nils-herrmann/clean_scirepeval huggingface dataset:
Automated robot-assisted surgical skill evaluation: Predictive analytics approach.

Surgical skill assessment has predominantly been a subjective task. Recently, technological advances such as robot-assisted surgery have created great opportunities for objective surgical evaluation. In this paper, we introduce a predictive framework for objective skill assessment based on movement trajectory data. Our aim is to build a classification framework to automatically evaluate the performance of surgeons with different levels of expertise.



Eight global movement features are extracted from movement trajectory data captured by a da Vinci robot for surgeons with two levels of expertise - novice and expert. Three classification methods - k-nearest neighbours, logistic regression and support vector machines - are applied.



The result shows that the proposed framework can classify surgeons' expertise as novice or expert with an accuracy of 82.3% for knot tying and 89.9% for a suturing task.



This study demonstrates and evaluates the ability of machine learning methods to automatically classify expert and novice surgeons using global movement features.
Image Inpainting using Block-wise Procedural Training with Annealed Adversarial Counterpart

Recent advances in deep generative models have shown promising potential in image inpanting, which refers to the task of predicting missing pixel values of an incomplete image using the known context. However, existing methods can be slow or generate unsatisfying results with easily detectable flaws. In addition, there is often perceivable discontinuity near the holes and require further post-processing to blend the results. We present a new approach to address the difficulty of training a very deep generative model to synthesize high-quality photo-realistic inpainting. Our model uses conditional generative adversarial networks (conditional GANs) as the backbone, and we introduce a novel block-wise procedural training scheme to stabilize the training while we increase the network depth. We also propose a new strategy called adversarial loss annealing to reduce the artifacts. We further describe several losses specifically designed for inpainting and show their effectiveness. Extensive experiments and user-study show that our approach outperforms existing methods in several tasks such as inpainting, face completion and image harmonization. Finally, we show our framework can be easily used as a tool for interactive guided inpainting, demonstrating its practical value to solve common real-world challenges.
Cascaded Regressor based 3D Face Reconstruction from a Single Arbitrary View Image

State-of-the-art methods reconstruct three-dimensional (3D) face shapes from a single image by fitting 3D face models to input images or by directly learning mapping functions between two-dimensional (2D) images and 3D faces. However, they are often difficult to use in real-world applications due to expensive online optimization or to the requirement of frontal face images. This paper approaches the 3D face reconstruction problem as a regression problem rather than a model fitting problem. Given an input face image along with some pre-defined facial landmarks on it, a series of shape adjustments to the initial 3D face shape are computed through cascaded regressors based on the deviations between the input landmarks and the landmarks obtained from the reconstructed 3D faces. The cascaded regressors are offline learned from a set of 3D faces and their corresponding 2D face images in various views. By treating the landmarks that are invisible in large view angles as missing data, the proposed method can handle arbitrary view face images in a unified way with the same regressors. Experiments on the BFM and Bosphorus databases demonstrate that the proposed method can reconstruct 3D faces from arbitrary view images more efficiently and more accurately than existing methods.
FAMPER: A fully autonomous mobile robot for pipeline exploration

Pipeline-based applications have become an integral part of life. However, knowing that the pipeline systems can be largely deployed in an inaccessible and hazardous environment, active monitoring and frequent inspection of the pipeline systems are highly expensive using the traditional maintenance systems. Robot agents have been considered as an attractive alternative. Although many different types of pipeline exploration robots have been proposed, they were suffered from various limitations. In this paper, we present the design and implementation of a single-moduled fully autonomous mobile pipeline exploration robot, called FAMPER, that can be used for the inspection of 150mm pipelines. This robot consists of four wall-press caterpillars operated by two DC motors each. The speed of each caterpillar is controlled independently to provide steering capability to go through 45 degree elbows, 90 degree elbows, T-branches, and Y-branches. The uniqueness of this paper is to show the opportunity of using 4 caterpillar configuration for superior performance in all types of complex networks of pipelines. The robot system has been developed and experimented in different pipeline layouts.
A Review of Imaging Techniques for Plant Phenotyping

Given the rapid development of plant genomic technologies, a lack of access to plant phenotyping capabilities limits our ability to dissect the genetics of quantitative traits. Effective, high-throughput phenotyping platforms have recently been developed to solve this problem. In high-throughput phenotyping platforms, a variety of imaging methodologies are being used to collect data for quantitative studies of complex traits related to the growth, yield and adaptation to biotic or abiotic stress (disease, insects, drought and salinity). These imaging techniques include visible imaging (machine vision), imaging spectroscopy (multispectral and hyperspectral remote sensing), thermal infrared imaging, fluorescence imaging, 3D imaging and tomographic imaging (MRT, PET and CT). This paper presents a brief review on these imaging techniques and their applications in plant phenotyping. The features used to apply these imaging techniques to plant phenotyping are described and discussed in this review.
The effects of social media based brand communities on brand community markers, value creation practices, brand trust and brand loyalty

