nils-herrmann/clean_scirepeval huggingface dataset:
Discriminative and Geometry Aware Unsupervised Domain Adaptation

Domain adaptation (DA) aims to generalize a learning model across training and testing data despite the mismatch of their data distributions. In light of a theoretical estimation of upper error bound, we argue in this paper that an effective DA method should 1) search a shared feature subspace where source and target data are not only aligned in terms of distributions as most state of the art DA methods do, but also discriminative in that instances of different classes are well separated; 2) account for the geometric structure of the underlying data manifold when inferring data labels on the target domain. In comparison with a baseline DA method which only cares about data distribution alignment between source and target, we derive three different DA models, namely CDDA, GA-DA, and DGA-DA, to highlight the contribution of Close yet Discriminative DA(CDDA) based on 1), Geometry Aware DA (GA-DA) based on 2), and finally Discriminative and Geometry Aware DA (DGA-DA) implementing jointly 1) and 2). Using both synthetic and real data, we show the effectiveness of the proposed approach which consistently outperforms state of the art DA methods over 36 image classification DA tasks through 6 popular benchmarks. We further carry out in-depth analysis of the proposed DA method in quantifying the contribution of each term of our DA model and provide insights into the proposed DA methods in visualizing both real and synthetic data. Index Terms Domain adaptation, Transfer Learning, Visual classification, Discriminative learning, Data distribution matching, Data manifold geometric structure alignment.
Improved Statistical Machine Translation Using Paraphrases

Parallel corpora are crucial for training SMT systems. However, for many language pairs they are available only in very limited quantities. For these language pairs a huge portion of phrases encountered at run-time will be unknown. We show how techniques from paraphrasing can be used to deal with these otherwise unknown source language phrases. Our results show that augmenting a stateof-the-art SMT system with paraphrases leads to significantly improved coverage and translation quality. For a training corpus with 10,000 sentence pairs we increase the coverage of unique test set unigrams from 48% to 90%, with more than half of the newly covered items accurately translated, as opposed to none in current approaches.
Hospital Nurses' Work Environment Characteristics and Patient Safety Outcomes: A Literature Review.

This integrative literature review assesses the relationship between hospital nurses' work environment characteristics and patient safety outcomes and recommends directions for future research based on examination of the literature. Using an electronic search of five databases, 18 studies published in English between 1999 and 2016 were identified for review. All but one study used a cross-sectional design, and only four used a conceptual/theoretical framework to guide the research. No definition of work environment was provided in most studies. Differing variables and instruments were used to measure patient outcomes, and findings regarding the effects of work environment on patient outcomes were inconsistent. To clarify the relationship between nurses' work environment characteristics and patient safety outcomes, researchers should consider using a longitudinal study design, using a theoretical foundation, and providing clear operational definitions of concepts. Moreover, given the inconsistent findings of previous studies, they should choose their measurement methodologies with care.
Digital video tampering detection: An overview of passive techniques

Video tampering is a process of malicious alteration of video content, so as to conceal an object, an event or change the meaning conveyed by the imagery in the video. Fast proliferation of video acquisition devices and powerful video editing software tools have made video tampering an easy task. Hence, the authentication of video files (especially in surveillance applications like bank ATM videos, medical field and legal concerns) are becoming important. Video tampering detection aims to find the traces of tampering and thereby evaluate the authenticity and integrity of the video file. These methods can be classified into active and passive (blind) methods. In this paper, we present a survey on passive video tampering detection methods. Passive video tampering detection methods are classified into the following three categories based on the type of forgery they address: Detection of double or multiple compressed videos, Region tampering detection and Video inter-frame forgery detection. At first, we briefly present the preliminaries of video files required for understanding video tampering forgery. The existing papers surveyed are presented concisely; the features used and their limitations are summarized in a compact tabular form. Finally, we have identified some open issues that help to identify new research areas in passive video tampering detection.
Chronic obstructive pulmonary disease and cervico-thoracic musculoskeletal dysfunction

