nils-herrmann/clean_scirepeval huggingface dataset:
Contextual information renders experts vulnerable to making erroneous identifications.

We investigated whether experts can objectively focus on feature information in fingerprints without being misled by extraneous information, such as context. We took fingerprints that have previously been examined and assessed by latent print experts to make positive identification of suspects. Then we presented these same fingerprints again, to the same experts, but gave a context that suggested that they were a no-match, and hence the suspects could not be identified. Within this new context, most of the fingerprint experts made different judgements, thus contradicting their own previous identification decisions. Cognitive aspects involved in biometric identification can explain why experts are vulnerable to make erroneous identifications.
Aggressive, Tense or Shy? Identifying Personality Traits from Crowd Videos

We present a real-time algorithm to automatically classify the dynamic behavior or personality of a pedestrian based on his or her movements in a crowd video. Our classification criterion is based on Personality Trait Theory. We present a statistical scheme that dynamically learns the behavior of every pedestrian in a scene and computes that pedestrian’s motion model. This model is combined with global crowd characteristics to compute the movement patterns and motion dynamics, which can also be used to predict the crowd movement and behavior. We highlight its performance in identifying the personalities of different pedestrians in lowand high-density crowd videos. We also evaluate the accuracy by comparing the results with a user study.
Cylinder Detection in Large-Scale Point Cloud of Pipeline Plant

The huge number of points scanned from pipeline plants make the plant reconstruction very difficult. Traditional cylinder detection methods cannot be applied directly due to the high computational complexity. In this paper, we explore the structural characteristics of point cloud in pipeline plants and define a structure feature. Based on the structure feature, we propose a hierarchical structure detection and decomposition method that reduces the difficult pipeline-plant reconstruction problem in R3 into a set of simple circle detection problems in R2. Experiments with industrial applications are presented, which demonstrate the efficiency of the proposed structure detection method.
Elevated Blood Lead Levels in Children Associated With the Flint Drinking Water Crisis: A Spatial Analysis of Risk and Public Health Response.

We analyzed differences in pediatric elevated blood lead level incidence before and after Flint, Michigan, introduced a more corrosive water source into an aging water system without adequate corrosion control.



We reviewed blood lead levels for children younger than 5 years before (2013) and after (2015) water source change in Greater Flint, Michigan. We assessed the percentage of elevated blood lead levels in both time periods, and identified geographical locations through spatial analysis.



Incidence of elevated blood lead levels increased from 2.4% to 4.9% (P < .05) after water source change, and neighborhoods with the highest water lead levels experienced a 6.6% increase. No significant change was seen outside the city. Geospatial analysis identified disadvantaged neighborhoods as having the greatest elevated blood lead level increases and informed response prioritization during the now-declared public health emergency.



The percentage of children with elevated blood lead levels increased after water source change, particularly in socioeconomically disadvantaged neighborhoods. Water is a growing source of childhood lead exposure because of aging infrastructure.
Tracking Benchmark Databases for Video-Based Sign Language Recognition

A survey of video databases that can be used within a continuous sign language recognition scenario to measure the performance of head and hand tracking algorithms either w.r.t. a tracking error rate or w.r.t. a word error rate criterion is presented in this work. Robust tracking algorithms are required as the signing hand frequently moves in front of the face, may temporarily disappear, or cross the other hand. Only few studies consider the recognition of continuous sign language, and usually special devices such as colored gloves or blue-boxing environments are used to accurately track the regions-of-interest in sign language processing. Ground-truth labels for hand and head positions have been annotated for more than 30k frames in several publicly available video databases of different degrees of difficulty, and preliminary tracking results are presented.
A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning

For safe and efficient planning and control in autonomous driving, we need a driving policy which can achieve desirable driving quality in long-term horizon with guaranteed safety and feasibility. Optimization-based approaches, such as Model Predictive Control (MPC), can provide such optimal policies, but their computational complexity is generally unacceptable for real-time implementation. To address this problem, we propose a fast integrated planning and control framework that combines learningand optimization-based approaches in a two-layer hierarchical structure. The first layer, defined as the policy layer, is established by a neural network which learns the long-term optimal driving policy generated by MPC. The second layer, called the execution layer, is a short-term optimization-based controller that tracks the reference trajecotries given by the policy layer with guaranteed short-term safety and feasibility. Moreover, with efficient and highly-representative features, a small-size neural network is sufficient in the policy layer to handle many complicated driving scenarios. This renders online imitation learning with Dataset Aggregation (DAgger) so that the performance of the policy layer can be improved rapidly and continuously online. Several exampled driving scenarios are demonstrated to verify the effectiveness and efficiency of the proposed framework.
Hierarchical Attribute-Based Access Control with Authentication for Outsourced Data in Cloud Computing