Social media based brand communities are communities initiated on the platform of social media. In this article, we explore whether brand communities based on social media (a special type of online brand communities) have positive effects on the main community elements and value creation practices in the communities as well as on brand trust and brand loyalty. A survey based empirical study with 441 respondents was conducted. The results of structural equation modeling show that brand communities established on social media have positive effects on community markers (i.e., shared consciousness, shared rituals and traditions, and obligations to society), which have positive effects on value creation practices (i.e., social networking, community engagement, impressions management, and brand use). Such communities could enhance brand loyalty through brand use and impression management practices. We show that brand trust has a full mediating role in converting value creation practices into brand loyalty. Implications for practice and future research opportunities are discussed.
A 10-week randomized trial comparing eccentric vs. concentric hamstring strength training in well-trained soccer players.

To compare the effects of a 10-week training program with two different exercises -- traditional hamstring curl (HC) and Nordic hamstrings (NH), a partner exercise focusing the eccentric phase -- on muscle strength among male soccer players.



Subjects were 21 well-trained players who were randomized to NH training (n = 11) or HC training (n = 10). The programs were similar, with a gradual increase in the number of repetitions from two sets of six reps to three sets of eight to 12 reps over 4 weeks, and then increasing load during the final 6 weeks of training. Strength was measured as maximal torque on a Cybex dynamometer before and after the training period.



In the NH group, there was an 11% increase in eccentric hamstring torque measured at 60 degrees s(-1), as well as a 7% increase in isometric hamstring strength at 90 degrees, 60 degrees and 30 degrees of knee flexion. Since there was no effect on concentric quadriceps strength, there was a significant increase in the hamstrings:quadriceps ratio from 0.89 +/- 0.12 to 0.98 +/- 0.17 (11%) in the NH group. No changes were observed in the HC group.



NH training for 10 weeks more effectively develops maximal eccentric hamstring strength in well-trained soccer players than a comparable program based on traditional HC.
Prediction and analysis of Pakistan election 2013 based on sentiment analysis

The significance of social media has already been proven in provoking transformation of public opinion for developed countries in improving democratic process of elections. On the contrary, developing countries lacking basic necessities of life possess monopolistic electoral system in which candidates are elected based on tribes, family backgrounds, or landlord influences. They extort voters to cast votes against their promises for the provision of basic needs. Similarly voters also poll votes for personal interests being unaware of party manifesto or national interest. These issues can be addressed by social media, resulting as ongoing process of improvement for presently adopted electoral procedures. People of Pakistan utilized social media to garner support and campaign for political parties in General Elections 2013. Political leaders, parties, and people of Pakistan disseminated party's agenda and advocacy of party's ideology on Twitter without much campaigning cost. To study effectiveness of social media inferred from individual's political behavior, large scale analysis, sentiment detection & tweet classification was done in order to classify, predict and forecast election results. The experimental results depicts that social media content can be used as an effective indicator for capturing political behaviors of different parties Positive, negative and neutral behavior of the party followers as well as party's campaign impact can be predicted from the analysis. The analytical findings proved to be having considerable correspondence with actual results as published by Election Commission of Pakistan..
Technology roadmapping in review : A tool for making sustainable new product development decisions

Typically, firms decide whether or not to develop a new product based on their resources, capabilities and the return on investment that the product is estimated to generate. We propose that firms adopt a broader heuristic for making new product development choices. Our heuristic approach requires moving beyond traditional finance-based thinking, and suggests that firms concentrate on technological trajectories by combining technology roadmapping, information technology (IT) and supply chain management to make more sustainable new product development decisions. Using the proposed holistic heuristic methods, versus relying on traditional finance-based decision-making tools (e.g., emphasizing net present value or internal rate of return projections), enables firms to plan beyond the short-term and immediate set of technologies at hand. Our proposed heuristic approach enables firms to forecast technologies and markets, and hence, new product priorities in the longer term. Investments in new products should, as a result, generate returns over a longer period than traditionally expected, giving firms more sustainable investments. New products are costly and need to have a 0040-1625/$ – see front matter D 2003 Elsevier Inc. All rights reserved. doi:10.1016/S0040-1625(03)00064-7 * Corresponding author.  E-mail addresses: ijpetrick@psu.edu (I.J. Petrick), aie1@psu.edu (A.E. Echols). 1 Tel.: +1-814-863-0642. I.J. Petrick, A.E. Echols / Technological Forecasting & Social Change 71 (2004) 81–100 82 durable presence in the market. Transaction costs and resources will be saved, as firms make new product development decisions less frequently. D 2003 Elsevier Inc. All rights reserved.
A White-Box DES Implementation for DRM Applications

