nils-herrmann/clean_scirepeval huggingface dataset:
Towards Trusted Social Networks with Blockchain Technology

Large-scale rumor spreading could pose severe social and economic damages. The emergence of online social networks along with the new media can even make rumor spreading more severe. Effective control of rumor spreading is of theoretical and practical significance. This paper takes the first step to understand how the blockchain technology can help limit the spread of rumors. Specifically, we develop a new paradigm for social networks embedded with the blockchain technology, which employs decentralized contracts to motivate trust networks as well as secure information exchange contract. We design a blockchain-based sequential algorithm which utilizes virtual information credits for each peer-topeer information exchange. We validate the effectiveness of the blockchain-enabled social network on limiting the rumor spreading. Simulation results validate our algorithm design in avoiding rapid and intense rumor spreading, and motivate better mechanism design for trusted social networks.
What about N? A methodological study of sample-size reporting in focus group studies

Focus group studies are increasingly published in health related journals, but we know little about how researchers use this method, particularly how they determine the number of focus groups to conduct. The methodological literature commonly advises researchers to follow principles of data saturation, although practical advise on how to do this is lacking. Our objectives were firstly, to describe the current status of sample size in focus group studies reported in health journals. Secondly, to assess whether and how researchers explain the number of focus groups they carry out.



We searched PubMed for studies that had used focus groups and that had been published in open access journals during 2008, and extracted data on the number of focus groups and on any explanation authors gave for this number. We also did a qualitative assessment of the papers with regard to how number of groups was explained and discussed.



We identified 220 papers published in 117 journals. In these papers insufficient reporting of sample sizes was common. The number of focus groups conducted varied greatly (mean 8.4, median 5, range 1 to 96). Thirty seven (17%) studies attempted to explain the number of groups. Six studies referred to rules of thumb in the literature, three stated that they were unable to organize more groups for practical reasons, while 28 studies stated that they had reached a point of saturation. Among those stating that they had reached a point of saturation, several appeared not to have followed principles from grounded theory where data collection and analysis is an iterative process until saturation is reached. Studies with high numbers of focus groups did not offer explanations for number of groups. Too much data as a study weakness was not an issue discussed in any of the reviewed papers.



Based on these findings we suggest that journals adopt more stringent requirements for focus group method reporting. The often poor and inconsistent reporting seen in these studies may also reflect the lack of clear, evidence-based guidance about deciding on sample size. More empirical research is needed to develop focus group methodology.
Transitioning between Convolutional and Fully Connected Layers in Neural Networks

Digital pathology has advanced substantially over the last decade however tumor localization continues to be a challenging problem due to highly complex patterns and textures in the underlying tissue bed. The use of convolutional neural networks (CNNs) to analyze such complex images has been well adopted in digital pathology. However in recent years, the architecture of CNNs have altered with the introduction of inception modules which have shown great promise for classification tasks. In this paper, we propose a modified “transition” module which learns global average pooling layers from filters of varying sizes to encourage class-specific filters at multiple spatial resolutions. We demonstrate the performance of the transition module in AlexNet and ZFNet, for classifying breast tumors in two independent datasets of scanned histology sections, of which the transition module was superior.
A Good Practice Towards Top Performance of Face Recognition: Transferred Deep Feature Fusion

Unconstrained face recognition performance evaluations have traditionally focused on Labeled Faces in the Wild (LFW) dataset for imagery and the YouTubeFaces (YTF) dataset for videos in the last couple of years. Spectacular progress in this field has resulted in a saturation on verification and identification accuracies for those benchmark datasets. In this paper, we propose a unified learning framework named transferred deep feature fusion targeting at the new IARPA Janus Bechmark A (IJB-A) face recognition dataset released by NIST face challenge. The IJB-A dataset includes real-world unconstrained faces from 500 subjects with full pose and illumination variations which are much harder than the LFW and YTF datasets. Inspired by transfer learning, we train two advanced deep convolutional neural networks (DCNN) with two different large datasets in source domain, respectively. By exploring the complementarity of two distinct DCNNs, deep feature fusion is utilized after feature extraction in target domain. Then, template specific linear SVMs is adopted to enhance the discrimination of framework. Finally, multiple matching scores corresponding different templates are merged as the final results. This simple unified framework outperforms the state-of-the-art by a wide margin on IJB-A dataset. Based on the proposed approach, we have submitted our IJB-A results to National Institute of Standards and Technology (NIST) for official evaluation.
Distributional Inclusion Hypothesis for Tensor-based Composition

