nils-herrmann/clean_scirepeval huggingface dataset:
1 50 years of Computational Wind Engineering : Past , present and future 1

In the past 50 years, Computational Wind Engineering (CWE) has undergone a successful transition from an emerging field into an increasingly established field in wind engineering research, practice and education. This paper provides a perspective on the past, present and future of CWE. It addresses three key illustrations of the success of CWE: (1) the establishment of CWE as an individual research and application area in wind engineering with its own successful conference series under the umbrella of the International Association of Wind Engineering (IAWE); (2) the increasing range of topics covered in CWE; and (3) the history of overview and review papers in CWE. The paper also outlines some of the earliest achievements in CWE and the resulting development of best practice guidelines. It provides some views on the complementary relationship between reduced-scale wind-tunnel testing and CFD. It re-iterates some important quotes made by CWE and/or CFD researchers in the past, many of which are still equally valid today and which are provided without additional comments, to let the quotes speak for themselves. Next, as application examples to the foregoing sections, the paper provides a more detailed view on CFD simulation of pedestrian-level wind conditions around buildings, CFD simulation of natural ventilation of buildings and CFD simulation of wind-driven rain on building facades. Finally, a non-exhaustive perspective on the future of CWE is provided.
Behavioural analysis of crime against women using a graph based clustering approach

Crime against women is increasing at an alarming rate in almost all parts of India. Women in the Indian society have been victims of humiliation, torture and exploitation. It has even existed in the past but only in the recent years the issues have been brought to the open for concern. According to the latest data released by the National Crime Records Bureau (NCRB), crime against women have increased more than doubled over the past ten years. While a number of analyses have been done in the field of crime pattern detection, none have done an extensive study on the crime against women in India. The present paper describes a behavioural analysis of crime against women in India from the year 2001 to 2014. The study evaluates the efficacy of Infomap clustering algorithm for detecting communities of states and union territories in India based on crimes. As it is a graph based clustering approach, all the states of India along with the union territories have been considered as nodes of the graph and similarity among the nodes have been measured based on different types of crimes. Each community is a group of states and / or union territories which are similar based on crime trends. Initially, the method finds the communities based on current year crime data, subsequently at the end of a year when new crime data for the next year is available, the graph is modified and new communities are formed. The process is repeated year wise that helps to predict how crime against women has significantly increased in various states of India over the past years. It also helps in rapid visualisation and identification of states which are densely affected with crimes. This approach proves to be quite effective and can also be used for analysing the global crime scenario.
MoleculeNet: a benchmark for molecular machine learning†
†Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc02664a


Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods. This work introduces MoleculeNet, a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem open source library). MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance. However, this result comes with caveats. Learnable representations still struggle to deal with complex tasks under data scarcity and highly imbalanced classification. For quantum mechanical and biophysical datasets, the use of physics-aware featurizations can be more important than choice of particular learning algorithm.
Evaluation of Cannabinoid and Terpenoid Content: Cannabis Flower Compared to Supercritical CO2 Concentrate.

A recent cannabis use survey revealed that 60% of cannabis users rely on smelling the flower to select their cannabis. Olfactory indicators in plants include volatile compounds, principally represented by the terpenoid fraction. Currently, medicinal- and adult-use cannabis is marketed in the United States with relatively little differentiation between products other than by a common name, association with a species type, and Δ-9 tetrahydrocannabinol/cannabidiol potency. Because of this practice, how terpenoid compositions may change during an extraction process is widely overlooked. Here we report on a comparative study of terpenoid and cannabinoid potencies of flower and supercritical fluid CO2 (SC-CO2) extract from six cannabis chemovars grown in Washington State. To enable this comparison, we employed a validated high-performance liquid chromatography/diode array detector methodology for quantification of seven cannabinoids and developed an internal gas chromatography-mass spectrometry method for quantification of 42 terpenes. The relative potencies of terpenoids and cannabinoids in flower versus concentrate were significantly different. Cannabinoid potency increased by factors of 3.2 for Δ-9 tetrahydrocannabinol and 4.0 for cannabidiol in concentrates compared to flower. Monoterpenes were lost in the extraction process; a ketone increased by 2.2; an ether by 2.7; monoterpene alcohols by 5.3, 7 and 9.4; and sesquiterpenes by 5.1, 4.2, 7.7, and 8.9. Our results demonstrate that the product of SC-CO2 extraction may have a significantly different chemotypic fingerprint from that of cannabis flower. These results highlight the need for more complete characterization of cannabis and associated products, beyond cannabinoid content, in order to further understand health-related consequences of inhaling or ingesting concentrated forms.
The St. Thomas Common Sense Symposium: Designing Architectures for Human-Level Intelligence

