nils-herrmann/clean_scirepeval huggingface dataset:
Reducing Length of Stay Using a Robotic-assisted Approach for Retromuscular Ventral Hernia Repair: A Comparative Analysis From the Americas Hernia Society Quality Collaborative.

The aim of this study was to compare length of stay (LOS) after robotic-assisted and open retromuscular ventral hernia repair (RVHR).



RVHR has traditionally been performed by open techniques. Robotic-assisted surgery enables surgeons to perform minimally invasive RVHR, but with unknown benefit. Using real-world evidence, this study compared LOS after open (o-RVHR) and robotic-assisted (r-RVHR) approach.



Multi-institutional data from patients undergoing elective RVHR in the Americas Hernia Society Quality Collaborative between 2013 and 2016 were analyzed. Propensity score matching was used to compare median LOS between o-RVHR and r-RVHR groups. This work was supported by an unrestricted grant from Intuitive Surgical, and all clinical authors have declared direct or indirect relationships with Intuitive Surgical.



In all, 333 patients met inclusion criteria for a 2:1 match performed on 111 r-RVHR patients using propensity scores, with 222 o-RVHR patients having similar characteristics as the robotic-assisted group. Median LOS [interquartile range (IQR)] was significantly decreased for r-RVHR patients [2 days (IQR 2)] compared with o-RVHR patients [3 days (IQR 3), P < 0.001]. No differences in 30-day readmissions or surgical site infections were observed. Higher surgical site occurrences were noted with r-RVHR, consisting mostly of seromas not requiring intervention.



Using real-world evidence, a robotic-assisted approach to RVHR offers the clinical benefit of reduced postoperative LOS. Ongoing monitoring of this technique should be employed through continuous quality improvement to determine the long-term effect on hernia recurrence, complications, patient satisfaction, and overall cost.
A field trial of privacy nudges for facebook

Anecdotal evidence and scholarly research have shown that Internet users may regret some of their online disclosures. To help individuals avoid such regrets, we designed two modifications to the Facebook web interface that nudge users to consider the content and audience of their online disclosures more carefully. We implemented and evaluated these two nudges in a 6-week field trial with 28 Facebook users. We analyzed participants' interactions with the nudges, the content of their posts, and opinions collected through surveys. We found that reminders about the audience of posts can prevent unintended disclosures without major burden; however, introducing a time delay before publishing users' posts can be perceived as both beneficial and annoying. On balance, some participants found the nudges helpful while others found them unnecessary or overly intrusive. We discuss implications and challenges for designing and evaluating systems to assist users with online disclosures.
Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies

Construction projects are initiated in dynamic environment which result in circumstances of high uncertainty and risks due to accumulation of many interrelated parameters. The purpose of this study is to use novel analytic tools to evaluate the construction projects and their overall risks under incomplete and uncertain situations. It was also aimed to place the risk in a proper category and predict the level of it in advance to develop strategies and counteract the high-risk factors. The study covers identifying the key risk criteria of construction projects at King Abdulaziz University (KAU), and assessing the criteria by the integrated hybrid methodologies. The proposed hybrid methodologies were initiated with a survey for data collection. The relative importance index (RII) method was applied to prioritize the project risks based on the data obtained. The construction projects were then categorized by fuzzy AHP and fuzzy TOPSIS methodologies. Fuzzy AHP (FAHP) was used to create favorable weights for fuzzy linguistic variable of construction projects overall risk. The fuzzy TOPSIS method is very suitable for solving group decision making problems under the fuzzy environment. It attempted to incorporate vital qualitative attributes in performance analysis of construction projects and transformed the qualitative data into equivalent quantitative measures. Thirty construction projects were studied with respect to five main criteria that are the time, cost, quality, safety and environment sustainability. The results showed that these novel methodologies are able to assess the overall risks of construction projects, select the project that has the lowest risk with the contribution of relative importance index. This approach will have potential applications in the future.
The Effect of Noise on Sofware Engineers' Performance

Noise, dened as an unwanted sound, is one of the commonest factors that could aect people’s performance in their daily work activities. e soware engineering research community has marginally investigated the eects of noise on soware engineers’ performance.  We studied if noise aects soware engineers’ performance in: (i) comprehending functional requirements and (ii) xing faults in source code.  We conducted two experiments with nal-year undergraduate students in Computer Science. In the rst experiment, we asked 55 students to comprehend functional requirements exposing them or not to noise, while in the second experiment 42 students were asked to x faults in Java code.  e participants in the second experiment, when exposed to noise, had signicantly worse performance in xing faults in source code. On the other hand, we did not observe any statistically signicant dierence in the rst experiment.  Fixing faults in source code seems to be more vulnerable to noise than comprehending functional requirements.
A NEXT GENERATION SMART CONTRACT & DECENTRALIZED APPLICATION PLATFORM

