nils-herrmann/clean_scirepeval huggingface dataset:
Derotational casting for progressive infantile scoliosis.

Serial cast correction by using the Cotrel derotation technique is one of several potential treatments for progressive infantile scoliosis. This study reviews our early experience to identify which, if any, patients are likely to benefit from or fail this technique.



We followed all patients treated at our institutions for progressive infantile scoliosis since 2003 prospectively at 1 institution and retrospectively at the other 2. Data, including etiology, Cobb angles, rib vertebral angle difference, Moe-Nash rotation, and space available for the lung, were recorded over time.



Fifty-five patients with progressive infantile scoliosis had more than 1 year of follow-up from the initiation of casting. The diagnosis of progressive scoliosis was made based upon either a progressive Cobb angle or a rib vertebral angle difference of more than 20 degrees at presentation. All but 6 patients responded to cast correction. Nine patients have undergone surgery to date, 6 because of worsening and 3 by parent choice. As shown in the table, initiation of cast correction at a younger age, moderate curve size (<60 degrees), and an idiopathic diagnosis carry a better prognosis than an older age of initiation, curve >60 degrees, and a nonidiopathic diagnosis. The space available for the lung improved from 0.89 to 0.93. No patient experienced worsening of rib deformities.



Serial cast correction for infantile scoliosis often results in full correction in infants with idiopathic curves less than 60 degrees if started before 20 months of age. Cast correction for older patients with larger curves or nonidiopathic diagnosis still frequently results in curve improvement along with improvement in chest and body shape.



Derotational cast correction seems to play a role in the treatment of progressive infantile scoliosis with cures in young patients and reductions in curve size with a delay in surgery in older and syndromic patients.
Unsupervised Induction of Semantic Roles within a Reconstruction-Error Minimization Framework

We introduce a new approach to unsupervised estimation of feature-rich semantic role labeling models. Our model consists of two components: (1) an encoding component: a semantic role labeling model which predicts roles given a rich set of syntactic and lexical features; (2) a reconstruction component: a tensor factorization model which relies on roles to predict argument fillers. When the components are estimated jointly to minimize errors in argument reconstruction, the induced roles largely correspond to roles defined in annotated resources. Our method performs on par with most accurate role induction methods on English and German, even though, unlike these previous approaches, we do not incorporate any prior linguistic knowledge about the languages.
Efficient Fully Homomorphic Encryption from (Standard) LWE

We present a fully homomorphic encryption scheme that is based solely on the(standard) learning with errors (LWE) assumption. Applying known results on LWE, the security of our scheme is based on the worst-case hardness of ``short vector problems'' on arbitrary lattices. Our construction improves on previous works in two aspects:\begin{enumerate}\item We show that ``somewhat homomorphic'' encryption can be based on LWE, using a new {\em re-linearization} technique. In contrast, all previous schemes relied on complexity assumptions related to ideals in various rings. \item We deviate from the "squashing paradigm'' used in all previous works. We introduce a new {\em dimension-modulus reduction} technique, which shortens the cipher texts and reduces the decryption complexity of our scheme, {\em without introducing additional assumptions}. \end{enumerate}Our scheme has very short cipher texts and we therefore use it to construct an asymptotically efficient LWE-based single-server private information retrieval (PIR) protocol. The communication complexity of our protocol (in the public-key model) is $k \cdot \polylog(k)+\log \dbs$ bits per single-bit query (here, $k$ is a security parameter).
Recent Advances in Augmented Reality

The field of Augmented Reality (AR) has existed for just over one decade, but the growth and progress in the past few years has been remarkable. In 1997, the first author published a survey  (based on a 1995 SIGGRAPH course lecture) that defined the field, described many problems, and summarized the developments up to that point. Since then, the field has grown rapidly. In the late 1990s, several conferences specializing in this area were started, including the International Workshop and Symposium on Augmented Reality [29], the International Symposium on Mixed Reality [30], and the Designing Augmented Reality Environments workshop. Some wellfunded interdisciplinary consortia were formed that focused on AR, notably the Mixed Reality Systems Laboratory [50] in Japan and Project ARVIKA [61] in Germany. A freely-available software toolkit (the ARToolkit) for rapidly building AR applications is now available . Because of this wealth of new developments, an updated survey is needed to guide and encourage further research in this exciting area.
Decision support and intelligent systems in the textile and apparel supply chain: An academic review of research articles

