nils-herrmann/clean_scirepeval huggingface dataset:
Guided Bone Regeneration with Collagen Membranes and Particulate Graft Materials: A Systematic Review and Meta-Analysis.

The aim of this meta-analysis was to evaluate different methods for guided bone regeneration using collagen membranes and particulate grafting materials in implant dentistry.



An electronic database search and hand search were performed for all relevant articles dealing with guided bone regeneration in implant dentistry published between 1980 and 2014. Only randomized clinical trials and prospective controlled studies were included. The primary outcomes of interest were survival rates, membrane exposure rates, bone gain/defect reduction, and vertical bone loss at follow-up. A meta-analysis was performed to determine the effects of presence of membrane cross-linking, timing of implant placement, membrane fixation, and decortication.



Twenty studies met the inclusion criteria. Implant survival rates were similar between simultaneous and subsequent implant placement. The membrane exposure rate of cross-linked membranes was approximately 30% higher than that of non-cross-linked membranes. The use of anorganic bovine bone mineral led to sufficient newly regenerated bone and high implant survival rates. Membrane fixation was weakly associated with increased vertical bone gain, and decortication led to higher horizontal bone gain (defect depth).



Guided bone regeneration with particulate graft materials and resorbable collagen membranes is an effective technique for lateral alveolar ridge augmentation. Because implant survival rates for simultaneous and subsequent implant placement were similar, simultaneous implant placement is recommended when possible. Additional techniques like membrane fixation and decortication may represent beneficial implications for the practice.
PROC OF THE IEEE NOVEMBER Gradient Based Learning Applied to Document Recognition

Multilayer Neural Networks trained with the backpropa gation algorithm constitute the best example of a successful Gradient Based Learning technique Given an appropriate network architecture Gradient Based Learning algorithms can be used to synthesize a complex decision surface that can classify high dimensional patterns such as handwritten char acters with minimal preprocessing This paper reviews var ious methods applied to handwritten character recognition and compares them on a standard handwritten digit recog nition task Convolutional Neural Networks that are specif ically designed to deal with the variability of D shapes are shown to outperform all other techniques Real life document recognition systems are composed of multiple modules including eld extraction segmenta tion recognition and language modeling A new learning paradigm called Graph Transformer Networks GTN al lows such multi module systems to be trained globally using Gradient Based methods so as to minimize an overall per formance measure Two systems for on line handwriting recognition are de scribed Experiments demonstrate the advantage of global training and the exibility of Graph Transformer Networks A Graph Transformer Network for reading bank check is also described It uses Convolutional Neural Network char acter recognizers combined with global training techniques to provides record accuracy on business and personal checks It is deployed commercially and reads several million checks per day
FindU: Privacy-preserving personal profile matching in mobile social networks

Making new connections according to personal preferences is a crucial service in mobile social networking, where the initiating user can find matching users within physical proximity of him/her. In existing systems for such services, usually all the users directly publish their complete profiles for others to search. However, in many applications, the users' personal profiles may contain sensitive information that they do not want to make public. In this paper, we propose FindU, the first privacy-preserving personal profile matching schemes for mobile social networks. In FindU, an initiating user can find from a group of users the one whose profile best matches with his/her; to limit the risk of privacy exposure, only necessary and minimal information about the private attributes of the participating users is exchanged. Several increasing levels of user privacy are defined, with decreasing amounts of exchanged profile information. Leveraging secure multi-party computation (SMC) techniques, we propose novel protocols that realize two of the user privacy levels, which can also be personalized by the users. We provide thorough security analysis and performance evaluation on our schemes, and show their advantages in both security and efficiency over state-of-the-art schemes.
Feature Engineering for Second Language Acquisition Modeling

Knowledge tracing serves as a keystone in delivering personalized education. However, few works attempted to model students’ knowledge state in the setting of Second Language Acquisition. The Duolingo Shared Task on Second Language Acquisition Modeling (Settles et al., 2018) provides students’ trace data that we extensively analyze and engineer features from for the task of predicting whether a student will correctly solve a vocabulary exercise. Our analyses of students’ learning traces reveal that factors like exercise format and engagement impact their exercise performance to a large extent. Overall, we extracted 23 different features as input to a Gradient Tree Boosting framework, which resulted in an AUC score of between 0.80 and 0.82 on the official test set.
Self-Attentive Model for Headline Generation

