nils-herrmann/clean_scirepeval huggingface dataset:
A new dynamic foot abduction orthosis for clubfoot treatment.

Recurrent clubfoot deformity after successful initial correction with the use of the Ponseti method continues to be a common problem and is often caused by noncompliance with wear of the traditional foot abduction brace. The purpose of this study was to assess the results of a newly designed dynamic foot abduction orthosis in terms of (1) parental compliance and (2) effectiveness in preventing recurrent clubfoot deformities. Twenty-eight patients (49 clubfeet) who were treated with a dynamic foot abduction orthosis in accordance with the Ponseti method were included in this study. Of the 28 patients, 18 had idiopathic clubfeet (31 clubfeet), 2 had complex idiopathic clubfeet (4 clubfeet), 5 had myelodysplasia (8 clubfeet), and 3 were syndromic (6 clubfeet). The mean duration of follow-up was 29 months (range, 24-36 months). Noncompliance was reported in only 2 (7.1%) of the 28 patients in the new orthosis compared with the authors' previously reported 41% (21/51) noncompliance rate in patients treated with the use of the traditional foot abduction brace. The two patients in this study, in which parents were noncompliant with orthosis wear, developed recurrent deformities. There were 2 patients (7%) who experienced skin blistering in the new orthosis compared with 12 (23.5%) of 51 patients who experienced blistering with the use of traditional abduction brace in the authors' previously reported study. Logistic regression modeling compliance and recurrence revealed that noncompliance with the foot abduction orthosis was most predictive of recurrence of deformity (odds ratio, 27; 95% confidence interval, 2.2-326; P = 0.01). The articulating foot abduction orthosis is well tolerated by patients and parents and results in a higher compliance rate and a lower complication rate than what were observed with the traditional foot abduction orthosis.
Healthcare informatics and analytics framework

Healthcare industry is constantly undergoing changes owing to the advancements made in its medical and technological dimensions. Healthcare Informatics has evolved over decades by leveraging upon best of the computer technologies available. It has come a long way from being just a database to a comprehensive source of useful information for analytics and research. Emergence of technology paradigms like Big Data, Internet of Things (IoT), Complex Even Processing (CEP), Cloud Computing etc are changing the way things were. Healthcare informatics is now at juncture to find a complete solution to all its woes by adopting an integrated solution framework based on Big Data, IoT and CEP. This paper proposes one such framework which can act as reference for implementing a holistic healthcare informatics and analytics ecosystem.
Encrypted Traffic Classification Using Statistical Features

Traffic classification plays an important role in many aspects of network management such as identifying type of the transferred data, detection of malware applications, applying policies to restrict network accesses and so on. Basic methods in this field were using some obvious traffic features like port number and protocol type to classify the traffic type. However, recent changes in applications make these features imperfect for such tasks. As a remedy, network traffic classification using machine learning techniques is now evolving. In this article, a new semi-supervised learning is proposed which utilizes clustering algorithms and label propagation techniques. The clustering part is based on graph theory and minimum spanning tree algorithm. In the next level, some pivot data instances are selected for the expert to vote for their classes, and the identified class labels will be used for similar data instances with no labels. In the last part, the decision tree algorithm is used to construct the classification model. The results show that the proposed method has a precise and accurate performance in classification of encrypted traffic for the network applications. It also provides desirable results for plain un-encrypted traffic classification, especially for unbalanced streams of data.
Development of Computational Thinking Skills through Unplugged Activities in Primary School

Computational thinking is nowadays being widely adopted and investigated. Educators and researchers are using two main approaches to teach these skills in schools: with computer programming exercises, and with unplugged activities that do not require the use of digital devices or any kind of specific hardware. While the former is the mainstream approach, the latter is especially important for schools that do not have proper technology resources, Internet connections or even electrical power. However, there is a lack of investigations that prove the effectiveness of the unplugged activities in the development of computational thinking skills, particularly for primary schools. This paper, which summarizes a quasi-experiment carried out in two primary schools in Spain, tries to shed some light on this regard. The results show that students in the experimental groups, who took part in the unplugged activities, enhanced their computational thinking skills significantly more than their peers in the control groups who did not participate during the classes, proving that the unplugged approach may be effective for the development of this ability.
Identifying Mislabeled Training Data

