nils-herrmann/clean_scirepeval huggingface dataset:
Detecting offensive tweets via topical feature discovery over a large scale twitter corpus

In this paper, we propose a novel semi-supervised approach for detecting profanity-related offensive content in Twitter. Our approach exploits linguistic regularities in profane language via statistical topic modeling on a huge Twitter corpus, and detects offensive tweets using automatically these generated features. Our approach performs competitively with a variety of machine learning (ML) algorithms. For instance, our approach achieves a true positive rate (TP) of 75.1% over 4029 testing tweets using Logistic Regression, significantly outperforming the popular keyword matching baseline, which has a TP of 69.7%, while keeping the false positive rate (FP) at the same level as the baseline at about 3.77%. Our approach provides an alternative to large scale hand annotation efforts required by fully supervised learning approaches.
Homomorphic Secret Sharing: Optimizations and Applications

We continue the study of Homomorphic Secret Sharing (HSS), recently introduced by Boyle et al. (Crypto 2016, Eurocrypt 2017). A (2-party) HSS scheme splits an input <i>x</i> into shares (<i></i>x<sup>0</sup>,<i>x</i><sup>1</sup>) such that (1) each share computationally hides <i>x</i>, and (2) there exists an efficient homomorphic evaluation algorithm $\Eval$ such that for any function (or "program") <i></i> from a given class it holds that Eval(<i>x</i><sup>0</sup>,<i>P</i>)+Eval(<i>x</i><sup>1</sup>,<i>P</i>)=<i>P</i>(<i>x</i>). Boyle et al. show how to construct an HSS scheme for branching programs, with an inverse polynomial error, using discrete-log type assumptions such as DDH.
 We make two types of contributions.
 <b>Optimizations</b>. We introduce new optimizations that speed up the previous optimized implementation of Boyle et al. by more than a factor of 30, significantly reduce the share size, and reduce the rate of leakage induced by selective failure.
 <b>Applications.</b> Our optimizations are motivated by the observation that there are natural application scenarios in which HSS is useful even when applied to simple computations on short inputs. We demonstrate the practical feasibility of our HSS implementation in the context of such applications.
Passives and impersonals 1

This paper argues that the term ‘passive’ has been systematically misapplied to a class of impersonal constructions that suppress the realization of a syntactic subject. The reclassification of these constructions highlights a typological contrast between two types of verbal diathesis and clarifies the status of putative ‘passives of unaccusatives ’ and ‘transitive passives’ in Balto-Finnic and Balto-Slavic. Impersonal verb forms differ from passives in two key respects : they are insensitive to the argument structure of a verb and can be formed from unergatives or unaccusatives, and they may retain direct objects. As with other subjectless forms of personal verbs, there is a strong tendency to interpret the suppressed subject of an impersonal as an indefinite human agent. Hence impersonalization is often felicitious only for verbs that select human subjects.
Isolation, identification, and characterization of Aeromonas hydrophila from juvenile farmed pangasius (Pangasianodon hypophthalmus)

Bacterial samples had been isolated from clinically detected diseased juvenile Pangasius, collected from Mymensingh, Bangladesh. Primarily, the isolates were found as Gram-negative, motile, oxidase-positive, fermentative, and O/129 resistant Aeromonas bacteria. The species was exposed as Aeromonas hydrophila from esculin hydrolysis test. Ten isolates of A. hydrophila were identified from eye lesions, kidney, and liver of the infected fishes. Further characterization of A. hydrophila was accomplished using API-20E and antibiotic sensitivity test. Isolates were highly resistant to amoxyclav among ten different antibiotics. All isolates were found as immensely pathogenic to healthy fishes while intraperitoneal injection. Histopathologically, necrotic hematopoietic tissues with pyknotic nuclei, mild hemorrhage, and wide vacuolation in kidney, liver, and muscle were principally noticed due to Aeromonad infection. So far, this is the first full note on characterizing A. hydrophila from diseased farmed Pangasius in Bangladesh. The present findings will provide further direction to develop theranostic strategies of A. hydrophila infection.
Deep View Morphing

Recently, convolutional neural networks (CNN) have been successfully applied to view synthesis problems. However, such CNN-based methods can suffer from lack of texture details, shape distortions, or high computational complexity. In this paper, we propose a novel CNN architecture for view synthesis called Deep View Morphing that does not suffer from these issues. To synthesize a middle view of two input images, a rectification network first rectifies the two input images. An encoder-decoder network then generates dense correspondences between the rectified images and blending masks to predict the visibility of pixels of the rectified images in the middle view. A view morphing network finally synthesizes the middle view using the dense correspondences and blending masks. We experimentally show the proposed method significantly outperforms the state-of-the-art CNN-based view synthesis method.
Long-term exposure to air pollution and incidence of cardiovascular events in women.

