nils-herrmann/clean_scirepeval huggingface dataset:
Building Theories from Case Study

This paper describes the process of inducting theory using case studies-from specifying the research questions to reaching closure. Some features of the process, such as problem definition and construct validation, are similar to hypothesis-testing research. Others, such as within-case analysis and replication logic, are unique to the inductive, case-oriented process. Overall, the process described here is highly iterative and tightly linked to data. This research approach is especially appropriate in new topic areas. The resultant theory is often novel, testable, and empirically valid. Finally, framebreaking insights, the tests of good theory (e.g., parsimony, logical coherence), and convincing grounding in the evidence are the key criteria for evaluating this type of research.
A Web of Hate: Tackling Hateful Speech in Online Social Spaces

Online social platforms are beset with hateful speech content that expresses hatred for a person or group of people. Such content can frighten, intimidate, or silence platform users, and some of it can inspire other users to commit violence. Despite widespread recognition of the problems posed by such content, reliable solutions even for detecting hateful speech are lacking. In the present work, we establish why keyword-based methods are insufficient for detection. We then propose an approach to detecting hateful speech that uses content produced by self-identifying hateful communities as training data. Our approach bypasses the expensive annotation process often required to train keyword systems and performs well across several established platforms, making substantial improvements over current state-of-the-art approaches.
Scientometric analysis and mapping of scientific articles on Behcet's disease.

Behçet's disease (BD) is a systemic vasculitis disease with oral and genital aphthous ulceration, uveitis, skin manifestations, arthritis and neurological involvement. Many investigators have published articles on BD in the last two decades since introduction of diagnosis criteria by the International Study Group for Behçet's Disease in 1990. However, there is no scientometric analysis available for this increasing amount of literature.



A scientometric analysis method was used to achieve a view of scientific articles about BD which were published between 1990 and 2010, by data retrieving from ISI Web of Science. The specific features such as publication year, language of article, geographical distribution, main journal in this field, institutional affiliation and citation characteristics were retrieved and analyzed. International collaboration was analyzed using Intcoll and Pajek softwares.



There was a growing trend in the number of BD articles from 1990 to 2010. The number of citations to BD literature also increased around 5.5-fold in this period. The countries found to have the highest output were Turkey, Japan, the USA and England; the first two universities were from Turkey. Most of the top 10 journals publishing BD articles were in the field of rheumatology, consistent with the subject areas of the articles. There was a correlation between the citations per paper and the impact factor of the publishing journal.



This is the first scientometric analysis of BD, showing the scientometric characteristics of ISI publications on BD.
Self-Supervised Monocular Image Depth Learning and Confidence Estimation

Convolutional Neural Networks (CNNs) need large amounts of data with ground truth annotation, which is a challenging problem that has limited the development and fast deployment of CNNs for many computer vision tasks. We propose a novel framework for depth estimation from monocular images with corresponding confidence in a selfsupervised manner. A fully differential patch-based cost function is proposed by using the Zero-Mean Normalized Cross Correlation (ZNCC) that takes multi-scale patches as a matching strategy. This approach greatly increases the accuracy and robustness of the depth learning. In addition, the proposed patch-based cost function can provide a 0 to 1 confidence, which is then used to supervise the training of a parallel network for confidence map learning and estimation. Evaluation on KITTI dataset shows that our method outperforms the state-of-the-art results.
Analysis of Hotel Guest Satisfaction Ratings and Reviews : An Application in Singapore

The hotel industry in Singapore is an important part of the hospitality and tourism infrastructure and a strategic part of Singapore’s growth story. Hotels are primarily viewed as a service industry with intangible areas of guest experience and service levels. The research objective of this paper is to better understand the hotel guest satisfaction and the areas that hotel management can change, in order to get better results. For this purpose, an analysis of hotel guest satisfaction ratings based on attributes such as Location, Sleep quality, Rooms, Service quality, Value for money and Cleanliness was performed. Further, text analysis of customer reviews was also performed to better understand the positive and negative sentiments of hotel guests. We focused on identifying the attributes that differentiate one hotel from another, and then using these attribute insights to make recommendation to hotel management, on how they can improve their operations, guest satisfaction and generally differentiate themselves from their competition. Data from an online website, Trip Advisor, was used to analyse and compare customer ratings and reviews on five hotels. Statistical data analysis techniques were used to identify the key attributes that are most important in choosing hotels and are critical to focus on in order to ensure guest satisfaction expectations are met. Based on text analytics, the key results from this study indicated that hotel guests look for a good room and a hotel with a pool and good service. Based on the ratings analysis, the most important attributes for guest satisfaction turned out to be Rooms, Value for money and Location.
Feature location in source code: a taxonomy and survey