once a principal goal in the field of artificial intelligence. But most researchers in recent years have retreated from that ambitious aim. Instead, each developed some special technique that could deal with some class of problem well, but does poorly at almost everything else. We are convinced, however, that no one such method will ever turn out to be “best,” and that instead, the powerful AI systems of the future will use a diverse array of resources that, together, will deal with a great range of problems. To build a machine that’s resourceful enough to have humanlike common sense, we must develop ways to combine the advantages of multiple methods to represent knowledge, multiple ways to make inferences, and multiple ways to learn. We held a two-day symposium in St. Thomas, U.S. Virgin Islands, to discuss such a project—to develop new architectural schemes that can bridge between different strategies and representations. This article reports on the events and ideas developed at this meeting and subsequent thoughts by the authors on how to make progress.
Automatic Annotation Suggestions and Custom Annotation Layers in WebAnno

In this paper, we present a flexible approach to the efficient and exhaustive manual annotation of text documents. For this purpose, we extend WebAnno (Yimam et al., 2013) an open-source web-based annotation  While it was previously limited to specific annotation layers, our extension allows adding and configuring an arbitrary number of layers through a web-based UI. These layers can be annotated separately or simultaneously, and support most types of linguistic annotations such as spans, semantic classes, dependency relations, lexical chains, and morphology. Further, we tightly integrate a generic machine learning component for automatic annotation suggestions of span annotations. In two case studies, we show that automatic annotation suggestions, combined with our split-pane UI concept, significantly reduces annotation time.
Analysis and Optimization of fastText Linear Text Classifier

The paper  shows that simple linear classifier can compete with complex deep learning algorithms in text classification applications. Combining bag of words (BoF) and linear classification techniques, fastText  attains same or only slightly lower accuracy than deep learning algorithms [2-9] that are orders of magnitude slower. We proved formally that fastText can be transformed into a simpler equivalent classifier, which unlike fastText does not have any hidden layer. We also proved that the necessary and sufficient dimensionality of the word vector embedding space is exactly the number of document classes. These results help constructing more optimal linear text classifiers with guaranteed maximum classification capabilities. The results are proven exactly by pure formal algebraic methods without attracting any empirical data.
Ethnic diversity and economic development

This paper analyzes the role that different indices and dimensions of ethnicity play in the process of economic development. Firstly, we discuss the advantages and disadvantages of alternative data sources for the construction of indices of religious and ethnic heterogeneity. Secondly, we compare the index of fractionalization and the index of polarization. We argue that an index of the family of discrete polarization measures is the adequate indicator to measure potential conflict. We find that ethnic (religious) polarization has a large and negative effect on economic development through the reduction of investment and the increase of government consumption and the probability of a civil conflict. D 2004
Ambient Vibration Tests of Structures−a Review

A literature review on the subject of ambient vibration testing is presented, and a review of a recent study to illustrate state-of-the-art in the application of the ambient vibration method. Reports on testing of full-scale structures by the ambient vibration method began to appear regularly around 1970, with about 3/4 of all contributions devoted to the experiments in buildings, dams, chimneys and silos, and about 1/4 to bridges. Since 1985, there are only about 3 papers per year, world wide, dealing with this subject. The reviewed study is of a seven-story reinforced concrete building in Van Nuys, California, damaged by the 17 January 1994 Northridge earthquake and its early aftershocks. Two detailed ambient vibration surveys were conducted soon after the earthquake. The apparent frequencies and twoand three-dimensional mode shapes for longitudinal, transverse and vertical vibrations are presented in this paper. The attempts to detect the highly localized damage by simple spectral analyses of the ambient noise data were not successful. It is suggested that very high spatial resolution of recording is required to identify localized column and beam damage. The loss of axial capacity of the damaged columns could be seen in the vertical response of the columns, but similar moderate or weak damage typically would not be noticed in ambient vibration surveys. 1 Assistant Professor,   Research Associate Professor,
Discussion of "Evidence-based Health Informatics: How Do We Know What We Know?".

