nils-herrmann/clean_scirepeval huggingface dataset:
Detection of Harassment on Web 2 . 0

Web 2.0 has led to the development and evolution of web-based communities and applications. These communities provide places for information sharing and collaboration. They also open the door for inappropriate online activities, such as harassment, in which some users post messages in a virtual community that are intentionally offensive to other members of the community. It is a new and challenging task to detect online harassment; currently few systems attempt to solve this problem. In this paper, we use a supervised learning approach for detecting harassment. Our technique employs content features, sentiment features, and contextual features of documents. The experimental results described herein show that our method achieves significant improvements over several baselines, including Term FrequencyInverse Document Frequency (TFIDF) approaches. Identification of online harassment is feasible when TFIDF is supplemented with sentiment and contextual feature attributes.
Manifold regularized cross-modal embedding for zero-shot learning

Zero-Shot Learning (ZSL) aims at classifying previously unseen class samples and has gained its popularity in applications where samples of some categories are scarce for training. The basic idea to address this issue is transferring knowledge from the seen classes to the unseen classes through mapping the visual feature to an embedding space spanned by class semantic information. The class semantic information can be obtained from human-labeled attributes or text corpus in an unsupervised fashion. Therefore, the embedding function from visual space to the embedding space is extremely important. However, the existing embedding approaches to ZSL mainly focus on aligning pairwise semantic consistency from heterogeneous spaces but ignore the intrinsic structure of the locally homogeneous isomorph. In order to preserve the locally visual structure in the embedding process, this paper proposes a Manifold regularized Cross-Modal Embedding (MCME) approach for ZSL by formulating the manifold constraint for intrinsic structure of the visual features as well as aligning pairwise consistency. The linear, closed-form solution makes MCME efficient to compute. Furthermore, rather than applying the embedding function learned from the seen classes directly, we also propose a new domain adaptation strategy to overcome the domain-shift problem during the knowledge transfer process. The MCME with the domain adaptation method is called MCME-DA. Extensive experiments on the benchmark datasets of AwA and CUB validate the superiority and promise of MCME and MCME-DA.
Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position

-Suggested by the structure of the visual nervous system, a new algorithm is proposed for pattern recognlt~on This algorithm can be reahzed with a multllayered network consisting of neuron-hke cells The network, "neocognltron", is self-organized by unsupervised learnmg, and acquires the abdlty to recognize stimulus patterns according to the &fferences in their shapes Any patterns which we human beings judge to be ahke are also judged to be of the same category by the neocognltron The neocognltron recognizes stimulus patterns correctly without being affected by shifts m position or even by considerable d~stortlons in shape of the stimulus patterns Visual pattern recognition Unsupervised learning Neural network model Deformatmn-reslstant Self-orgamzatmn Visual nervous system Posltlon-mvarlant Multdayered network Simulation
Dermatoscopy of facial actinic keratosis, intraepidermal carcinoma, and invasive squamous cell carcinoma: a progression model.

Little is known about the dermoscopic features of keratinocyte skin cancer.



We sought to determine the dermoscopic features of facial actinic keratosis (AK), intraepidermal carcinoma (IEC), moderately to poorly differentiated invasive squamous cell carcinoma (SCC), and well-differentiated SCC of the keratoacanthoma type.



This was a retrospective analysis of dermoscopic images of histopathologically diagnosed keratinocyte skin cancer.



A total of 243 (70 AK, 71 IEC, 78 SCC, and 24 keratoacanthomas) tumors of the face from 243 patients (mean age: 71.1 years; range: 44-94 years) were analyzed. The majority of patients had a fair skin type, history of melanoma or nonmelanoma skin cancer, and multiple AK. A red pseudonetwork was significantly associated with AK (P < .001), whereas dotted/glomerular vessels, diffuse yellow opaque scales, and microerosions were significantly more prevalent among IEC (P < .001). Hairpin vessels, linear-irregular vessels, targetoid hair follicles, white structureless areas, a central mass of keratin, and ulceration were significantly associated with invasive SCC (P < .001 for all criteria). Similar patterns as in SCC were observed among keratoacanthomas.



The retrospective design of our study and the lack of assessment of sensitivity and specificity of the dermoscopic criteria are limitations.



Based on our findings we propose a progression model of facial AK developing into IEC and invasive SCC.
Measurement of lumbar multifidus muscle contraction with rehabilitative ultrasound imaging.