This article provides a comprehensive review of research articles related to the application of decision support and intelligent systems in the textile and apparel supply chains. Data were obtained from 77 articles published from 1994 to 2009 in 35 journals. The articles were categorized according to their applicability into three basic sectors – textile production, apparel manufacture, and distribution/sales. They were further categorized into 16 subsectors based on their operational and management/control processes. A comprehensive list of categorized journal articles identified in this study provides insights and relevant references for both researchers and practitioners on the application of decision support and intelligent systems to various stages of a textile and apparel supply chain. In light of the developed classification framework, we identify gaps in extending the use of the decision support and artificial intelligent systems in the industry and suggest potential and applicable research areas for further consideration in this subject area.
Bluetooth based home automation system using cell phone

Technology is a never ending process. To be able to design a product using the current technology that will be beneficial to the lives of others is a huge contribution to the community. This paper presents the design and implementation of a low cost but yet flexible and secure cell phone based home automation system. The design is based on a stand alone Arduino BT board and the home appliances are connected to the input/ output ports of this board via relays. The communication between the cell phone and the Arduino BT board is wireless. This system is designed to be low cost and scalable allowing variety of devices to be controlled with minimum changes to its core. Password protection is being used to only allow authorised users from accessing the appliances at home.
The tear trough and lid/cheek junction: anatomy and implications for surgical correction.

The tear trough and the lid/cheek junction become more visible with age. These landmarks are adjacent, forming in some patients a continuous indentation or groove below the infraorbital rim. Numerous, often conflicting procedures have been described to improve the appearance of the region. The purpose of this study was to evaluate the anatomy underlying the tear trough and the lid/cheek junction and to evaluate the procedures designed to correct them.



Twelve fresh cadaver lower lid and midface dissections were performed (six heads). The orbital regions were dissected in layers, and medical photography was performed.



In the subcutaneous plane, the tear trough and lid/cheek junction overlie the junction of the palpebral and orbital portions of the orbicularis oculi muscle and the cephalic border of the malar fat pad. In the submuscular plane, these landmarks differ. Along the tear trough, the orbicularis muscle is attached directly to the bone. Along the lid/cheek junction, the attachment is ligamentous by means of the orbicularis retaining ligament.



The tear trough and lid/cheek junction are primarily explained by superficial (subcutaneous) anatomical features. Atrophy of skin and fat is the most likely explanation for age-related visibility of these landmarks. "Descent" of this region with age is unlikely (the structures are fixed to bone). Bulging orbital fat accentuates these landmarks. Interventions must extend significantly below the infraorbital rim. Fat or synthetic filler may be best placed in the intraorbicularis plane (tear trough) and in the suborbicularis plane (lid/cheek junction).
A data mining approach for location prediction in mobile environments

Mobility prediction is one of the most essential issues that need to be explored for mobility management in mobile computing systems. In this paper, we propose a new algorithm for predicting the next inter-cell movement of a mobile user in a Personal Communication Systems network. In the first phase of our threephase algorithm, user mobility patterns are mined from the history of mobile user trajectories. In the second phase, mobility rules are extracted from these patterns, and in the last phase, mobility predictions are accomplished by using these rules. The performance of the proposed algorithm is evaluated through simulation as compared to two other prediction methods. The performance results obtained in terms of Precision and Recall indicate that our method can make more accurate predictions than the other methods.
A Lossy Image Codec Based on Index Coding

In this paper we propose a new lossy image codec based on index coding. Both J. Shapiro’s embedded zerotree wavelet algorithm, and A. Said and W. A. Pearlman’s codetree algorithm (which is the state-of-the-art method today) use spatial orientation tree structures to implicitly locate the significant wavelet transform coefficients. Here a direct approach to find the positions of these significant coefficients is presented. The new algorithm combines the discrete wavelet transform, differential coding, variable-length coding of integers, ordered bit plane transmission, and adaptive arithmetic coding. The encoding can be stopped at any point, which allows a target rate or distortion metric to be met exactly. The bits in the bit stream are generated in the order of importance, yielding a fully embedded code to successively approximate the original image source; thus it’s well suited for progressive image transmission. The decoder can also terminate the decoding at any point, and produce a lower bit rate reconstruction image. Our algorithm is very simple in its form (which will make the encoding and decoding very fast), requires no training of any kind or prior knowledge of image sources, and has a clear geometric structure. The image coding results of it are quite competitive with almost all previous reported image compression algorithms on standard test images. 456 1068.0314/96$5.00
Goals and Tactics on the Dark Side of Knowledge Management