This article is part of a For-Discussion-Section of Methods of Information in Medicine about the paper "Evidence-based Health Informatics: How Do We Know What We Know?" written by Elske Ammenwerth . It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the Ammenwerth paper. In subsequent issues the discussion can continue through letters to the editor. With these comments on the paper "Evidence-based Health Informatics: How do we know what we know?", written by Elske Ammenwerth , the journal seeks to stimulate a broad discussion on the challenges of evaluating information processing and information technology in health care. An international group of experts has been invited by the editor of Methods to comment on this paper. Each of the invited commentaries forms one section of this paper.
Junction Tree Variational Autoencoder for Molecular Graph Generation

We seek to automate the design of molecules based on specific chemical properties. In computational terms, this task involves continuous embedding and generation of molecular graphs. Our primary contribution is the direct realization of molecular graphs, a task previously approached by generating linear SMILES strings instead of graphs. Our junction tree variational autoencoder generates molecular graphs in two phases, by first generating a tree-structured scaffold over chemical substructures, and then combining them into a molecule with a graph message passing network. This approach allows us to incrementally expand molecules while maintaining chemical validity at every step. We evaluate our model on multiple tasks ranging from molecular generation to optimization. Across these tasks, our model outperforms previous state-of-the-art baselines by a significant margin.
Perceived Exclusion in the Workplace : The Moderating Effects of Gender on Work-Related Attitudes and Psychological Health

Research examining the nature and consequences of social exclusion indicates that such behavior is multifaceted and has deleterious effects on the intended targets. However, relatively little research has specifically assessed the impact of such behavior on employees who perceive of themselves as being excluded within their place of work. Even less has examined gender differences in relation to exclusionary behavior. The current research investigated the moderating effect of gender on the relation between perceived exclusion at work and work-related attitudes and psychological health. Participants included 223 working students (64 men and 159 women). Hierarchical moderated regression analyses on work attitudes (supervisor satisfaction, coworker satisfaction) and psychological health supported initial predictions. At higher levels of perceived exclusion men indicated lower satisfaction and psychological health compared to women. Findings are discussed in terms of potential workplace implications and limitations of the current research.
Efficient secure similarity computation on encrypted trajectory data

Outsourcing database to clouds is a scalable and cost-effective way for large scale data storage, management, and query processing. Trajectory data contain rich spatio-temporal relationships and reveal many forms of individual sensitive information (e.g., home address, health condition), which necessitate them to be encrypted before being outsourced for privacy concerns. However, efficient query processing over encrypted trajectory data is a very challenging task. Though some achievements have been reported very recently for simple queries (e.g., SQL queries, kNN queries) on encrypted data, there is rather limited progress on secure evaluation of trajectory queries because they are more complex and need special treatment. In this paper, we focus on secure trajectory similarity computation that is the cornerstone of secure trajectory query processing. More specifically, we propose an efficient solution to securely compute the similarity between two encrypted trajectories, which reveals nothing about the trajectories, but the final result. We theoretically prove that our solution is secure against the semi-honest adversaries model as all the intermediate information in our protocols can be simulated in polynomial time. Finally we empirically study the efficiency of the proposed method, which demonstrates the feasibility of our solution.
On Deep Multi-View Representation Learning: Objectives and Optimization

We consider learning representations (features) in the set ting in which we have access to multiple unlabeled views of the data for learning while only one view i s available for downstream tasks. Previous work on this problem has proposed several techniqu es based on deep neural networks, typically involving either autoencoder-like networks wit h a reconstruction objective or paired feedforward networks with a batch-style correlation-based obj ective. We analyze several techniques based on prior work, as well as new variants, and compare them empirically on image, speech, and text tasks. We find an advantage for correlation-based re pres ntation learning, while the best results on most tasks are obtained with our new variant, deep canonically correlated autoencoders (DCCAE). We also explore a stochastic optimization procedu r for minibatch correlation-based objectives and discuss the time/performance trade-offs fo r kernel-based and neural network-based implementations.
Towards Robust Evaluations of Continual Learning

The experiments used in current continual learning research do not faithfully assess fundamental challenges of learning continually. We examine standard evaluations and show why these evaluations make some types of continual learning approaches look better than they are. In particular, current evaluations are biased towards continual learning approaches that treat previous models as a prior (e.g., EWC, VCL). We introduce desiderata for continual learning evaluations and explain why their absence creates misleading comparisons. Our analysis calls for a reprioritization of research effort by the community.
CRITICAL DISCOURSE ANALYSIS AND ITS CRITICS

