nils-herrmann/clean_scirepeval huggingface dataset:
An Analysis of Lateral Crural Repositioning and Its Effect on Alar Rim Position.

Cephalically malpositioned lateral crura are a frequent cause of alar rim retraction during both primary and revision rhinoplasty.



To demonstrate the efficacy of lateral crural repositioning as an isolated maneuver as and combined with adjunctive grafts for lowering the alar rim.



From August through December 2014, we retrospectively reviewed the cases of 54 patients (102 hemi-noses) who had lateral crural repositioning performed by the same surgeon between 2007 and 2013. Only patients with standardized photographs taken preoperatively and at least 6 months postoperatively were included in the study.



All primary and revision cases were completed via an open rhinoplasty approach and had lateral crural repositioning performed.



Preoperative and postoperative photographs were compared using Adobe Photoshop CS via a modified Gunter technique to measure the degree of alar rim retraction on lateral views. The change in angle was then calculated, and statistical analysis conducted using a paired t test.



A total of 54 patients (102 hemi-noses) met inclusion criteria; 42 (79%) were women, and the average patient age was 41.3 years. Forty-five (83%) of the cases were revision rhinoplasties, and the average time to obtaining postoperative photographs was 11.3 months. The mean (SD) anterior nostril apex angles preoperatively (31.3° [8.9°]) and postoperatively (24.5° [6.8°]) indicated a net decrease of 6.8° (P < .001). Excluding patients who had other rim-lowering grafts (alar rim and/or composite grafts), we saw a similar result comparing preoperative (31.1° [8.2°]) and postoperative (24.5° [6.7°]) angles, with a net decrease of 6.6° degrees (P < .001). When examining patients who underwent lateral crural repositioning alone compared with those who also had lateral crural strut grafts, we saw a decrease of 6.9° (P < .001) and 6.7° degrees (P < .001), respectively.



Repositioning of the lower lateral cartilages results in a statistically significant lowering of the alar rim. Our data suggest that when combined with lateral crural repositioning, the addition of adjunctive grafts does not add significantly to the rim-lowering effect.
Measuring User Engagement

User engagement refers to the quality of the user experience that emphasizes the positive aspects of interacting with an online application and, in particular, the desire to use that application longer and repeatedly. User engagement is a key concept in the design of online applications (whether for desktop, tablet or mobile), motivated by the observation that successful applications are not just used, but are engaged with. Users invest time, attention, and emotion in their use of technology, and seek to satisfy pragmatic and hedonic needs. Measurement is critical for evaluating whether online applications are able to successfully engage users, and may inform the design of and use of applications. User engagement is a multifaceted, complex phenomenon; this gives rise to a number of potential measurement approaches. Common ways to evaluate user engagement include using self-report measures, e.g., questionnaires; observational methods, e.g. facial expression analysis, speech analysis; neuro-physiological signal processing methods, e.g., respiratory and cardiovascular accelerations and decelerations, muscle spasms; and web analytics, e.g., number of site visits, click depth. These methods represent various trade-offs in terms of the setting (laboratory versus “in the wild”), object of measurement (user behaviour, affect or cognition) and scale of data collected. For instance, small-scale user studies are deep and rich, but limited in terms of generalizability, whereas large-scale web analytic studies are powerful but negate users’ motivation and context. The focus of this book is how user engagement is currently being measured and various considerations for its measurement. Our goal is to leave readers with an appreciation of the various ways in which to measure user engagement, and their associated strengths and weaknesses. We emphasize the multifaceted nature of user engagement and the unique contextual constraints that come to bear upon attempts to measure engagement in different settings, and across different user groups and web domains. At the same time, this book advocates for the development of “good” measures and good measurement practices that will advance the study of user engagement and improve our understanding of this construct, which has become so vital in our wired world.
Tumor-associated macrophages: unwitting accomplices in breast cancer malignancy

Deleterious inflammation is a primary feature of breast cancer. Accumulating evidence demonstrates that macrophages, the most abundant leukocyte population in mammary tumors, have a critical role at each stage of cancer progression. Such tumor-associated macrophages facilitate neoplastic transformation, tumor immune evasion and the subsequent metastatic cascade. Herein, we discuss the dynamic process whereby molecular and cellular features of the tumor microenvironment act to license tissue-repair mechanisms of macrophages, fostering angiogenesis, metastasis and the support of cancer stem cells. We illustrate how tumors induce, then exploit trophic macrophages to subvert innate and adaptive immune responses capable of destroying malignant cells. Finally, we discuss compelling evidence from murine models of cancer and early clinical trials in support of macrophage-targeted intervention strategies with the potential to dramatically reduce breast cancer morbidity and mortality.
Pattern Recognition and Image Analysis