This paper presents a new approach to identifying and eliminating mislabeled training instances for supervised learning. The goal of this approach is to improve classiication accuracies produced by learning algorithms by improving the quality of the training data. Our approach uses a set of learning algorithms to create classiiers that serve as noise lters for the training data. We evaluate single algorithm, majority vote and consensus lters on ve datasets that are prone to labeling errors. Our experiments illustrate that ltering signiicantly improves classiication accuracy for noise levels up to 30%. An analytical and empirical evaluation of the precision of our approach shows that consensus lters are conservative at throwing away good data at the expense of retaining bad data and that majority lters are better at detecting bad data at the expense of throwing away good data. This suggests that for situations in which there is a paucity of data, consensus lters are preferable, whereas majority vote lters are preferable for situations with an abundance of data.
Adaptive bootstrapping of recommender systems using decision trees

Recommender systems perform much better on users for which they have more information. This gives rise to a problem of satisfying users new to a system. The problem is even more acute considering that some of these hard to profile new users judge the unfamiliar system by its ability to immediately provide them with satisfying recommendations, and may quickly abandon the system when disappointed. Rapid profiling of new users by a recommender system is often achieved through a bootstrapping process - a kind of an initial interview - that elicits users to provide their opinions on certain carefully chosen items or categories. The elicitation process becomes particularly effective when adapted to users' responses, making best use of users' time by dynamically modifying the questions to improve the evolving profile. In particular, we advocate a specialized version of decision trees as the most appropriate tool for this task. We detail an efficient tree learning algorithm, specifically tailored to the unique properties of the problem. Several extensions to the tree construction are also introduced, which enhance the efficiency and utility of the method. We implemented our methods within a movie recommendation service. The experimental study delivered encouraging results, with the tree-based bootstrapping process significantly outperforming previous approaches.
A Short Text Similarity Algorithm for Finding Similar Police 110 Incidents

Finding similar police 110 incidents from the incident dataset plays an important role in recognising related cases from which the investigators could find more clues and make a better decision on police deployment. We aim at finding 110 incidents with similar case features and semantic compared against a given incident. A short text similarity algorithm is presented. Our algorithm is developed from a novel semantic similarity algorithm Word Mover'd Distance(WMD). In order to emphasize the significance of case features in incident text, the method introduces the traditional term frequency-inverted document frequency(TF-IDF) as term weights to the WMD. Then the algorithm is verified on the practical dataset of public security department to find similar incidents, and experiments show that the algorithm is effective and can improve the accuracy in finding similar police incidents.
A Weighted Finite-State Transducer (WFST)-Based Language Model for Online Indic Script Handwriting Recognition

Though designing of classifies for Indic script handwriting recognition has been researched with enough attention, use of language model has so far received little exposure. This paper attempts to develop a weighted finite-state transducer (WFST) based language model for improving the current recognition accuracy. Both the recognition hypothesis (i.e. the segmentation lattice) and the lexicon are modeled as two WFSTs. Concatenation of these two FSTs accept a valid word(s) which is (are) present in the recognition lattice. A third FST called error FST is also introduced to retrieve certain words which were missing in the previous concatenation operation. The proposed model has been tested for online Bangla handwriting recognition though the underlying principle can equally be applied for recognition of offline or printed words. Experiment on a part of ISI-Bangla handwriting database shows that while the present classifiers (without using any language model) can recognize about 73% word, use of recognition and lexicon FSTs improve this result by about 9% giving an average word-level accuracy of 82%. Introduction of error FST further improves this accuracy to 93%. This remarkable improvement in word recognition accuracy by using FST-based language model would serve as a significant revelation for the research in handwriting recognition, in general and Indic script handwriting recognition, in particular.
Dynamic-CSR : A Format for Dynamic Sparse-Matrix Updates