For digital rights management (drm) software implementations incorporating cryptography, white-box cryptography (cryptographic implementation designed to withstand the white-box attack context) is more appropriate than traditional black-box cryptography. In the whitebox context, the attacker has total visibility into software implementation and execution. Our objective is to prevent extraction of secret keys from the program. We present methods to make such key extraction difficult, with focus on symmetric block ciphers implemented by substitution boxes and linear transformations. A des implementation (useful also for triple-des) is presented as a concrete example.
Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts

Sentiment analysis of short texts is challenging because of the limited contextual information they usually contain. In recent years, deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been applied to text sentiment analysis with comparatively remarkable results. In this paper, we describe a jointed CNN and RNN architecture, taking advantage of the coarse-grained local features generated by CNN and long-distance dependencies learned via RNN for sentiment analysis of short texts. Experimental results show an obvious improvement upon the state-of-the-art on three benchmark corpora, MR, SST1 and SST2, with 82.28%, 51.50% and 89.95% accuracy, respectively.
Minimum-Current-Stress Scheme of Dual Active Bridge DC–DC Converter With Unified Phase-Shift Control

To reduce current stress and improve efficiency of dual active bridge (DAB) dc-dc converters, various control schemes have been proposed in recent decades. Most control schemes for directly minimizing power losses from power loss modeling analysis and optimization aspect of the adopted converter are too difficult and complicated to implement in real-time digital microcontrollers. Thus, this paper focuses on a simple solution to reduce current stress and improve the efficiency of the adopted DAB converter. However, traditional current-stress-optimized (CSO) schemes have some drawbacks, such as inductance dependency and an additional load-current sensor. In this paper, a simple CSO scheme with a unified phase-shift (UPS) control, which can be equivalent to the existing conventional phase-shift controls, is proposed for DAB dc-dc converters to realize current stress optimization. The simple CSO scheme can overcome those drawbacks of traditional CSO schemes, gain the minimum current stress, and improve efficiency. Then, a comparison of single-phase-shift (SPS) control, simple CSO scheme with dual-phase-shift (CSO-DPS) control, simple CSO scheme with extended-phase-shift (CSO-EPS) control, and simple CSO scheme with UPS (CSO-UPS) control is analyzed in detail. Finally, experimental results verify the excellent performance of the proposed CSO-UPS control scheme and the correctness of theoretical analysis.
ZVS Soft Switching Boundaries for Dual Active Bridge DC–DC Converters Using Frequency Domain Analysis

Dual active bridge (DAB) converters offer an unmatched capability to transfer energy in either direction between two dc sources, while also providing galvanic isolation and high conversion efficiency. However, to operate at higher efficiencies, the bridges must operate with zero voltage switching (ZVS) over as wide an operating range as possible. The conventional approach to determine ZVS operation uses time domain analysis with ideal ac coupling inductances, which only approximately identifies the ZVS boundaries. This paper proposes a new approach using harmonic decomposition of the bridge switching patterns, which gives an explicit theoretical solution under all operating conditions, while also accommodating more complex ac coupling structures, practical impedance nonidealities, and the switching impact of dead-time and device capacitance. The methodology is confirmed by matching analytical predictions with experimental results for selected DAB systems.
Fusion and binarization of CNN features for robust topological localization across seasons

The extreme variability in the appearance of a place across the four seasons of the year is one of the most challenging problems in life-long visual topological localization for mobile robotic systems and intelligent vehicles. Traditional solutions to this problem are based on the description of images using hand-crafted features, which have been shown to offer moderate invariance against seasonal changes. In this paper, we present a new proposal focused on automatically learned descriptors, which are processed by means of a technique recently popularized in the computer vision community: Convolutional Neural Networks (CNNs). The novelty of our approach relies on fusing the image information from multiple convolutional layers at several levels and granularities. In addition, we compress the redundant data of CNN features into a tractable number of bits for efficient and robust place recognition. The final descriptor is reduced by applying simple compression and binarization techniques for fast matching using the Hamming distance. An exhaustive experimental evaluation confirms the improved performance of our proposal (CNN-VTL) with respect to state-of-the-art methods over varied long-term datasets recorded across seasons.
FaceDate: a mobile cloud computing app for people matching