Feature location is the activity of identifying an initial location in the source code that implements functionality in a software system. Many feature location techniques have been introduced that automate some or all of this process, and a comprehensive overview of this large body of work would be beneficial to researchers and practitioners. This paper presents a systematic literature survey of feature location techniques. Eighty-nine articles from 25 venues have been reviewed and classified within the taxonomy in order to organize and structure existing work in the field of feature location. The paper also discusses open issues and defines future directions in the field of feature location.
Building Theories from Case Study Research

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The security of RFID readers with IDS/IPS solution using Raspberry Pi

This paper presents developed detection rules for LLRP (Low Level Reader Protocol) interface, implementation of IDS (Intrusion Detection System) and IPS (Intrusion Prevention System) solution for UHF (Ultra High Frequency) RFID (Radio Frequency Identification) reader using Raspberry Pi 3. Developed security solution is used for securing current RFID readers supporting LLRP. The paper also deals with a comparison of IPS/IDS solutions and subsequently selection of the appropriate one for the implementation using Raspberry Pi. Main contribution is the securing of RFID readers which are usually available without any encryption support on the market. The functionality is tested with software metasploit framework.
Exploring Implicit Hierarchical Structures for Recommender Systems

Items in real-world recommender systems exhibit certain hierarchical structures. Similarly, user preferences also present hierarchical structures. Recent studies show that incorporating the explicit hierarchical structures of items or user preferences can improve the performance of recommender systems. However, explicit hierarchical structures are usually unavailable, especially those of user preferences. Thus, there’s a gap between the importance of hierarchical structures and their availability. In this paper, we investigate the problem of exploring the implicit hierarchical structures for recommender systems when they are not explicitly available. We propose a novel recommendation framework HSR to bridge the gap, which enables us to capture the implicit hierarchical structures of users and items simultaneously. Experimental results on two real world datasets demonstrate the effectiveness of the proposed framework.
Syphilis and HIV co-infection. Epidemiology, treatment and molecular typing of Treponema pallidum.

The studies included in this PhD thesis examined the interactions of syphilis, which is caused by Treponema pallidum, and HIV. Syphilis reemerged worldwide in the late 1990s and hereafter increasing rates of early syphilis were also reported in Denmark. The proportion of patients with concurrent HIV has been substantial, ranging from one third to almost two thirds of patients diagnosed with syphilis some years. Given that syphilis facilitates transmission and acquisition of HIV the two sexually transmitted diseases are of major public health concern. Further, syphilis has a negative impact on HIV infection, resulting in increasing viral loads and decreasing CD4 cell counts during syphilis infection. Likewise, HIV has an impact on the clinical course of syphilis; patients with concurrent HIV are thought to be at increased risk of neurological complications and treatment failure. Almost ten per cent of Danish men with syphilis acquired HIV infection within five years after they were diagnosed with syphilis during an 11-year study period. Interestingly, the risk of HIV declined during the later part of the period. Moreover, HIV-infected men had a substantial increased risk of re-infection with syphilis compared to HIV-uninfected men. As one third of the HIV-infected patients had viral loads >1,000 copies/ml, our conclusion supported the initiation of cART in more HIV-infected MSM to reduce HIV transmission. During a five-year study period, including the majority of HIV-infected patients from the Copenhagen area, we observed that syphilis was diagnosed in the primary, secondary, early and late latent stage. These patients were treated with either doxycycline or penicillin and the rate of treatment failure was similar in the two groups, indicating that doxycycline can be used as a treatment alternative - at least in an HIV-infected population. During a four-year study period, the T. pallidum strain type distribution was investigated among patients diagnosed by PCR testing of material from genital lesions. In total, 22 strain types were identified. HIV-infected patients were diagnosed with nine different strains types and a difference by HIV status was not observed indicating that HIV-infected patients did not belong to separate sexual networks. In conclusion, concurrent HIV remains common in patients diagnosed with syphilis in Denmark, both in those diagnosed by serological testing and PCR testing. Although the rate of syphilis has stabilized in recent years, a spread to low-risk groups is of concern, especially due to the complex symptomatology of syphilis. However, given the efficient treatment options and the targeted screening of pregnant women and persons at higher risk of syphilis, control of the infection seems within reach. Avoiding new HIV infections is the major challenge and here cART may play a prominent role.
An MRF Model-Based Active Learning Framework for the Spectral-Spatial Classification of Hyperspectral Imagery