Fine particulate air pollution has been linked to cardiovascular disease, but previous studies have assessed only mortality and differences in exposure between cities. We examined the association of long-term exposure to particulate matter of less than 2.5 microm in aerodynamic diameter (PM2.5) with cardiovascular events.



We studied 65,893 postmenopausal women without previous cardiovascular disease in 36 U.S. metropolitan areas from 1994 to 1998, with a median follow-up of 6 years. We assessed the women's exposure to air pollutants using the monitor located nearest to each woman's residence. Hazard ratios were estimated for the first cardiovascular event, adjusting for age, race or ethnic group, smoking status, educational level, household income, body-mass index, and presence or absence of diabetes, hypertension, or hypercholesterolemia.



A total of 1816 women had one or more fatal or nonfatal cardiovascular events, as confirmed by a review of medical records, including death from coronary heart disease or cerebrovascular disease, coronary revascularization, myocardial infarction, and stroke. In 2000, levels of PM2.5 exposure varied from 3.4 to 28.3 microg per cubic meter (mean, 13.5). Each increase of 10 microg per cubic meter was associated with a 24% increase in the risk of a cardiovascular event (hazard ratio, 1.24; 95% confidence interval [CI], 1.09 to 1.41) and a 76% increase in the risk of death from cardiovascular disease (hazard ratio, 1.76; 95% CI, 1.25 to 2.47). For cardiovascular events, the between-city effect appeared to be smaller than the within-city effect. The risk of cerebrovascular events was also associated with increased levels of PM2.5 (hazard ratio, 1.35; 95% CI, 1.08 to 1.68).



Long-term exposure to fine particulate air pollution is associated with the incidence of cardiovascular disease and death among postmenopausal women. Exposure differences within cities are associated with the risk of cardiovascular disease.
‘ Clear as Mud ’ : Toward Greater Clarity in Generic Qualitative Research

We have observed a growth in the number of qualitative studies that have no guiding set of philosophic assumptions in the form of one of the established qualitative methodologies. This lack of allegiance to an established qualitative approach presents many challenges for “generic qualitative” studies, one of which is that the literature lacks debate about how to do a generic study well. We encourage such debate and offer four basic requirements as a point of departure: noting the researchers’ position, distinguishing method and methodology, making explicit the approach to rigor, and identifying the researchers’ analytic lens.
Model-Based Systems Engineering in Support of Complex Systems Development

A team from APL has been using model-based systems engineering (MBSE) methods within a conceptual modeling process to support and unify activities related to system-of-systems architecture development; modeling, simulation, and analysis efforts; and system capability trade studies. These techniques have been applied to support analysis of complex systems, particularly in the net-centric operations and warfare domain, which has proven particularly challenging to the modeling, simulation, and analysis community because of its complexity, information richness, and broad scope. In particular, the APL team has used MBSE techniques to provide structured models of complex systems incorporating input from multiple diverse stakeholders odel-based systems engineering techniques facilitate complex system design and documentation processes. A rigorous, iterative conceptual development process based on the Unified Modeling Language (UML) or the Systems Modeling Language (SysML) and consisting of domain modeling, use case development, and behavioral and structural modeling supports design, architecting, analysis, modeling and simulation, test and evaluation, and program management activities. The resulting model is more useful than traditional documentation because it represents structure, data, and functions, along with associated documentation, in a multidimensional, navigable format. Beyond benefits to project documentation and stakeholder communication, UMLand SysML-based models also support direct analysis methods, such as functional thread extraction. The APL team is continuing to develop analysis techniques using conceptual models to reduce the risk of design and test errors, reduce costs, and improve the quality of analysis and supporting modeling and simulation activities in the development of complex systems. Model-Based Systems Engineering in Support of Complex Systems Development
Implementing Neural Turing Machines

Neural Turing Machines (NTMs) are an instance of Memory Augmented Neural Networks, a new class of recurrent neural networks which decouple computation from memory by introducing an external memory unit. NTMs have demonstrated superior performance over Long Short-Term Memory Cells in several sequence learning tasks. A number of open source implementations of NTMs exist but are unstable during training and/or fail to replicate the reported performance of NTMs. This paper presents the details of our successful implementation of a NTM. Our implementation learns to solve three sequential learning tasks from the original NTM paper. We find that the choice of memory contents initialization scheme is crucial in successfully implementing a NTM. Networks with memory contents initialized to small constant values converge on average 2 times faster than the next best memory contents initialization scheme.
Multimodal transport routing problem considering transshipment and accessibility: The case of the “One Belt One Road” initiative