According to the distributional inclusion hypothesis, entailment between words can be measured via the feature inclusions of their distributional vectors. In recent work, we showed how this hypothesis can be extended from words to phrases and sentences in the setting of compositional distributional semantics. This paper focuses on inclusion properties of tensors; its main contribution is a theoretical and experimental analysis of how feature inclusion works in different concrete models of verb tensors. We present results for relational, Frobenius, projective, and holistic methods and compare them to the simple vector addition, multiplication, min, and max models. The degrees of entailment thus obtained are evaluated via a variety of existing wordbased measures, such as Weed’s and Clarke’s, KL-divergence, APinc, balAPinc, and two of our previously proposed metrics at the phrase/sentence level. We perform experiments on three entailment datasets, investigating which version of tensor-based composition achieves the highest performance when combined with the sentence-level measures.
Improving low-resource CD-DNN-HMM using dropout and multilingual DNN training

We investigate two strategies to improve the contextdependent deep neural network hidden Markov model (CDDNN-HMM) in low-resource speech recognition. Although outperforming the conventional Gaussian mixture model (GMM) HMM on various tasks, CD-DNN-HMM acoustic modeling becomes challenging with limited transcribed speech, e.g., less than 10 hours. To resolve this issue, we firstly exploit dropout which prevents overfitting in DNN finetuning and improves model robustness under data sparseness. Then, the effectiveness of multilingual DNN training is evaluated when additional auxiliary languages are available. The hidden layer parameters of the target language are shared and learned over multiple languages. Experiments show that both strategies boost the recognition performance significantly. Combining them results in further reduction in word error rate, achieving 11.6% and 6.2% relative improvement on two limited data conditions.
Prenatal Correction of X-Linked Hypohidrotic Ectodermal Dysplasia.

Genetic deficiency of ectodysplasin A (EDA) causes X-linked hypohidrotic ectodermal dysplasia (XLHED), in which the development of sweat glands is irreversibly impaired, an condition that can lead to life-threatening hyperthermia. We observed normal development of mouse fetuses with Eda mutations after they had been exposed in utero to a recombinant protein that includes the receptor-binding domain of EDA. We administered this protein intraamniotically to two affected human twins at gestational weeks 26 and 31 and to a single affected human fetus at gestational week 26; the infants, born in week 33 (twins) and week 39 (singleton), were able to sweat normally, and XLHED-related illness had not developed by 14 to 22 months of age.
Application integration on the user interface level: An ontology-based approach

Integration of software applications can be achieved on different levels: the data level, the business logic level, and the user interface level. Integration on the user interface level means assembling the user interfaces of existing applications in a framework allowing seamless, unified interaction with those applications. While integration on the user interface level is desirable both from an software engineering as well as from a usability point of view, most current approaches require detailed knowledge of the integrated applications and make the implementation of a common interaction that involves different applications a difficult issue. In this paper, we propose a framework using ontologies for application integration on the user interface level by encapsulating the applications in plugins. Our approach is to use different ontologies for characterizing applications and the interactions possible with them, and for semantically annotating information objects exchanged between applications. Thus, the domainindependent and the domain-specific parts are untangled, which makes the framework applicable to different domains. An instance-based reasoner is used to process the ontologies and to compute the possible interactions, thus enabling integration at run-time. In an example from the domain of emergency management, we show how our approach helps implementing cross-application interactions more easily, thus significantly lowering the barriers for interoperability. This is a draft version of the article “Application Integration on the User Interface Level:an OntologyBased Approach”, which has been accepted for publication in the Data and Knowledge Engineering Journal (DKE), 2010. Application Integration on the User Interface Level: an Ontology-Based Approach Heiko Paulheim, Florian Probst SAP Research Center Darmstadt, Bleichstrasse 8, 64283 Darmstadt, Germany
Modeling opinion dynamics in social networks

Our opinions and judgments are increasingly shaped by what we read on social media -- whether they be tweets and posts in social networks, blog posts, or review boards. These opinions could be about topics such as consumer products, politics, life style, or celebrities. Understanding how users in a network update opinions based on their neighbor's opinions, as well as what global opinion structure is implied when users iteratively update opinions, is important in the context of viral marketing and information dissemination, as well as targeting messages to users in the network.
 In this paper, we consider the problem of modeling how users update opinions based on their neighbors' opinions. We perform a set of online user studies based on the celebrated conformity experiments of Asch . Our experiments are carefully crafted to derive quantitative insights into developing a model for opinion updates (as opposed to deriving psychological insights). We show that existing and widely studied theoretical models do not explain the entire gamut of experimental observations we make. This leads us to posit a new, nuanced model that we term the BVM. We present preliminary theoretical and simulation results on the convergence and structure of opinions in the entire network when users iteratively update their respective opinions according to the BVM. We show that consensus and polarization of opinions arise naturally in this model under easy to interpret initial conditions on the network.
Zero-shot User Intent Detection via Capsule Neural Networks