Hyperspectral image classification has attracted extensive research efforts in the recent decades. The main difficulty lies in the few labeled samples versus high dimensional features. The spectral-spatial classification method using Markov random field (MRF) has been shown to perform well in improving the classification performance. Moreover, active learning (AL), which iteratively selects the most informative unlabeled samples and enlarges the training set, has been widely studied and proven useful in remotely sensed data. In this paper, we focus on the combination of MRF and AL in the classification of hyperspectral images, and a new MRF model-based AL (MRF-AL) framework is proposed. In the proposed framework, the unlabeled samples whose predicted results vary before and after the MRF processing step is considered as uncertain. In this way, subset is firstly extracted from the entire unlabeled set, and AL process is then performed on the samples in the subset. Moreover, hybrid AL methods which combine the MRF-AL framework with either the passive random selection method or the existing AL methods are investigated. To evaluate and compare the proposed AL approaches with other state-of-the-art techniques, experiments were conducted on two hyperspectral data sets. Results demonstrated the effectiveness of the hybrid AL methods, as well as the advantage of the proposed MRF-AL framework.
Analysis of time-frequency representations for musical onset detection with convolutional neural network

In this paper a convolutional neural network is applied to the problem of note onset detection in audio recordings. Two time-frequency representations are analysed, showing the superiority of standard spectrogram over enhanced autocorrelation (EAC) used as the input to the convolutional network. Experimental evaluation is based on a dataset containing 10,939 annotated onsets, with total duration of the audio recordings of over 45 min.
Approximate kernel k-means: solution to large scale kernel clustering

Digital data explosion mandates the development of scalable tools to organize the data in a meaningful and easily accessible form. Clustering is a commonly used tool for data organization. However, many clustering algorithms designed to handle large data sets assume linear separability of data and hence do not perform well on real world data sets. While kernel-based clustering algorithms can capture the non-linear structure in data, they do not scale well in terms of speed and memory requirements when the number of objects to be clustered exceeds tens of thousands. We propose an approximation scheme for kernel k-means, termed approximate kernel k-means, that reduces both the computational complexity and the memory requirements by employing a randomized approach. We show both analytically and empirically that the performance of approximate kernel k-means is similar to that of the kernel k-means algorithm, but with dramatically reduced run-time complexity and memory requirements.
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks

In recent years, state-of-the-art methods in computer vision have utilized increasingly deep convolutional neural network architectures (CNNs), with some of the most successful models employing hundreds or even thousands of layers. A variety of pathologies such as vanishing/exploding gradients make training such deep networks challenging. While residual connections and batch normalization do enable training at these depths, it has remained unclear whether such specialized architecture designs are truly necessary to train deep CNNs. In this work, we demonstrate that it is possible to train vanilla CNNs with ten thousand layers or more simply by using an appropriate initialization scheme. We derive this initialization scheme theoretically by developing a mean field theory for signal propagation and by characterizing the conditions for dynamical isometry, the equilibration of singular values of the input-output Jacobian matrix. These conditions require that the convolution operator be an orthogonal transformation in the sense that it is norm-preserving. We present an algorithm for generating such random initial orthogonal convolution kernels and demonstrate empirically that they enable efficient training of extremely deep architectures.
Improving Semantic Textual Similarity with Phrase Entity Alignment

Semantic Textual Similarity (STS) measures the degree of semantic equivalence between two segments of text, even though the similar context is expressed using different words. The textual segments are word phrases, sentences, paragraphs or documents. The similarity can be measured using lexical, syntactic and semantic information embedded in the sentences. The STS task in SemEval workshop is viewed as a regression problem, where real-valued output is clipped to the range 0-5 on a sentence pair. In this paper, empirical evaluations are carried using lexical, syntactic and semantic features on STS 2016 dataset. A new syntactic feature, Phrase Entity Alignment (PEA) is proposed. A phrase entity is a conceptual unit in a sentence with a subject or an object and its describing words. PEA aligns phrase entities present in the sentences based on their similarity scores. STS score is measured by combing the similarity scores of all aligned phrase entities. The impact of PEA on semantic textual equivalence is depicted using Pearson correlation between system generated scores and the human annotations. The proposed system attains a mean score of 0.7454 using random forest regression model. The results indicate that the system using the lexical, syntactic and semantic features together with PEA feature perform comparably better than existing systems.
Imparting Interpretability to Word Embeddings

