nils-herrmann/clean_scirepeval huggingface dataset:
Review on Bovine Babesiosis and its Economical Importance

Babesiosis is a tick-borne disease of cattle caused by the protozoan parasites. The causative agents of Babesiosis are specific for particular species of animals. In cattle: B. bovis and B. bigemina are the common species involved in babesiosis. Rhipicephalus (Boophilus) spp., the principal vectors of B. bovis and B. bigemina, are widespread in tropical and subtropical countries. Babesia multiplies in erythrocytes by asynchronous binary fission, resulting in considerable pleomorphism. Babesia produces acute disease by two principle mechanism; hemolysis and circulatory disturbance. Affected animals suffered from marked rise in body temperature, loss of appetite, cessation of rumination, labored breathing, emaciation, progressive hemolytic anemia, various degrees of jaundice (Icterus). Lesions include an enlarged soft and pulpy spleen, a swollen liver, a gall bladder distended with thick granular bile, congested dark-coloured kidneys and generalized anemia and jaundice. The disease can be diagnosis by identification of the agent by using direct microscopic examination, nucleic acid-based diagnostic assays, in vitro culture and animal inoculation as well as serological tests like indirect fluorescent antibody, complement fixation and Enzyme-linked immunosorbent assays tests. Babesiosis occurs throughout the world. However, the distribution of the causative protozoa is governed by the geographical and seasonal distribution of the insect vectors. Recently Babesia becomes the most widespread parasite due to exposure of 400 million cattle to infection through the world, with consequent heavy economic losses such as mortality, reduction in meat and milk yield and indirectly through control measures of ticks. Different researches conducted in Ethiopia reveal the prevalence of the disease in different parts of the country. The most commonly used compounds for the treatment of babesiosis are diminazene diaceturate, imidocarb, and amicarbalide. Active prevention and control of Babesiosis is achieved by three main methods: immunization, chemoprophylaxis and vector control.
Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning

Learning visual features from unlabeled image data is an important yet challenging task, which is often achieved by training a model on some annotation-free information. We consider spatial contexts, for which we solve so-called jigsaw puzzles, i.e., each image is cut into grids and then disordered, and the goal is to recover the correct configuration. Existing approaches formulated it as a classification task by defining a fixed mapping from a small subset of configurations to a class set, but these approaches ignore the underlying relationship between different configurations and also limit their application to more complex scenarios. This paper presents a novel approach which applies to jigsaw puzzles with an arbitrary grid size and dimensionality. We provide a fundamental and generalized principle, that weaker cues are easier to be learned in an unsupervised manner and also transfer better. In the context of puzzle recognition, we use an iterative manner which, instead of solving the puzzle all at once, adjusts the order of the patches in each step until convergence. In each step, we combine both unary and binary features on each patch into a cost function judging the correctness of the current configuration. Our approach, by taking similarity between puzzles into consideration, enjoys a more reasonable way of learning visual knowledge. We verify the effectiveness of our approach in two aspects. First, it is able to solve arbitrarily complex puzzles, including high-dimensional puzzles, that prior methods are difficult to handle. Second, it serves as a reliable way of network initialization, which leads to better transfer performance in a few visual recognition tasks including image classification, object detection, and semantic segmentation.
Who reads research articles? An altmetrics analysis of Mendeley user categories

Vincent Larivière École de bibliothéconomie et des sciences de l’information, Université de Montréal, C.P. 6128, Succ. CentreVille, Montréal, QC H3C 3J7, Canada, and Observatoire des Sciences et des Technologies (OST), Centre Interuniversitaire de Recherche sur la Science et la Technologie (CIRST), Université du Québec à Montréal, CP 8888, Succ. Centre-Ville, Montréal, QC H3C 3P8, Canada. E-mail: vincent.lariviere@umontreal.ca
Laughing Metaphorically : Metaphor and Humour in Discourse

