nils-herrmann/clean_scirepeval huggingface dataset:
Superimposition of 3D cone-beam CT models of orthognathic surgery patients.

To evaluate the registration of 3D models from cone-beam CT (CBCT) images taken before and after orthognathic surgery for the assessment of mandibular anatomy and position.



CBCT scans were taken before and after orthognathic surgery for ten patients with various malocclusions undergoing maxillary surgery only. 3D models were constructed from the CBCT images utilizing semi-automatic segmentation and manual editing. The cranial base was used to register 3D models of pre- and post-surgery scans (1 week). After registration, a novel tool allowed the visual and quantitative assessment of post-operative changes via 2D overlays of superimposed models and 3D coloured displacement maps.



3D changes in mandibular rami position after surgical procedures were clearly illustrated by the 3D colour-coded maps. The average displacement of all surfaces was 0.77 mm (SD=0.17 mm), at the posterior border 0.78 mm (SD=0.25 mm), and at the condyle 0.70 mm (SD=0.07 mm). These displacements were close to the image spatial resolution of 0.60 mm. The average interobserver differences were negligible. The range of the interobserver errors for the average of all mandibular rami surface distances was 0.02 mm (SD=0.01 mm).



Our results suggest this method provides a valid and reproducible assessment of craniofacial structures for patients undergoing orthognathic surgery. This technique may be used to identify different patterns of ramus and condylar remodelling following orthognathic surgery.
PERSONAL NETWORKING IN RUSSIA AND CHINA : BLAT AND GUANXI

Blat and guanxi are terms describing personal networks in Russia and China respectively. They have existed and been practiced for centuries and remain highly relevant today. By employing a social network perspective, we analyze the architecture of blat and guanxi and some of the similarities between them. We also argue that personal networks in Russia and China are products of specific cultural heritages and as such have their own particular configurations and characteristics. Following a dynamic perspective, we examine how blat and guanxi have evolved over time: while they were similar in a number of dimensions during the command economy period, there are interesting signs of increasing differentiation in the post-command decade. The paper outlines important insights for Western managers and expatriates in terms of surviving in and effectively using blat and guanxi.
Facial anatomy.

Botulinum toxin acts at the neuromuscular endplate, which requires precise delivery of the drug to achieve a desired clinical result. A thorough understanding of the complex anatomic structures of the face and their effect on facial form and function, coupled with an appreciation of facial esthetics and the balanced muscle actions that produce resting and active facial form, will result in accurate and reproducible clinical effects. Utilizing some general anatomic principles in combination with specific individual facial features and variations will enable the physician to consistently find the optimum injection sites and combination of other therapies for desired outcomes.
Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection

Due to the rise of technology, the possibility of fraud in different areas such as banking has increased. Credit card fraud is a crucial problem in banking and its danger is ever increasing. This paper proposes an advanced data mining method, considering both the feature selection and the decision cost for accuracy enhancement of credit card fraud detection. After selecting the best and most effective features, using an extended wrapper method, an ensemble classification is performed. The extended feature selection approach includes a prior feature filtering and a wrapper approach using C4.5 decision tree. Ensemble classification is performed using cost sensitive decision trees in a decision forest framework. A locally gathered fraud detection dataset is used to estimate the proposed method. The method is assessed using accuracy, recall, and F-measure as the evaluation metrics and compared with the basic classification algorithms including ID3, J48, Naïve Bayes, Bayesian Network, and NB tree. The experiments carried out show that considering the F-measure as the evaluation metric, the proposed approach yields 1.8 to 2.4 percent performance improvement compared to the other classifiers.
Learning to Parse Natural Language Commands to a Robot Control System

As robots become more ubiquitous and capable of performing complex tasks, the importance of enabling untrained users to interact with them has increased. In response, unconstrained natural-language interaction with robots has emerged as a significant research area. We discuss the problem of parsing natural language commands to actions and control structures that can be readily implemented in a robot execution system. Our approach learns a parser based on example pairs of English commands and corresponding control language expressions. We evaluate this approach in the context of following route instructions through an indoor environment, and demonstrate that our system can learn to translate English commands into sequences of desired actions, while correctly capturing the semantic intent of statements involving complex control structures. The procedural nature of our formal representation allows a robot to interpret route instructions online while moving through a previously unknown environment. 1  In this paper, we discuss our work on grounding natural language–interpreting human language into semantically informed structures in the context of robotic perception and actuation. To this end, we explore the question of interpreting natural language commands so they can be executed by a robot, specifically in the context of following route instructions through a map. Natural language (NL) is a rich, intuitive mechanism by which humans can interact with systems around them, offering sufficient signal to support robot task planning. Human route instructions include complex language constructs, which robots must be able to execute without being given a fully specified world model such as a map. Our goal is to investigate whether it is possible to learn a parser that produces · All authors are affiliated with the University of Washington, Seattle, USA. · Email: {cynthia,eherbst,lsz,fox}@cs.washington.edu
Content-based citation analysis: The next generation of citation analysis