This paper presents an enhancement to the recent framework of histogram aggregation , that enables to improve the matching accuracy while preserving a low computational complexity. The original algorithm uses a fronto-parallel support window for cost aggregation, which leads to inaccurate results in the presence of significant surface slant. We address the problem by considering a pre-defined set of discrete orientation hypotheses for the aggregation window. It is shown that a single orientation hypothesis in the Disparity Space Image is usually representative of a large interval of possible 3D slants, and that handling slant in the disparity space has the advantage of avoiding visibility issues. We also propose a fast recognition scheme in the Disparity Space Image volume for selecting the most likely orientation hypothesis for aggregation. The experiments clearly prove the effectiveness of the approach.
TrackMeNot: Enhancing the privacy of Web Search

Most search engines can potentially infer the preferences and interests of a user based on her history of search queries. While search engines can use these inferences for a variety of tasks, including targeted advertisements, such tasks do impose an serious threat to user privacy. In 2006, after AOL disclosed the search queries of 650,000 users, TrackMeNot was released as a simple browser extension that sought to hide user search preferences in a cloud of queries. The first versions of TrackMeNot, though used extensively in the past three years, was fairly simplistic in design and did not provide any strong privacy guarantees. In this paper, we present the new design and implementation of TrackMeNot, which address many of the limitations of the first release. TrackMeNot addresses two basic problems. First, using a model for characterizing search queries, TrackMeNot provides a mechanism for obfuscating the search preferences of a user from a search engine. Second, TrackMeNot prevents the leakage of information revealing the use of obfuscation to a search engine via several potential side channels in existing browsers such as clicks, cookies etc. Finally, we show that TrackMeNot cannot be detected by current search bot detection mechanisms and demonstrate the effectiveness of TrackMeNot in obfuscating user interests by testing its efficiency on a major search engine.
Preliminary safety assessment for a sectorless ATM concept

In a sectorless air traffic management concept the airspace is no longer divided into sectors but regarded as one piece. A number of aircraft, not necessarily in the same airspace region, are assigned to each air traffic controller who is then responsible for these aircraft from their entry into the airspace to their exit. These individually assigned flights can be anywhere in the airspace and therefore in different traffic situations. This means that air traffic controllers will manage flights which may not be geographically connected. Such a concept change will necessitate also a significant change in the controllers' routines and the support tools. Naturally, the question of safety arises regarding new procedures and systems. This paper provides a preliminary safety assessment for a sectorless air traffic management concept. The assessment is based on the Single European Sky ATM Research (SESAR) Safety Reference Material which was originally developed for SESAR purposes. This success-based approach stresses the positive contribution of a new concept while traditional approaches mainly consider the negative effect of possible hazards and failures. Based on validation activities including realtime simulations we have developed safety acceptance criteria and safety objectives for a sectorless air traffic management (ATM) concept. Starting from these we have sketched the safety performance requirement model and deduce the first safety requirements for normal conditions, abnormal conditions and in the case of internal system failures.
Dielectric resonators and filters

High-performance DRs and filters are widely used in wireless and satellite communication systems due to their superior characteristics, such as high unloaded Q, excellent temperature stability, and smaller size compared to their air-filled counterpart. Temperature-stable high-Q dielectric materials with a wide range of dielectric constants from 20 to 100 are available. The properties of a DR such as resonant frequency and unloaded Q can be accurately determined by full-wave EM simulations. TEoi single-mode filters offer the advantages of design simplicity and flexibility in layout options over HEn dual-mode filters, while dual-mode filters have significiint advantages in the mass and volume of the products. Mixing the TEoi and HE\\ modes, dielectric loaded resonator filters achieve the advantages of dual-mode HEn DR filters and the excellent spurious performances of the TEqi mode ring resonator filters.
Investigating Bidirectional Recurrent Neural Network Language Models for Speech Recognition

Recurrent neural network language models (RNNLMs) are powerful language modeling techniques. Significant performance improvements have been reported in a range of tasks including speech recognition compared to n-gram language models. Conventional n-gram and neural network language models are trained to predict the probability of the next word given its preceding context history. In contrast, bidirectional recurrent neural network based language models consider the context from future words as well. This complicates the inference process, but has theoretical benefits for tasks such as speech recognition as additional context information can be used. However to date, very limited or no gains in speech recognition performance have been reported with this form of model. This paper examines the issues of training bidirectional recurrent neural network language models (bi-RNNLMs) for speech recognition. A bi-RNNLM probability smoothing technique is proposed, that addresses the very sharp posteriors that are often observed in these models. The performance of the bi-RNNLMs is evaluated on three speech recognition tasks: broadcast news; meeting transcription (AMI); and low-resource systems (Babel data). On all tasks gains are observed by applying the smoothing technique to the bi-RNNLM. In addition consistent performance gains can be obtained by combining bi-RNNLMs with n-gram and uni-directional RNNLMs.
Simulating Search Engines

