nils-herrmann/clean_scirepeval huggingface dataset:
Anatomy of the medial femoral circumflex artery and its surgical implications.

The primary source for the blood supply of the head of the femur is the deep branch of the medial femoral circumflex artery (MFCA). In posterior approaches to the hip and pelvis the short external rotators are often divided. This can damage the deep branch and interfere with perfusion of the head. We describe the anatomy of the MFCA and its branches based on dissections of 24 cadaver hips after injection of neoprene-latex into the femoral or internal iliac arteries. The course of the deep branch of the MFCA was constant in its extracapsular segment. In all cases there was a trochanteric branch at the proximal border of quadratus femoris spreading on to the lateral aspect of the greater trochanter. This branch marks the level of the tendon of obturator externus, which is crossed posteriorly by the deep branch of the MFCA. As the deep branch travels superiorly, it crosses anterior to the conjoint tendon of gemellus inferior, obturator internus and gemellus superior. It then perforates the joint capsule at the level of gemellus superior. In its intracapsular segment it runs along the posterosuperior aspect of the neck of the femur dividing into two to four subsynovial retinacular vessels. We demonstrated that obturator externus protected the deep branch of the MFCA from being disrupted or stretched during dislocation of the hip in any direction after serial release of all other soft-tissue attachments of the proximal femur, including a complete circumferential capsulotomy. Precise knowledge of the extracapsular anatomy of the MFCA and its surrounding structures will help to avoid iatrogenic avascular necrosis of the head of the femur in reconstructive surgery of the hip and fixation of acetabular fractures through the posterior approach.
Conservative voxelization

We propose a novel hardware-accelerated voxelization algorithm for polygonal models. Compared with previous approaches, our algorithm has a major advantage that it guarantees the conservative correctness in voxelization: every voxel intersecting the input model is correctly recognized. This property is crucial for applications like collision detection, occlusion culling and visibility processing. We also present an efficient and robust implementation of the algorithm in the GPU. Experiments show that our algorithm has a lower memory consumption than previous approaches and is more efficient when the volume resolution is high. In addition, our algorithm requires no preprocessing and is suitable for voxelizing deformable models.
Recognition of Mexican banknotes via their color and texture features

a Universidad Autónoma del Estado de Hidalgo, Escuela Superior de Tizayuca, Carretera Federal Pachuca – Tizayuca km 2.5, CP 43800, Tizayuca, Hidalgo, Mexico b Universidad Autónoma del Estado de México, Av. Jardín Zumpango s/n, Fraccionamiento El Tecojote, CP 56259, Texcoco-Estado de México, Mexico c Centro de Investigación y de Estudios Avanzados del IPN, Departamento de Computación, Av. Instituto Politécnico Nacional 2508, San Pedro Zacatenco, CP 07360, México DF, Mexico
Virtual machine allocation policies against co-resident attacks in cloud computing

While the services-based model of cloud computing makes more and more IT resources available to a wider range of customers, the massive amount of data in cloud platforms is becoming a target for malicious users. Previous studies show that attackers can co-locate their virtual machines (VMs) with target VMs on the same server, and obtain sensitive information from the victims using side channels. This paper investigates VM allocation policies and practical countermeasures against this novel kind of co-resident attack by developing a set of security metrics and a quantitative model. A security analysis of three VM allocation policies commonly used in existing cloud computing platforms reveals that the server's configuration, oversubscription and background traffic have a large impact on the ability to prevent attackers from co-locating with the targets. If the servers are properly configured, and oversubscription is enabled, the best policy is to allocate new VMs to the server with the most VMs. Based on these results, a new strategy is introduced that effectively decreases the probability of attackers achieving co-residence. The proposed solution only requires minor changes to current allocation policies, and hence can be easily integrated into existing cloud platforms to mitigate the threat of co-resident attacks.
A fine-grained taxonomy of tables on the web

We propose a classification taxonomy over a large crawl of HTML tables on the Web, focusing primarily on those tables that express structured knowledge. The taxonomy separates tables into two top-level classes: a) those used for layout purposes, including navigational and formatting; and b) those containing relational knowledge, including listings, attribute/value, matrix, enumeration, and form. We then propose a classification algorithm for automatically detecting a subset of the classes in our taxonomy, namely layout tables and attribute/value tables. We report on the performance of our system over a large sample of manually annotated HTML tables on the Web.
Music Recommendation Based on Acoustic Features and User Access Patterns