Sparse matrices are a core component in many numerical simulations, and their efficiency is essential to achieving high performance. Dynamic sparse-matrix allocation (insertion) can benefit a number of problems such as sparse-matrix factorization, sparse-matrix-matrix addition, static analysis (e.g., points-to analysis), computing transitive closure, and other graph algorithms. Existing sparse-matrix formats are poorly designed to handle dynamic updates. The compressed sparse-row (CSR) format is fully compact and must be rebuilt after each new entry. Ellpack (ELL) stores a constant number of entries per row, which allows for efficient insertion and sparse matrix-vector multiplication (SpMV) but is memory inefficient and strictly limits row size. The coordinate (COO) format stores a list of entries and is efficient for both memory use and insertion time; however, it is much less efficient at SpMV. Hybrid ellpack (HYB) compromises by using a combination of ELL and COO but degrades in performance as the COO portion fills up. Rows that use the COO portion require it to be completely traversed during every SpMV operation. In this paper we present dynamic compressed sparse row (DCSR), a sparse matrix format that allows for asynchronous dynamic updates, and extend it to an improved SpMM operation. Through the use of DCSR we demonstrate an improved sparse matrix-matrix multiplication (SpMM) algorithm and evaluate it using algebraic multigrid (AMG). AMG is a multigrid operation in which the hierarchy of operators is created from the matrix itself as opposed to the geometry of the mesh. This operation can be expressed in terms of SpMM, SpMV, and primitive parallel operations. DCSR provides significant performance improvements for SpMM through increased memory efficiency due to asynchronous dynamic updates.
DESC: enabling secure data exchange based on smart contracts

Dear editor, As data are one of the most important factors of today’s intelligent systems but are relatively scarce, people try exchanging data with other organizations or public marketplaces. However, data exchange is now lack of a secure execution mechanism to automatically and fairly protect the rights which are seriously concerned by data owners. For instance, data owners cannot limit the identity of a data transferee who is banned to take the data by any way. Moreover, data transferees are also afraid that their applications for exchange may be unfairly treated. For instance, data owners could dynamically change data price when multiple transferees submit their applications at the same time. A data marketplace can take over the role of a trusted third party during data exchange, but single point failure is always a serious problem in a large-scale distributed system. Furthermore, these marketplaces usually use contracts based on natural languages which may be maliciously misinterpreted and cannot be automatically executed. Numerous researches have been presented to provide digital rights management (DRM) . For instance, Zhu et al.  presented a privacypreserving video subscription scheme with the limitation of expire date. However, rights of data owners are more complex than rights of digital content owners. Decentralized and trustworthy enforcement for access control policies is a long-term issue. Han et al.  proposed an optimized mechanism which is a trusted and decentralized access control framework for the client/server architecture. These studies still need third party authorities which are used to execute or supervise the rights control, leading to a few trust and security issues. Existing studies  on attribute-based access control have been applied to many fields, such as e-commerce and Internet of Things. This article introduces attribute-based access control and proposes a secure framework for data exchange based on smart contracts (DESC) to enable automatic and fair access control. Featuring with decentralized, fair, transparent, immutable and traceable advantages, blockchain 2.0  provides a generalized framework for implementing decentralized compute resources named smart contracts which define rights and policies in mathematical and programming forms. Once the predefined smart contract is triggered by a transaction, it can automatically execute the specific contractual clauses. To find out which data rights are seriously concerned by data owners, we investigate the DRM mechanisms and several data exchange platforms: GBDEX, national engineering lab for big data distribution and exchange technologies. Then,
Drug-Drug Interaction Extraction via Convolutional Neural Networks

Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM) with a large number of manually defined features. Recently, convolutional neural networks (CNN), a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%.
Esports Analytics Through Encounter Detection

Esports is computer games played in a competitive environment, and analytics in this domain is focused on player and team behavior.  games are among the most played digital games in the world. In these games, teams of players fight against each other in enclosed arena environments, with a complex gameplay focused on tactical combat. Here we present a technique for segmenting matches into spatio‐ temporally defined components referred to as encounters, enabling performance analysis. We apply encounter‐based analysis to match data from the popular esport game DOTA, and present win probability predictions based on encounters. Finally, metrics for evaluating team performance during match runtime are proposed.
An integrative framework of COBIT and TOGAF for designing IT governance in local government

To implement the good IT governance in local governance, we need an IT governance design for mature and appropriate models. There are some problems that appear in designing information technology governance in local government. The problem is how to control the information technology risk management at the operational level and designing information technology in mapping an integrated framework. So this led to the adoption of information technology in local government to be inefficient. This study proposes an integrated model framework of COBIT and TOGAF to design more comprehensive IT Governance to increase the efficiency of the application of IT in local government. An integrated model to solve the problem of IT risk management control are using the COBIT 5 framework. To solve the problem in designing an integrated IT governance is used TOGAF 9.1 framework. The result is the integration model combines three aspects: governance area, 6 principles and management area are used for the effective application of IT governance in local government.
A New Discrete Particle Swarm Optimization Algorithm