Traditional citation analysis has been widely applied to detect patterns of scientific collaboration, map the landscapes of scholarly disciplines, assess the impact of research outputs, and observe knowledge transfer across domains. It is, however, limited, as it assumes all citations are of similar value and weights each equally. Content-based citation analysis (CCA) addresses a citation’s value by interpreting each one based on its context at both the syntactic and semantic levels. This paper provides a comprehensive overview of CAA research in terms of its theoretical foundations, methodical approaches, and example applications. In addition, we highlight how increased computational capabilities and publicly available full-text resources have opened this area of research to vast possibilities, which enable deeper citation analysis, more accurate citation prediction, and increased knowledge discovery.
Capsid-CPSF6 Interaction Licenses Nuclear HIV-1 Trafficking to Sites of Viral DNA Integration.

HIV-1 integration into the host genome favors actively transcribed genes. Prior work indicated that the nuclear periphery provides the architectural basis for integration site selection, with viral capsid-binding host cofactor CPSF6 and viral integrase-binding cofactor LEDGF/p75 contributing to selection of individual sites. Here, by investigating the early phase of infection, we determine that HIV-1 traffics throughout the nucleus for integration. CPSF6-capsid interactions allow the virus to bypass peripheral heterochromatin and penetrate the nuclear structure for integration. Loss of interaction with CPSF6 dramatically alters virus localization toward the nuclear periphery and integration into transcriptionally repressed lamina-associated heterochromatin, while loss of LEDGF/p75 does not significantly affect intranuclear HIV-1 localization. Thus, CPSF6 serves as a master regulator of HIV-1 intranuclear localization by trafficking viral preintegration complexes away from heterochromatin at the periphery toward gene-dense chromosomal regions within the nuclear interior.
The practice of crowdsourcing is transforming the Web and giving rise to a new field

social systems, social search, social media, collective intelligence, wikinomics, crowd wisdom, smart mobs, mass collaboration, and human computation. The topic has been discussed extensively in books, popular press, and academia.1,5,15,23,29,35 But this body of work has considered mostly efforts in the physical world.23,29,30 Some do consider crowdsourcing systems on the Web, but only certain system types28,33 or challenges (for example, how to evaluate users12). This survey attempts to provide a global picture of crowdsourcing systems on the Web. We define and classify such systems, then describe a broad sample of systems. The sample CROWDSOURCING SYSTEMS enlist a multitude of humans to help solve a wide variety of problems. Over the past decade, numerous such systems have appeared on the World-Wide Web. Prime examples include Wikipedia, Linux, Yahoo! Answers,  Turk-based systems, and much effort is being directed toward developing many more. As is typical for an emerging area, this effort has appeared under many names, including peer production, user-powered systems, user-generated content, collaborative systems, community systems, Crowdsourcing Systems on the World-Wide Web DOI:10.1145/1924421.1924442
Information-Centric Networking for the Internet-of-Things

Information-centric networking(ICN) opens new opportunities in the IoT domain due to its in-network caching capability. This significantly reduces read latency and the load on the origin server. In-network caching in ICN however introduces its own set of challenges because of its ubiquitous caches. Maintaining cache consistency without incurring high overhead is an important problem that needs to be handled in ICN to prevent a client from retrieving stale data. We propose a cache consistency approach based on the rate at which an IoT application generates its data. Our technique is lightweight and can be deployed easily in real network. Our simulation results demonstrate that our proposed algorithm significantly reduces the network traffic as well as the load on the origin server while serving fresh content to the clients.
Recent advances with liposomes as pharmaceutical carriers

Liposomes — microscopic phospholipid bubbles with a bilayered membrane structure — have received a lot of attention during the past 30 years as pharmaceutical carriers of great potential. More recently, many new developments have been seen in the area of liposomal drugs — from clinically approved products to new experimental applications, with gene delivery and cancer therapy still being the principal areas of interest. For further successful development of this field, promising trends must be identified and exploited, albeit with a clear understanding of the limitations of these approaches.
Control + Data Flow Model Directed Inputs Generation B . Approximate Taint Flow Inference