As an ubiquitous method in natural language processing, word embeddings are extensively employed to map semantic properties of words into a dense vector representation. They capture semantic and syntactic relations among words but the vector corresponding to the words are only meaningful relative to each other. Neither the vector nor its dimensions have any absolute, interpretable meaning. We introduce an additive modification to the objective function of the embedding learning algorithm that encourages the embedding vectors of words that are semantically related a predefined concept to take larger values along a specified dimension, while leaving the original semantic learning mechanism mostly unaffected. In other words, we align words that are already determined to be related, along predefined concepts. Therefore, we impart interpretability to the word embedding by assigning meaning to its vector dimensions. The predefined concepts are derived from an external lexical resource, which in this paper is chosen as Roget’s Thesaurus. We observe that alignment along the chosen concepts is not limited to words in the Thesaurus and extends to other related words as well. We quantify the extent of interpretability and assignment of meaning from our experimental results. We also demonstrate the preservation of semantic coherence of the resulting vector space by using word-analogy and word-similarity tests. These tests show that the interpretability-imparted word embeddings that are obtained by the proposed framework do not sacrifice performances in common benchmark tests.
Triejoin: A Simple, Worst-Case Optimal Join Algorithm

Recent years have seen exciting developments in join algorithms. In 2008, Atserias, Grohe and Marx (henceforth AGM) proved a tight bound on the maximum result size of a full conjunctive query, given constraints on the input rel ation sizes. In 2012, Ngo, Porat, R «e and Rudra (henceforth NPRR) devised a join algorithm with worst-case running time proportional to the AGM bound . Our commercial database system LogicBlox employs a novel join algorithm, leapfrog triejoin, which compared conspicuously well to the NPRR algorithm in preliminary benchmarks. This spurred us to analyze the complexity of leapfrog triejoin. In this pa per we establish that leapfrog triejoin is also worst-case o ptimal, up to a log factor, in the sense of NPRR. We improve on the results of NPRR by proving that leapfrog triejoin achieves worst-case optimality for finer-grained classes o f database instances, such as those defined by constraints on projection cardinalities. We show that NPRR is not worstcase optimal for such classes, giving a counterexamplewher e leapfrog triejoin runs inO(n log n) time and NPRR runs in Θ(n) time. On a practical note, leapfrog triejoin can be implemented using conventional data structures such as B-trees, and extends naturally to ∃1 queries. We believe our algorithm offers a useful addition to the existing toolbox o f join algorithms, being easy to absorb, simple to implement, and having a concise optimality proof.
CBinfer: Exploiting Frame-to-Frame Locality for Faster Convolutional Network Inference on Video Streams

The last few years have brought advances in computer vision at an amazing pace, grounded on new findings in deep neural network construction and training as well as the availability of large labeled datasets. Applying these networks to images demands a high computational effort and pushes the use of state-of-the-art networks on real-time video data out of reach of embedded platforms. Many recent works focus on reducing network complexity for real-time inference on embedded computing platforms. We adopt an orthogonal viewpoint and propose a novel algorithm exploiting the spatio-temporal sparsity of pixel changes. This optimized inference procedure resulted in an average speed-up of 9.1× over cuDNN on the Tegra X2 platform at a negligible accuracy loss of < 0.1% and no retraining of the network for a semantic segmentation application. Similarly, an average speed-up of 7.0× has been achieved for a pose detection DNN on static camera video surveillance data. These throughput gains combined with a lower power consumption result in an energy efficiency of 511 GOp/s/W compared to 70 GOp/s/W for the baseline.
Improving Consistency-Based Semi-Supervised Learning with Weight Averaging

Presently the most successful approaches to semi-supervised learning are based on consistency regularization, whereby a model is trained to be robust to small perturbations of its inputs and parameters. To understand consistency regularization, we conceptually explore how loss geometry interacts with training procedures. The consistency loss dramatically improves generalization performance over supervisedonly training; however, we show that SGD struggles to converge on the consistency loss and continues to make large steps that lead to changes in predictions on the test data. Motivated by these observations, we propose to train consistencybased methods with Stochastic Weight Averaging (SWA), a recent approach which averages weights along the trajectory of SGD with a modified learning rate schedule. We also propose fast-SWA, which further accelerates convergence by averaging multiple points within each cycle of a cyclical learning rate schedule. With weight averaging, we achieve the best known semi-supervised results on CIFAR-10 and CIFAR-100, over many different quantities of labeled training data. For example, we achieve 5.0% error on CIFAR-10 with only 4000 labels, compared to the previous best result in the literature of 6.3%.