Rehabilitative Ultrasound Imaging (RUSI) has been validated as a noninvasive method to measure activation of selected muscles. The purpose of this study was to determine the relationship between muscle thickness change, as measured by ultrasonography, and electromyography (EMG) activity of the lumbar multifidus (LM) muscle in normal subjects. Bipolar fine wire electrodes were inserted into the LM at the L4 level of five subjects. Simultaneous EMG and RUSI data (muscle thickness) were collected while subjects performed increasingly demanding postural response tasks thought to activate the LM muscle. To determine the relationship between muscle thickness change and EMG activity, the normalized EMG data were correlated to normalized RUSI data. To determine if the tasks increased the demand on the LM, the mean EMG data were compared over each of the four tasks. Muscle thickness change as measured by RUSI was highly correlated with EMG activity of LM in asymptomatic subjects (r=.79,P<.001). Mean EMG data showed increasing levels of activation across tasks (19-34% of maximum voluntary isometric contraction (MVIC)). The results of the repeated measures ANOVA demonstrated theses differences were significant (F(3,12)=25.39,P<.001). Measurement of muscle thickness change utilizing RUSI is a valid and potentially useful method to measure activation of the LM muscle in a narrow range (19-34% of MVIC) in an asymptomatic population.
ModelarDB: Modular Model-Based Time Series Management with Spark and Cassandra

Industrial systems, e.g., wind turbines, generate big amounts of data from reliable sensors with high velocity. As it is unfeasible to store and query such big amounts of data, only simple aggregates are currently stored. However, aggregates remove fluctuations and outliers that can reveal underlying problems and limit the knowledge to be gained from historical data. As a remedy, we present the distributed Time Series Management System (TSMS) ModelarDB that uses models to store sensor data. We thus propose an online, adaptive multi-model compression algorithm that maintains data values within a user-defined error bound (possibly zero). We also propose (i) a database schema to store time series as models, (ii) methods to push-down predicates to a key-value store utilizing this schema, (iii) optimized methods to execute aggregate queries on models, (iv) a method to optimize execution of projections through static code-generation, and (v) dynamic extensibility that allows new models to be used without recompiling the TSMS. Further, we present a general modular distributed TSMS architecture and its implementation, ModelarDB, as a portable library, using Apache Spark for query processing and Apache Cassandra for storage. An experimental evaluation shows that, unlike current systems, ModelarDB hits a sweet spot and offers fast ingestion, good compression, and fast, scalable online aggregate query processing at the same time. This is achieved by dynamically adapting to data sets using multiple models. The system degrades gracefully as more outliers occur and the actual errors are much lower than the bounds.  Søren Kejser Jensen, Torben Bach Pedersen, Christian Thomsen. ModelarDB: Modular Model-Based Time Series Management with Spark and Cassandra. PVLDB, 11(11): 1688-1701, 2018.
A Compositional Framework for Grounding Language Inference, Generation, and Acquisition in Video

We present an approach to simultaneously reasoning about a video clip and an entire natural-language sentence. The compositional nature of language is exploited to construct models which represent the meanings of entire sentences composed out of the meanings of the words in those sentences mediated by a grammar that encodes the predicate-argument relations. We demonstrate that these models faithfully represent the meanings of sentences and are sensitive to how the roles played by participants (nouns), their characteristics (adjectives), the actions performed (verbs), the manner of such actions (adverbs), and changing spatial relations between participants (prepositions) affect the meaning of a sentence and how it is grounded in video. We exploit this methodology in three ways. In the first, a video clip along with a sentence are taken as input and the participants in the event described by the sentence are highlighted, even when the clip depicts multiple similar simultaneous events. In the second, a video clip is taken as input without a sentence and a sentence is generated that describes an event in that clip. In the third, a corpus of video clips is paired with sentences which describe some of the events in those clips and the meanings of the words in those sentences are learned. We learn these meanings without needing to specify which attribute of the video clips each word in a given sentence refers to. The learned meaning representations are shown to be intelligible to humans.
Analyzing Opinion Spammers’ Network Behavior in Online Review Systems

Review systems are indisputably important resource for users when making various decisions on products or services. Consequently, they become increas- ingly targeted by attackers who deliberately inject biased reviews, so called opinion spams, aiming to influence normal users’ decisions for financial gain. In this paper, we perform an empirical analysis of opinion spammers on one of the famous online review systems, Yelp. Specifically, we analyze two different types of networks: implicit and explicit networks of opinion spammers and those of non- spammers. Through analyzing the network characteristics in different networks, we show similarities and differences between opinion spammers and non-spammers in terms of statistical characteristics and network properties. More specifically, through extensive analysis on Yelp dataset, we show that (i) the explicit network of non-spammers exhibits typical “small-world” properties of social networks. (ii) the implicit network of non-spammers is close to random networks. (iii) in both explicit and implicit networks, opin- ion spammers form near-isolated communities with dense inner connections among themselves, while exhibiting the lower level of “small-world” properties.
Deep learning approach in multi-scale prediction of turbulent mixing-layer