When Satoshi Nakamoto first set the Bitcoin blockchain into motion in January 2009, he was  simultaneously introducing two radical and untested concepts. The first is the "bitcoin", a decentralized  peer-to-peer online currency that maintains a value without any backing, intrinsic value or central issuer. So  far, the "bitcoin" as a currency unit has taken up the bulk of the public attention, both in terms of the political  aspects of a currency without a central bank and its extreme upward and downward volatility in price.  However, there is also another, equally important, part to Satoshi's grand experiment: the concept of a proof of  work-based blockchain to allow for public agreement on the order of transactions. Bitcoin as an application can  be described as a first-to-file system: if one entity has 50 BTC, and simultaneously sends the same 50 BTC to  A and to B, only the transaction that gets confirmed first will process. There is no intrinsic way of determining  from two transactions which came earlier, and for decades this stymied the development of decentralized  digital currency. Satoshi's blockchain was the first credible decentralized solution. And now, attention is  rapidly starting to shift toward this second part of Bitcoin's technology, and how the blockchain concept can be  used for more than just money.
A Combinatorial Noise Model for Quantum Computer Simulation

Quantum computers (QCs) have many potential hardware implementations ranging from solid-state silicon-based structures to electron-spin qubits on liquid helium. However, all QCs must contend with gate infidelity and qubit state decoherence over time. Quantum error correcting codes (QECCs) have been developed to protect program qubit states from such noise. Previously, Monte Carlo noise simulators have been developed to model the effectiveness of QECCs in combating decoherence. The downside to this random sampling approach is that it may take days or weeks to produce enough samples for an accurate measurement. We present an alternative noise modeling approach that performs combinatorial analysis rather than random sampling. This model tracks the progression of the most likely error states of the quantum program through its course of execution. This approach has the potential for enormous speedups versus the previous Monte Carlo methodology. We have found speedups with the combinatorial model on the order of 100X-1,000X over the Monte Carlo approach when analyzing applications utilizing the [[7,1,3]] QECC. The combinatorial noise model has significant memory requirements, and we analyze its scaling properties relative to the size of the quantum program. Due to its speedup, this noise model is a valuable alternative to traditional Monte Carlo simulation.
Photoshopping the selfie: Self photo editing and photo investment are associated with body dissatisfaction in adolescent girls.

Social media engagement by adolescent girls is high. Despite its appeal, there are potential negative consequences for body dissatisfaction and disordered eating from social media use. This study aimed to examine, in a cross-sectional design, the relationship between social media use in general, and social media activities related to taking "selfies" and sharing specifically, with overvaluation of shape and weight, body dissatisfaction, and dietary restraint.



Participants were 101 grade seven girls (M(age)  = 13.1, SD = 0.3), who completed self-report questionnaires of social media use and body-related and eating concerns measures.



Results showed that girls who regularly shared self-images on social media, relative to those who did not, reported significantly higher overvaluation of shape and weight, body dissatisfaction, dietary restraint, and internalization of the thin ideal. In addition, among girls who shared photos of themselves on social media, higher engagement in manipulation of and investment in these photos, but not higher media exposure, were associated with greater body-related and eating concerns, including after accounting for media use and internalization of the thin ideal.



Although cross-sectional, these findings suggest the importance of social media activities for body-related and eating concerns as well as potential avenues for targeted social-media-based intervention.
Applications of Probabilistic Programming (Master's thesis, 2015)

This thesis describes work on two applications of probabilistic programming: the learning of probabilistic program code given specifications, in particular program code of one-dimensional samplers; and the facilitation of sequential Monte Carlo inference with help of data-driven proposals. The latter is presented with experimental results on a linear Gaussian model and a non-parametric dependent Dirichlet process mixture of objects model for object recognition and tracking. We begin this work by providing a brief introduction to probabilistic programming. In the second Chapter we present an approach to automatic discovery of samplers in the form of probabilistic programs. Specifically, we learn the procedure code of samplers for one-dimensional distributions. We formulate a Bayesian approach to this problem by specifying a grammar-based prior over probabilistic program code. We use an approximate Bayesian computation method to learn the programs, whose executions generate samples that statistically match observed data or analytical characteristics of distributions of interest. In our experiments we leverage different probabilistic programming systems, including Anglican and Probabilistic C, to perform Markov chain Monte Carlo sampling over the space of programs. Experimental results have demonstrated that, using the proposed methodology, we can learn approximate and even some exact samplers. Finally, we show that our results are competitive with regard to genetic programming methods.
Thread Arcs: an email thread visualization