Particle Swarm Optimization (PSO) has been shown to perform very well on a wide range of optimization problems. One of the drawbacks to PSO is that the base algorithm assumes continuous variables. In this paper, we present a version of PSO that is able to optimize over discrete variables. This new PSO algorithm, which we call Integer and Categorical PSO (ICPSO), incorporates ideas from Estimation of Distribution Algorithms (EDAs) in that particles represent probability distributions rather than solution values, and the PSO update modifies the probability distributions. In this paper, we describe our new algorithm and compare its performance against other discrete PSO algorithms. In our experiments, we demonstrate that our algorithm outperforms comparable methods on both discrete benchmark functions and NK landscapes, a mathematical framework that generates tunable fitness landscapes for evaluating EAs.
Feature representation for statistical-learning-based object detection: A review

Statistical learning based object detection is an important topic in computer vision. It learns visual representation from annotated exemplars to identify semantic defined objects in images. High-performance object detection is usually carried out in feature space and effective feature representation can improve the performance significantly. Feature representation is the encoding process which maps raw image pixels inside local regions into discriminant feature space. The motivation of this paper is to present a review on feature representation in recent object detection methods. Visual features applied in object detection are categorized according to the differences in computation and visual properties. The most valued features are introduced and discussed in details. Representative extensions are introduced briefly for comparison. Descriptive power, robustness, compactness as well as computational efficiency are viewed as important properties. According to these properties, discussions are presented on the advantages and drawbacks of features. Besides, generic techniques such as dimension reduction and combination are introduced. Through this review, we would like to draw the feature sketch and provide new insights for feature utilization, in order to tackle with future challenges of object detection.
Crowdsourcing Annotations for Visual Object Detection

A large number of images with ground truth object bounding boxes are critical for learning object detectors, which is a fundamental task in compute vision. In this paper, we study strategies to crowd-source bounding box annotations. The core challenge of building such a system is to effectively control the data quality with minimal cost. Our key observation is that drawing a bounding box is significantly more difficult and time consuming than giving answers to multiple choice questions. Thus quality control through additional verification tasks is more cost effective than consensus based algorithms. In particular, we present a system that consists of three simple sub-tasks — a drawing task, a quality verification task and a coverage verification task. Experimental results demonstrate that our system is scalable, accurate, and cost-effective.
Return-Oriented Flush-Reload Side Channels on ARM and Their Implications for Android Devices

Cache side-channel attacks have been extensively studied on x86 architectures, but much less so on ARM processors. The technical challenges to conduct side-channel attacks on ARM, presumably, stem from the poorly documented ARM cache implementations, such as cache coherence protocols and cache flush operations, and also the lack of understanding of how different cache implementations will affect side-channel attacks. This paper presents a systematic exploration of vectors for flush-reload attacks on ARM processors. flush-reload attacks are among the most well-known cache side-channel attacks on x86. It has been shown in previous work that they are capable of exfiltrating sensitive information with high fidelity. We demonstrate in this work a novel construction of flush-reload side channels on last-level caches of ARM processors, which, particularly, exploits return-oriented programming techniques to reload instructions. We also demonstrate several attacks on Android OS (e.g., detecting hardware events and tracing software execution paths) to highlight the implications of such attacks for Android devices.
Why experts make errors

Expert latent f ingerprint examiners were presented with fingerprints taken from real criminal cases. Half of the prints had been previously judged as individualizations and the other half as exclusions. We re-presented the same prints to the same experts who had judged them previously, but provided biasing contextual information in both the individualizations and exclusions. A control set of individualizations and exclusions was also re-presented as part of the study. The control set had no biasing contextual information associated with it. Each expert examined a total of eight past decisions. Two-thirds of the experts made inconsistent decisions. The f indings are discussed in terms of psychological and cognitive vulnerabilities.
Predicting Price Changes in Ethereum

The market capitalization of publicly traded cryptocurrencies is currently above $230 billion (Bovaird, 2017). Bitcoin, the most valuable cryptocurrency, serves primarily as a digital store of value (Van Alstyne, 2014), and its price predictability has been well-studied (Hegazy and Mumford, 2016). However, Ethereum has the second-highest market capitalization and supports much more functionality than Bitcoin. While its price predictability is sparsely covered in published literature, the technology’s additional functionality may cause Ether’s price predictability to differ significantly from that of Bitcoin. These characteristics are outlined in the following subsection; the underlying details of Bitcoin (Nakamoto, 2008) and Ethereum (Buterin, 2013) are elided, as they are described in depth in the cited papers.