The common ground between figurative language and humour (in the form of jokes) is what Koestler (1964) termed the bisociation of ideas. In both jokes and metaphors, two disparate concepts are brought together, but the nature and the purpose of this conjunction is different in each case. This paper focuses on this notion of boundaries and attempts to go further by asking the question “when does a metaphor become a joke?”. More specifically, the main research questions of the paper are: (a) How do speakers use metaphor in discourse for humorous purposes? (b) What are the (metaphoric) cognitive processes that relate to the creation of humour in discourse? (c) What does the study of humour in discourse reveal about the nature of metaphoricity? This paper answers these questions by examining examples taken from a three-hour conversation, and considers how linguistic theories of humour (Raskin, 1985; Attardo and Raskin, 1991; Attardo, 1994; 2001) and cognitive theories of metaphor and blending (Lakoff and Johnson, 1980; Fauconnier and Turner, 2002) can benefit from each other. Boundaries in Humour and Metaphor The goal of this paper is to explore the relationship between metaphor (and, more generally, blending) and humour, in order to attain a better understanding of the cognitive processes that are involved or even contribute to laughter in discourse. This section will present briefly research in both areas and will identify possible common ground between the two. More specifically, the notion of boundaries will be explored in both areas. The following section explores how metaphor can be used for humorous purposes in discourse by applying relevant theories of humour and metaphor to conversational data. Linguistic theories of humour highlight the importance of duality and tension in humorous texts. Koestler (1964: 51) in discussing comic creativity notes that: The sudden bisociation of an idea or event with two habitually incompatible matrices will produce a comic effect, provided that the narrative, the semantic pipeline, carries the right kind of emotional tension. When the pipe is punctured, and our expectations are fooled, the now redundant tension gushes out in laughter, or is spilled in the gentler form of the sou-rire [my emphasis]. This oft-quoted passage introduces the basic themes and mechanisms that later were explored extensively within contemporary theories of humour: a humorous text must relate to two different and opposing in some way scenarios; this duality is not
International environmental law and global public health.

The environment continues to be a source of ill-health for many people, particularly in developing countries. International environmental law offers a viable strategy for enhancing public health through the promotion of increased awareness of the linkages between health and environment, mobilization of technical and financial resources, strengthening of research and monitoring, enforcement of health-related standards, and promotion of global cooperation. An enhanced capacity to utilize international environmental law could lead to significant worldwide gains in public health.
Affective Sentiment and Emotional

Sentiment and emotional analysis on online collaborative software development forums can be very useful to gain important insights into the behaviors and personalities of the developers. Such information can later on be used to increase productivity of developers by making recommendations on how to behave best in order to get a task accomplished. However, due to the highly technical nature of the data present in online collaborative software development forums, mining sentiments and emotions becomes a very challenging task. In this work we present a new approach for mining sentiments and emotions from software development datasets using Interaction Process Analysis(IPA) labels and machine learning. We also apply distance metric learning as a preprocessing step before training a feed forward neural network and report the precision, recall, F1 and accuracy.
Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems

IoT in industry is a rapidly developing area. Numerous IoT research and application projects have been done by universities or in joint industryuniversity consortia in recent years. However an important question to be further addressed is on value creation by IoT industry applications. IoT applications in the sense of this paper are solutions using IoT technologies to improve industrial manufacturing processes, enable new and efficient ways to operate production plants, create new service or supervision means for industrial installations, offer an optimized infrastructure, reduce operational cost or improve human safety in industrial areas. The present paper brings together Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems, 153–206.  Creation for Industry experts from academia, research and industry offering a view on the IoT application in industrial environment, the challenges, expected evolution of IoT technology and use in future factories, on connected and holistic processes. The paper is intended to contribute to an IoT supported paradigm change in manufacturing, industrial service and over life sustainable industrial activities.
Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising

In display advertising, click through rate (CTR) prediction is the problem of estimating the probability that an advertisement (ad) is clicked when displayed to a user in a specific context. Due to its easy implementation and promising performance, logistic regression (LR) model has been widely used for CTR prediction, especially in industrial systems. However, it is not easy for LR to capture the nonlinear information, such as the conjunction information, from user features and ad features. In this paper, we propose a novel model, called coupled group lasso (CGL), for CTR prediction in display advertising. CGL can seamlessly integrate the conjunction information from user features and ad features for modeling. Furthermore, CGL can automatically eliminate useless features for both users and ads, which may facilitate fast online prediction. Scalability of CGL is ensured through feature hashing and distributed implementation. Experimental results on real-world data sets show that our CGL model can achieve state-of-the-art performance on webscale CTR prediction tasks.   W&CP volume 32.
Sentiment analysis: A combined approach

Sentiment analysis is an important current research area. This paper combines rule-based classification, supervised learning andmachine learning into a new combinedmethod. Thismethod is tested onmovie reviews, product reviews and MySpace comments. The results show that a hybrid classification can improve the classification effectiveness in terms of microand macro-averaged F1. F1 is a measure that takes both the precision and recall of a classifier’s effectiveness into account. In addition, we propose a semi-automatic, complementary approach in which each classifier can contribute to other classifiers to achieve a good level of effectiveness.
On Permutation Representations for Scheduling Problems