A simulation methodology is proposed to evaluate the performance of large-scale Web search engines hosted by datacenters. The salient features of the methodology are the use of models of parallel computing to overcome the complexities associated with the simulation of hardware and system software details; a circulating tokens approach to represent sequences of operations that compete for search engine resources; benchmark programs to measure the cost of relevant operations; and simulations driven by real user traces to consider the dynamics of user behavior. An experimental evaluation of the methodology, which ranges from clusters of processors to single multithreaded processors, shows that it can generate respective simulation programs capable of predicting performance in a precise and efficient manner.
QELAR: A Machine-Learning-Based Adaptive Routing Protocol for Energy-Efficient and Lifetime-Extended Underwater Sensor Networks

Underwater sensor network (UWSN) has emerged in recent years as a promising networking technique for various aquatic applications. Due to specific characteristics of UWSNs, such as high latency, low bandwidth, and high energy consumption, it is challenging to build networking protocols for UWSNs. In this paper, we focus on addressing the routing issue in UWSNs. We propose an adaptive, energy-efficient, and lifetime-aware routing protocol based on reinforcement learning, QELAR. Our protocol assumes generic MAC protocols and aims at prolonging the lifetime of networks by making residual energy of sensor nodes more evenly distributed. The residual energy of each node as well as the energy distribution among a group of nodes is factored in throughout the routing process to calculate the reward function, which aids in selecting the adequate forwarders for packets. We have performed extensive simulations of the proposed protocol on the Aqua-sim platform and compared with one existing routing protocol (VBF) in terms of packet delivery rate, energy efficiency, latency, and lifetime. The results show that QELAR yields 20 percent longer lifetime on average than VBF.
Filling the periorbital hollows with hyaluronic acid gel: Long-term review of outcomes and complications.

Hyaluronic acid gel has been used for rejuvenation of the periorbital tissues since 2004. The in-office, nonsurgical procedural nature has resulted in the growing popularity of the use of fillers. The periorbital region poses unique challenges to the cosmetic surgeon. Malar edema, blue-gray dyschromia, and contour irregularities are well reported short-term complications. We present a long-term follow-up review of complications associated with periocular injection of hyaluronic acid gel fillers.



Retrospective review of 147 patients with at least a 5-year follow-up period since the first injection. Five senior masked reviewers were asked to grade photographs of patients in comparison with preinjection photographs. Grading was based on a reference photographic key looking specifically at the presence and severity of malar edema, blue-gray dyschromia, and contour irregularity.



Malar edema (11%), blue-gray dyschromia (31.3%), and contour irregularities (30.5%) continue to be problems in longer term follow-up of patients. The vast majority (90%) of these complications were mild and required no intervention. There is no statistically significant difference in the mean volumes injected by severity grading of each measured complication.



Hyaluronic acid gel fillers of the periorbital region are well tolerated in our long-term follow-up experience. The vast majority of cases of malar edema, blue-gray dyschromia, and contour irregularities are mild and do not require intervention. Conservative treatment and tailored treatment approaches, in terms of volume and frequency of injections, may decrease the tendency of these adverse effects to occur.
Real Time Drowsy Driver Identification Using Eye Blink Detection

HUMAN COMPUTER INTERFACE (HCI) systems are designed for use in assisting people in various aspects. Driving support systems such as navigating systems are getting commoner by the day. The capability of driving support systems to detect the level of driver’s alertness is very important in ensuring road safety. By observation of blink pattern and eye movements, driver fatigue can be detected early enough to prevent collisions caused by drowsiness. The analysis of face image are widely used in security systems, face recognition, criminal focusing etc. In this specific study a non recursive system have been designed to detect shutting of eyes of the person driving an automobile. Real time detection of driver’s eyes is processed using image processing in MATLAB to detect whether the eye remains closed more than the fixed duration thus indicating condition of fatigue and raise an alarm which could prevent a collision. The driving support systems have been found lacking in detecting the influence of drug or alcohol causing great degree of risks to the commuters. This study has found that eye blink patterns are starkly different for persons under the influence of drugs and can be easily detected by the system designed by us.
Recommendation to Groups