Headline generation is a special type of text summarization task. While the amount of available training data for this task is almost unlimited, it still remains challenging, as learning to generate headlines for news articles implies that the model has strong reasoning about natural language. To overcome this issue, we applied recent Universal Transformer architecture paired with byte-pair encoding technique and achieved new state-of-the-art results on the New York Times Annotated corpus with ROUGE-L F1-score 24.84 and ROUGE-2 F1-score 13.48. We also present the new RIA corpus and reach ROUGE-L F1-score 36.81 and ROUGE-2 F1-score 22.15 on it.
Strategies Used in the Translation of Interlingual Subtitling

This study was an attempt to identify the interlingual strategies employed to translate English subtitles into Persian and to determine their frequency, as well. Contrary to many countries, subtitling is a new field in Iran. The study, a corpus-based, comparative, descriptive, non-judgmental analysis of an English-Persian parallel corpus, comprised English audio scripts of five movies of different genres, with Persian subtitles. The study’s theoretical framework was based on Gottlieb’s (1992) classification of subtitling translation strategies. The results indicated that all Gottlieb’s proposed strategies were applicable to the corpus with some degree of variation of distribution among different film genres. The most frequently used strategy was “transfer” at 54.06%; the least frequently used strategies were “transcription” and “decimation” both at 0.81%. It was concluded that the film genre plays a crucial role in using different strategies.
Implementation of a feral cat management program on a university campus.

In August 1998, Texas AM University implemented on campus a trap-test-vaccinate-alter-return-monitor (TTVARM) program to manage the feral cat population. TTVARM is an internationally recognized term for trapping and neutering programs aimed at management of feral cat populations. In this article we summarize results of the program for the period August 1998 to July 2000. In surgery laboratories, senior veterinary students examined cats that were humanely trapped once a month and tested them for feline leukemia and feline immunodeficiency virus infections, vaccinated, and surgically neutered them. They euthanized cats testing positive for either infectious disease. Volunteers provided food and observed the cats that were returned to their capture sites on campus and maintained in managed colonies. The program placed kittens and tame cats for adoption; cats totaled 158. Of the majority of 158 captured cats, there were less kittens caught in Year 2 than in Year 1. The proportion of tame cats trapped was significantly greater in Year 2 than in Year 1. The prevalence found for feline leukemia and feline immunodeficiency virus ELISA test positives was 5.8% and 6.5%, respectively. Following surgery, 101 cats returned to campus. The project recaptured, retested, and revaccinated more than one-fourth of the cats due for their annual vaccinations. The program placed 32 kittens, juveniles, and tame adults for adoption. The number of cat complaints received by the university's pest control service decreased from Year 1 to Year 2.
An End-to-End Trainable Neural Network Model with Belief Tracking for Task-Oriented Dialog

We present a novel end-to-end trainable neural network model for task-oriented dialog systems. The model is able to track dialog state, issue API calls to knowledge base (KB), and incorporate structured KB query results into system responses to successfully complete task-oriented dialogs. The proposed model produces well-structured system responses by jointly learning belief tracking and KB result processing conditioning on the dialog history. We evaluate the model in a restaurant search domain using a dataset that is converted from the second Dialog State Tracking Challenge (DSTC2) corpus. Experiment results show that the proposed model can robustly track dialog state given the dialog history. Moreover, our model demonstrates promising results in producing appropriate system responses, outperforming prior end-to-end trainable neural network models using per-response accuracy evaluation metrics.
Credit scoring with macroeconomic variables using survival analysis

Survival analysis can be applied to build models fo r time to default on debt. In this paper we report an application of survival analysis to model default o n a large data set of credit card accounts. We exp lore the hypothesis that probability of default is affec ted by general conditions in the economy over time. These macroeconomic variables cannot readily be inc luded in logistic regression models. However, survival analysis provides a framework for their in clusion as time-varying covariates. Various macroeconomic variables, such as interest rate and unemployment rate, are included in the analysis. We show that inclusion of these indicators improves model fit and affects probability of default yielding a modest improvement in predictions of def ault on an independent test set.
Question Generation from SQL Queries Improves Neural Semantic Parsing

We study how to learn a semantic parser of state-of-the-art accuracy with less supervised training data. We conduct our study on WikiSQL, the largest hand-annotated semantic parsing dataset to date. First, we demonstrate that question generation is an effective method that empowers us to learn a state-ofthe-art neural network based semantic parser with thirty percent of the supervised training data. Second, we show that applying question generation to the full supervised training data further improves the state-of-the-art model. In addition, we observe that there is a logarithmic relationship between the accuracy of a semantic parser and the amount of training data.
Correlation between toe flexor strength and ankle dorsiflexion ROM during the
countermovement jump