The knowledge management literature focuses on the bright side of KM; it barely mentions the dark side, in which knowledge is distorted, suppressed, or misappropriated due to personal or organizational motives. This paper presents a framework for categorizing dark side examples, and uses it to organize 30 examples culled primarily from recent news accounts. The paper’s contributions include its method for exploring the dark side of KM, its initial categorization of dark side goals and tactics, and its demonstration that dark side manipulation may occur throughout processes of knowledge creation and use.
Semantic Role Labeling Using Different Syntactic Views

Semantic role labeling is the process of annotating the predicate-argument structure in text with semantic labels. In this paper we present a state-of-the-art baseline semantic role labeling system based on Support Vector Machine classifiers. We show improvements on this system by: i) adding new features including features extracted from dependency parses, ii) performing feature selection and calibration and iii) combining parses obtained from semantic parsers trained using different syntactic views. Error analysis of the baseline system showed that approximately half of the argument identification errors resulted from parse errors in which there was no syntactic constituent that aligned with the correct argument. In order to address this problem, we combined semantic parses from a Minipar syntactic parse and from a chunked syntactic representation with our original baseline system which was based on Charniak parses. All of the reported techniques resulted in performance improvements.
Wide Table Layout Optimization based on Column Ordering and Duplication

Modern data analytical tasks often witness very wide tables, from a few hundred columns to a few thousand. While it is commonly agreed that column stores are an appropriate data format for wide tables and analytical workloads, the physical order of columns has not been investigated. Column ordering plays a critical role in I/O performance, because in wide tables accessing the columns in a single horizontal partition may involve multiple disk seeks. An optimal column ordering will incur minimal cumulative disk seek costs for the set of queries applied to the data. In this paper, we aim to find such an optimal column layout to maximize I/O performance. Specifically, we study two problems for column stores on HDFS: column ordering and column duplication. Column ordering seeks an approximately optimal order of columns; column duplication complements column ordering in that some columns may be duplicated multiple times to reduce contention among the queries' diverse requirements on the column order. We consider an actual fine-grained cost model for column accesses and propose algorithms that take a query workload as input and output a column ordering strategy with or without storage redundancy that significantly improves the overall I/O performance. Experimental results over real-life data and production query workloads confirm the effectiveness of the proposed algorithms in diverse settings.
Automatic text summarization of Wikipedia articles

The main objective of a text summarization system is to identify the most important information from the given text and present it to the end users. In this paper, Wikipedia articles are given as input to system and extractive text summarization is presented by identifying text features and scoring the sentences accordingly. The text is first pre-processed to tokenize the sentences and perform stemming operations. We then score the sentences using the different text features. Two novel approaches implemented are using the citations present in the text and identifying synonyms. These features along with the traditional methods are used to score the sentences. The scores are used to classify the sentence to be in the summary text or not with the help of a neural network. The user can provide what percentage of the original text should be in the summary. It is found that scoring the sentences based on citations gives the best results.
Multi-source learning with block-wise missing data for Alzheimer's disease prediction

With the advances and increasing sophistication in data collection techniques, we are facing with large amounts of data collected from multiple heterogeneous sources in many applications. For example, in the study of Alzheimer's Disease (AD), different types of measurements such as neuroimages, gene/protein expression data, genetic data etc. are often collected and analyzed together for improved predictive power. It is believed that a joint learning of multiple data sources is beneficial as different data sources may contain complementary information, and feature-pruning and data source selection are critical for learning interpretable models from high-dimensional data. Very often the collected data comes with block-wise missing entries; for example, a patient without the MRI scan will have no information in the MRI data block, making his/her overall record incomplete. There has been a growing interest in the data mining community on expanding traditional techniques for single-source complete data analysis to the study of multi-source incomplete data. The key challenge is how to effectively integrate information from multiple heterogeneous sources in the presence of block-wise missing data. In this paper we first investigate the situation of complete data and present a unified ``bi-level" learning model for multi-source data. Then we give a natural extension of this model to the more challenging case with incomplete data. Our major contributions are threefold: (1) the proposed models handle both feature-level and source-level analysis in a unified formulation and include several existing feature learning approaches as special cases; (2) the model for incomplete data avoids direct imputation of the missing elements and thus provides superior performances. Moreover, it can be easily generalized to other applications with block-wise missing data sources; (3) efficient optimization algorithms are presented for both the complete and incomplete models. We have performed comprehensive evaluations of the proposed models on the application of AD diagnosis. Our proposed models compare favorably against existing approaches.
An Entity-Centric Approach for Privacy and Identity Management in Cloud Computing