Music recommendation is receiving increasing attention as the music industry develops venues to deliver music over the Internet. The goal of music recommendation is to present users lists of songs that they are likely to enjoy. Collaborative-filtering and content-based recommendations are two widely used approaches that have been proposed for music recommendation. However, both approaches have their own disadvantages: collaborative-filtering methods need a large collection of user history data and content-based methods lack the ability of understanding the interests and preferences of users. To overcome these limitations, this paper presents a novel dynamic music similarity measurement strategy that utilizes both content features and user access patterns. The seamless integration of them significantly improves the music similarity measurement accuracy and performance. Based on this strategy, recommended songs are obtained by a means of label propagation over a graph representing music similarity. Experimental results on a real data set collected from http://www.newwisdom.net demonstrate the effectiveness of the proposed approach.
Output Reachable Set Estimation and Verification for Multilayer Neural Networks

In this brief, the output reachable estimation and safety verification problems for multilayer perceptron (MLP) neural networks are addressed. First, a conception called maximum sensitivity is introduced, and for a class of MLPs whose activation functions are monotonic functions, the maximum sensitivity can be computed via solving convex optimization problems. Then, using a simulation-based method, the output reachable set estimation problem for neural networks is formulated into a chain of optimization problems. Finally, an automated safety verification is developed based on the output reachable set estimation result. An application to the safety verification for a robotic arm model with two joints is presented to show the effectiveness of the proposed approaches.
On Evaluating and Comparing Conversational Agents

Conversational agents are exploding in popularity. However, much work remains in the area of non goal-oriented conversations, despite significant growth in research interest over recent years. To advance the state of the art in conversational AI, Amazon launched the Alexa Prize, a 2.5-million dollar university competition where sixteen selected university teams built conversational agents to deliver the best social conversational experience. Alexa Prize provided the academic community with the unique opportunity to perform research with a live system used by millions of users. The subjectivity associated with evaluating conversations is key element underlying the challenge of building non-goal oriented dialogue systems. In this paper, we propose a comprehensive evaluation strategy with multiple metrics designed to reduce subjectivity by selecting metrics which correlate well with human judgement. The proposed metrics provide granular analysis of the conversational agents, which is not captured in human ratings. We show that these metrics can be used as a reasonable proxy for human judgment. We provide a mechanism to unify the metrics for selecting the top performing agents, which has also been applied throughout the Alexa Prize competition. To our knowledge, to date it is the largest setting for evaluating agents with millions of conversations and hundreds of thousands of ratings from users. We believe that this work is a step towards an automatic evaluation process for conversational AIs.
Analysis of Efficient CNN Design Techniques for Semantic Segmentation

Majority of CNN architecture design is aimed at achieving high accuracy in public benchmarks by increasing the complexity. Typically, they are over-specified by a large margin and can be optimized by a factor of 10-100x with only a small reduction in accuracy. In spite of the increase in computational power of embedded systems, these networks are still not suitable for embedded deployment. There is a large need to optimize for hardware and reduce the size of the network by orders of magnitude for computer vision applications. This has led to a growing community which is focused on designing efficient networks. However, CNN architectures are evolving rapidly and efficient architectures seem to lag behind. There is also a gap in understanding the hardware architecture details and incorporating it into the network design. The motivation of this paper is to systematically summarize efficient design techniques and provide guidelines for an application developer. We also perform a case study by benchmarking various semantic segmentation algorithms for autonomous driving.
Safety and Feasibility of using the EksoTM Bionic Exoskeleton to Aid Ambulation after Spinal Cord Injury

The sudden inability to walk is one of the most glaring impairments following spinal cord injury (SCI). Regardless of time since injury, recovery of walking has been found to be one of the top priorities for those with SCI as well as their rehabilitation professionals . Despite clinical management and promising basic science research advances, a recent multicenter prospective study revealed that 59% of those with SCI are unable to ambulate without assistance from others at one year following injury . The worldwide incidence of SCI is between 10.4-83 per million , and there are approximately 265,000 persons with SCI living in the United States . Thus, there is a tremendous consumer demand to improve ambulation outcomes following SCI.
Vector graphics complexes

Basic topological modeling, such as the ability to have several faces share a common edge, has been largely absent from vector graphics. We introduce the vector graphics complex (VGC) as a simple data structure to support fundamental topological modeling operations for vector graphics illustrations. The VGC can represent any arbitrary non-manifold topology as an immersion in the plane, unlike planar maps which can only represent embeddings. This allows for the direct representation of incidence relationships between objects and can therefore more faithfully capture the intended semantics of many illustrations, while at the same time keeping the geometric flexibility of stacking-based systems. We describe and implement a set of topological editing operations for the VGC, including glue, unglue, cut, and uncut. Our system maintains a global stacking order for all faces, edges, and vertices without requiring that components of an object reside together on a single layer. This allows for the coordinated editing of shared vertices and edges even for objects that have components distributed across multiple layers. We introduce VGC-specific methods that are tailored towards quickly achieving desired stacking orders for faces, edges, and vertices.
A Survey on Wireless Indoor Localization from the Device Perspective

