nils-herrmann/clean_scirepeval huggingface dataset:
Reduced-Complexity Approach to Iterative Detection of Coded SOQPSK

We develop a reduced-complexity approach for the detection of coded shaped-offset quadrature phase-shift keying (SOQPSK), a highly bandwidth-efficient and popular constant-envelope modulation. The complexity savings result from viewing the signal as a continuous-phase modulation (CPM). We give a simple and convenient closed-form expression for a recursive binary-to-ternary precoder for SOQPSK. The recursive nature of this formulation is necessary in serially concatenated systems where SOQPSK serves as the inner code. We show that the proposed detectors are optimal in the full-response case, and are near-optimal in the partial-response case due to some additional complexity reducing approximations. In all cases, the proposed detectors achieve large coding gains for serially concatenated coded SOQPSK. These gains are similar to those reported recently by Li and Simon, which were obtained using a more complicated cross-correlated trellis-coded quadrature modulation (XTCQM) interpretation.
Beyond Dashboards: On the Many Facets of Metrics and Feedback in Agile Organizations

One of the key principles in agile development is that the team should use continuous feedback cycles to maintain awareness both about the product, the process and the involved people. In healthy agile environments, people see a lot of value in the collection of metrics and in their collaborative analysis to support a continuous improvement process. In pseudo agile environments, metrics are often perceived as a threat and they make people uncomfortable. In theses conditions, they can do more harm than good. In this paper, we argue that the acceptance of a data-driven continuous improvement process is heavily influenced by the nature of the channels through which the metrics are published. We argue for the need to go beyond typical dashboards and propose additional engagement channels. We discuss the notions of awareness and story telling and illustrate them with various experiments conducted with agile teams.
Relationship between Gender Roles and Sexual Assertiveness in Married Women


Evidence indicates that sexual assertiveness is one of the important factors affecting sexual satisfaction. According to some studies, traditional gender norms conflict with women's capability in expressing sexual desires. This study examined the relationship between gender roles and sexual assertiveness in married women in Mashhad, Iran.



This cross-sectional study was conducted on 120 women who referred to Mashhad health centers through convenient sampling in 2014-15. Data were collected using Bem Sex Role Inventory (BSRI) and Hulbert index of sexual assertiveness. Data were analyzed using SPSS 16 by Pearson and Spearman's correlation tests and linear Regression Analysis.



The mean scores of sexual assertiveness was 54.93±13.20. According to the findings, there was non-significant correlation between Femininity and masculinity score with sexual assertiveness (P=0.069 and P=0.080 respectively). Linear regression analysis indicated that among the predictor variables, only Sexual function satisfaction was identified as the sexual assertiveness summary predictor variables (P=0.001).



Based on the results, sexual assertiveness in married women does not comply with gender role, but it is related to Sexual function satisfaction. So, counseling psychologists need to consider this variable when designing intervention programs for modifying sexual assertiveness and find other variables that affect sexual assertiveness.
Cell-aware Stacked LSTMs for Modeling Sentences

We propose a method of stacking multiple long short-term memory (LSTM) layers for modeling sentences. In contrast to the conventional stacked LSTMs where only hidden states are fed as input to the next layer, our architecture accepts both hidden and memory cell states of the preceding layer and fuses information from the left and the lower context using the soft gating mechanism of LSTMs. Thus the proposed stacked LSTM architecture modulates the amount of information to be delivered not only in horizontal recurrence but also in vertical connections, from which useful features extracted from lower layers are effectively conveyed to upper layers. We dub this architecture Cell-aware Stacked LSTM (CAS-LSTM) and show from experiments that our models achieve state-of-the-art results on benchmark datasets for natural language inference, paraphrase detection, and sentiment classification.
Stem cell-based biological tooth repair and regeneration

Teeth exhibit limited repair in response to damage, and dental pulp stem cells probably provide a source of cells to replace those damaged and to facilitate repair. Stem cells in other parts of the tooth, such as the periodontal ligament and growing roots, play more dynamic roles in tooth function and development. Dental stem cells can be obtained with ease, making them an attractive source of autologous stem cells for use in restoring vital pulp tissue removed because of infection, in regeneration of periodontal ligament lost in periodontal disease, and for generation of complete or partial tooth structures to form biological implants. As dental stem cells share properties with mesenchymal stem cells, there is also considerable interest in their wider potential to treat disorders involving mesenchymal (or indeed non-mesenchymal) cell derivatives, such as in Parkinson's disease.
Insiders Behaving Badly: Addressing Bad Actors and Their Actions