Entities (e.g., users, services) have to authenticate themselves to service providers (SPs) in order to use their services. An entity provides personally identifiable information (PII) that uniquely identifies it to an SP. In the traditional application-centric Identity Management (IDM) model, each application keeps trace of identities of the entities that use it. In cloud computing, entities may have multiple accounts associated with different SPs, or one SP. Sharing PIIs of the same entity across services along with associated attributes can lead to mapping of PIIs to the entity. We propose an entity-centric approach for IDM in the cloud. The approach is based on: (1) active bundles—each including a payload of PII, privacy policies and a virtual machine that enforces the policies and uses a set of protection mechanisms to protect themselves, (2) anonymous identification to mediate interactions between the entity and cloud services using entity’s privacy policies. The main characteristics of the approach are: it is independent of third party, gives minimum information to the SP and provides ability to use identity data on untrusted hosts.
Task offloading decision in fog computing system

Fog computing is an emerging paradigm of cloud computing which to meet the growing computation demand of mobile application. It can help mobile devices to overcome resource constraints by offloading the computationally intensive tasks to cloud servers. The challenge of the cloud is to minimize the time of data transfer and task execution to the user, whose location changes owing to mobility, and the energy consumption for the mobile device. To provide satisfactory computation performance is particularly challenging in the fog computing environment. In this paper, we propose a novel fog computing model and offloading policy which can effectively bring the fog computing power closer to the mobile user. The fog computing model consist of remote cloud nodes and local cloud nodes, which is attached to wireless access infrastructure. And we give task offloading policy taking into account executi+on, energy consumption and other expenses. We finally evaluate the performance of our method through experimental simulations. The experimental results show that this method has a significant effect on reducing the execution time of tasks and energy consumption of mobile devices.
RASCAL: A Domain Specific Language for Source Code Analysis and Manipulation

Many automated software engineering tools require tight integration of techniques for source code analysis and manipulation. State-of-the-art tools exist for both, but the domains have remained notoriously separate because different computational paradigms fit each domain best. This impedance mismatch hampers the development of new solutions because the desired functionality and scalability can only be achieved by repeated and ad hoc integration of different techniques. Rascal is a domain-specific language that takes away most of this boilerplate by integrating source code analysis and manipulation at the conceptual, syntactic, semantic and technical level. We give an overview of the language and assess its merits by implementing a complex refactoring.
CEDD: Color and Edge Directivity Descriptor: A Compact Descriptor for Image Indexing and Retrieval

This paper deals with a new low level feature that is extracted from the images and can be used for indexing and retrieval. This feature is called “Color and Edge Directivity Descriptor” and incorporates color and texture information in a histogram. CEDD size is limited to 54 bytes per image, rendering this descriptor suitable for use in large image databases. One of the most important attribute of the CEDD is the low computational power needed for its extraction, in comparison with the needs of the most MPEG-7 descriptors. The objective measure called ANMRR is used to evaluate the performance of the proposed feature. An online demo that implements the proposed feature in an image retrieval system is available at: http://orpheus.ee.duth.gr/image_retrieval.
Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss

Convolutional networks (ConvNets) have achieved great successes in various challenging vision tasks. However, the performance of ConvNets would degrade when encountering the domain shift. The domain adaptation is more significant while challenging in the field of biomedical image analysis, where cross-modality data have largely different distributions. Given that annotating the medical data is especially expensive, the supervised transfer learning approaches are not quite optimal. In this paper, we propose an unsupervised domain adaptation framework with adversarial learning for cross-modality biomedical image segmentations. Specifically, our model is based on a dilated fully convolutional network for pixel-wise prediction. Moreover, we build a plug-and-play domain adaptation module (DAM) to map the target input to features which are aligned with source domain feature space. A domain critic module (DCM) is set up for discriminating the feature space of both domains. We optimize the DAM and DCM via an adversarial loss without using any target domain label. Our proposed method is validated by adapting a ConvNet trained with MRI images to unpaired CT data for cardiac structures segmentations, and achieved very promising results.