nils-herrmann/clean_scirepeval huggingface dataset:
A Hybrid Hierarchical Model for Multi-Document Summarization

Scoring sentences in documents given abstract summaries created by humans is important in extractive multi-document summarization. In this paper, we formulate extractive summarization as a two step learning problem building a generative model for pattern discovery and a regression model for inference. We calculate scores for sentences in document clusters based on their latent characteristics using a hierarchical topic model. Then, using these scores, we train a regression model based on the lexical and structural characteristics of the sentences, and use the model to score sentences of new documents to form a summary. Our system advances current state-of-the-art improving ROUGE scores by ∼7%. Generated summaries are less redundant and more coherent based upon manual quality evaluations.
Issues in Strategic Decision Modelling

Models are invaluable tools for strategic planning. Models help key decision makers develop a shared conceptual understanding of complex decisions, identify sensitivity factors and test management scenarios. Different modelling approaches are specialist areas in themselves. Model development can be onerous, expensive, time consuming, and often bewildering. It is also an iterative process where the true magnitude of the effort, time and data required is often not fully understood until well into the process. This paper explores the traditional approaches to strategic planning modelling commonly used in organisations and considers the application of a real-options approach to match and benefit from the increasing uncertainty in today’s rapidly changing world.
Bayes-Optimal Hierarchical Multilabel Classification

Hierarchical multilabel classification allows a sample to belong to multiple class labels residing on a hierarchy, which can be a tree or directed acyclic graph (DAG). However, popular hierarchical loss functions, such as the H-loss, can only be defined on tree hierarchies (but not on DAGs), and may also under- or over-penalize misclassifications near the bottom of the hierarchy. Besides, it has been relatively unexplored on how to make use of the loss functions in hierarchical multilabel classification. To overcome these deficiencies, we first propose hierarchical extensions of the Hamming loss and ranking loss which take the mistake at every node of the label hierarchy into consideration. Then, we first train a general learning model, which is independent of the loss function. Next, using Bayesian decision theory, we develop Bayes-optimal predictions that minimize the corresponding risks with the trained model. Computationally, instead of requiring an exhaustive summation and search for the optimal multilabel, the resultant optimization problem can be efficiently solved by a greedy algorithm. Experimental results on a number of real-world data sets show that the proposed Bayes-optimal classifier outperforms state-of-the-art methods.
Therapeutic satisfaction and subjective effects of different strains of pharmaceutical-grade cannabis.

In The Netherlands, pharmaceutical-grade cultivated cannabis is distributed for medicinal purposes as commissioned by the Ministry of Health. Few studies have thus far described its therapeutic efficacy or subjective (adverse) effects in patients. The aims of this study are to assess the therapeutic satisfaction within a group of patients using prescribed pharmaceutical-grade cannabis and to compare the subjective effects among the available strains with special focus on their delta-9-tetrahydrocannabinol and cannabidiol content. In a cross-sectional and natural design, users of pharmaceutical-grade cannabis were investigated with questionnaires. Medical background of the patients was asked as well as experienced therapeutic effects and characteristics of cannabis use. Subjective effects were measured with psychometric scales and used to compare among the strains of cannabis used across this group of patients. One hundred two patients were included; their average age was 53 years and 76% used it for more than a year preceding this study. Chronic pain (53%; n = 54) was the most common medical indication for using cannabis followed by multiple sclerosis (23%; n = 23), and 86% (n = 88) of patients (almost) always experienced therapeutic satisfaction when using pharmaceutical cannabis. Dejection, anxiety, and appetite stimulation were found to differ among the 3 strains of cannabis. These results show that patients report therapeutic satisfaction with pharmaceutical cannabis, mainly pain alleviation. Some subjective effects were found to differ among the available strains of cannabis, which is discussed in relation to their different tetrahydrocannabinol/cannabidiol content. These results may aid in further research and critical appraisal for medicinally prescribed cannabis products.
Minimizing Network Traffic Features for Android Mobile Malware Detection