Access control is one of the most important security mechanisms in cloud computing. Attributed based encryption provides an approach that allows data owners to integrate data access policies within the encrypted data. However, little work has been done to explore flexible authorization in specifying the data user's privileges and enforcing the data owner's policy in cloud based environments. In this paper, we propose a hierarchical attribute based access control scheme by extending ciphertext-policy attribute-based encryption (CP-ABE) with a hierarchical structure of multiauthorities and exploiting attribute-based signature (ABS). The proposed scheme not only achieves scalability due to its hierarchical structure, but also inherits fine-grained access control with authentication in supporting write privilege on outsourced data in cloud computing. In addition, we decouple the task of policy management from security enforcement by using the extensible access control markup language (XACML) framework. Extensive analysis shows that our scheme is both efficient and scalable in dealing with access control for outsourced data in cloud computing.
Video game-based coordinative training improves ataxia in children with degenerative ataxia.

Degenerative ataxias in children present a rare condition where effective treatments are lacking. Intensive coordinative training based on physiotherapeutic exercises improves degenerative ataxia in adults, but such exercises have drawbacks for children, often including a lack of motivation for high-frequent physiotherapy. Recently developed whole-body controlled video game technology might present a novel treatment strategy for highly interactive and motivational coordinative training for children with degenerative ataxias.



We examined the effectiveness of an 8-week coordinative training for 10 children with progressive spinocerebellar ataxia. Training was based on 3 Microsoft Xbox Kinect video games particularly suitable to exercise whole-body coordination and dynamic balance. Training was started with a laboratory-based 2-week training phase and followed by 6 weeks training in children's home environment. Rater-blinded assessments were performed 2 weeks before laboratory-based training, immediately prior to and after the laboratory-based training period, as well as after home training. These assessments allowed for an intraindividual control design, where performance changes with and without training were compared.



Ataxia symptoms were significantly reduced (decrease in Scale for the Assessment and Rating of Ataxia score, p = 0.0078) and balance capacities improved (dynamic gait index, p = 0.04) after intervention. Quantitative movement analysis revealed improvements in gait (lateral sway: p = 0.01; step length variability: p = 0.01) and in goal-directed leg placement (p = 0.03).



Despite progressive cerebellar degeneration, children are able to improve motor performance by intensive coordination training. Directed training of whole-body controlled video games might present a highly motivational, cost-efficient, and home-based rehabilitation strategy to train dynamic balance and interaction with dynamic environments in a large variety of young-onset neurologic conditions.



This study provides Class III evidence that directed training with Xbox Kinect video games can improve several signs of ataxia in adolescents with progressive ataxia as measured by SARA score, Dynamic Gait Index, and Activity-specific Balance Confidence Scale at 8 weeks of training.
Named Entity Recognition in Bengali: A Conditional Random Field Approach

This paper reports about the development of a Named Entity Recognition (NER) system for Bengali using the statistical Conditional Random Fields (CRFs). The system makes use of the different contextual information of the words along with the variety of features that are helpful in predicting the various named entity (NE) classes. A portion of the partially NE tagged Bengali news corpus, developed from the archive of a leading Bengali newspaper available in the web, has been used to develop the system. The training set consists of 150K words and has been manually annotated with a NE tagset of seventeen tags. Experimental results of the 10-fold cross validation test show the effectiveness of the proposed CRF based NER system with an overall average Recall, Precision and F-Score values of 93.8%, 87.8% and 90.7%, respectively.
UGC Video Sharing: Measurement and Analysis

User-generated content (UGC) site has become a new killer Internet application in the recent four years. Among those popular sites, YouTube is the most representative and successful one providing a new generation of short video sharing service. Today, YouTube is a dominant provider of online video in the Internet, and is still growing fast. Understanding the features of YouTube and similar video sharing sites is thus crucial to their sustainable development and to network traffic engineering. We investigate the YouTube site from two perspectives, internal and external. Using traces crawled in a 1.5-year span, we systematic measure the characteristics of YouTube videos. We find that YouTube videos have noticeably different statistics compared to traditional streaming videos, ranging from length, access pattern, to their active life span. The series of datasets also allows us to identify the growth trend of this fast evolving Internet site in various aspects, which has seldom been explored before. We also look closely at the social networking aspect of YouTube, as this is a key driving force toward its success. In particular, we find that the links to related videos generated by uploaders’ choices form a small-world network. This suggests that the videos have strong correlations with each other, and creates opportunities for developing novel caching or peer-to-peer distribution schemes to efficiently deliver videos to end users. We also provide an in-depth study into the effects of the external links of YouTube. We collected nearly one million videos’ external link information, and traced different types of videos for more than two months. Our study shows interesting characteristics of external links of YouTube. In particular, we find that views from external links are independent from total views in each category. Also, videos benefit more from external links in the early stage. Our work can serve as a initial step for the study of the external environment.
Virtual TryOn Topics in Realistic , Individualized Dressing in Virtual Reality