Fuzz testing is an active testing technique which consists in automatically generating and sending malicious inputs to an application in order to hopefully trigger a vulnerability. Fuzzing entails such questions as: Where to fuzz? Which parameter to fuzz? What kind of anomaly to introduce? Where to observe its effects? etc. Different test contexts depending on the degree of knowledge assumed about the target: recompiling the application (white-box), interacting only at the target interface (blackbox), dynamically instrumenting a binary (grey-box). In this paper, we focus on black-box test contest, and specifically address the questions: How to obtain a notion of coverage on unstructured inputs? How to capture human testers intuitions and use it for the fuzzing? How to drive the search in various directions? We specifically address the problems of detecting Memory Corruption in PDF interpreters and Cross Site Scripting (XSS) in web applications. We detail our approaches which use genetic algorithm, inference and anti-random testing. We empirically evaluate our implementations of XSS fuzzer KameleonFuzz and of PDF fuzzer ShiftMonkey.
A novel pore extraction method for heterogeneous fingerprint images using Convolutional Neural Networks

Most fingerprint recognition systems use Level 1 characteristics (ridge flow, orientation, and frequency) and Level 2 features (minutiae points) to recognize individuals. Level 3 features (sweat pores, incipient ridges and ultra-thin characteristics of the ridges) are less frequently adopted because they can be extracted only from high resolution images, but they have the potential of improving all the steps of the biometric recognition process. In particular, sweat pores can be used for quality assessment, liveness detection, biometric matching in live applications, and matching of partial latent fingerprints in forensic applications. Currently, each type of fingerprint acquisition technique (touch-based, touchless, or latent) requires a different algorithm for pore extraction. In this paper, we propose the first method in the literature able to extract the coordinates of the pores from touch-based, touchless, and latent fingerprint images. Our method uses specifically designed and trained Convolutional Neural Networks (CNN) to estimate and refine the centroid of each pore. Results show that our method is feasible and achieved satisfactory accuracy for all the types of evaluated images, with a better performance with respect to the compared state-of-the-art methods.
A Deep Neural Network Compression Pipeline : Pruning , Quantization , Huffman Encoding

Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, We introduce a three stage pipeline: pruning, quantization and Huffman encoding, that work together to reduce the storage requirement of neural networks by 35× to 49× without affecting their accuracy. Our method first prunes the network by learning only the important connections. Next, we quantize the weights to enforce weight sharing, finally, we apply Huffman encoding. After the first two steps we retrain the network to fine tune the remaining connections and the quantized centroids. Pruning, reduces the number of connections by 9× to 13×; Quantization then reduces the number of bits that represent each connection from 32 to 5. On the ImageNet dataset, our method reduced the storage required by AlexNet by 35×, from 240MB to 6.9MB, without loss of accuracy. Our method reduced the size of VGG16 by 49× from 552MB to 11.3MB, again with no loss of accuracy. This allows fitting the model into on-chip SRAM cache rather than off-chip DRAM memory, which has 180× less access energy.
Particular object retrieval with integral max-pooling of CNN activations

Recently, image representation built upon Convolutional Neural Network (CNN) has been shown to provide effective descriptors for image search, outperforming pre-CNN features as short-vector representations. Yet such models are not compatible with geometry-aware re-ranking methods and still outperformed, on some particular object retrieval benchmarks, by traditional image search systems relying on precise descriptor matching, geometric re-ranking, or query expansion. This work revisits both retrieval stages, namely initial search and re-ranking, by employing the same primitive information derived from the CNN. We build compact feature vectors that encode several image regions without the need to feed multiple inputs to the network. Furthermore, we extend integral images to handle max-pooling on convolutional layer activations, allowing us to efficiently localize matching objects. The resulting bounding box is finally used for image reranking. As a result, this paper significantly improves existing CNN-based recognition pipeline: We report for the first time results competing with traditional methods on the challenging Oxford5k and Paris6k datasets.
Automatic Taxonomy Construction for Eye Colors Data without Using Context Information

Learning is one of the challenge subtasks of Ontology Learning. A taxonomy describes hyponymy between concepts, and it plays an important role in information systems and categorization tasks. Works in this field prefer to utilize context of sentences to obtain a precise analysis of the corpus, since context can provide more semantic information for building a precise interpretation of the semantic classes. In this paper, we propose a method, called SenseRefined, for automatic taxonomy construction without using context information. It's a part work of one of our project. The proposed method is specially applied for eye colors data, and all the domain phrases are extracted out before building taxonomy. Word senses are extracted out as a way to provide semantic information, because we find the senses of words are more like the definitions of the concepts, which often contain hypernym concepts in. Experiments were carried out using SenseRefined method and Substring Matching method, where the latter one is baseline. Experimental results show the effectiveness of our approach.
Power Electronics and Motor Drives in Electric, Hybrid Electric, and Plug-In Hybrid Electric Vehicles

