maintaining sufficient representative uncertainty that these points retain the opportunity to "come in'' smoothly from infinity if the camera makes larger movements. Feature initialization is undelayed in the sense that even distant features are immediately used to improve camera motion estimates, acting initially as bearing references but not permanently labeled as such. The inverse depth parametrization remains well behaved for features at all stages of SLAM processing, but has the drawback in computational terms that each point is represented by a 6-D state vector as opposed to the standard three of a Euclidean XYZ representation. We show that once the depth estimate of a feature is sufficiently accurate, its representation can safely be converted to the Euclidean XYZ form, and propose a linearity index that allows automatic detection and conversion to maintain maximum efficiency---only low parallax features need be maintained in inverse depth form for long periods. We present a real-time implementation at 30 Hz, where the parametrization is validated in a fully automatic 3-D SLAM system featuring a handheld single camera with no additional sensing. Experiments show robust operation in challenging indoor and outdoor environments with a very large ranges of scene depth, varied motion, and also real time 360deg loop closing.
Models and algorithms for three-stage two-dimensional bin packing

We consider the three-stage two-dimensional bin packing problem (2BP) which occurs in real-world applications such as glass, paper, or steel cutting. We present new integer linear programming formulations: Models for a restricted version and the original version of the problem are developed. Both involve polynomial numbers of variables and constraints only and effectively avoid symmetries. Those models are solved using CPLEX. Furthermore, a branch-and-price (B&P) algorithm is presented for a set covering formulation of the unrestricted problem. We consider stabilizing the column generation process of the B&P algorithm using dual-optimal inequalities. Fast column generation is performed by applying a hierarchy of four methods: (a) a fast greedy heuristic, (b) an evolutionary algorithm, (c) solving a restricted form of the pricing problem using CPLEX, and finally (d) solving the complete pricing problem using CPLEX. Computational experiments on standard benchmark instances document the benefits of the new approaches: The restricted version of the ILP model can be used for quickly obtaining nearly optimal solutions. The unrestricted version is computationally more expensive. Column generation provides a strong lower bound for 3-stage 2BP. The combination of all four pricing algorithms and column generation stabilization in the proposed B&P framework yields the best results in terms of the average objective value, the average run-time, and the number of instances solved to proven optimality. 1 This work is supported by the Austrian Science Fund (FWF) under grant P16263-N04.
A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models

Generative adversarial networks (GANs) are a recently proposed class of generative models in which a generator is trained to optimize a cost function that is being simultaneously learned by a discriminator. While the idea of learning cost functions is relatively new to the field of generative modeling, learning costs has long been studied in control and reinforcement learning (RL) domains, typically for imitation learning from demonstrations. In these fields, learning the cost function underlying observed behavior is known as inverse reinforcement learning (IRL) or inverse optimal control. While at first the connection between cost learning in RL and cost learning in generative modeling may appear to be a superficial one, we show in this paper that certain IRL methods are in fact mathematically equivalent to GANs. In particular, we demonstrate an equivalence between a sample-based algorithm for maximum entropy IRL and a GAN in which the generator’s density can be evaluated and is provided as an additional input to the discriminator. Interestingly, maximum entropy IRL is a special case of an energy-based model. We discuss the interpretation of GANs as an algorithm for training energy-based models, and relate this interpretation to other recent work that seeks to connect GANs and EBMs. By formally highlighting the connection between GANs, IRL, and EBMs, we hope that researchers in all three communities can better identify and apply transferable ideas from one domain to another, particularly for developing more stable and scalable algorithms: a major challenge in all three domains.
SSD: Single Shot MultiBox Detector

We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. SSD is simple relative to methods that require object proposals because it completely eliminates proposal generation and subsequent pixel or feature resampling stages and encapsulates all computation in a single network. This makes SSD easy to train and straightforward to integrate into systems that require a detection component. Experimental results on the PASCAL VOC, COCO, and ILSVRC datasets confirm that SSD has competitive accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference. For 300× 300 input, SSD achieves 74.3% mAP on VOC2007 test at 59 FPS on a Nvidia Titan X and for 512 × 512 input, SSD achieves 76.9% mAP, outperforming a comparable state-of-the-art Faster R-CNN model. Compared to other single stage methods, SSD has much better accuracy even with a smaller input image size. Code is available at: https://github.com/weiliu89/caffe/tree/ssd .
Monte Carlo Tree Search in Hex

Hex, the classic board game invented by Piet Hein in 1942 and independently by John Nash in 1948, has been a domain of AI research since Claude Shannon's seminal work in the 1950s. Until the Monte Carlo Go revolution a few years ago, the best computer Hex players used knowledge-intensive alpha-beta search. Since that time, strong Monte Carlo Hex players have appeared that are on par with the best alpha-beta Hex players. In this paper, we describe MoHex, the Monte Carlo tree search Hex player that won gold at the 2009 Computer Olympiad. Our main contributions to Monte Carlo tree search include using inferior cell analysis and connection strategy computation to prune the search tree. In particular, we run our random game simulations not on the actual game position, but on a reduced equivalent board.
GoGoGo : Improving Deep Neural Network Based Go Playing AI with Residual Networks