User intent detection plays a critical role in question-answering and dialog systems. Most previous works treat intent detection as a classification problem where utterances are labeled with predefined intents. However, it is labor-intensive and time-consuming to label users’ utterances as intents are diversely expressed and novel intents will continually be involved. Instead, we study the zero-shot intent detection problem, which aims to detect emerging user intents where no labeled utterances are currently available. We propose two capsule-based architectures: INTENTCAPSNET that extracts semantic features from utterances and aggregates them to discriminate existing intents, and INTENTCAPSNET-ZSL which gives INTENTCAPSNET the zero-shot learning ability to discriminate emerging intents via knowledge transfer from existing intents. Experiments on two real-world datasets show that our model not only can better discriminate diversely expressed existing intents, but is also able to discriminate emerging intents when no labeled utterances are available.
Strategies and techniques for node placement in wireless sensor networks: A survey

The major challenge in designing wireless sensor networks (WSNs) is the support of the functional, such as data latency, and the non-functional, such as data integrity, requirements while coping with the computation, energy and communication constraints. Careful node placement can be a very effective optimization means for achieving the desired design goals. In this paper, we report on the current state of the research on optimized node placement in WSNs. We highlight the issues, identify the various objectives and enumerate the different models and formulations. We categorize the placement strategies into static and dynamic depending on whether the optimization is performed at the time of deployment or while the network is operational, respectively. We further classify the published techniques based on the role that the node plays in the network and the primary performance objective considered. The paper also highlights open problems in this area of research.
Improved Representation Learning for Predicting Commonsense Ontologies

Recent work in learning ontologies (hierarchical and partially-ordered structures) has leveraged the intrinsic geometry of spaces of learned representations to make predictions that automatically obey complex structural constraints. We explore two extensions of one such model, the order-embedding (Vendrov et al.) model for hierarchical relation learning, with an aim towards improved performance on text data for commonsense knowledge representation. Our first model jointly learns ordering relations and nonhierarchical knowledge in the form of raw text. Our second extension exploits the partial order structure of the training data to find long-distance triplet constraints among embeddings which are poorly enforced by the pairwise training procedure. We find that both incorporating free text and augmented training constraints improve over the original order-embedding model and other strong baselines.
A Column Generation Approach for Graph Coloring

We present a method for solving the independent set formulation of the graph coloring problem (where there is one variable for each independent set in the graph). We use a column generation method for implicit optimization of the linear program at each node of the branch-and-bound tree. This approach, while requiring the solution of a diicult subproblem as well as needing sophisticated branching rules, solves small to moderate size problems quickly. We have also implemented an exact graph coloring algorithm based on DSATUR for comparison. Implementation details and computational experience are presented.
Application of Bitcoin Data-Structures & Design Principles to Supply Chain Management

Heretofore the concept of " blockchain " has not been precisely defined. Accordingly the potential useful applications of this technology have been largely inflated. This work sidesteps the question of what constitutes a blockchain as such and focuses on the architectural components of the Bitcoin cryptocurrency, insofar as possible, in isolation. We consider common problems inherent in the design of effective supply chain management systems. With each identified problem we propose a solution that utilizes one or more component aspects of Bitcoin. This culminates in five design principles for increased efficiency in supply chain management systems through the application of incentive mechanisms and data structures native to the Bitcoin cryptocurrency protocol.
Running with Technology: Evaluating the Impact of Interacting with Wearable Devices on Running Movement

The use of wearable devices during running has become commonplace. Although there is ongoing research on interaction techniques for use while running, the effects of the resulting interactions on the natural movement patterns have received little attention so far. While previous studies on pedestrians reported increased task load and reduced walking speed while interacting, running movement further restricts interaction and requires minimizing interferences, e.g. to avoid injuries and maximize comfort. In this paper, we aim to shed light on how interacting with wearable devices affects running movement. We present results from a motion-tracking study (N=12) evaluating changes in movement and task load when users interact with a smartphone, a smartwatch, or a pair of smartglasses while running. In our study, smartwatches required less effort than smartglasses when using swipe input, resulted in less interference with the running movement and were preferred overall. From our results, we infer a number of guidelines regarding interaction design targeting runners.
Stunting, wasting and associated factors among children aged 6–24 months in Dabat health and demographic surveillance system site: A community based cross-sectional study in Ethiopia