Conservative non-pharmacological evidence-based management options for Chronic Obstructive Pulmonary Disease (COPD) primarily focus on developing physiological capacity. With co-morbidities, including those of the musculoskeletal system, contributing to the overall severity of the disease, further research was needed. This thesis presents a critical review of the active and passive musculoskeletal management approaches currently used in COPD. The evidence for using musculoskeletal interventions in COPD management was inconclusive. Whilst an evaluation of musculoskeletal changes and their influence on pulmonary function was required, it was apparent that this would necessitate a significant programme of research. In view of this a narrative review of musculoskeletal changes in the cervico-thoracic region was undertaken. With a paucity of literature exploring chest wall flexibility and recent clinical guidelines advocating research into thoracic mobility exercises in COPD, a focus on thoracic spine motion analysis literature was taken. On critically reviewing the range of current in vivo measurement techniques it was evident that soft tissue artefact was a potential source of measurement error. As part of this thesis, soft tissue artefact during thoracic spine axial rotation was quantified. Given the level was deemed unacceptable, an alternative approach was developed and tested for intra-rater reliability. This technique, in conjunction with a range of other measures, was subsequently used to evaluate cervico-thoracic musculoskeletal changes and their relationship with pulmonary function in COPD. In summary, subjects with COPD were found to have reduced spinal motion, altered posture and increased muscle sensitivity compared to controls. Reduced spinal motion and altered neck posture were associated with reduced pulmonary function and having diagnosed COPD. Results from this thesis provide evidence to support inception of a clinical trial of flexibility or mobility exercises
Folktale similarity based on ontological abstraction Marijn Schraagen Digital

This paper presents a method to compute similarity of folktales based on conceptual overlap at various levels of abstraction as defined in Dutch WordNet. The method is applied on a corpus of Dutch folktales and evaluated using a comparison to traditional folktale similarity analysis based on the Aarne–Thompson–Uther (ATU) classification system. Document similarity computed by the presented method is in agreement with traditional analysis for a certain amount of folktale pairs, but differ for other pairs. However, it can be argued that the current approach computes an alternative, data-driven type of similarity. Using WordNet instead of a domainspecific ontology or classification system ensures applicability of the method outside of the folktale domain.
Mental models and deduction

According to the mental-model theory of deductive reasoning, reasoners use the meanings of assertions together with general knowledge to construct mental models of the possibilities compatible with the premises. Each model represents what is true in a possibility. A conclusion is held to be valid if it holds in all the models of the premises. Recent evidence described here shows that the fewer models an inference calls for, the easier the inference is. Errors arise because reasoners fail to consider all possible models, and because models do not normally represent what is false, even though reasoners can construct counterexamples to refute invalid conclusions.
Transactional Locking II

The transactional memory programming paradigm is gaining momentum as the approach of choice for replacing locks in concurrent programming. This paper introduces the transactional locking II (TL2) algorithm, a software transactional memory (STM) algorithm based on a combination of commit-time locking and a novel global version-clock based validation technique. TL2 improves on state-of-the-art STMs in the following ways: (1) unlike all other STMs it fits seamlessly with any systems memory life-cycle, including those using malloc/free (2) unlike all other lock-based STMs it efficiently avoids periods of unsafe execution, that is, using its novel version-clock validation, user code is guaranteed to operate only on consistent memory states, and (3) in a sequence of high performance benchmarks, while providing these new properties, it delivered overall performance comparable to (and in many cases better than) that of all former STM algorithms, both lock-based and non-blocking. Perhaps more importantly, on various benchmarks, TL2 delivers performance that is competitive with the best hand-crafted fine-grained concurrent structures. Specifically, it is ten-fold faster than a single lock. We believe these characteristics make TL2 a viable candidate for deployment of transactional memory today, long before hardware transactional support is available.
Aggregate production : Fines generation during rock crushing

The energy required to crush rocks is proportional to the amount of new surface area that is created; hence, a very important percentage of the energy consumed to produce construction aggregates is spent in producing non-commercial fines. Data gathered during visits to quarries, an extensive survey and laboratory experiments are used to explore the role of mineralogy and fracture mode in fines production during the crushing of single aggregates and aggregates within granular packs. Results show that particle-level loading conditions determine the failure mode, resulting particle shape and fines generation. Point loading (both single particles and grains in loose packings) produces clean fractures and a small percentage of fines. In choked operations, high inter-particle coordination controls particle-level loading conditions, causesmicro-fractures on new aggregate faces and generates a large amount of fines. The generation of fines increases when shear is imposed during crushing. Aggregates produced in current crushing operations show the effects of multiple loading conditions and fracture modes. Results support the producers' empirical observations that the desired cubicity of aggregates is obtained at the expense of increased fines generation when standard equipment is used.
Multiangle Social Network Recommendation Algorithms and Similarity Network Evaluation

Multiangle social network recommendation algorithms (MSN) and a new assessment method, called similarity network evaluation (SNE), are both proposed. From the viewpoint of six dimensions, the MSN are classified into six algorithms, including user-based algorithm from resource point (UBR), user-based algorithm from tag point (UBT), resource-based algorithm from tag point (RBT), resource-based algorithm from user point (RBU), tag-based algorithm from resource point (TBR), and tag-based algorithm from user point (TBU). Compared with the traditional recall/precision (RP) method, the SNE is more simple, effective, and visualized. The simulation results show that TBR and UBR are the best algorithms, RBU and TBU are the worst ones, and UBT and RBT are in the medium levels.
Deep Neural Networks for YouTube Recommendations