Recommender systems have traditionally recommended items to individual users, but there has recently been a proliferation of recommenders that address their recommendations to groups of users. The shift of focus from an individual to a group makes more of a difference than one might at first expect. This chapter discusses the most important new issues that arise, organizing them in terms of four subtasks that can or must be dealt with by a group recommender: 1. acquiring information about the user’s preferences; 2. generating recommendations; 3. explaining recommendations; and 4. helping users to settle on a final decision. For each issue, we discuss how it has been dealt with in existing group recommender systems and what open questions call for further research.
Sentiment-specific word embedding for Indonesian sentiment analysis

Word embedding has been proven to improve performance of sentiment classification systems. A new proposed word embedding called sentiment-specific word embedding exploits sentiment information, in order to reach better performance of sentiment analysis systems using word embedding. In this paper, we build Indonesian sentiment-specific word embedding and apply it for sentiment analysis. We compare performance of sentiment classification based on sentiment-specific word embedding with other popular feature representations such as bag of words, Term Frequency-Inverse Document Frequency (TF-IDF), and also generic word embedding. Although our sentiment-specific word embedding models achieve a higher score than the Word2Vec embedding, the lexical representations, bag of words and TF-IDF still gained better performance.
Machine Learning that Matters

Much of current machine learning (ML) research has lost its connection to problems of import to the larger world of science and society. From this perspective, there exist glaring limitations in the data sets we investigate, the metrics we employ for evaluation, and the degree to which results are communicated back to their originating domains. What changes are needed to how we conduct research to increase the impact that ML has? We present six Impact Challenges to explicitly focus the field’s energy and attention, and we discuss existing obstacles that must be addressed. We aim to inspire ongoing discussion and focus on ML that matters.
Stock Market Prediction: Using Historical Data Analysis

The Stock market process is full of uncertainty and it’s affected by many factors. Hence the Stock market prediction is one of the important exertions in business and finance. There are two types of analysis possible for prediction, fundamental and technical. In this paper both fundamental and technical analysis are considered. fundamental analysis is done using social media data by applying sentiment analysis process. Social media data has high impact today than ever, it can helpful in predicting the trend of the stock market and Technical analysis is done using historical data of stock prices by applying machine learning algorithms. The method involves collecting news and also collect social media data and extracting sentiments expressed by individual. Then the correlation between the sentiments and stock values is analysed. The learned model can then be used to make future predictions about stock value. It’s can be shown that this method is able to predict the stock performance and sentiment and social data are also closely correlated with recent news.  prediction, Big data, social media analytics, machine learning.
A Neural Transducer

Sequence-to-sequence models have achieved impressive results on various tasks. However, they are unsuitable for tasks that require incremental predictions to be made as more data arrives or tasks that have long input sequences and output sequences. This is because they generate an output sequence conditioned on an entire input sequence. In this paper, we present a Neural Transducer that can make incremental predictions as more input arrives, without redoing the entire computation. Unlike sequence-to-sequence models, the Neural Transducer computes the next-step distribution conditioned on the partially observed input sequence and the partially generated sequence. At each time step, the transducer can decide to emit zero to many output symbols. The data can be processed using an encoder and presented as input to the transducer. The discrete decision to emit a symbol at every time step makes it difficult to learn with conventional backpropagation. It is however possible to train the transducer by using a dynamic programming algorithm to generate target discrete decisions. Our experiments show that the Neural Transducer works well in settings where it is required to produce output predictions as data come in. We also find that the Neural Transducer performs well for long sequences even when attention mechanisms are not used.
StaticGreedy: solving the scalability-accuracy dilemma in influence maximization

Influence maximization, defined as a problem of finding a set of seed nodes to trigger a maximized spread of influence, is crucial to viral marketing on social networks. For practical viral marketing on large scale social networks, it is required that influence maximization algorithms should have both guaranteed accuracy and high scalability. However, existing algorithms suffer a scalability-accuracy dilemma: conventional greedy algorithms guarantee the accuracy with expensive computation, while the scalable heuristic algorithms suffer from unstable accuracy
 In this paper, we focus on solving this scalability-accuracy dilemma. We point out that the essential reason of the dilemma is the surprising fact that the submodularity, a key requirement of the objective function for a greedy algorithm to approximate the optimum, is not guaranteed in all conventional greedy algorithms in the literature of influence maximization. Therefore a greedy algorithm has to afford a huge number of Monte Carlo simulations to reduce the pain caused by unguaranteed submodularity. Motivated by this critical finding, we propose a static greedy algorithm, named StaticGreedy, to strictly guarantee the submodularity of influence spread function during the seed selection process. The proposed algorithm makes the computational expense dramatically reduced by two orders of magnitude without loss of accuracy. Moreover, we propose a dynamical update strategy which can speed up the StaticGreedy algorithm by 2-7 times on large scale social networks.