Achievement of solutions in Navier-Stokes equation is one of challenging quests, especially for its closure problem. For achievement of particular solutions, there are variety of numerical simulations including Direct Numerical Simulation (DNS) or Large Eddy Simulation (LES). These methods analyze flow physics through efficient reduced-order modeling such as proper orthogonal decomposition or Koopman method, showing prominent fidelity in fluid dynamics. Generative adversarial network (GAN) is a reprint of neurons in brain as combinations of linear operations, using competition between generator and discriminator. Current paper propose deep learning network for prediction of small-scale movements with large-scale inspections only, using GAN. Therefore DNS result of three-dimensional mixing-layer was filtered blurring out the small-scaled structures, then is predicted of its detailed structures, utilizing Generative Adversarial Network (GAN). This enables multi-resolution analysis being asked to predict fine-resolution solution with only inspection of blurry one. Within the grid scale, current paper present deep learning approach of modeling small scale features in turbulent flow. The presented method is expected to have its novelty in utilization of unprocessed simulation data, achievement of 3D structures in prediction by processing 3D convolutions, and predicting precise solution with less computational costs.
Intergroup emotions: explaining offensive action tendencies in an intergroup context.

Three studies tested the idea that when social identity is salient, group-based appraisals elicit specific emotions and action tendencies toward out-groups. Participants' group memberships were made salient and the collective support apparently enjoyed by the in-group was measured or manipulated. The authors then measured anger and fear (Studies 1 and 2) and anger and contempt (Study 3), as well as the desire to move against or away from the out-group. Intergroup anger was distinct from intergroup fear, and the inclination to act against the out-group was distinct from the tendency to move away from it. Participants who perceived the in-group as strong were more likely to experience anger toward the out-group and to desire to take action against it. The effects of perceived in-group strength on offensive action tendencies were mediated by anger.
The Effect of Powder Mixing Procedures on a-SiAlON Roman Shuba and I -

Various procedures of mixing starting powders for hot-pressing α-SiAlON ceramics were studied. They included different milling methods (attrition milling, ball milling, and sonication), liquid medium (water, isopropyl alcohol, and pyridine), and atmospheres (ambient air and nitrogen). These mixing procedures resulted in markedly different densification behavior and fired ceramics. As the powders experienced increasing oxidation because of mixing, the densification temperature decreased, the amount of residual glass increased, and α-SiAlON was destabilized and replaced by ß-SiAlON and AlN polytypes during hot pressing. These effects were mitigated when pyridine, nitrogen, and sonication were used. Several protocols that yielded nearly phase-pure, glass-free dense α-SiAlON were thus identified.  Reprinted from Journal of the American Ceramic Society, Volume 89, Issue 3, 2006, pages 1110-1113.  http://repository.upenn.edu/mse_papers/103 The Effect of Powder Mixing Procedures on a-SiAlON Roman Shuba and I-Wei Chen Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104-6272 Various procedures of mixing starting powders for hot-pressing a-SiAlON ceramics were studied. They included different milling methods (attrition milling, ball milling, and sonication), liquid medium (water, isopropyl alcohol, and pyridine), and atmospheres (ambient air and nitrogen). These mixing procedures resulted in markedly different densification behavior and fired ceramics. As the powders experienced increasing oxidation because of mixing, the densification temperature decreased, the amount of residual glass increased, and a-SiAlON was destabilized and replaced by b-SiAlON and AlN polytypes during hot pressing. These effects were mitigated when pyridine, nitrogen, and sonication were used. Several protocols that yielded nearly phase-pure, glass-free dense a-SiAlON were thus identified.
The efficacy of violence prediction: a meta-analytic comparison of nine risk assessment tools.

Actuarial risk assessment tools are used extensively to predict future violence, but previous studies comparing their predictive accuracies have produced inconsistent findings as a result of various methodological issues. We conducted meta-analyses of the effect sizes of 9 commonly used risk assessment tools and their subscales to compare their predictive efficacies for violence. The effect sizes were extracted from 28 original reports published between 1999 and 2008, which assessed the predictive accuracy of more than one tool. We used a within-subject design to improve statistical power and multilevel regression models to disentangle random effects of variation between studies and tools and to adjust for study features. All 9 tools and their subscales predicted violence at about the same moderate level of predictive efficacy with the exception of Psychopathy Checklist--Revised (PCL-R) Factor 1, which predicted violence only at chance level among men. Approximately 25% of the total variance was due to differences between tools, whereas approximately 85% of heterogeneity between studies was explained by methodological features (age, length of follow-up, different types of violent outcome, sex, and sex-related interactions). Sex-differentiated efficacy was found for a small number of the tools. If the intention is only to predict future violence, then the 9 tools are essentially interchangeable; the selection of which tool to use in practice should depend on what other functions the tool can perform rather than on its efficacy in predicting violence. The moderate level of predictive accuracy of these tools suggests that they should not be used solely for some criminal justice decision making that requires a very high level of accuracy such as preventive detention.
Computational models for text summarization