YouTube represents one of the largest scale and most sophisticated industrial recommendation systems in existence. In this paper, we describe the system at a high level and focus on the dramatic performance improvements brought by deep learning. The paper is split according to the classic two-stage information retrieval dichotomy: first, we detail a deep candidate generation model and then describe a separate deep ranking model. We also provide practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user-facing impact.
Learning a Deep Embedding Model for Zero-Shot Learning

Zero-shot learning (ZSL) models rely on learning a joint embedding space where both textual/semantic description of object classes and visual representation of object images can be projected to for nearest neighbour search. Despite the success of deep neural networks that learn an end-to-end model between text and images in other vision problems such as image captioning, very few deep ZSL model exists and they show little advantage over ZSL models that utilise deep feature representations but do not learn an end-to-end embedding. In this paper we argue that the key to make deep ZSL models succeed is to choose the right embedding space. Instead of embedding into a semantic space or an intermediate space, we propose to use the visual space as the embedding space. This is because that in this space, the subsequent nearest neighbour search would suffer much less from the hubness problem and thus become more effective. This model design also provides a natural mechanism for multiple semantic modalities (e.g.,~attributes and sentence descriptions) to be fused and optimised jointly in an end-to-end manner. Extensive experiments on four benchmarks show that our model significantly outperforms the existing models.
Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index

This paper proposes genetic algorithms (GAs) approach to feature discretization and the determination of connection weights for artificial neural networks (ANNs) to predict the stock price index. Previous research proposed many hybrid models of ANN and GA for the method of training the network, feature subset selection, and topology optimization. In most of these studies, however, GA is only used to improve the learning algorithm itself. In this study, GA is employed not only to improve the learning algorithm, but also to reduce the complexity in feature space. GA optimizes simultaneously the connection weights between layers and the thresholds for feature discretization. The genetically evolved weights mitigate the well-known limitations of the gradient descent algorithm. In addition, globally searched feature discretization reduces the dimensionality of the feature space and eliminates irrelevant factors. Experimental results show that GA approach to the feature discretization model outperforms the other two conventional models. q 2000
Sales prediction for a pharmaceutical distribution company: A data mining based approach

For pharmaceutical distribution companies it is essential to obtain good estimates of medicine needs, due to the short shelf life of many medicines and the need to control stock levels, so as to avoid excessive inventory costs while guaranteeing customer demand satisfaction, and thus decreasing the possibility of loss of customers due to stock outages. In this paper we explore the use of the time series data mining technique for the sales prediction of individual products of a pharmaceutical distribution company in Portugal. Through data mining techniques, the historical data of product sales are analyzed to detect patterns and make predictions based on the experience contained in the data. The results obtained with the technique as well as with the proposed method suggest that the performed modelling may be considered appropriate for the short term product sales prediction.
Attend and Diagnose: Clinical Time Series Analysis Using Attention Models

With widespread adoption of electronic health records, there is an increased emphasis for predictive models that can effectively deal with clinical time-series data. Powered by Recurrent Neural Network (RNN) architectures with Long ShortTerm Memory (LSTM) units, deep neural networks have achieved state-of-the-art results in several clinical prediction tasks. Despite the success of RNNs, its sequential nature prohibits parallelized computing, thus making it inefficient particularly when processing long sequences. Recently, architectures which are based solely on attention mechanisms have shown remarkable success in transduction tasks in NLP, while being computationally superior. In this paper, for the first time, we utilize attention models for clinical time-series modeling, thereby dispensing recurrence entirely. We develop the SAnD (Simply Attend and Diagnose) architecture, which employs a masked, self-attention mechanism, and uses positional encoding and dense interpolation strategies for incorporating temporal order. Furthermore, we develop a multi-task variant of SAnD to jointly infer models with multiple diagnosis tasks. Using the recent MIMIC-III benchmark datasets, we demonstrate that the proposed approach achieves state-of-the-art performance in all tasks, outperforming LSTM models and classical baselines with handengineered features.
Low-Rank Bilinear Pooling for Fine-Grained Classification