With the marvelous development of wireless techniques and ubiquitous deployment of wireless systems indoors, myriad indoor location-based services (ILBSs) have permeated into numerous aspects of modern life. The most fundamental functionality is to pinpoint the location of the target via wireless devices. According to how wireless devices interact with the target, wireless indoor localization schemes roughly fall into two categories: device based and device free. In device-based localization, a wireless device (e.g., a smartphone) is attached to the target and computes its location through cooperation with other deployed wireless devices. In device-free localization, the target carries no wireless devices, while the wireless infrastructure deployed in the environment determines the target’s location by analyzing its impact on wireless signals.
 This article is intended to offer a comprehensive state-of-the-art survey on wireless indoor localization from the device perspective. In this survey, we review the recent advances in both modes by elaborating on the underlying wireless modalities, basic localization principles, and data fusion techniques, with special emphasis on emerging trends in (1) leveraging smartphones to integrate wireless and sensor capabilities and extend to the social context for device-based localization, and (2) extracting specific wireless features to trigger novel human-centric device-free localization. We comprehensively compare each scheme in terms of accuracy, cost, scalability, and energy efficiency. Furthermore, we take a first look at intrinsic technical challenges in both categories and identify several open research issues associated with these new challenges.
Design Guidelines for Spatial Modulation

A new class of low-complexity, yet energy-efficient Multiple-Input Multiple-Output (MIMO) transmission techniques, namely, the family of Spatial Modulation (SM) aided MIMOs (SM-MIMO), has emerged. These systems are capable of exploiting the spatial dimensions (i.e., the antenna indices) as an additional dimension invoked for transmitting information, apart from the traditional Amplitude and Phase Modulation (APM). SM is capable of efficiently operating in diverse MIMO configurations in the context of future communication systems. It constitutes a promising transmission candidate for large-scale MIMO design and for the indoor optical wireless communication while relying on a single-Radio Frequency (RF) chain. Moreover, SM may be also viewed as an entirely new hybrid modulation scheme, which is still in its infancy. This paper aims for providing a general survey of the SM design framework as well as of its intrinsic limits. In particular, we focus our attention on the associated transceiver design, on spatial constellation optimization, on link adaptation techniques, on distributed/cooperative protocol design issues, and on their meritorious variants.
Small-size internal antenna for LTE/WWAN operation in the laptop computer

A new antenna structure of internal LTE/WWAN laptop computer antenna formed by a coupled-fed loop antenna connected with two branch radiators is presented. The two branch radiators consist of one longer strip and one shorter strip, both contributing multi-resonant modes to enhance the bandwidth of the antenna. The antenna's lower band is formed by a dual-resonant mode mainly contributed by the longer branch strip, while the upper band is formed by three resonant modes contributed respectively by one higher-order resonant mode of the longer branch strip, one resonant mode of the coupled-fed loop antenna alone, and one resonant mode of the shorter branch strip. The antenna's lower and upper bands can therefore cover the desired 698~960 and 1710~2690 MHz bands, respectively. The proposed antenna is suitable to be mounted at the top shielding metal wall of the display ground of the laptop computer and occupies a small volume of 4 × 10 × 75 mm3 above the top shielding metal wall, which makes it promising to be embedded inside the casing of the laptop computer as an internal antenna.
Wide Air Gap and Large-Scale Bearingless Segment Motor With Six Stator Elements

In recent years, the demand for high purity spinning processes has been growing in certain industry branches, such as the semiconductor, biotechnological, pharmaceutical, and chemical industry. Therefore, the cleanness specifications have been tightened, and hermetically sealed process chambers are preferred. This paper presents an advantageous solution for such an application featuring a large scale, wide air gap, and a high accelerating bearingless segment motor. Bearingless slice motors allow complete magnetic levitation in combination with a very compact and economic design. The disc-shaped rotor holds permanent magnets generating magnetic flux in the air gap. Hence, three degrees of freedom are passively stabilized by reluctance forces. Thus, only the radial rotor position and the rotor angle have to be controlled actively. The announced bearingless segment motor is a subtype of the bearingless slice motor, featuring separate independent stator elements. This leads to a reduction of stator iron, cost, and weight and, in addition, leaves space for sensors and electronics enabling a very compact system design.
The spacing effect depends on an encoding deficit, retrieval, and time in working memory: evidence from once-presented words.