Abstractive text summarization is a blossoming area of natural language processing research in which short textual summaries are generated from longer input documents. Existing state-of-the-art methods take long time to train, and are limited to functioning on relatively short input sequences. We evaluate neural network architectures with simplified encoder stages, which naturally support arbitrarily long input sequences in a computationally efficient manner.ive text summarization is a blossoming area of natural language processing research in which short textual summaries are generated from longer input documents. Existing state-of-the-art methods take long time to train, and are limited to functioning on relatively short input sequences. We evaluate neural network architectures with simplified encoder stages, which naturally support arbitrarily long input sequences in a computationally efficient manner.
Recommendation as link prediction: a graph kernel-based machine learning approach

Recommender systems have demonstrated commercial success in multiple industries. In digital libraries they have the potential to be used as a support tool for traditional information retrieval functions. Among the major recommendation algorithms, the successful collaborative filtering (CF) methods explore the use of user-item interactions to infer user interests. Based on the finding that transitive user-item associations can alleviate the data sparsity problem in CF, multiple heuristic algorithms were designed to take advantage of the user-item interaction networks with both direct and indirect interactions. However, the use of such graph representation was still limited in learning-based algorithms. In this paper, we propose a graph kernel-based recommendation framework. For each user-item pair, we inspect its associative interaction graph (AIG) that contains the users, items, and interactions n steps away from the pair. We design a novel graph kernel to capture the AIG structures and use them to predict possible user-item interactions. The framework demonstrates improved performance on an online bookstore dataset, especially when a large number of suggestions are needed.
Embeddedness and the Price of Legal Services in the Large Law Firm Market

The determination of prices is a key function of markets yet it is just beginning to be studied by sociologists. Most theories view prices as a consequence of economic processes. By contrast, we consider how social structure shapes prices. Building on embeddedness arguments and original fieldwork at large law firms, we propose that a firm’s embedded relationships influence prices by prompting private information flows and informal governance arrangements that add unique value to goods and services. We test our arguments with a separate longitudinal dataset on the pricing of legal services by law firms that represent corporate America. We find that embeddedness can significantly increase and decrease prices net of standard variables and in markets for both complex and routine legal services. Moreover, results show that three forms of embeddedness embedded ties, board memberships, and status affect prices in different directions and have different magnitudes of effects that depend on the complexity of the legal service.
Self-Paced Learning for Latent Variable Models

Latent variable models are a powerful tool for addressing several tasks in machine learning. However, the algorithms for learning the parameters of latent variable models are prone to getting stuck in a bad local optimum. To alleviate this problem, we build on the intuition that, rather than considering all samples simultaneously, the algorithm should be presented with the training data in a meaningful order that facilitates learning. The order of the samples is determined by how easy they are. The main challenge is that typically we are not provided with a readily computable measure of the easiness of samples. We address this issue by proposing a novel, iterative self-paced learning algorithm where each iteration simultaneously selects easy samples and learns a new parameter vector. The number of samples selected is governed by a weight that is annealed until the entire training data has been considered. We empirically demonstrate that the self-paced learning algorithm outperforms the state of the art method for learning a latent structural SVM on four applications: object localization, noun phrase coreference, motif finding and handwritten digit recognition.
Effects of Website Interactivity on Online Retail Shopping Behavior

Motivations to engage in retail online shopping can include both utilitarian and hedonic shopping dimensions. To cater to these consumers, online retailers can create a cognitively and esthetically rich shopping environment, through sophisticated levels of interactive web utilities and features, offering not only utilitarian benefits and attributes but also providing hedonic benefits of enjoyment. Since the effect of interactive websites has proven to stimulate online consumer’s perceptions, this study presumes that websites with multimedia rich interactive utilities and features can influence online consumers’ shopping motivations and entice them to modify or even transform their original shopping predispositions by providing them with attractive and enhanced interactive features and controls, thus generating a positive attitude towards products and services offered by the retailer. This study seeks to explore the effects of Web interactivity on online consumer behavior through an attitudinal model of technology acceptance.