The concept of the “One Belt and One Road”(hereinafter referred to as: OBOR) was first initiated by the Chinese government in 2013. This inter-regional initiative has been rapidly and well promoted. Transport system (especially the multimodal transport system) of the OBOR is both the condition and basis for the implementation of the initiative. This paper analyzed the multimodal transport routing problem considering transshipment and accessibility. An integrated time-spatial three-dimensional super-network was constructed for depicting the OBOR multimodal network. Based on that, a mathematical model considering transshipment and accessibility in OBOR was established. Case study of the OBOR was conducted using the proposed model. Problems with different situations of were analyzed.
Unsupervised Image to Sequence Translation with Canvas-Drawer Networks

Encoding images as a series of high-level constructs, such as brush strokes or discrete shapes, can often be key to both human and machine understanding. In many cases, however, data is only available in pixel form. We present a method for generating images directly in a high-level domain (e.g. brush strokes), without the need for real pairwise data. Specifically, we train a ”canvas” network to imitate the mapping of high-level constructs to pixels, followed by a high-level ”drawing” network which is optimized through this mapping towards solving a desired image recreation or translation task. We successfully discover sequential vector representations of symbols, large sketches, and 3D objects, utilizing only pixel data. We display applications of our method in image segmentation, and present several ablation studies comparing various configurations.
Effective surgical safety checklist implementation.

Research suggests that surgical safety checklists can reduce mortality and other postoperative complications. The real world impact of surgical safety checklists on patient outcomes, however, depends on the effectiveness of hospitals' implementation processes.



We studied implementation processes in 5 Washington State hospitals by conducting semistructured interviews with implementation leaders and surgeons from September to December 2009. Interviews were transcribed, analyzed, and compared with findings from previous implementation research to identify factors that distinguish effective implementation.



Qualitative analysis suggested that effectiveness hinges on the ability of implementation leaders to persuasively explain why and adaptively show how to use the checklist. Coordinated efforts to explain why the checklist is being implemented and extensive education regarding its use resulted in buy-in among surgical staff and thorough checklist use. When implementation leaders did not explain why or show how the checklist should be used, staff neither understood the rationale behind implementation nor were they adequately prepared to use the checklist, leading to frustration, disinterest, and eventual abandonment despite a hospital-wide mandate.



The impact of surgical safety checklists on patient outcomes is likely to vary with the effectiveness of each hospital's implementation process. Further research is needed to confirm these findings and reveal additional factors supportive of checklist implementation.
EEG oscillations: From correlation to causality.

Already in his first report on the discovery of the human EEG in 1929, Berger showed great interest in further elucidating the functional roles of the alpha and beta waves for normal mental activities. Meanwhile, most cognitive processes have been linked to at least one of the traditional frequency bands in the delta, theta, alpha, beta, and gamma range. Although the existing wealth of high-quality correlative EEG data led many researchers to the conviction that brain oscillations subserve various sensory and cognitive processes, a causal role can only be demonstrated by directly modulating such oscillatory signals. In this review, we highlight several methods to selectively modulate neuronal oscillations, including EEG-neurofeedback, rhythmic sensory stimulation, repetitive transcranial magnetic stimulation (rTMS), and transcranial alternating current stimulation (tACS). In particular, we discuss tACS as the most recent technique to directly modulate oscillatory brain activity. Such studies demonstrating the effectiveness of tACS comprise reports on purely behavioral or purely electrophysiological effects, on combination of behavioral effects with offline EEG measurements or on simultaneous (online) tACS-EEG recordings. Whereas most tACS studies are designed to modulate ongoing rhythmic brain activity at a specific frequency, recent evidence suggests that tACS may also modulate cross-frequency interactions. Taken together, the modulation of neuronal oscillations allows to demonstrate causal links between brain oscillations and cognitive processes and to obtain important insights into human brain function.
Improved polar decoder based on deep learning

Deep learning recently shows strong competitiveness to improve polar code decoding. However, suffering from prohibitive training and computation complexity, the conventional deep neural network (DNN) is only possible for very short code length. In this paper, the main problems of deep learning in decoding are well solved. We first present the multiple scaled belief propagation (BP) algorithm, aiming at obtaining faster convergence and better performance. Based on this, deep neural network decoder (NND) with low complexity and latency, is proposed for any code length. The training only requires a small set of zero codewords. Besides, its computation complexity is close to the original BP. Experiment results show that the proposed (64,32) NND with 5 iterations achieves even lower bit error rate (BER) than the 30-iteration conventional BP and (512, 256) NND also outperforms conventional BP decoder with same iterations. The hardware architecture of basic computation block is given and folding technique is also considered, saving about 50% hardware cost.
Unsupervised Streaming Feature Selection in Social Media