In the course of the project Virtual Try-On new VR technologies have been developed, which form the basis for a realistic, three dimensional, (real-time) simulation and visualization of individualized garments put on by virtual counterparts of real customers. To provide this cloning and dressing of people in VR, a complete process chain is being build up starting with the touchless 3-dimensional scanning of the human body up to a photo-realistic 3-dimensional presentation of the virtual customer dressed in the chosen pieces of clothing. The emerging platform for interactive selection and configuration of virtual garments, the „virtual shop“, will be accessible in real fashion boutiques as well as over the internet, thereby supplementing the conventional distribution channels.
An Application of Zero-Inflated Poisson Regression for Software Fault Prediction

Poisson regression model is widely used in software quality modeling. W h e n the response variable of a data set includes a large number of zeros, Poisson regression model will underestimate the probability of zeros. A zero-inflated model changes the mean structure of the pure Poisson model. The predictive quality is therefore improved. I n this paper, we examine a full-scale industrial software system and develop two models, Poisson regression and zero-inflated Poisson regression. To our knowledge, this is the first study that introduces the zero-inflated Poisson regression model in software reliability. Comparing the predictive qualities of the two competing models, we conclude that for this system, the zero-inflated Poisson regression model is more appropriate in theory and practice.
Structured Control Nets for Deep Reinforcement Learning

In recent years, Deep Reinforcement Learning has made impressive advances in solving several important benchmark problems for sequential decision making. Many control applications use a generic multilayer perceptron (MLP) for non-vision parts of the policy network. In this work, we propose a new neural network architecture for the policy network representation that is simple yet effective. The proposed Structured Control Net (SCN) splits the generic MLP into two separate sub-modules: a nonlinear control module and a linear control module. Intuitively, the nonlinear control is for forward-looking and global control, while the linear control stabilizes the local dynamics around the residual of global control. We hypothesize that this will bring together the benefits of both linear and nonlinear policies: improve training sample efficiency, final episodic reward, and generalization of learned policy, while requiring a smaller network and being generally applicable to different training methods. We validated our hypothesis with competitive results on simulations from OpenAI MuJoCo, Roboschool, Atari, and a custom 2D urban driving environment, with various ablation and generalization tests, trained with multiple black-box and policy gradient training methods. The proposed architecture has the potential to improve upon broader control tasks by incorporating problem specific priors into the architecture. As a case study, we demonstrate much improved performance for locomotion tasks by emulating the biological central pattern generators (CPGs) as the nonlinear part of the architecture.
Compact band-notched ultra wideband printed monopole antenna

A simple and compact microstrip-fed ultra wideband (UWB) printed monopole antenna with band-notched performance is proposed in this paper. The antenna is composed of a cirque ring with a small strip bar, so that the antenna occupies about 8.29 GHz bandwidth covering 3.18-11.47 GHz with expected band rejection of 5.09 GHz to 5.88 GHz. A quasi-omnidirectional and quasi-symmetrical radiation pattern is also obtained. This kind of band-notched UWB antenna requires no external filters and thus greatly simplifies the system design of UWB wireless communication.
Image-based action recognition using hint-enhanced deep neural networks

While human action recognition from still images finds wide applications in computer vision, it remains a very challenging problem. Compared with videobased ones, image-based action representation and recognition are impossible to access the motion cues of action, which largely increases the difficulties in dealing with pose variances and cluttered backgrounds. Motivated by the recent success of convolutional neural networks (CNN) in learning discriminative features from objects in the presence of variations and backgrounds, in this paper, we investigate the potentials of CNN in image-based action recognition. A new action recognition method is proposed by implicitly integrating pose hints into the CNN framework, i.e., we use a CNN originally learned for object recognition as a base network and then transfer it to action recognition by training the base network jointly with inference of poses. Such a joint training scheme can guide the network towards pose inference and meanwhile prevent the unrelated knowledge inherited from the base network. For further performance improvement, the training data is augmented by enriching the pose-related samples. The experimental results on three benchmark datasets have demonstrated the effectiveness of our method.
Evaluation of a Simple Lamination Stacking Method for the Teeth of an Axial Flux Permanent-Magnet Synchronous Machine With Concentrated Stator Windings