Smartphones have emerged as one of the dominant computing platforms in today's era where Android has been the first choice for users as well as app developers due to its open source nature and feature rich apps. Such popularity has come hand-in-hand with an equivalent increase in malware targeting Android. Since mobile devices allow easy-to-use, touch-sensitive, and anywhere-anytime access to its resources, the mobile-specific applications like SMS, MMS, Bluetooth, e-mail, and other services may pose serious threats and lead to financial losses and privacy leakages. In recent time, high attention is drawn by the researchers for detecting Android malware; very fewer community have considered network traffic analysis in their detection models. The majority of these models have considered the detection primarily on traffic features that distinguish malware traffic from normal traffic. In this paper, we have proposed an algorithm to prioritize network traffic features with an aim to minimize the number of features to be analyzed to give high detection accuracy along with reduced training and testing time. To this extent, we have used statistical tests to rank the features. Results demonstrate that using prioritized features for detection not only reduces the training and testing time, but also gives slightly higher detection accuracy than using all the features together by measuring Fmeasure, a widely used measure for detection accuracy. The training time of 300 applications is reduced from 11.7 seconds to 5.8 seconds and testing time of 230 applications is reduced from 25.1 seconds to 17.3 seconds, hence reduction of around 50% and 31% in training time and testing time respectively. We believe this time difference will have a larger impact if there are thousands of files to be tested.
Least squares linear discriminant analysis

Linear Discriminant Analysis (LDA) is a well-known method for dimensionality reduction and classification. LDA in the binaryclass case has been shown to be equivalent to linear regression with the class label as the output. This implies that LDA for binary-class classifications can be formulated as a least squares problem. Previous studies have shown certain relationship between multivariate linear regression and LDA for the multi-class case. Many of these studies show that multivariate linear regression with a specific class indicator matrix as the output can be applied as a preprocessing step for LDA. However, directly casting LDA as a least squares problem is challenging for the multi-class case. In this paper, a novel formulation for multivariate linear regression is proposed. The equivalence relationship between the proposed least squares formulation and LDA for multi-class classifications is rigorously established under a mild condition, which is shown empirically to hold in many applications involving high-dimensional data. Several LDA extensions based on the equivalence relationship are discussed.
Working Paper : Designing a Data Governance Framework

Data governance is an emerging research area getting attention from information systems (IS) scholars and practitioners. In this paper I take a look at existing literature and current state-of-the-art in data governance. I found out that there is only a limited amount of existing scientific literature, but many practitioners are already treating data as a valuable corporate asset. The paper describes an action design research project that will be conducted in 2012-2016 and is expected to result in a generic data governance framework.
Ambient intelligence: A survey

In this article we survey ambient intelligence (AmI), including its applications, some of the technologies it uses, and its social and ethical implications. The applications include AmI at home, care of the elderly, healthcare, commerce, and business, recommender systems, museums and tourist scenarios, and group decision making. Among technologies, we focus on ambient data management and artificial intelligence; for example planning, learning, event-condition-action rules, temporal reasoning, and agent-oriented technologies. The survey is not intended to be exhaustive, but to convey a broad range of applications, technologies, and technical, social, and ethical challenges.
Markov Random Field modeling, inference & learning in computer vision & image understanding: A survey

In this paper, we present a comprehensive survey of Markov Random Fields (MRFs) in computer vision and image understanding, with respect to the modeling, the inference and the learning. While MRFs were introduced into the computer vision eld about two decades ago, they started to become a ubiquitous tool for solving visual perception problems around the turn of the millennium following the emergence of efficient inference methods. During the past decade, a variety of MRF models as well as inference and learning methods have been developed for addressing numerous low, mid and high-level vision problems. While most of the literature concerns pairwise MRFs, in recent years we have also witnessed signi cant progress in higher-order MRFs, which substantially enhances the expressiveness of graph-based models and expands the domain of solvable problems. This survey provides a compact and informative summary of the major literature in this research topic.
A Blending and Inter-Modal Transportation Model for the Coal Distribution Problem

⎯The problem of how to plan coal fuel blending and distribution from overseas coal sources to domestic power plants through some possible seaports by certain types of fleet in order to meet operational and environmental requirements is a complex task. The aspects under consideration includes each coal source contract’s supply, quality and price, each power plant’s demand, environmental requirements and limit on maximum number of different coal sources that can supply it, installation of blending facilities, selection of fleet types, and transient seaport’s capacity limit on fleet types. A coal blending and inter-model transportation model is explored to find optimal blending and distribution decisions for coal fuel from overseas contracts to domestic power plants. The objective in this study is to minimize total logistics costs, including procurement cost, shipping cost, and inland delivery cost. The developed model is one type of mix-integer zero-one programming problems. A real-world case problem is presented using the coal logistics system of a local electric utility company to demonstrate the benefit of the proposed approach. A well-known optimization package, AMPL-CPLEX, is utilized to solve this problem. Results from this study suggest that the obtained solution is better than the rule-of-thumb solution and the developed model provides a tool for management to conduct capacity expansion planning and power generation options. Keywords⎯Blending and inter-modal transportation model, Integer programming, Coal fuel. ∗ Corresponding author’s email: cmliu@fcu.edu.tw International Journal of Operations Research
NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition

Scene text recognition has attracted a great many researches for decades due to its importance to various applications. Existing sequence-to-sequence (seq2seq) recognizers mainly adopt Connectionist Temporal Classification (CTC) or Attention based Recurrent or Convolutional networks, and have made great progresses in scene text recognition. However, we observe that current methods suffer from slow training speed because the internal recurrence of RNNs limits training parallelization, and high complexity because of stacking too many convolutional layers in order to extract features over long input sequence. To tackle the above problems, this paper presents a no-recurrence seq2seq model, named NRTR, that relies only on the attention mechanism dispensing with recurrences and convolutions entirely. NRTR consists of an encoder that transforms an input sequence to the hidden feature representation, and a decoder that generates an output sequence of characters from the encoder output. Both of the encoder and the decoder are based on self-attention module to learn positional dependencies, which could be trained with more parallelization and less complexity. Besides, we also propose a modality-transform block to effectively transform the input image to the corresponding sequence, which could be used by the encoder directly. NRTR is end-to-end trainable and is not confined to any predefined lexicon. Extensive experiments on various benchmarks, including the IIIT5K, SVT and ICDAR datasets, show that NRTR achieves the state-of-the-art or highlycompetitive performances in both lexicon-free and lexicon-based scene text recognition tasks, while requiring only one order of magnitude less time for model training compared to current methods.
Democratic Leadership : The Lessons of Exemplary Models for Democratic Governance

Since leadership plays a vital role in democratic movements, understanding the nature of democratic leadership is essential. However, the definition of democratic leadership is unclear (Gastil, 1994). Also, little research has defined democratic leadership in the context of democratic movements. The leadership literature has paid no attention to democratic leadership in such movements, focusing on democratic leadership within small groups and organizations. This study proposes a framework of democratic leadership in democratic movements. The framework includes contexts, motivations, characteristics, and outcomes of democratic leadership. The study considers sacrifice, courage, symbolism, citizen participation, and vision as major characteristics in the display of democratic leadership in various political, social, and cultural contexts. Applying the framework to Nelson Mandela, Lech Walesa, and Dae Jung Kim; the study considers them as exemplary models of democratic leadership in democratic movements for achieving democracy. They have showed crucial characteristics of democratic leadership, offering lessons for democratic governance.
A language-independent neural network for event detection

Event detection remains a challenge because of the difficulty of encoding the word semantics in various contexts. Previous approaches have heavily depended on language-specific knowledge and preexisting natural language processing tools. However, not all languages have such resources and tools available compared with English language. A more promising approach is to automatically learn effective features from data, without relying on language-specific resources. In this study, we develop a language-independent neural network to capture both sequence and chunk information from specific contexts and use them to train an event detector for multiple languages without any manually encoded features. Experiments show that our approach can achieve robust, efficient and accurate results for various languages. In the ACE 2005 English event detection task, our approach achieved a 73.4% F-score with an average of 3.0% absolute improvement compared with state-of-the-art. Additionally, our experimental results are competitive for Chinese and Spanish.
The 2018 Nucleic Acids Research database issue and the online molecular biology database collection

The 2018 Nucleic Acids Research Database Issue contains 181 papers spanning molecular biology. Among them, 82 are new and 84 are updates describing resources that appeared in the Issue previously. The remaining 15 cover databases most recently published elsewhere. Databases in the area of nucleic acids include 3DIV for visualisation of data on genome 3D structure and RNArchitecture, a hierarchical classification of RNA families. Protein databases include the established SMART, ELM and MEROPS while GPCRdb and the newcomer STCRDab cover families of biomedical interest. In the area of metabolism, HMDB and Reactome both report new features while PULDB appears in NAR for the first time. This issue also contains reports on genomics resources including Ensembl, the UCSC Genome Browser and ENCODE. Update papers from the IUPHAR/BPS Guide to Pharmacology and DrugBank are highlights of the drug and drug target section while a number of proteomics databases including proteomicsDB are also covered. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been updated, reviewing 138 entries, adding 88 new resources and eliminating 47 discontinued URLs, bringing the current total to 1737 databases. It is available at http://www.oxfordjournals.org/nar/database/c/.
Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey From Data to Methodology

