nils-herrmann/clean_scirepeval huggingface dataset:
Feature extraction for co-occurrence-based cosine similarity score of text documents

A major challenge in topic classification (TC) is the high dimensionality of the feature space. Therefore, feature extraction (FE) plays a vital role in topic classification in particular and text mining in general. FE based on cosine similarity score is commonly used to reduce the dimensionality of datasets with tens or hundreds of thousands of features, which can be impossible to process further. In this study, TF-IDF term weighting is used to extract features. Selecting relevant features and determining how to encode them for a learning machine method have a vast impact on the learning machine methods ability to extract a good model. Two different weighting methods (TF-IDF and TF-IDF Global) were used and tested on the Reuters-21578 text categorization test collection. The obtained results emerged a good candidate for enhancing the performance of English topics FE. Simulation results the Reuters-21578 text categorization show the superiority of the proposed algorithm.
Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging

In this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches. We demonstrate for common sequence tagging tasks that the seed value for the random number generator can result in statistically significant (p < 10−4) differences for state-of-the-art systems. For two recent systems for NER, we observe an absolute difference of one percentage point F1-score depending on the selected seed value, making these systems perceived either as state-of-the-art or mediocre. Instead of publishing and reporting single performance scores, we propose to compare score distributions based on multiple executions. Based on the evaluation of 50.000 LSTMnetworks for five sequence tagging tasks, we present network architectures that produce both superior performance as well as are more stable with respect to the remaining hyperparameters. The full experimental results are published in (Reimers and Gurevych, 2017).1 The implementation of our network is publicly
Social Network Analysis: Selected Methods and Applications

A social network (SN) is a network containing nodes – social entities (people or groups of people) and links between these nodes. Social networks are examples of more general concept of complex networks and SNs are usually free-scale and have power distribution of node degree. Overall, several types of social networks can be enumerated: (i) simple SNs, (ii) multi-layered SNs (with many links between a pair of nodes), (iii) bipartite or multi-modal, heterogeneous SNs (with two or many different types of nodes), (iv) multidimensional SNs (reflecting the data warehousing multidimensional modelling concept), and some more specific like (v) temporal SNs, (vi) large scale SNs, and (vii) virtual SNs. For all these social networks suitable analytical methods may be applied commonly called social network analysis (SNA). They cover in particular: appropriate structural measures, efficient algorithms for their calculation, statistics and data mining methods, e.g. extraction of social communities (clustering). Some types of social networks have their own measures and methods developed. Several real application domains of SNA may be distinguished: classification of nodes for the purpose of marketing, evaluation of organizational structure versus communication structures in companies, recommender systems for hidden knowledge acquisition and for user support in web 2.0, analysis of social groups on web forums and prediction of their evolution. The above SNA methods and applications will be discussed in some details. J. Pokorný, V. Snášel, K. Richta (Eds.): Dateso 2012, pp. 151–151, ISBN 978-80-7378-171-2.
The Ring of Gyges: Investigating the Future of Criminal Smart Contracts

Thanks to their anonymity (pseudonymity) and elimination of trusted intermediaries, cryptocurrencies such as Bitcoin have created or stimulated growth in many businesses and communities. Unfortunately, some of these are criminal, e.g., money laundering, illicit marketplaces, and ransomware. Next-generation cryptocurrencies such as Ethereum will include rich scripting languages in support of smart contracts, programs that autonomously intermediate transactions. In this paper, we explore the risk of smart contracts fueling new criminal ecosystems. Specifically, we show how what we call criminal smart contracts (CSCs) can facilitate leakage of confidential information, theft of cryptographic keys, and various real-world crimes (murder, arson, terrorism).
 We show that CSCs for leakage of secrets (a la Wikileaks) are efficiently realizable in existing scripting languages such as that in Ethereum. We show that CSCs for theft of cryptographic keys can be achieved using primitives, such as Succinct Non-interactive ARguments of Knowledge (SNARKs), that are already expressible in these languages and for which efficient supporting language extensions are anticipated. We show similarly that authenticated data feeds, an emerging feature of smart contract systems, can facilitate CSCs for real-world crimes (e.g., property crimes).
 Our results highlight the urgency of creating policy and technical safeguards against CSCs in order to realize the promise of smart contracts for beneficial goals.
Local Convolutional Features with Unsupervised Training for Image Retrieval

Patch-level descriptors underlie several important computer vision tasks, such as stereo-matching or content-based image retrieval. We introduce a deep convolutional architecture that yields patch-level descriptors, as an alternative to the popular SIFT descriptor for image retrieval. The proposed family of descriptors, called Patch-CKN, adapt the recently introduced Convolutional Kernel Network (CKN), an unsupervised framework to learn convolutional architectures. We present a comparison framework to benchmark current deep convolutional approaches along with Patch-CKN for both patch and image retrieval, including our novel "RomePatches" dataset. Patch-CKN descriptors yield competitive results compared to supervised CNN alternatives on patch and image retrieval.
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms

In continuous action domains, standard deep reinforcement learning algorithms like DDPG suffer from inefficient exploration when facing sparse or deceptive reward problems. Conversely, evolutionary and developmental methods focusing on exploration like Novelty Search, QualityDiversity or Goal Exploration Processes explore more robustly but are less efficient at fine-tuning policies using gradient-descent. In this paper, we present the GEP-PG approach, taking the best of both worlds by sequentially combining a Goal Exploration Process and two variants of DDPG. We study the learning performance of these components and their combination on a low dimensional deceptive reward problem and on the larger Half-Cheetah benchmark. We show that DDPG fails on the former and that GEP-PG improves over the best DDPG variant in both environments. Supplementary videos and discussion can be found at frama.link/gep_pg, the code at github.com/flowersteam/geppg.
PUSHOVER ANALYSIS OF REINFORCED CONCRETE FRAME STRUCTURES : A CASE STUDY

Recent earthquakes in which many concrete structures have been severely damaged or collapsed, have indicated the need for evaluating the seismic adequacy of existing buildings. About 60% of the land area of our country is susceptible to damaging levels of seismic hazard. We can’t avoid future earthquakes, but preparedness and safe building construction practices can certainly reduce the extent of damage and loss. In order to strengthen and resist the buildings for future earthquakes, some procedures have to be adopted. One of the procedures is the nonlinear static pushover analysis which is becoming a popular tool for seismic performance evaluation of existing and new structures. By conducting this push over analysis, we can know the weak zones in the structure and then we will decide whether the particular part is to be retrofitted or rehabilitated according to the requirement. In the present study the push over analysis is performed on multistoried frame structures by using most common software SAP2000 . To achieve this objective, two framed buildings with 5 and 12 stories respectively were analyzed. The results obtained from this study show that properly designed frames will perform well under seismic loads.
The Unscented Kalman Filter for Nonlinear Estimation

The Extended Kalman Filter (EKF) has become a standard technique used in a number of nonlinear estimation and machine learning applications. These include estimating the state of a nonlinear dynamic system, estimating parameters for nonlinear system identification (e.g., learning the weights of a neural network), and dual estimation (e.g., the ExpectationMaximization (EM) algorithm)where both states and parameters are estimated simultaneously. This paper points out the flaws in using the EKF, and introduces an improvement, the Unscented Kalman Filter (UKF), proposed by Julier and Uhlman . A central and vital operation performed in the Kalman Filter is the propagation of a Gaussian random variable (GRV) through the system dynamics. In the EKF, the state distribution is approximated by a GRV, which is then propagated analytically through the first-order linearization of the nonlinear system. This can introduce large errors in the true posterior mean and covariance of the transformed GRV, which may lead to sub-optimal performance and sometimes divergence of the filter. The UKF addresses this problem by using a deterministic sampling approach. The state distribution is again approximated by a GRV, but is now represented using a minimal set of carefully chosen sample points. These sample points completely capture the true mean and covariance of the GRV, and when propagated through the true nonlinear system, captures the posterior mean and covariance accurately to the 3rd order (Taylor series expansion) for any nonlinearity. The EKF, in contrast, only achieves first-order accuracy. Remarkably, the computational complexity of the UKF is the same order as that of the EKF. Julier and Uhlman demonstrated the substantial performance gains of the UKF in the context of state-estimation for nonlinear control. Machine learning problems were not considered. We extend the use of the UKF to a broader class of nonlinear estimation problems, including nonlinear system identification, training of neural networks, and dual estimation problems. Our preliminary results were presented in . In this paper, the algorithms are further developed and illustrated with a number of additional examples. This work was sponsored by the NSF under grant grant IRI-9712346
Visual Question Answering with Question Representation Update (QRU)

Our method aims at reasoning over natural language questions and visual images. Given a natural language question about an image, our model updates the question representation iteratively by selecting image regions relevant to the query and learns to give the correct answer. Our model contains several reasoning layers, exploiting complex visual relations in the visual question answering (VQA) task. The proposed network is end-to-end trainable through back-propagation, where its weights are initialized using pre-trained convolutional neural network (CNN) and gated recurrent unit (GRU). Our method is evaluated on challenging datasets of COCO-QA [19] and VQA  and yields state-of-the-art performance.
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation

Sequence-to-sequence models have shown strong performance across a broad range of applications. However, their application to parsing and generating text using Abstract Meaning Representation (AMR) has been limited, due to the relatively limited amount of labeled data and the nonsequential nature of the AMR graphs. We present a novel training procedure that can lift this limitation using millions of unlabeled sentences and careful preprocessing of the AMR graphs. For AMR parsing, our model achieves competitive results of 62.1 SMATCH, the current best score reported without significant use of external semantic resources. For AMR generation, our model establishes a new state-of-the-art performance of BLEU 33.8. We present extensive ablative and qualitative analysis including strong evidence that sequencebased AMR models are robust against ordering variations of graph-to-sequence conversions.
A Neural Network for Factoid Question Answering over Paragraphs

Text classification methods for tasks like factoid question answering typically use manually defined string matching rules or bag of words representations. These methods are ineffective when question text contains very few individual words (e.g., named entities) that are indicative of the answer. We introduce a recursive neural network (rnn) model that can reason over such input by modeling textual compositionality. We apply our model, qanta, to a dataset of questions from a trivia competition called quiz bowl. Unlike previous rnn models, qanta learns word and phrase-level representations that combine across sentences to reason about entities. The model outperforms multiple baselines and, when combined with information retrieval methods, rivals the best human players.
Supervised Hashing for Image Retrieval via Image Representation Learning

Hashing is a popular approximate nearest neighbor search approach for large-scale image retrieval. Supervised hashing, which incorporates similarity/dissimilarity information on entity pairs to improve the quality of hashing function learning, has recently received increasing attention. However, in the existing supervised hashing methods for images, an input image is usually encoded by a vector of hand-crafted visual features. Such hand-crafted feature vectors do not necessarily preserve the accurate semantic similarities of images pairs, which may often degrade the performance of hashing function learning. In this paper, we propose a supervised hashing method for image retrieval, in which we automatically learn a good image representation tailored to hashing as well as a set of hash functions. The proposed method has two stages. In the first stage, given the pairwise similarity matrix S over training images, we propose a scalable coordinate descent method to decompose S into a product of HH where H is a matrix with each of its rows being the approximate hash code associated to a training image. In the second stage, we propose to simultaneously learn a good feature representation for the input images as well as a set of hash functions, via a deep convolutional network tailored to the learned hash codes in H and optionally the discrete class labels of the images. Extensive empirical evaluations on three benchmark datasets with different kinds of images show that the proposed method has superior performance gains over several state-of-the-art supervised and unsupervised hashing methods.
Extremely Low Bit Neural Network: Squeeze the Last Bit Out With ADMM

Although deep learning models are highly effective for various learning tasks, their high computational costs prohibit the deployment to scenarios where either memory or computational resources are limited. In this paper, we focus on compressing and accelerating deep models with network weights represented by very small numbers of bits, referred to as extremely low bit neural network. We model this problem as a discretely constrained optimization problem. Borrowing the idea from Alternating Direction Method of Multipliers (ADMM), we decouple the continuous parameters from the discrete constraints of network, and cast the original hard problem into several subproblems. We propose to solve these subproblems using extragradient and iterative quantization algorithms that lead to considerably faster convergency compared to conventional optimization methods. Extensive experiments on image recognition and object detection verify that the proposed algorithm is more effective than state-of-theart approaches when coming to extremely low bit neural network.
The Role of Strategic Information Systems (SIS) in Supporting and Achieving the Competitive Advantages (CA): An Empirical Study on Saudi Banking Sector

The purpose of this research paper is to identify the significant role of Strategic Information Systems (SIS) in supporting the Competitive Advantage (CA). It also explains its role on the dimensions that increase the competitive advantage which are the operational efficiency, information quality and innovation. In order to achieve the goal of this study and to collect the primary data, the researchers designed a survey, in the form of an electronic questionnaire. This survey instrument consisted of 20 questions. It was distributed to members of the study sample, which contains the managers at all levels, and the employees in the Saudi banking sector. The number of the participants included in the survey was 147. The results of this study revealed that there is a significant role of strategic information systems on increasing operational efficiency, improving the quality of information and promoting innovation. This in turn enabled the organizations to achieve higher levels of competitive advantages. The strategic information systems have deep consequences for organizations that adopt them; managers could achieve great and sustainable competitive advantages from such systems if carefully considered and developed. On the other hand, this study was conducted in the banking sector in KSA context. So, more research is needed in other sectors and in the context of other countries; to confirm and generalize the results. Finally, the paper’s primary value lies in its ability to provide the evidence that the strategic information systems play a significant role in supporting and achieving the competitive advantages in Saudi Arabia, particularly in the banking sector. Since there was a lack of such research in the Saudi context, this paper can provide a theoretical basis for future researchers as well as practical implications for managers. Strategic information systems (SIS); competitive advantage (CA); operational efficiency; information quality; innovation
Towards a Dynamic Theory of Strategy – Michael Porter (SMJ 1991)

Develop and implement an internally consistent set of goals and functional policies (this is, a solution to the agency problem) • These internally consistent set of goals and policies aligns the firm’s strengths and weaknesses with external (industry) opportunities and threats (SWOT) in a dynamic balance • The firm’s strategy has to be concerned with the exploitation of its “distinctive competences” (early reference to RBV)
PD-L1 Expression as a Predictive Biomarker in Cancer Immunotherapy.