The spacing effect in list learning occurs because identical massed items suffer encoding deficits and because spaced items benefit from retrieval and increased time in working memory. Requiring the retrieval of identical items produced a spacing effect for recall and recognition, both for intentional and incidental learning. Not requiring retrieval produced spacing only for intentional learning because intentional learning encourages retrieval. Once-presented words provided baselines for these effects. Next, massed and spaced word pairs were judged for matches on their first three letters, forcing retrieval. The words were not identical, so there was no encoding deficit. Retrieval could and did cause spacing only for the first word of each pair; time in working memory, only for the second.
Broadband Planar Transition to Dielectric Image Line by Substrate Truncated Microstrip Line for Millimeter-Wave Circuit Integration

In this letter, a broadband planar substrate truncated tapered microstrip line-to-dielectric image line transition on a single substrate is proposed. The design uses substrate truncated microstrip line which helps to minimize the losses due to the surface wave generation on thick microstrip line. Generalized empirical equations are proposed for the transition design and validated for different dielectric constants in millimeter-wave frequency band. Full-wave simulations are carried out using high frequency structural simulator. A back-to-back transition prototype of Ku-band is fabricated and measured. The measured return loss for 80-mm-long structure is better than 10 dB and the insertion loss is better than 2.5 dB in entire Ku-band (40% impedance bandwidth).
A Framework for Tag-Based Research Paper Recommender System: An IR Approach

The Internet and the World Wide Web provide a way to store and share information, especially in academic fields. Community-based research paper sharing systems, such as CiteULike, have become popular among researchers. This paper proposes a framework for a tag-based research paper recommender system. The proposed approach exploits the use of sets of tags for recommending research papers to each user. The preliminary evaluation shows that user self-defined tags could be used as a profile for each individual user. This recommender system demonstrated an encouraging preliminary result with the overall accuracy percentage up to 91.66%.
Three-level phase-shift ZVS-PWM DC-DC converter with high frequency transformer for high performance arc welding machines

This paper is concerned with some experimental results and practical evaluations of a three-level phase shifted ZVS-PWM DC-DC converter with neutral point clamping diodes and flying capacitor for a variety of gas metal arc welding machines. This new DC-DC converter suitable for high power applications is implemented by modifying the high-frequency-linked half bridge soft switching PWM DC-DC converter with two active edge resonant cells (AERCs) in high side and low side DC rails, which is previously-developed put into practice by the authors. The operating principle of the three-level phase-shift ZVS-PWM DC-DC converter and its experimental and simulation results including power regulation characteristics vs. phase-shifted angle and power conversion efficiency characteristics in addition to power loss analysis are illustrated and evaluated comparatively from a practical point of view, along with the remarkable advantageous features as compared with previously-developed one.
Learning Deep Latent Spaces for Multi-Label Classification

Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming at better relating feature and label domain data for improved classification, we uniquely perform joint feature and label embedding by deriving a deep latent space, followed by the introduction of label-correlation sensitive loss function for recovering the predicted label outputs. Our C2AE is achieved by integrating the DNN architectures of canonical correlation analysis and autoencoder, which allows end-to-end learning and prediction with the ability to exploit label dependency. Moreover, our C2AE can be easily extended to address the learning problem with missing labels. Our experiments on multiple datasets with different scales confirm the effectiveness and robustness of our proposed method, which is shown to perform favorably against state-of-the-art methods for multi-label classification.
Crowd Density Estimation Based on Local Binary Pattern Co-Occurrence Matrix

Crowd density estimation is important for intelligent video surveillance. Many methods based on texture features have been proposed to solve this problem. Most of the existing algorithms only estimate crowd density on the whole image while ignore crowd density in local region. In this paper, we propose a novel texture descriptor based on Local Binary Pattern (LBP) Co-occurrence Matrix (LBPCM) for crowd density estimation. LBPCM is constructed from several overlapping cells in an image block, which is going to be classified into different crowd density levels. LBPCM describes both the statistical properties and the spatial information of LBP and thus makes full use of LBP for local texture features. Additionally, we both extract LBPCM on gray and gradient images to improve the performance of crowd density estimation. Finally, the sliding window technique is used to detect the potential crowded area. The experimental results show the proposed method has better performance than other texture based crowd density estimation methods.