With the requirements for reducing emissions and improving fuel economy, automotive companies are developing electric, hybrid electric, and plug-in hybrid electric vehicles. Power electronics is an enabling technology for the development of these environmentally friendlier vehicles and implementing the advanced electrical architectures to meet the demands for increased electric loads. In this paper, a brief review of the current trends and future vehicle strategies and the function of power electronic subsystems are described. The requirements of power electronic components and electric motor drives for the successful development of these vehicles are also presented.
The Stellar Consensus Protocol : A Federated Model for Internet-level Consensus

This paper introduces a new model for consensus called federated Byzantine agreement (FBA). FBA achieves robustness through quorum slices—individual trust decisions made by each node that together determine system-level quorums. Slices bind the system together much the way individual networks’ peering and transit decisions now unify the Internet. We also present the Stellar Consensus Protocol (SCP), a construction for FBA. Like all Byzantine agreement protocols, SCP makes no assumptions about the rational behavior of attackers. Unlike prior Byzantine agreement models, which presuppose a unanimously accepted membership list, SCP enjoys open membership that promotes organic network growth. Compared to decentralized proof of-work and proof-of-stake schemes, SCP has modest computing and financial requirements, lowering the barrier to entry and potentially opening up financial systems to new participants.
A Taxonomy and Qualitative Comparison of Program Analysis Techniques for Security Assessment of Android Apps

In parallel with the meteoric rise of mobile software, we are witnessing an alarming escalation in the number and sophistication of the security threats targeted at mobile platforms, particularly Android, as the dominant platform. While existing research has made significant progress towards detection and mitigation of Android security, gaps and challenges remain. This paper contributes a comprehensive taxonomy to classify and characterize the state-of-the-art research in this area. We have carefully followed the systematic literature review process, and analyzed the results of more than 100 research papers, resulting in the most comprehensive and elaborate investigation of the literature in this area of research. The systematic analysis of the research literature has revealed patterns, trends, and gaps in the existing literature, and underlined key challenges and opportunities that will shape the focus of future research efforts.
Multi-column deep neural networks for image classification

Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible, wide and deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional winner-take-all neurons yield large network depth, resulting in roughly as many sparsely connected neural layers as found in mammals between retina and visual cortex. Only winner neurons are trained. Several deep neural columns become experts on inputs preprocessed in different ways; their predictions are averaged. Graphics cards allow for fast training. On the very competitive MNIST handwriting benchmark, our method is the first to achieve near-human performance. On a traffic sign recognition benchmark it outperforms humans by a factor of two. We also improve the state-of-the-art on a plethora of common image classification benchmarks.
Alternating Direction Algorithms for 1-Problems in Compressive Sensing

In this paper, we propose and study the use of alternating direction algorithms for several `1-norm minimization problems arising from sparse solution recovery in compressive sensing, including the basis pursuit problem, the basis-pursuit denoising problems of both unconstrained and constrained forms, as well as others. We present and investigate two classes of algorithms derived from either the primal or the dual forms of the `1-problems. The construction of the algorithms consists of two main steps: (1) to reformulate an `1-problem into one having partially separable objective functions by adding new variables and constraints; and (2) to apply an exact or inexact alternating direction method to the resulting problem. The derived alternating direction algorithms can be regarded as first-order primal-dual algorithms because both primal and dual variables are updated at each and every iteration. Convergence properties of these algorithms are established or restated when they already exist. Extensive numerical results in comparison with several state-of-the-art algorithms are given to demonstrate that the proposed algorithms are efficient, stable and robust. Moreover, we present numerical results to emphasize two practically important but perhaps overlooked points. One point is that algorithm speed should always be evaluated relative to appropriate solution accuracy; another is that whenever erroneous measurements possibly exist, the `1-norm fidelity should be the fidelity of choice in compressive sensing.
Effective Page Recommendation Algorithms Based on Distributed Learning Automata

Different efforts have been done to address the problem of information overload on the Internet. Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests by extracting knowledge from the previous users’ interactions. In this paper, we propose an algorithm to solve the web page recommendation problem. In our algorithm, we use distributed learning automata to learn the behavior of previous users’ and recommend pages to the current user based on learned pattern. Our experiments on real data set show that the proposed algorithm performs better than the other algorithms that we compared to and, at the same time, it is less complex than other algorithms with respect to memory usage and computational cost too.