The resurgence of cancer immunotherapy stems from an improved understanding of the tumor microenvironment. The PD-1/PD-L1 axis is of particular interest, in light of promising data demonstrating a restoration of host immunity against tumors, with the prospect of durable remissions. Indeed, remarkable clinical responses have been seen in several different malignancies including, but not limited to, melanoma, lung, kidney, and bladder cancers. Even so, determining which patients derive benefit from PD-1/PD-L1-directed immunotherapy remains an important clinical question, particularly in light of the autoimmune toxicity of these agents. The use of PD-L1 (B7-H1) immunohistochemistry (IHC) as a predictive biomarker is confounded by multiple unresolved issues: variable detection antibodies, differing IHC cutoffs, tissue preparation, processing variability, primary versus metastatic biopsies, oncogenic versus induced PD-L1 expression, and staining of tumor versus immune cells. Emerging data suggest that patients whose tumors overexpress PD-L1 by IHC have improved clinical outcomes with anti-PD-1-directed therapy, but the presence of robust responses in some patients with low levels of expression of these markers complicates the issue of PD-L1 as an exclusionary predictive biomarker. An improved understanding of the host immune system and tumor microenvironment will better elucidate which patients derive benefit from these promising agents.
European standards for the documentation of historic buildings and their relationship with CIDOC-CRM

Integration of architectural datasets concerning historic buildings depends on their interoperability, which has as first step a mapping to a common schema. The paper investigates current approaches and proposes mapping to a CIDOC-CRM extension as the common glue to overcome the fragmentation of datasets provided by large national institutions such as MIBAC in Italy, EH in the UK, and so on, and by EU projects, each one structured according to a different metadata schema. The paper describes the mapping of the MA-CA MIBAC-ICCD schemas, probably the most comprehensive, to CRM.
Image processing with neural networks - a review

We review more than 200 applications of neural networks in image processing and discuss the present and possible future role of neural networks, especially feed-forward neural networks, Kohonen feature maps and Hop1eld neural networks. The various applications are categorised into a novel two-dimensional taxonomy for image processing algorithms. One dimension speci1es the type of task performed by the algorithm: preprocessing, data reduction=feature extraction, segmentation, object recognition, image understanding and optimisation. The other dimension captures the abstraction level of the input data processed by the algorithm: pixel-level, local feature-level, structure-level, object-level,ion level of the input data processed by the algorithm: pixel-level, local feature-level, structure-level, object-level, object-set-level and scene characterisation. Each of the six types of tasks poses speci1c constraints to a neural-based approach. These speci1c conditions are discussed in detail. A synthesis is made of unresolved problems related to the application of pattern recognition techniques in image processing and speci1cally to the application of neural networks. Finally, we present an outlook into the future application of neural networks and relate them to novel developments. ? 2002 Pattern Recognition Society.
A comprehensive framework for verification , validation , and uncertainty quantification in scientific computing

An overview of a comprehensive framework is given for estimating the predictive uncertainty of scientific computing applications. The framework is comprehensive in the sense that it treats both types of uncertainty (aleatory and epistemic), incorporates uncertainty due to the mathematical form of the model, and it provides a procedure for including estimates of numerical error in the predictive uncertainty. Aleatory (random) uncertainties in model inputs are treated as random variables, while epistemic (lack of knowledge) uncertainties are treated as intervals with no assumed probability distributions. Approaches for propagating both types of uncertainties through the model to the system response quantities of interest are briefly discussed. Numerical approximation errors (due to discretization, iteration, and computer round off) are estimated using verification techniques, and the conversion of these errors into epistemic uncertainties is discussed. Model form uncertainty is quantified using (a) model validation procedures, i.e., statistical comparisons of model predictions to available experimental data, and (b) extrapolation of this uncertainty structure to points in the application domain where experimental data do not exist. Finally, methods for conveying the total predictive uncertainty to decision makers are presented. The different steps in the predictive uncertainty framework are illustrated using a simple example in computational fluid dynamics applied to a hypersonic wind tunnel.