This article briefly reviews the rise of Critical Discourse Analysis and teases out a detailed analysis of the various critiques that have been levelled at CDA and its practitioners over the last twenty years, both by scholars working within the “critical” paradigm and by other critics. A range of criticisms are discussed which target the underlying premises, the analytical methodology and the disputed areas of reader response and the integration of contextual factors. Controversial issues such as the predominantly negative focus of much CDA scholarship, and the status of CDA as an emergent “intellectual orthodoxy”, are also reviewed. The conclusions offer a summary of the principal criticisms that emerge from this overview, and suggest some ways in which these problems could be attenuated.
Universal Language Model Fine-Tuning with Subword Tokenization for Polish

Universal Language Model for Fine-tuning [6] (ULMFiT) is one of the first NLP methods for efficient inductive transfer learning. Unsupervised pretraining results in improvements on many NLP tasks for English. In this paper, we describe a new method that uses subword tokenization to adapt ULMFiT to languages with high inflection. Our approach results in a new state-of-the-art for the Polish language, taking first place in Task 3 of PolEval’18. After further training, our final model outperformed the second best model by 35%. We have open-sourced our pretrained models and code.
Minutiae-based Fingerprint Extraction and Recognition

In our electronically inter-connected society, reliable and user-friendly recognition and verification system is essential in many sectors of our life. The person’s physiological or behavioral characteristics, known as biometrics, are important and vital methods that can be used for identification and verification. Fingerprint recognition is one of the most popular biometric techniques used in automatic personal identification and verification. Many researchers have addressed the fingerprint classification problem and many approaches to automatic fingerprint classification have been presented in the literature; nevertheless, the research on this topic is still very active. Although significant progress has been made in designing automatic fingerprint identification systems over the past two decades, a number of design factors (lack of reliable minutia extraction algorithms, difficulty in quantitatively defining a reliable match between fingerprint images, poor image acquisition, low contrast images, the difficulty of reading the fingerprint for manual workers, etc.) create bottlenecks in achieving the desired performance. Nowadays, investigating the influence of the fingerprint quality on recognition performances also gains more and more attention. A fingerprint is the pattern of ridges and valleys on the surface of a fingertip. Each individual has unique fingerprints. Most fingerprint matching systems are based on four types of fingerprint representation schemes  grayscale image (Bazen et al., 2000), phase image (Thebaud, 1999), skeleton image (Feng, 2006; Hara & Toyama, 2007), and minutiae (Ratha et al., 2000; Bazen & Gerez, 2003). Due to its distinctiveness, compactness, and compatibility with features used by human fingerprint experts, minutiae-based representation has become the most widely adopted fingerprint representation scheme. The uniqueness of a fingerprint is exclusively determined by the local ridge characteristics and their relationships. The ridges and valleys in a fingerprint alternate, flowing in a local constant direction. The two most prominent local ridge characteristics are: 1) ridge ending and, 2) ridge bifurcation. A ridge ending is defined as the point where a ridge ends abruptly. A ridge bifurcation is defined as the point where a ridge forks or diverges into branch ridges. Collectively, these features are called minutiae. Detailed description of fingerprint minutiae will be given in the next section. The widespread deployment of fingerprint recognition systems in various applications has caused concerns that compromised fingerprint templates may be used to make fake fingers, which could then be used to deceive all fingerprint systems the same person is enrolled in.
Integrated Billing Solutions in the Internet of Things

The Internet of Things is one of the most promising technological developments in information technology. It promises huge financial and nonfinancial benefits across supply chains, in product life cycle and customer relationship applications as well as in smart environments. However, the adoption process of the Internet of Things has been slower than expected. One of the main reasons for this is the missing profitability for each individual stakeholder. Costs and benefits are not equally distributed. Cost benefit sharing models have been proposed to overcome this problem and to enable new areas of application. However, these cost benefit sharing approaches are complex, time consuming, and have failed to achieve broad usage. In this chapter, an alternative concept, suggesting flexible pricing and trading of information, is proposed. On the basis of a beverage supply chain scenario, a prototype installation, based on an open source billing solution and the Electronic Product Code Information Service (EPCIS), is shown as a proof of concept and an introduction to different pricing options. This approach allows a more flexible and scalable solution for cost benefit sharing and may enable new business models for the Internet of Things. University of Bremen, Planning and Control of Production Systems, Germany