This study assessed the relationships between peak toe flexor muscle strength, ankle dorsiflexion range of motion, and countermovement jump height.  Eighteen healthy volunteers participated in the study. Each participant completed tests for peak toe flexor muscle strength, ankle dorsiflexion range of motion, and countermovement jump height.  The results showed (1) a moderate correlation between ankle dorsiflexion range of motion and countermovement jump height and (2) a high correlation between peak first toe flexor muscle strength and countermovement jump height. Peak first toe flexor muscle strength and ankle dorsiflexion range of motion are the main contributors to countermovement jump performance.  These findings indicate that the measurement of peak first toe flexor muscle strength and ankle dorsiflexion range of motion may be useful in clinical practice for improving jump performance in athletes training for sports such as volleyball and basketball.
BSIF: Binarized statistical image features

This paper proposes a method for constructing local image descriptors which efficiently encode texture information and are suitable for histogram based representation of image regions. The method computes a binary code for each pixel by linearly projecting local image patches onto a subspace, whose basis vectors are learnt from natural images via independent component analysis, and by binarizing the coordinates in this basis via thresholding. The length of the binary code string is determined by the number of basis vectors. Image regions can be conveniently represented by histograms of pixels' binary codes. Our method is inspired by other descriptors which produce binary codes, such as local binary pattern and local phase quantization. However, instead of heuristic code constructions, the proposed approach is based on statistics of natural images and this improves its modeling capacity. The experimental results show that our method improves accuracy in texture recognition tasks compared to the state-of-the-art.
BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer’s Disease

Alzheimer's disease (AD), the most common form of dementia, shares many aspects of abnormal brain aging. We present a novel magnetic resonance imaging (MRI)-based biomarker that predicts the individual progression of mild cognitive impairment (MCI) to AD on the basis of pathological brain aging patterns. By employing kernel regression methods, the expression of normal brain-aging patterns forms the basis to estimate the brain age of a given new subject. If the estimated age is higher than the chronological age, a positive brain age gap estimation (BrainAGE) score indicates accelerated atrophy and is considered a risk factor for conversion to AD. Here, the BrainAGE framework was applied to predict the individual brain ages of 195 subjects with MCI at baseline, of which a total of 133 developed AD during 36 months of follow-up (corresponding to a pre-test probability of 68%). The ability of the BrainAGE framework to correctly identify MCI-converters was compared with the performance of commonly used cognitive scales, hippocampus volume, and state-of-the-art biomarkers derived from cerebrospinal fluid (CSF). With accuracy rates of up to 81%, BrainAGE outperformed all cognitive scales and CSF biomarkers in predicting conversion of MCI to AD within 3 years of follow-up. Each additional year in the BrainAGE score was associated with a 10% greater risk of developing AD (hazard rate: 1.10 [CI: 1.07-1.13]). Furthermore, the post-test probability was increased to 90% when using baseline BrainAGE scores to predict conversion to AD. The presented framework allows an accurate prediction even with multicenter data. Its fast and fully automated nature facilitates the integration into the clinical workflow. It can be exploited as a tool for screening as well as for monitoring treatment options.
Word Embedding for Response-To-Text Assessment of Evidence

Manually grading the Response to Text Assessment (RTA) is labor intensive. Therefore, an automatic method is being developed for scoring analytical writing when the RTA is administered in large numbers of classrooms. Our long-term goal is to also use this scoring method to provide formative feedback to students and teachers about students’ writing quality. As a first step towards this goal, interpretable features for automatically scoring the evidence rubric of the RTA have been developed. In this paper, we present a simple but promising method for improving evidence scoring by employing the word embedding model. We evaluate our method on corpora of responses written by upper elementary students.
CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM

The representation of geometry in real-time 3D perception systems continues to be a critical research issue. Dense maps capture complete surface shape and can be augmented with semantic labels, but their high dimensionality makes them computationally costly to store and process, and unsuitable for rigorous probabilistic inference. Sparse feature-based representations avoid these problems, but capture only partial scene information and are mainly useful for localisation only. We present a new compact but dense representation of scene geometry which is conditioned on the intensity data from a single image and generated from a code consisting of a small number of parameters. We are inspired by work both on learned depth from images, and auto-encoders. Our approach is suitable for use in a keyframe-based monocular dense SLAM system: While each keyframe with a code can produce a depth map, the code can be optimised efficiently jointly with pose variables and together with the codes of overlapping keyframes to attain global consistency. Conditioning the depth map on the image allows the code to only represent aspects of the local geometry which cannot directly be predicted from the image. We explain how to learn our code representation, and demonstrate its advantageous properties in monocular SLAM.
Open Information Extraction to KBP Relations in 3 Hours