This paper describes Thread Arcs, a novel interactive visualization technique designed to help people use threads found in email. Thread Arcs combine the chronology of messages with the branching tree structure of a conversational thread in a mixed-model visualization by Venolia and Neustaedter (2003) that is stable and compact. By quickly scanning and interacting with Thread Arcs, people can see various attributes of conversations and find relevant messages in them easily. We tested this technique against other visualization techniques with users' own email in a functional prototype email client. Thread Arcs proved an excellent match for the types of threads found in users' email for the qualities users wanted in small-scale visualizations.
The Cerebellum, Sensitive Periods, and Autism

Cerebellar research has focused principally on adult motor function. However, the cerebellum also maintains abundant connections with nonmotor brain regions throughout postnatal life. Here we review evidence that the cerebellum may guide the maturation of remote nonmotor neural circuitry and influence cognitive development, with a focus on its relationship with autism. Specific cerebellar zones influence neocortical substrates for social interaction, and we propose that sensitive-period disruption of such internal brain communication can account for autism's key features.
Intra-platform Envelopment: The Coopetitive Dynamics between the Platform Owner and Platform Complementors ABSTRACT Platform owners can exert a substantial level of influence on their complementors, and this influence can have negative consequences for the firms and entrepreneurs offering complement

Platform owners can exert a substantial level of influence on their complementors, and this influence can have negative consequences for the firms and entrepreneurs offering complementary products. Such competitive influence can vary from indirect actions such as new policies that change the “rule of the game” within the platform, to direct actions such as launching a product that openly competes with some complementors’ products. Despite the fact that these actions are not uncommon, little prior work has explicitly addressed this topic, particularly regarding the coopetitive dynamics that take place when a platform owner directly competes with its platform complementors. This present paper addressees this gap by focusing on intra-platform envelopment, conceptualized as the platform owner’s absorbing the features or functionalities of products offered by its complementors. Our theorizing centers on the consequences that such envelopment has on the affected complementors, and the strategic levers that complementors can use to mitigate or foster the impact. We empirically examine the effect of intra-platform envelopment in two major mobile platforms, Apple iOS and Google Play app stores. We find that the effect of envelopment by the platform owner is heterogeneous and context-specific, and that it also depends on platform governance, specifically how the platform owner implements its envelopment strategy.
An Optimized Transformerless Photovoltaic Grid-Connected Inverter

Unipolar sinusoidal pulsewidth modulation (SPWM) full-bridge inverter brings high-frequency common-mode voltage, which restricts its application in transformerless photovoltaic grid-connected inverters. In order to solve this problem, an optimized full-bridge structure with two additional switches and a capacitor divider is proposed in this paper, which guarantees that a freewheeling path is clamped to half input voltage in the freewheeling period. Sequentially, the high-frequency common-mode voltage has been avoided in the unipolar SPWM full-bridge inverter, and the output current flows through only three switches in the power processing period. In addition, a clamping branch makes the voltage stress of the added switches be equal to half input voltage. The operation and clamping modes are analyzed, and the total losses of power device of several existing topologies and proposed topology are fairly calculated. Finally, the common-mode performance of these topologies is compared by a universal prototype inverter rated at 1 kW.
Machine learning in digital games: a survey

Artificial intelligence for digital games constitutes the implementation of a set of algorithms and techniques from both traditional and modern artificial intelligence in order to provide solutions to a range of game dependent problems. However, the majority of current approaches lead to predefined, static and predictable game agent responses, with no ability to adjust during game-play to the behaviour or playing style of the player. Machine learning techniques provide a way to improve the behavioural dynamics of computer controlled game agents by facilitating the automated generation and selection of behaviours, thus enhancing the capabilities of digital game artificial intelligence and providing the opportunity to create more engaging and entertaining game-play experiences. This paper provides a survey of the current state of academic machine learning research for digital game environments, with respect to the use of techniques from neural networks, evolutionary computation and reinforcement learning for game agent control.
Biometrics from Brain Electrical Activity: A Machine Learning Approach

The potential of brain electrical activity generated as a response to a visual stimulus is examined in the context of the identification of individuals. Specifically, a framework for the visual evoked potential (VEP)-based biometrics is established, whereby energy features of the gamma band within VEP signals were of particular interest. A rigorous analysis is conducted which unifies and extends results from our previous studies, in particular, with respect to 1) increased bandwidth, 2) spatial averaging, 3) more robust power spectrum features, and 4) improved classification accuracy. Simulation results on a large group of subject support the analysis
Visual Transfer between Atari Games using Competitive Reinforcement Learning