Pooling second-order local feature statistics to form a high-dimensional bilinear feature has been shown to achieve state-of-the-art performance on a variety of fine-grained classification tasks. To address the computational demands of high feature dimensionality, we propose to represent the covariance features as a matrix and apply a low-rank bilinear classifier. The resulting classifier can be evaluated without explicitly computing the bilinear feature map which allows for a large reduction in the compute time as well as decreasing the effective number of parameters to be learned. To further compress the model, we propose a classifier co-decomposition that factorizes the collection of bilinear classifiers into a common factor and compact per-class terms. The co-decomposition idea can be deployed through two convolutional layers and trained in an end-to-end architecture. We suggest a simple yet effective initialization that avoids explicitly first training and factorizing the larger bilinear classifiers. Through extensive experiments, we show that our model achieves state-of-the-art performance on several public datasets for fine-grained classification trained with only category labels. Importantly, our final model is an order of magnitude smaller than the recently proposed compact bilinear model , and three orders smaller than the standard bilinear CNN model [19].
Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks

Mammography is the most widely used method to screen breast cancer. Because of its mostly manual nature, variability in mass appearance, and low signal-to-noise ratio, a significant number of breast masses are missed or misdiagnosed. In this work, we present how Convolutional Neural Networks can be used to directly classify pre-segmented breast masses in mammograms as benign or malignant, using a combination of transfer learning, careful pre-processing and data augmentation to overcome limited training data. We achieve state-of-the-art results on the DDSM dataset, surpassing human performance, and show interpretability of our model.
Inverse halftoning algorithm using edge-based lookup table approach

The inverse halftoning algorithm is used to reconstruct a gray image from an input halftone image. Based on the recently published lookup table (LUT) technique, this paper presents a novel edge-based LUT method for inverse halftoning which improves the quality of the reconstructed gray image. The proposed method first uses the LUT-based inverse halftoning method as a preprocessing step to transform the given halftone image to a base gray image, and then the edges are extracted and classified from the base gray image. According to these classified edges, a novel edge-based LUT is built up to reconstruct the gray image. Based on a set of 30 real training images with both low- and high-frequency contents, experimental results demonstrated that the proposed method achieves a better image quality when compared to the currently published two methods, by Chang et al. and Mes$80e and Vaidyanathan.
A Conundrum of Permissions: Installing Applications on an Android Smartphone

Each time a user installs an application on their Android phone they are presented with a full screen of information describing what access they will be granting that application. This information is intended to help them make two choices: whether or not they trust that the application will not damage the security of their device and whether or not they are willing to share their information with the application, developer, and partners in question. We performed a series of semi-structured interviews in two cities to determine whether people read and understand these permissions screens, and to better understand how people perceive the implications of these decisions. We find that the permissions displays are generally viewed and read, but not understood by Android users. Alarmingly, we find that people are unaware of the security risks associated with mobile apps and believe that app marketplaces test and reject applications. In sum, users are not currently well prepared to make informed privacy and security decisions around installing applications.
New Linear-Time Algorithms for Edge-Coloring Planar Graphs

We show efficient algorithms for edge-coloring planar graphs. Our main result is a linear-time algorithm for coloring planar graphs with maximum degree Δ with max {Δ,9} colors. Thus the coloring is optimal for graphs with maximum degree Δ≥9. Moreover for Δ=4,5,6 we give linear-time algorithms that use Δ+2 colors. These results improve over the algorithms of Chrobak and Yung ( 1989) and of Chrobak and Nishizeki (J. Algorithms 11:102–116, 1990) which color planar graphs using max {Δ,19} colors in linear time or using max {Δ,9} colors in $\mathcal{O}(n\log n)$ time.
Research Note - Gamification of Technology-Mediated Training: Not All Competitions Are the Same

Gamification, an application of game design elements to non-gaming contexts, is proposed as a way to add engagement in technology-mediated training programs. But there is hardly any information on how to adapt game design elements to improve learning outcomes and promote learner engagement. To address the issue, we focus on a popular game design element, competition, and specifically examine the effects of different competitive structures – whether a person faces a higher-skilled, lower-skilled, or equally-skilled competitor – on learning and engagement. We study a gamified training design for databases, where trainees play a trivia-based mini-game with a competitor after each e-training module. Trainees who faced a lower-skilled competitor reported higher self-efficacy beliefs and better learning outcomes, supporting the effect of peer appraisal, a less examined aspect of social cognitive theory. But trainees who faced equallyskilled competitors reported higher levels of engagement, supporting the balance principle of flow theory. Our study findings indicate that no one competitive structure can address learning and engagement outcomes simultaneously. The choice of competitive structures depends on the priority of the outcomes in training. Our findings provide one explanation for the mixed findings on the effect of competitive gamification designs in technology mediated training.