Though there is a marked decline in burden of undernutrition, about 44 and 10% of children under five are stunted and wasted, respectively in Ethiopia. The highest prevalence of wasting occurs in young children (6-23 months), however literature are limited in these population groups. Therefore, this study aimed to assess stunting, wasting and associated factors among children aged 6-24 months in Dabat Health and Demographic Surveillance System (HDSS) site, northwest Ethiopia.



A community based cross-sectional study was conducted in Dabat HDSS site from May 01 to June 29, 2015. A total of 587 mother-child pairs were included in the study. A multivariate logistic regression analysis was carried out to identify factors associated with stunting and wasting, separately.



The prevalence of stunting and wasting among children aged 6-24 months were 58.1 and 17.0%, respectively. Poor wealth status [Adjusted Odds Ratio (AOR) = 2.20; 95% Confidence Interval (CI): 1.42, 3.40], unavailability of latrine [AOR = 1.76; 95% CI: 1.17, 2.66], child age: 12-24 months [AOR = 3.24; 95% CI: 2.24, 4.69], not receiving maternal postnatal vitamin-A supplementation [AOR = 1.54; 95%: 1.02, 2.33] and source of family food: own food production [AOR = 1.71; 95% CI: 1.14, 2.57] were significantly associated with higher odds of stunting. However, only history of diarrheal morbidity was significantly associated with wasting [AOR = 2.06; 95% CI: 1.29, 3.30].



In this community, the magnitude of stunting and wasting exists as a severe public health concern. Therefore, improving socio-economic status, latrine and maternal postnatal vitamin-supplementation coverage are essential to mitigate the high burden of stunting. Besides, reducing the childhood diarrheal morbidity as well as strengthening early diagnosis and management of the problem are crucial to curve the high prevalence of wasting.
Protein quality assessment: impact of expanding understanding of protein and amino acid needs for optimal health.

Protein quality describes characteristics of a protein in relation to its ability to achieve defined metabolic actions. Traditionally, this has been discussed solely in the context of a protein's ability to provide specific patterns of amino acids to satisfy the demands for synthesis of protein as measured by animal growth or, in humans, nitrogen balance. As understanding of protein's actions expands beyond its role in maintaining body protein mass, the concept of protein quality must expand to incorporate these newly emerging actions of protein into the protein quality concept. New research reveals increasingly complex roles for protein and amino acids in regulation of body composition and bone health, gastrointestinal function and bacterial flora, glucose homeostasis, cell signaling, and satiety. The evidence available to date suggests that quality is important not only at the minimum Recommended Dietary Allowance level but also at higher intakes. Currently accepted methods for measuring protein quality do not consider the diverse roles of indispensable amino acids beyond the first limiting amino acid for growth or nitrogen balance. As research continues to evolve in assessing protein's role in optimal health at higher intakes, there is also need to continue to explore implications for protein quality assessment.
A document query search using an extended centrality with the word2vec

While everyday document search is done by keyword-based queries to search engines, we have situations that need deep search of documents such as scrutinies of patents, legal documents, and so on. In such cases, using document queries, instead of keyword-based queries, can be more helpful because it exploits more information from the query document. This paper studies a scheme of document search based on document queries. In particular, it uses centrality vectors, instead of tf-idf vectors, to represent query documents, combined with the Word2vec method to capture the semantic similarity in contained words. This scheme improves the performance of document search and provides a way to find documents not only lexically, but semantically close to a query document.
A Social-Cognitive Approach to Motivation and Personality

Past work has documented and described major patterns of adaptive and maladaptive behavior: the mastery.oriented and the helpless patterns. In this article, we present a research-based model that accounts for these patterns in terms of underlying psychological processes. The model specifies how individuals' implicit theories orient them toward particular goals and how these goals set up the different patterns. Indeed, we show how each feature (cognitive, affective, and behavioral) of the adaptive and maladaptive patterns can be seen to follow directly from different goals. We then examine the generality of the model and use it to illuminate phenomena in a wide variety of domains. Finally, we place the model in its broadest context and examine its implications for our understanding of motivational and personality processes.