We present a framework for describing insiders and their actions based on the organization, the environment, the system, and the individual. Using several real examples of unwelcome insider action (hard drive removal, stolen intellectual property, tax fraud, and proliferation of e-mail responses), we show how the taxonomy helps in understanding how each situation arose and could have been addressed. The differentiation among types of threats suggests how effective responses to insider threats might be shaped, what choices exist for each type of threat, and the implications of each. Future work will consider appropriate strategies to address each type of insider threat in terms of detection, prevention, mitigation, remediation, and punishment.
OPTIMAL DESIGN OF WHEEL PROFILE FOR RAILWAY VEHICLES

Beyond the advantages in design of running gear and railway vehicles itself and introducing the active wheelset steering control systems many railroads and tram companies still use huge number of old fashion vehicles. Dynamic performance, safety and maintenance cost of which strongly depend on the wheelset dynamics and particularly on how good is design of wheel and rail profiles. The paper presents a procedure for design of a wheel profile based on geometrical wheel/rail (w/r) contact characteristics which uses numerical optimization technique. The procedure has been developed by Railway Engineering Group in Delft University of Technology. The optimality criteria formulated using the requirements to railway track and wheelset, are related to stability of wheelset, cost efficiency of design and minimum wear of wheels and rails. The shape of a wheel profile has been varied during optimization. A new wheel profile is obtained for given target rolling radii difference function ‘ r y ∆ − ’ and rail profile. Measurements of new and worn wheel and rail profiles has been used to define the target ‘ r y ∆ − ’ curve. Finally dynamic simulations of vehicle with obtained wheel profile have been performed in ADAMS/Rail program package in order to control w/r wear and safety requirements. The proposed procedure has been applied to design of wheel profile for trams. Numerical results are presented and discussed.
The Impact of referral channels in Online Customer Journey

This study scrutinizes the predictive relationship between three referral channels, search engine, social medial, and third-party advertising, and online consumer search and purchase. The results derived from vector autoregressive models suggest that the three channels have differential predictive relationship with sale measures. The predictive power of the three channels is also considerably different in referring customers among competing online shopping websites. In the short run, referrals from all three channels have a significantly positive predictive relationship with the focal online store’s sales amount and volume, but having no significant relationship with conversion. Only referrals from search engines to the rival website have a significantly negative predictive relationship with the focal website’s sales and volume. In the long run, referrals from all three channels have a significant positive predictive relationship with the focal website’s sales, conversion and sales volume. In contrast, referrals from all three channels to the competing online stores have a significant negative predictive relationship with the focal website’s sales, conversion and sales volume. Our results also show that search engine referrals explains the most of the variance in sales, while social media referrals explains the most of the variance in conversion and third party ads referrals explains the most of the variance in sales volume. This study offers new insights for IT and marketing practitioners in respect to better and deeper understanding on marketing attribution and how different channels perform in order to optimize the media mix and overall performance.
20 Geminoid : Teleoperated Android of an Existing Person

Why are people attracted to humanoid robots and androids? The answer is simple: because human beings are attuned to understand or interpret human expressions and behaviors, especially those that exist in their surroundings. As they grow, infants, who are supposedly born with the ability to discriminate various types of stimuli, gradually adapt and fine-tune their interpretations of detailed social clues from other voices, languages, facial expressions, or behaviors (Pascalis et al., 2002). Perhaps due to this functionality of nature and nurture, people have a strong tendency to anthropomorphize nearly everything they encounter. This is also true for computers or robots. In other words, when we see PCs or robots, some automatic process starts running inside us that tries to interpret them as human. The media equation theory (Reeves & Nass, 1996) first explicitly articulated this tendency within us. Since then, researchers have been pursuing the key element to make people feel more comfortable with computers or creating an easier and more intuitive interface to various information devices. This pursuit has also begun spreading in the field of robotics. Recently, researcher’s interests in robotics are shifting from traditional studies on navigation and manipulation to human-robot interaction. A number of researches have investigated how people respond to robot behaviors and how robots should behave so that people can easily understand them (Fong et al., 2003; Breazeal, 2004; Kanda et al., 2004). Many insights from developmental or cognitive psychologies have been implemented and examined to see how they affect the human response or whether they help robots produce smooth and natural communication with humans. However, human-robot interaction studies have been neglecting one issue: the "appearance versus behavior problem." We empirically know that appearance, one of the most significant elements in communication, is a crucial factor in the evaluation of interaction . The interactive robots developed so far had very mechanical outcomes that do appear as “robots.” Researchers tried to make such interactive robots “humanoid” by equipping them with heads, eyes, or hands so that their appearance more closely resembled human beings and to enable them to make such analogous human movements or gestures as staring, pointing, and so on. Functionality was considered the primary concern in improving communication with humans. In this manner, many studies have compared robots with different behaviors. Thus far, scant attention has been paid to robot appearances.
TeICC: targeted execution of inter-component communications in Android