We participated in both the English Slot Filling and Entity Linking in the 2013 TAC-KBP evaluation. Our Slot Filling system provides an answer to the following conjectures: Can Open Information Extraction (Open IE) form the basis of a high precision extractor for a set of target relations in an ontology? And, just as importantly, can this be done with a minimum of human knowledge engineering? We built rules to map Open IE extractions to KBP Slot Filling relations and found that just three hours of rule creation gave extractor precision of 0.79. Spending a total of 12 hours refining the rules increased precision slightly to 0.80 with recall near the median of other KBP systems.
PMSC: PatchMatch-Based Superpixel Cut for Accurate Stereo Matching

Estimating the disparity and normal direction of one pixel simultaneously, instead of only disparity, also known as 3D label methods, can achieve much higher subpixel accuracy in the stereo matching problem. However, it is extremely difficult to assign an appropriate 3D label to each pixel from the continuous label space  $\mathbb {R}^{3}$  while maintaining global consistency because of the infinite parameter space. In this paper, we propose a novel algorithm called PatchMatch-based superpixel cut to assign 3D labels of an image more accurately. In order to achieve robust and precise stereo matching between local windows, we develop a bilayer matching cost, where a bottom–up scheme is exploited to design the two layers. The bottom layer is employed to measure the similarity between small square patches locally by exploiting a pretrained convolutional neural network, and then, the top layer is developed to assemble the local matching costs in large irregular windows induced by the tangent planes of object surfaces. To optimize the spatial smoothness of local assignments, we propose a novel strategy to update 3D labels. In the procedure of optimization, both segmentation information and random refinement of PatchMatch are exploited to update candidate 3D label set for each pixel with high probability of achieving lower loss. Since pairwise energy of general candidate label sets violates the submodular property of graph cut, we propose a novel multilayer superpixel structure to group candidate label sets into candidate assignments, which thereby can be efficiently fused by  $\alpha $ -expansion graph cut. Extensive experiments demonstrate that our method can achieve higher subpixel accuracy in different data sets, and currently ranks first on the new challenging Middlebury 3.0 benchmark among all the existing methods.
fMRI resting state networks define distinct modes of long-distance interactions in the human brain

Functional magnetic resonance imaging (fMRI) studies of the human brain have suggested that low-frequency fluctuations in resting fMRI data collected using blood oxygen level dependent (BOLD) contrast correspond to functionally relevant resting state networks (RSNs). Whether the fluctuations of resting fMRI signal in RSNs are a direct consequence of neocortical neuronal activity or are low-frequency artifacts due to other physiological processes (e.g., autonomically driven fluctuations in cerebral blood flow) is uncertain. In order to investigate further these fluctuations, we have characterized their spatial and temporal properties using probabilistic independent component analysis (PICA), a robust approach to RSN identification. Here, we provide evidence that: i. RSNs are not caused by signal artifacts due to low sampling rate (aliasing); ii. they are localized primarily to the cerebral cortex; iii. similar RSNs also can be identified in perfusion fMRI data; and iv. at least 5 distinct RSN patterns are reproducible across different subjects. The RSNs appear to reflect "default" interactions related to functional networks related to those recruited by specific types of cognitive processes. RSNs are a major source of non-modeled signal in BOLD fMRI data, so a full understanding of their dynamics will improve the interpretation of functional brain imaging studies more generally. Because RSNs reflect interactions in cognitively relevant functional networks, they offer a new approach to the characterization of state changes with pathology and the effects of drugs.
Snort Performance Evaluation

Intrusion Detection Systems (IDS) are among the fastest growing technologies in computer security domain. These systems are designed to identify/ prevent any hostile intrusion into a network. Today, variety of open source and commercial IDS are available to match the users/ network requirements. It has been accepted that the open source intrusion detection systems enjoys more support over commercial systems on account of cost, flexible configuration, online support, cross platform implementation etc. Among open source IDS, Snort has secured a prominent place and has been considered as a de facto standard in the IDS market. Snort is the real time packet analyser and packet logger that perform packet payload inspection by using contents matching algorithms. The system has been considered as a good alternative to expensive and heavy duty Network Intrusion Detection systems (NIDS). This research work has focussed on analysing the performance of Snort under heavy traffic conditions. This has been done on a test bench specifically designed to replicate the current day network traffic. Snort has been evaluated on different operating systems (OS) platforms and hardware resources by subjecting it to different categories of input traffic. Attacks were also injected to determine the detection quality of the system under different conditions. Our results have identified a strong performance limitation of Snort; it was unable to withstand few hundred mega bits per second of network traffic. This has generated queries on the performance of Snort and opened a new debate on the efficacy of open source systems.