In this paper we concentrate on Job Shop Scheduling as a representative of constrained combinatorial problems. We introduce a new permutation representation for this problem. Three crossover operators , diierent in tending to preserve the relative order, the absolute order, and the position in the permutation, are deened. By experiment we observe the strongest phenotypical correlation between parents and oospring when respecting the absolute order. It is shown that a genetic algorithm using an operator which preserves the absolute order also obtains a superior solution quality.
A Discriminative Approach to Topic-Based Citation Recommendation

In this paper, we present a study of a novel problem, i.e. topic-based citation recommendation, which involves recommending papers to be referred to. Traditionally, this problem is usually treated as an engineering issue and dealt with using heuristics. This paper gives a formalization of topic-based citation recommendation and proposes a discriminative approach to this problem. Specifically, it proposes a two-layer Restricted Boltzmann Machine model, called RBMCS, which can discover topic distributions of paper content and citation relationship simultaneously. Experimental results demonstrate that RBM-CS can significantly outperform baseline methods for citation recommendation.
Global catches , exploitation rates , and rebuilding options for sharks

Adequate conservation and management of shark populations is becoming increasingly important on a global scale, especially because many species are exceptionally vulnerable to overfishing. Yet, reported catch statistics for sharks are incomplete, and mortality estimates have not been available for sharks as a group. Here, the global catch and mortality of sharks from reported and unreported landings, discards, and shark finning are being estimated at 1.44 million metric tons for the year 2000, and at only slightly less in 2010 (1.41 million tons). Based on an analysis of average shark weights, this translates into a total annual mortality estimate of about 100 million sharks in 2000, and about 97 million sharks in 2010, with a total range of possible values between 63 and 273 million sharks per year. Further, the exploitation rate for sharks as a group was calculated by dividing two independent mortality estimates by an estimate of total global biomass. As an alternative approach, exploitation rates for individual shark populations were compiled and averaged from stock assessments and other published sources. The resulting three independent estimates of the average exploitation rate ranged between 6.4% and 7.9% of sharks killed per year. This exceeds the average rebound rate for many shark populations, estimated from the life history information on 62 shark species (rebound rates averaged 4.9% per year), and explains the ongoing declines in most populations for which data exist. The consequences of these unsustainable catch and mortality rates for marine ecosystems could be substantial. Global total shark mortality, therefore, needs to be reduced drastically in order to rebuild depleted populations and restore marine ecosystems with functional top predators. & 2013 Elsevier Ltd. All rights reserved.
From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0

This paper presents Integrated Information Theory (IIT) of consciousness 3.0, which incorporates several advances over previous formulations. IIT starts from phenomenological axioms: information says that each experience is specific--it is what it is by how it differs from alternative experiences; integration says that it is unified--irreducible to non-interdependent components; exclusion says that it has unique borders and a particular spatio-temporal grain. These axioms are formalized into postulates that prescribe how physical mechanisms, such as neurons or logic gates, must be configured to generate experience (phenomenology). The postulates are used to define intrinsic information as "differences that make a difference" within a system, and integrated information as information specified by a whole that cannot be reduced to that specified by its parts. By applying the postulates both at the level of individual mechanisms and at the level of systems of mechanisms, IIT arrives at an identity: an experience is a maximally irreducible conceptual structure (MICS, a constellation of concepts in qualia space), and the set of elements that generates it constitutes a complex. According to IIT, a MICS specifies the quality of an experience and integrated information ΦMax its quantity. From the theory follow several results, including: a system of mechanisms may condense into a major complex and non-overlapping minor complexes; the concepts that specify the quality of an experience are always about the complex itself and relate only indirectly to the external environment; anatomical connectivity influences complexes and associated MICS; a complex can generate a MICS even if its elements are inactive; simple systems can be minimally conscious; complicated systems can be unconscious; there can be true "zombies"--unconscious feed-forward systems that are functionally equivalent to conscious complexes.
Formal Ontology and Information Systems

Research on ontology is becoming increasingly widespread in the computer science community, and its importance is being recognized in a multiplicity of research fields and application areas, including knowledge engineering, database design and integration, information retrieval and extraction. We shall use the generic term “information systems”, in its broadest sense, to collectively refer to these application perspectives. We argue in this paper that so-called ontologies present their own methodological and architectural peculiarities: on the methodological side, their main peculiarity is the adoption of a highly interdisciplinary approach, while on the architectural side the most interesting aspect is the centrality of the role they can play in an information system, leading to the perspective of ontology-driven information systems.
Leave-One-Out Kernel Optimization for Shadow Detection and Removal