The explosive growth of social media sites brings about massive amounts of high-dimensional data. Feature selection is effective in preparing high-dimensional data for data analytics. The characteristics of social media present novel challenges for feature selection. First, social media data is not fully structured and its features are usually not predefined, but are generated dynamically. For example, in Twitter, slang words (features) are created everyday and quickly become popular within a short period of time. It is hard to directly apply traditional batch-mode feature selection methods to find such features. Second, given the nature of social media, label information is costly to collect. It exacerbates the problem of feature selection without knowing feature relevance. On the other hand, opportunities are also unequivocally present with additional data sources; for example, link information is ubiquitous in social media and could be helpful in selecting relevant features. In this paper, we study a novel problem to conduct unsupervised streaming feature selection for social media data. We investigate how to exploit link information in streaming feature selection, resulting in a novel unsupervised streaming feature selection framework USFS. Experimental results on two real-world social media datasets show the effectiveness and efficiency of the proposed framework comparing with the state-of-the-art unsupervised feature selection algorithms.
Using a Random Forest to Inspire a Neural Network and Improving on It

Neural networks have become very popular in recent years because of the astonishing success of deep learning in various domains such as image and speech recognition. In many of these domains, specific architectures of neural networks, such as convolutional networks, seem to fit the particular structure of the problem domain very well, and can therefore perform in an astonishingly effective way. However, the success of neural networks is not universal across all domains. Indeed, for learning problems without any special structure, or in cases where the data is somewhat limited, neural networks are known not to perform well with respect to traditional machine learning methods such as random forests. In this paper, we show that a carefully designed neural network with random forest structure can have better generalization ability. In fact, this architecture is more powerful than random forests, because the back-propagation algorithm reduces to a more powerful and generalized way of constructing a decision tree. Furthermore, the approach is efficient to train and requires a small constant factor of the number of training examples. This efficiency allows the training of multiple neural networks in order to improve the generalization accuracy. Experimental results on 10 realworld benchmark datasets demonstrate the effectiveness of the proposed enhancements.
Sharing Knowledge and Expertise: The CSCW View of Knowledge Management

Knowledge Management (KM) is a diffuse and controversial term, which has been used by a large number of research disciplines. CSCW, over the last 20 years, has taken a critical stance towards most of these approaches, and instead, CSCW shifted the focus towards a practice-based perspective. This paper surveys CSCW researchers’ viewpoints on what has become called ‘knowledge sharing’ and ‘expertise sharing’. These are based in an understanding of the social contexts of knowledge work and practices, as well as in an emphasis on communication among knowledgeable humans. The paper provides a summary and overview of the two strands of knowledge and expertise sharing in CSCW, which, from an analytical standpoint, roughly represent ‘generations’ of research: an ‘object-centric’ and a ‘people-centric’ view. We also survey the challenges and opportunities ahead.
The Painful Shoulder: Shoulder Impingement Syndrome

Rotator cuff disorders are considered to be among the most common causes of shoulder pain and disability encountered in both primary and secondary care. The general pathology of subacromial impingment generally relates to a chronic repetitive process in which the conjoint tendon of the rotator cuff undergoes repetitive compression and micro trauma as it passes under the coracoacromial arch. However acute traumatic injuries may also lead to this condition. Diagnosis remains a clinical one, however advances in imaging modalities have enabled clinicians to have an increased understanding of the pathological process. Ultrasound scanning appears to be a justifiable and cost effective assessment tool following plain radiographs in the assessment of shoulder impingment, with MRI scans being reserved for more complex cases. A period of observed conservative management including the use of NSAIDs, physiotherapy with or without the use of subacromial steroid injections is a well-established and accepted practice. However, in young patients or following any traumatic injury to the rotator cuff, surgery should be considered early. If surgery is to be performed this should be done arthroscopically and in the case of complete rotator cuff rupture the tendon should be repaired where possible.
Redundant STT-MTJ-based nonvolatile flip-flops for low write-error-rate operations

This paper introduces redundant spin-transfer-torque (STT) magnetic tunnel junction (MTJ) based nonvolatile flip-flops (NVFFs) for low write-error rate (WER) operations. STT-MTJ NVFFs are key components for ultra-low power VLSI systems thanks to zero standby current, but suffers from write errors due to probabilistic switching, causing a failure backup/restore operation. To reduce the WER, redundant STT-MTJ devices are exploited in the proposed NVFFs. As one-bit information is redundantly represented, it is correctly stored upon a few bit write errors, lowering WERs compared to a conventional NVFF at the same write time. Three different redundant structures are presented and discussed in terms of WER and write energy dissipation. For performance comparisons, the proposed redundant STT-MTJ NVFFs are designed using hybrid 90nm CMOS and MTJ technologies and evaluated using NSSPICE that handles both transistors and MTJs. The simulation results show that the proposed NVFF reduces the write time to 36.2% and the write energy to 70.7% at a WER of 10-12 compared to the conventional NVFF.