is paper presents FaceDate, a novel mobile app that matches persons based on their facial looks. Each FaceDate user uploads their prole face photo and trains the app with photos of faces they like. Upon user request, FaceDate detects other users located in the proximity of the requester and performs face matching in real-time. If a mutual match is found, the two users are notied and given the option to start communicating. FaceDate is implemented over our Moitree middleware for mobile distributed computing assisted by the cloud. e app is designed to scale with the number of users, as face recognition is done in parallel at dierent users. FaceDate can be congured for (i) higher performance, in which case the face recognition is done in the cloud or (ii) higher privacy, in which case the face recognition is done on the mobiles. e experimental results with Android-based phones demonstrate that FaceDate achieves promising performance.
Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks

Early detection of the motor faults is essential and artificial neural networks are widely used for this purpose. The typical systems usually encapsulate two distinct blocks: feature extraction and classification. Such fixed and hand-crafted features may be a suboptimal choice and require a significant computational cost that will prevent their usage for real-time applications. In this paper, we propose a fast and accurate motor condition monitoring and early fault-detection system using 1-D convolutional neural networks that has an inherent adaptive design to fuse the feature extraction and classification phases of the motor fault detection into a single learning body. The proposed approach is directly applicable to the raw data (signal), and, thus, eliminates the need for a separate feature extraction algorithm resulting in more efficient systems in terms of both speed and hardware. Experimental results obtained using real motor data demonstrate the effectiveness of the proposed method for real-time motor condition monitoring.
Dynamics of Multiple-Seller and Multiple-Buyer Spectrum Trading in Cognitive Radio Networks: A Game-Theoretic Modeling Approach

We consider the problem of spectrum trading with multiple licensed users (i.e., primary users) selling spectrum opportunities to multiple unlicensed users (i.e., secondary users). The secondary users can adapt the spectrum buying behavior (i.e., evolve) by observing the variations in price and quality of spectrum offered by the different primary users or primary service providers. The primary users or primary service providers can adjust their behavior in selling the spectrum opportunities to secondary users to achieve the highest utility. In this paper, we model the evolution and the dynamic behavior of secondary users using the theory of evolutionary game. An algorithm for the implementation of the evolution process of a secondary user is also presented. To model the competition among the primary users, a noncooperative game is formulated where the Nash equilibrium is considered as the solution (in terms of size of offered spectrum to the secondary users and spectrum price). For a primary user, an iterative algorithm for strategy adaptation to achieve the solution is presented. The proposed game-theoretic framework for modeling the interactions among multiple primary users (or service providers) and multiple secondary users is used to investigate network dynamics under different system parameter settings and under system perturbation.
3-Sweep: extracting editable objects from a single photo

We introduce an interactive technique for manipulating simple 3D shapes based on extracting them from a single photograph. Such extraction requires understanding of the components of the shape, their projections, and relations. These simple cognitive tasks for humans are particularly difficult for automatic algorithms. Thus, our approach combines the cognitive abilities of humans with the computational accuracy of the machine to solve this problem. Our technique provides the user the means to quickly create editable 3D parts---human assistance implicitly segments a complex object into its components, and positions them in space. In our interface, three strokes are used to generate a 3D component that snaps to the shape's outline in the photograph, where each stroke defines one dimension of the component. The computer reshapes the component to fit the image of the object in the photograph as well as to satisfy various inferred geometric constraints imposed by its global 3D structure. We show that with this intelligent interactive modeling tool, the daunting task of object extraction is made simple. Once the 3D object has been extracted, it can be quickly edited and placed back into photos or 3D scenes, permitting object-driven photo editing tasks which are impossible to perform in image-space. We show several examples and present a user study illustrating the usefulness of our technique.
Bayesian Face Revisited: A Joint Formulation

In this paper, we revisit the classical Bayesian face recognition method by Baback Moghaddam et al. and propose a new joint formulation. The classical Bayesian method models the appearance difference between two faces. We observe that this “difference” formulation may reduce the separability between classes. Instead, we model two faces jointly with an appropriate prior on the face representation. Our joint formulation leads to an EM-like model learning at the training time and an efficient, closed-formed computation at the test time. On extensive experimental evaluations, our method is superior to the classical Bayesian face and many other supervised approaches. Our method achieved 92.4% test accuracy on the challenging Labeled Face in Wild (LFW) dataset. Comparing with current best commercial system, we reduced the error rate by 10%.