Effective analysis of applications (shortly apps) is essential to understanding apps' behavior. Two analysis approaches, i.e., static and dynamic, are widely used; although, both have well known limitations. Static analysis suffers from obfuscation and dynamic code updates. Whereas, it is extremely hard for dynamic analysis to guarantee the execution of all the code paths in an app and thereby, suffers from the code coverage problem. However, from a security point of view, executing all paths in an app might be less interesting than executing certain potentially malicious paths in the app. In this work, we use a hybrid approach that combines static and dynamic analysis in an iterative manner to cover their shortcomings. We use targeted execution of interesting code paths to solve the issues of obfuscation and dynamic code updates. Our targeted execution leverages a slicing-based analysis for the generation of data-dependent slices for arbitrary methods of interest (MOI) and on execution of the extracted slices for capturing their dynamic behavior. Motivated by the fact that malicious apps use Inter Component Communications (ICC) to exchange data , our main contribution is the automatic targeted triggering of MOI that use ICC for passing data between components. We implement a proof of concept, TelCC, and report the results of our evaluation.
Security and Privacy Approaches in Mixed Reality: A Literature Survey

Mixed reality (MR) technology development is now gaining momentum due to advances in computer vision, sensor fusion, and realistic display technologies. With most of the research and development focused on delivering the promise of MR, there is only barely a few working on the privacy and security implications of this technology. is survey paper aims to put in to light these risks, and to look into the latest security and privacy work on MR. Specically, we list and review the dierent protection approaches that have been proposed to ensure user and data security and privacy in MR. We extend the scope to include work on related technologies such as augmented reality (AR), virtual reality (VR), and human-computer interaction (HCI) as crucial components, if not the origins, of MR, as well as numerous related work from the larger area of mobile devices, wearables, and Internet-of-ings (IoT). We highlight the lack of investigation, implementation, and evaluation of data protection approaches in MR. Further challenges and directions on MR security and privacy are also discussed.
Really Uncertain Business Cycles

We propose uncertainty shocks as a new impulse driving business cycles. First, we demonstrate that uncertainty, measured by a number of proxies is strongly countercyclical. Second, we build a dynamic stochastic general equilibrium model that extends the benchmark neoclassical growth model along two dimensions. It allows for the existence of heterogeneous rms with non-convex adjustment costs in both capital and labor and time-variation in uncertainty that is modeled as a change in the variance of innovations to productivity. We nd that increases in uncertainty lead to large drops in economic activity. This occurs because a rise in uncertainty makes rms cautious, leading them to pause hiring and investment. It also reduces the reallocation of capital and labor across rms, leading to large falls in productivity growth. Finally, we show that because uncertainty makes rms cautious it signicantly reduces the response of the economy to stimulative policy, leading to pro-cyclical policy multipliers.  uncertainty, adjustment costs and business cycles.  Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the U.S. Census Bureau. All results have been reviewed to ensure that no condential information is disclosed. We would like to thank our formal discussants Eduardo Engel at the AEA (2009) and Frank Smets at the ECB (2009) for their comments and suggestions. We thank Gadi Barlevy, Patrick Kehoe, and Naryana Kocherlakota for most helpful comments and suggestions. We would like to thank seminar participants at the AEA, ECB, UC-Irvine, IMF, Warwick University, Kellogg Business School of Management, NBER summer institute (2009).  Nick Bloom, Department of Economics, Stanford University, Stanford, CA 94305, nbloom@stanford.edu.
Expression of CD24 in Human Bone Marrow-Derived Mesenchymal Stromal Cells Is Regulated by TGFβ3 and Induces a Myofibroblast-Like Genotype.