The game of Go has a long history and has been viewed as the most challanging classical game due to the enormous amount of possible moves and the lack of precise evaulation tools. AlphaGo [13], a Go-playing AI built by Google DeepMind, used a new approach of combining deep neural networks with tree search to solve the Go playing problem. It narrows down the search space by introducing ”policy network” that gives reasonable moves instead of all possible moves. It then combine the Monte Carlo tree search (MCTS) with a ”value network” that evaluates the winning chance of current state to search deeply for the future moves. In a game with the 18-time Go world champion winner Lee Sedol by 4 games to 1. This is the first time that an artificial intelligence program defeated the Go world champion.
Geometry of Compositionality

This paper proposes a simple test for compositionality (i.e., literal usage) of a word or phrase in a context-specific way. The test is computationally simple, relying on no external resources and only uses a set of trained word vectors. Experiments show that the proposed method is competitive with state of the art and displays high accuracy in context-specific compositionality detection of a variety of natural language phenomena (idiomaticity, sarcasm, metaphor) for different datasets in multiple languages. The key insight is to connect compositionality to a curious geometric property of word embeddings, which is of independent interest.
Design and use of 3 D typography for indoor Augmented Reality mobile applications

Augmented Reality (AR), a concept where the real word is being enhanced with computer generated objects and text, has evolved and become a popular tool to communicate information through. Research on how the technique can be optimized regarding the technical aspects has been made, but not regarding how typography in three dimensions should be designed and used in AR applications. Therefore this master’s thesis investigates three different design attributes of typography in three dimensions. The three attributes are: typeface style, color, and weight including depth, and how they affect the visibility of the text in an indoor AR environment. A user study was conducted, both with regular users but also with users that were considered experts in the field of typography and design, to investigate differences of the visibility regarding the typography’s design attributes. The result shows noteworthy differences between two pairs of AR simulations containing different typography among the regular users. This along with a slight favoritism of bright colored text against dark colored text, even though no notable different could be seen regarding color alone. Discussions regarding the design attributes of the typography affect the legibility of the text, and what could have been done differently to achieve an even more conclusive result. To summarize this thesis, the objective resulted in design guidelines regarding typography for indoor mobile AR applications. Skapande och användande av 3D-typografi i mobila Augmented Reality-applikationer för inomhusbruk
THE EFFECTS OF SOCIAL NETWORKING SITES ON STUDENTS ’ ACADEMIC PERFORMANCE IN LYCEUM OF THE PHILIPPINES – LAGUNA

In the advent of internet technology, social communication using the internet became the frontier of daily communication, collaboration and networking. This frontier is delineated by many social networking websites and portals. This study was conducted to determine the effects of Social Networking Sites (SNSs) on students’ academic performance in Lyceum of the Philippines – Laguna. To determine the effects of SNSs on academic performance, the researcher used the descriptive correlational method of research. An online survey questionnaire designed using Google docs was distributed to 203 randomly-selected studentrespondents from the six colleges in Lyceum of the Philippines – Laguna. The study found out that all students in Lyceum of the Philippines – Laguna have Facebook accounts and were significantly registered to more than one SNSs account. The results of the study showed a statistically significant positive relationship between time spent by students on SNSs and their academic performance. The student-respondents tended to use SNSs to interact with their friends and not use for academic reasons. Specifically, the study found out that the higher the time spent on SNSs showed the lower is the time spent on studying. Hence, correlation analysis showed an inverse relationship between the GPA and the number of SNSs accounts being maintained by the students, the frequency of SNSs use, since when they have SNSs, length of SNSs use, and the number of study hours. The results revealed that as these mentioned variables increases, the GPA also increases, showing that SNSs had made a contribution in the attainment of higher grades. However, such relationships are not significant. Therefore, the study found that SNSs usage had no significant relationship on student academic performance. The improvement in the grade relies on other factors within the teaching-learning process and thus could not be attributed to the use of SNS alone.
Reasoning about Entailment with Neural Attention

Automatically recognizing entailment relations between pairs of natural language sentences has so far been the dominion of classifiers employing hand engineered features derived from natural language processing pipelines. End-to-end differentiable neural architectures have failed to approach state-of-the-art performance until very recently. In this paper, we propose a neural model that reads two sentences to determine entailment using long short-term memory units. We extend this model with a word-by-word neural attention mechanism that encourages reasoning over entailments of pairs of words and phrases. Furthermore, we present a qualitative analysis of attention weights produced by this model, demonstrating such reasoning capabilities. On a large entailment dataset this model outperforms the previous best neural model and a classifier with engineered features by a substantial margin. It is the first generic end-to-end differentiable system that achieves state-of-the-art accuracy on a textual entailment dataset.