The objective of this work is to detect shadows in images. We pose this as the problem of labeling image regions, where each region corresponds to a group of superpixels. To predict the label of each region, we train a kernel Least-Squares Support Vector Machine (LSSVM) for separating shadow and non-shadow regions. The parameters of the kernel and the classifier are jointly learned to minimize the leave-one-out cross validation error. Optimizing the leave-one-out cross validation error is typically difficult, but it can be done efficiently in our framework. Experiments on two challenging shadow datasets, UCF and UIUC, show that our region classifier outperforms more complex methods. We further enhance the performance of the region classifier by embedding it in a Markov Random Field (MRF) framework and adding pairwise contextual cues. This leads to a method that outperforms the state-of-the-art for shadow detection. In addition we propose a new method for shadow removal based on region relighting. For each shadow region we use a trained classifier to identify a neighboring lit region of the same material. Given a pair of lit-shadow regions we perform a region relighting transformation based on histogram matching of luminance values between the shadow region and the lit region. Once a shadow is detected, we demonstrate that our shadow removal approach produces results that outperform the state of the art by evaluating our method using a publicly available benchmark dataset.
SDCNet: Video Prediction Using Spatially-Displaced Convolution

We present an approach for high-resolution video frame prediction by conditioning on both past frames and past optical flows. Previous approaches rely on resampling past frames, guided by a learned future optical flow, or on direct generation of pixels. Resampling based on flow is insufficient because it cannot deal with disocclusions. Generative models currently lead to blurry results. Recent approaches synthesis a pixel by convolving input patches with a predicted kernel. However, their memory requirement increases with kernel size. Here, we present spatially-displaced convolution (SDC) module for video frame prediction. We learn a motion vector and a kernel for each pixel and synthesize a pixel by applying the kernel at a displaced location in the source image, defined by the predicted motion vector. Our approach inherits the merits of both vector-based and kernel-based approaches, while ameliorating their respective disadvantages. We train our model on 428K unlabelled 1080p video game frames. Our approach produces state-of-the-art results, achieving an SSIM score of 0.904 on high-definition YouTube-8M videos, 0.918 on Caltech Pedestrian videos. Our model handles large motion effectively and synthesizes crisp frames with consistent motion.
Essay-Anchor Attentive Multi-Modal Bilinear Pooling for Textbook Question Answering

Textbook Question Answering (TQA)  is a newly proposed task to answer arbitrary questions in middle school curricula, which has particular challenges to understand the long essays in additional to the images. Bilinear models ,  are effective at learning high-level associations between questions and images, but are inefficient to handle the long essays. In this paper, we propose an Essay-anchor Attentive Multi-modal Bilinear pooling (EAMB), a novel method to encode the long essays into the joint space of the questions and images. The essay-anchors, embedded from the keywords, represent the essay information in a latent space. We propose a novel network architecture to pay special attention on the keywords in the questions, consequently encoding the essay information into the question features, and thus the joint space with the images. We then use the bilinear models to extract the multi-modal interactions to obtain the answers. EAMB successfully utilizes the redundancy of the pre-trained word embedding space to represent the essay-anchors. This avoids the extra learning difficulties from exploiting large network structures. Quantitative and qualitative experiments show the outperforming effects of EAMB on the TQA dataset.
Deep Bayesian Active Semi-Supervised Learning

In many applications the process of generating label information is expensive and time consuming. We present a new method that combines active and semi-supervised deep learning to achieve high generalization performance from a deep convolutional neural network with as few known labels as possible. In a setting where a small amount of labeled data as well as a large amount of unlabeled data is available, our method first learns the labeled data set. This initialization is followed by an expectation maximization algorithm, where further training reduces classification entropy on the unlabeled data by targeting a low entropy fit which is consistent with the labeled data. In addition the algorithm asks at a specified frequency an oracle for labels of data with entropy above a certain entropy quantile. Using this active learning component we obtain an agile labeling process that achieves high accuracy, but requires only a small amount of known labels. For the MNIST dataset we report an error rate of 2.06% using only 300 labels and 1.06% for 1,000 labels. These results are obtained without employing any special network architecture or data augmentation.