Human bone marrow-derived stromal cells (hBMSCs) derived from the adult organism hold great promise for diverse settings in regenerative medicine. Therefore a more complete understanding of hBMSC biology to fully exploit the cells' potential for clinical settings is important. The protein CD24 has been reported to be involved in a diverse range of processes such as cancer, adaptive immunity, inflammation, and autoimmune diseases in other cell types. Its expression in hBMSCs, which has not yet been analyzed, may add an important aspect in the understanding of hBMSC biology. The present study therefore analyzes the expression, regulation, and functional implication of the surface protein CD24 in hBMSCs. Methods used are stimulation studies with TGF beta as well as shRNA-mediated knockdown and overexpression of CD24 followed by microarray, immunocytochemistry, and flow cytometric analyses. To our knowledge, we demonstrate for the first time that the expression of CD24 is an inherent property of hBMSCs. Importantly, the data links the upregulation of CD24 to the adoption of a myofibroblast-like gene expression pattern in hBMSCs. We demonstrate that CD24 is an important modulator in transforming growth factor beta 3 (TGFβ3) signaling with a reciprocal regulatory relationship between these two proteins.
The Language of Fake News: Opening the Black-Box of Deep Learning Based Detectors

The digital information age has generated new outlets for content creators to publish so-called “fake news”, a new form of propaganda that is intentionally designed to mislead the reader. With the widespread effects of the fast dissemination of fake news, efforts have been made to automate the process of fake news detection. A promising solution that has come up recently is to use machine learning to detect patterns in the news sources and articles, specifically deep neural networks, which have been successful in natural language processing. However, deep networks come with lack of transparency in the decision-making process, i.e. the “black-box problem”, which obscures its reliability. In this paper, we open this “black-box” and we show that the emergent representations from deep neural networks capture subtle but consistent differences in the language of fake and real news: signatures of exaggeration and other forms of rhetoric. Unlike previous work, we test the transferability of the learning process to novel news topics. Our results demonstrate the generalization capabilities of deep learning to detect fake news in novel subjects only from language
Systematic review automation technologies

Systematic reviews, a cornerstone of evidence-based medicine, are not produced quickly enough to support clinical practice. The cost of production, availability of the requisite expertise and timeliness are often quoted as major contributors for the delay. This detailed survey of the state of the art of information systems designed to support or automate individual tasks in the systematic review, and in particular systematic reviews of randomized controlled clinical trials, reveals trends that see the convergence of several parallel research projects.We surveyed literature describing informatics systems that support or automate the processes of systematic review or each of the tasks of the systematic review. Several projects focus on automating, simplifying and/or streamlining specific tasks of the systematic review. Some tasks are already fully automated while others are still largely manual. In this review, we describe each task and the effect that its automation would have on the entire systematic review process, summarize the existing information system support for each task, and highlight where further research is needed for realizing automation for the task. Integration of the systems that automate systematic review tasks may lead to a revised systematic review workflow. We envisage the optimized workflow will lead to system in which each systematic review is described as a computer program that automatically retrieves relevant trials, appraises them, extracts and synthesizes data, evaluates the risk of bias, performs meta-analysis calculations, and produces a report in real time.
On the statistical properties of viral misinformation in online social media

The massive diffusion of online social media allows for the rapid and uncontrolled spreading of conspiracy theories, hoaxes, unsubstantiated claims, and false news. Such an impressive amount of misinformation can influence policy preferences and encourage behaviors strongly divergent from recommended practices. In this paper, we study the statistical properties of viral misinformation in online social media. By means of methods belonging to Extreme Value Theory, we show that the number of extremely viral posts over time follows a homogeneous Poisson process, and that the interarrival times between such posts are independent and identically distributed, following an exponential distribution. Moreover, we characterize the uncertainty around the rate parameter of the Poisson process through Bayesian methods. Finally, we are able to derive the predictive posterior probability distribution of the number of posts exceeding a certain threshold of shares over a finite interval of time.
A parametric bootstrap approach for ANOVA with unequal variances: Fixed and random models

This article is about testing the equality of several normal means when the variances are unknown and arbitrary, i.e., the set up of the one-way ANOVA. Even though several tests are available in the literature, none of them perform well in terms of type I error probability under various sample size and parameter combinations. In fact, the type I errors can be highly inflated for some of the commonly used tests; a serious issue that appears to have been overlooked. We propose a parametric bootstrap (PB) approach and compare it with three existing location-scale invariant tests – the Welch test, James test and the generalized F (GF) test. The Type I error rates and powers of the tests are evaluated using Monte Carlo simulation. Our studies show that the PB test is the best among the four tests with respect to Type I error rates. The PB test performs very satisfactorily even for small samples while the Welch test and the GF test exhibit poor Type I error properties when the sample sizes are small and/or the number of means to be compared is moderate to large. The James test performs better than the Welch test and the GF test. It is also noted that the same tests can be used to test the significance of the random effect variance component in a one-way random model under unequal error variances. Such models are widely used to analyze data from inter-laboratory studies. The methods are illustrated using some examples.