In this paper, a simple lamination stacking method for the teeth of an axial flux permanent-magnet synchronous machine with concentrated stator windings is proposed. In this simple lamination stacking method, only two lamination profiles are used and are stacked alternately. To evaluate the performance of this stacking method, a comparison is made between the proposed method with two profiles and a conventional stacking method that uses different profiles for each lamination layer, using a multilayer 2-D finite element model.
Gain Enhancement for Broadband Vertical Planar Printed Antenna With H-Shaped Resonator Structures

A novel broadband high gain vertical planar printed antenna is presented. The proposed antenna is composed of a bowtie-shaped electric dipole, a loop antenna that works as a magnetic dipole, a microstrip-to-coplanar stripline transition balun, and the H-shaped resonator (HSR) structures that are used for gain enhancement, all of which are printed in the same plane perpendicular to the ground. Prototypes of the HSR-loaded antenna and the unloaded one were fabricated and measured. Analyses and comparisons of the proposed antenna are made. The HSR-loaded antenna has a wide impedance bandwidth of 51.9%, ranging from 2.31 GHz to 3.93 GHz and a stable gain of about 7.4-9.7 dBi over the whole operating band. Compared with the unloaded structure, a gain increment of about 0.5-6.0 dB over the whole band is obtained. The good electrical performances make the antenna suitable for the wireless communication systems and phased arrays.
Car detection in low resolution aerial images

We present a system to detect passenger cars in aerial images along the road directions where cars appear as small objects. We pose this as a 3D object recognition problem to account for the variation in viewpoint and the shadow. We started from psychological tests to find important features for human detection of cars. Based on these observations, we selected the boundary of the car body, the boundary of the front windshield, and the shadow as the features. Some of these features are affected by the intensity of the car and whether or not there is a shadow along it. This information is represented in the structure of the Bayesian network that we use to integrate all features. Experiments show very promising results even on some very challenging images.
Research on cloud storage architecture and key technologies

This paper proposes a general architecture of cloud storage system, analyzes the functions of the components, and discusses the key technologies, etc. Cloud storage is a novel storage service mode which the service providers supply storage capacities and data storage services through the Internet to the clients; meanwhile, the clients needn't know the details and lowered structures and mechanisms. The proposed architecture of cloud storage is layered and cooperative, and the discussed key technologies involve deployment, storage virtualization, data organization, migration, security, etc. The operation mechanism including ecology chain, game theory, ant colony optimization, data life cycle management, maintenance and update, convergence and evolution mechanisms are analyzed too. So an overall and new viewpoint to cloud storage system is illustrated.
A Hybrid Multigroup Coclustering Recommendation Framework Based on Information Fusion

Collaborative Filtering (CF) is one of the most successful algorithms in recommender systems. However, it suffers from data sparsity and scalability problems. Although many clustering techniques have been incorporated to alleviate these two problems, most of them fail to achieve further significant improvement in recommendation accuracy. First of all, most of them assume each user or item belongs to a single cluster. Since usually users can hold multiple interests and items may belong to multiple categories, it is more reasonable to assume that users and items can join multiple clusters (groups), where each cluster is a subset of like-minded users and items they prefer. Furthermore, most of the clustering-based CF models only utilize historical rating information in the clustering procedure but ignore other data resources in recommender systems such as the social connections of users and the correlations between items. In this article, we propose HMCoC, a Hybrid Multigroup CoClustering recommendation framework, which can cluster users and items into multiple groups simultaneously with different information resources. In our framework, we first integrate information of user--item rating records, user social networks, and item features extracted from the DBpedia knowledge base. We then use an optimization method to mine meaningful user--item groups with all the information. Finally, we apply the conventional CF method in each cluster to make predictions. By merging the predictions from each cluster, we generate the top-n recommendations to the target users for return. Extensive experimental results demonstrate the superior performance of our approach in top-n recommendation in terms of MAP, NDCG, and F1 compared with other clustering-based CF models.
A novel manufacturing defect detection method using association rule mining techniques

In recent years, manufacturing processes have become more and more complex, and meeting high-yield target expectations and quickly identifying root-cause machinesets, the most likely sources of defective products, also become essential issues. In this paper, we first define the root-cause machineset identification problem of analyzing correlations between combinations of machines and the defective products. We then propose the Root-cause Machine Identifier (RMI) method using the technique of association rule mining to solve the problem efficiently and effectively. The experimental results of real datasets show that the actual root-cause machinesets are almost ranked in the top 10 by the proposed RMI method.