This paper explores the use of deep reinforcement learning agents to transfer knowledge from one environment to another. More specifically, the method takes advantage of asynchronous advantage actor critic (A3C) architecture to generalize a target game using an agent trained on a source game in Atari. Instead of fine-tuning a pre-trained model for the target game, we propose a learning approach to update the model using multiple agents trained in parallel with different representations of the target game. Visual mapping between video sequences of transfer pairs is used to derive new representations of the target game; training on these visual representations of the target game improves model updates in terms of performance, data efficiency and stability. In order to demonstrate the functionality of the architecture, Atari games Pong-v0 and Breakout-v0 are being used from the OpenAI gym environment; as the source and target environment.
Attack net penetration testing

T h e m o d e l i n g o f pe ne t r a t i on tes t ing as a Petr i ne t is su rp r i s ing ly usefu l . I t re ta ins key a d v a n t a g e s o f the f l aw h y p o t h e s i s and a t t ack tree a p p r o a c h e s whi l e p r o v i d i n g s o m e n e w benef i t s .
Crowdsourcing for book search evaluation: impact of hit design on comparative system ranking

The evaluation of information retrieval (IR) systems over special collections, such as large book repositories, is out of reach of traditional methods that rely upon editorial relevance judgments. Increasingly, the use of crowdsourcing to collect relevance labels has been regarded as a viable alternative that scales with modest costs. However, crowdsourcing suffers from undesirable worker practices and low quality contributions. In this paper we investigate the design and implementation of effective crowdsourcing tasks in the context of book search evaluation. We observe the impact of aspects of the Human Intelligence Task (HIT) design on the quality of relevance labels provided by the crowd. We assess the output in terms of label agreement with a gold standard data set and observe the effect of the crowdsourced relevance judgments on the resulting system rankings. This enables us to observe the effect of crowdsourcing on the entire IR evaluation process. Using the test set and experimental runs from the INEX 2010 Book Track, we find that varying the HIT design, and the pooling and document ordering strategies leads to considerable differences in agreement with the gold set labels. We then observe the impact of the crowdsourced relevance label sets on the relative system rankings using four IR performance metrics. System rankings based on MAP and Bpref remain less affected by different label sets while the Precision@10 and nDCG@10 lead to dramatically different system rankings, especially for labels acquired from HITs with weaker quality controls. Overall, we find that crowdsourcing can be an effective tool for the evaluation of IR systems, provided that care is taken when designing the HITs.
A Method for Compact Image Representation Using Sparse Matrix and Tensor Projections Onto Exemplar Orthonormal Bases

We present a new method for compact representation of large image datasets. Our method is based on treating small patches from a 2-D image as matrices as opposed to the conventional vectorial representation, and encoding these patches as sparse projections onto a set of exemplar orthonormal bases, which are learned a priori from a training set. The end result is a low-error, highly compact image/patch representation that has significant theoretical merits and compares favorably with existing techniques (including JPEG) on experiments involving the compression of ORL and Yale face databases, as well as a database of miscellaneous natural images. In the context of learning multiple orthonormal bases, we show the easy tunability of our method to efficiently represent patches of different complexities. Furthermore, we show that our method is extensible in a theoretically sound manner to higher-order matrices (¿tensors¿). We demonstrate applications of this theory to compression of well-known color image datasets such as the GaTech and CMU-PIE face databases and show performance competitive with JPEG. Lastly, we also analyze the effect of image noise on the performance of our compression schemes.
Fully Deep Blind Image Quality Predictor

In general, owing to the benefits obtained from original information, full-reference image quality assessment (FR-IQA) achieves relatively higher prediction accuracy than no-reference image quality assessment (NR-IQA). By fully utilizing reference images, conventional FR-IQA methods have been investigated to produce objective scores that are close to subjective scores. In contrast, NR-IQA does not consider reference images; thus, its performance is inferior to that of FR-IQA. To alleviate this accuracy discrepancy between FR-IQA and NR-IQA methods, we propose a blind image evaluator based on a convolutional neural network (BIECON). To imitate FR-IQA behavior, we adopt the strong representation power of a deep convolutional neural network to generate a local quality map, similar to FR-IQA. To obtain the best results from the deep neural network, replacing hand-crafted features with automatically learned features is necessary. To apply the deep model to the NR-IQA framework, three critical problems must be resolved: 1) lack of training data; 2) absence of local ground truth targets; and 3) different purposes of feature learning. BIECON follows the FR-IQA behavior using the local quality maps as intermediate targets for conventional neural networks, which leads to NR-IQA prediction accuracy that is comparable with that of state-of-the-art FR-IQA methods.