The automatic identification system (AIS) tracks vessel movement by means of electronic exchange of navigation data between vessels, with onboard transceiver, terrestrial, and/or satellite base stations. The gathered data contain a wealth of information useful for maritime safety, security, and efficiency. Because of the close relationship between data and methodology in marine data mining and the importance of both of them in marine intelligence research, this paper surveys AIS data sources and relevant aspects of navigation in which such data are or could be exploited for safety of seafaring, namely traffic anomaly detection, route estimation, collision prediction, and path planning.
Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance

In this work we introduce Deforming Autoencoders, a generative model for images that disentangles shape from appearance in an unsupervised manner. As in the deformable template paradigm, shape is represented as a deformation between a canonical coordinate system (‘template’) and an observed image, while appearance is modeled in deformation-invariant, template coordinates. We introduce novel techniques that allow this approach to be deployed in the setting of autoencoders and show that this method can be used for unsupervised group-wise image alignment. We show experiments with expression morphing in humans, hands, and digits, face manipulation, such as shape and appearance interpolation, as well as unsupervised landmark localization. We also achieve a more powerful form of unsupervised disentangling in template coordinates, that successfully decomposes face images into shading and albedo, allowing us to further manipulate face images.
Coding, playgrounds and literacy in early childhood education: The development of KIBO robotics and ScratchJr

This paper describes two programming environments explicitly designed for early childhood education, the screen-based ScratchJr; and the tangible robotic kit KIBO. Both of these tools were explicitly designed to support the learning of concepts and skills of computer science and engineering in a developmentally appropriate way. The design principles are based on the notion of "coding as playground" and "coding as literacy".
Can Cognitive Dissonance Theory Explain Consumer Behavior ?

Cognitive dissonance theory is applicable to very limited areas of consumer behavior according to the author. Published findings in support of the theory are equivocal; they fail to show that cognitive dissonance is the only possible cause of observed "dissonance-reducing" behavior. Experimental evidences are examined and their weaknesses pointed out by the author to justify his position. He also provides suggestions regarding the circumstances under which dissonance reduction may be useful in increasing the repurchase probability of a purchased brand.
PPEDNet: Pyramid Pooling Encoder-Decoder Network for Real-Time Semantic Segmentation

Image semantic segmentation is a fundamental problem and plays an important role in computer vision and artificial intelligence. Recent deep neural networks have improved the accuracy of semantic segmentation significantly. Meanwhile, the number of network parameters and floating point operations have also increased notably. The realworld applications not only have high requirements on the segmentation accuracy, but also demand real-time processing. In this paper, we propose a pyramid pooling encoder-decoder network named PPEDNet for both better accuracy and faster processing speed. Our encoder network is based on VGG16 and discards the fully connected layers due to their huge amounts of parameters. To extract context feature efficiently, we design a pyramid pooling architecture. The decoder is a trainable convolutional network for upsampling the output of the encoder, and finetuning the segmentation details. Our method is evaluated on CamVid dataset, achieving 7.214% mIOU accuracy improvement while reducing 17.9% of the parameters compared with the state-of-the-art algorithm.
Exploring Gamification Techniques and Applications for Sustainable Tourism

Tourism is perceived as an appropriate solution for pursuing sustainable economic growth due to its main characteristics. In the context of sustainable tourism, gamification can act as an interface between tourists (clients), organisations (companies, NGOs, public institutions) and community, an interface built in a responsible and ethical way. The main objective of this study is to identify gamification techniques and applications used by organisations in the hospitality and tourism industry to improve their sustainable activities. The first part of the paper examines the relationship between gamification and sustainability, highlighting the links between these two concepts. The second part identifies success stories of gamification applied in hospitality and tourism and reviews gamification benefits by analysing the relationship between tourism organisations and three main tourism stakeholders: tourists, tourism employees and local community. The analysis is made in connection with the main pillars of sustainability: economic, social and environmental. This study is positioning the role of gamification in the tourism and hospitality industry and further, into the larger context of sustainable development.