nils-herrmann/clean_scirepeval huggingface dataset:
Low-Rank Matrix Completion by Riemannian Optimization

The matrix completion problem consists of finding or approximating a low-rank matrix based on a few samples of this matrix. We propose a novel algorithm for matrix completion that minimizes the least square distance on the sampling set over the Riemannian manifold of fixed-rank matrices. The algorithm is an adaptation of classical non-linear conjugate gradients, developed within the framework of retraction-based optimization on manifolds. We describe all the necessary objects from differential geometry necessary to perform optimization over this lowrank matrix manifold, seen as a submanifold embedded in the space of matrices. In particular, we describe how metric projection can be used as retraction and how vector transport lets us obtain the conjugate search directions. Additionally, we derive second-order models that can be used in Newton’s method based on approximating the exponential map on this manifold to second order. Finally, we prove convergence of a regularized version of our algorithm under the assumption that the restricted isometry property holds for incoherent matrices throughout the iterations. The numerical experiments indicate that our approach scales very well for large-scale problems and compares favorable with the state-of-the-art, while outperforming most existing solvers.
The role of emotion regulation in autism spectrum disorder.

Autism spectrum disorder (ASD) is associated with amplified emotional responses and poor emotional control, but little is known about the underlying mechanisms. This article provides a conceptual and methodologic framework for understanding compromised emotion regulation (ER) in ASD.



After defining ER and related constructs, methods to study ER were reviewed with special consideration on how to apply these approaches to ASD. Against the backdrop of cognitive characteristics in ASD and existing ER theories, available research was examined to identify likely contributors to emotional dysregulation in ASD.



Little is currently known about ER in youth with ASD. Some mechanisms that contribute to poor ER in ASD may be shared with other clinical populations (e.g., physiologic arousal, degree of negative and positive affect, alterations in the amygdala and prefrontal cortex), whereas other mechanisms may be more unique to ASD (e.g., differences in information processing/perception, cognitive factors [e.g., rigidity], less goal-directed behavior and more disorganized emotion in ASD).



Although assignment of concomitant psychiatric diagnoses is warranted in some cases, poor ER may be inherent in ASD and may provide a more parsimonious conceptualization for the many associated socioemotional and behavioral problems in this population. Further study of ER in youth with ASD may identify meaningful subgroups of patients and lead to more effective individualized treatments.
INFANTS ’ ABILITY TO DISTINGUISH BETWEEN PURPOSEFUL AND NON-PURPOSEFUL BEHAVIORS

Prior studies (Gergely et al., 1995; Woodward, 1998) have found that infants focus on the goals of an action over other details. The current studies tested whether infants would distinguish between a behavior that seemed to be goal-directed and one that seemed not to be. Infants in one condition saw an actor grasp one of two toys that sat side by side on a stage. Infants in the other condition saw the actor drop her hand onto one of the toys in a manner that looked unintentional. Once infants had been habituated to these events, they were shown test events in which either the path of motion or the object that was touched had changed. Nine-month-olds differentiated between these two actions. When they saw the actor grasp the toy, they looked longer on trials with a change in goal object than on trials with a change in path. When they saw the actor drop her hand onto the toy, they looked equally at the two test events. These findings did not result from infants being more interested in grasping as compared to inert hands. In a second study, 5-month-old infants showed patterns similar to those seen in 9-month-olds. These findings have implications for theories of the development of the concept of intention. They argue against the claim that infants are innately predisposed to interpret any motion of an animate agent as intentional.
Embedding Deep Metric for Person Re-identication A Study Against Large Variations

Person re-identification is challenging due to the large variations of pose, illumination, occlusion and camera view. Owing to these variations, the pedestrian data is distributed as highly-curved manifolds in the feature space, despite the current convolutional neural networks (CNN)’s capability of feature extraction. However, the distribution is unknown, so it is difficult to use the geodesic distance when comparing two samples. In practice, the current deep embedding methods use the Euclidean distance for the training and test. On the other hand, the manifold learning methods suggest to use the Euclidean distance in the local range, combining with the graphical relationship between samples, for approximating the geodesic distance. From this point of view, selecting suitable positive (i.e. intra-class) training samples within a local range is critical for training the CNN embedding, especially when the data has large intra-class variations. In this paper, we propose a novel moderate positive sample mining method to train robust CNN for person re-identification, dealing with the problem of large variation. In addition, we improve the learning by a metric weight constraint, so that the learned metric has a better generalization ability. Experiments show that these two strategies are effective in learning robust deep metrics for person re-identification, and accordingly our deep model significantly outperforms the state-of-the-art methods on several benchmarks of person re-identification. Therefore, the study presented in this paper may be useful in inspiring new designs of deep models for person re-identification.
A Fast MPEG Video Encryption Algorithm

Multimedia data secmity is important for multimedia commerce. Previous cryptography studies have focused on text data. The encryption algorithms devdoped to secure text data may not be suitable to multimedia applications becattse of large data sizes and real time constraint. For multimedia applications, light weight encryption algorithms are attractive. We present a novel MPEG Video Encryption Algorithm, called VEA The basic idea of VEA is to use a secret key randomly changing the sign bits of all of the DCT coefficients of MPEG video. VEA’S encryption effects are achieved by the IDCT during MPEG video decompression processing. VEA adds minimum overhead to MPEG codecj one Mm&e XOR operation to each none zero DCT coefficient. A software implementation of VEA is fast enough to meet the real time requirement of MPEG video applications. Our experimental results show that VEA achieves satisfying results. We believe that it can be used to secure video-on-demand, tideo conferencing and video email applications.
Texture indexes and gray level size zone matrix. Application to cell nuclei classification

In this paper, we present a study on the characterization and the classification of textures. This study is performed using a set of values obtained by the computation of indexes. To obtain these indexes, we extract a set of data with two techniques: the computation of matrices which are statistical representations of the texture and the computation of "measures". These matrices and measures are subsequently used as parameters of a function bringing real or discrete values which give information about texture features. A model of texture characterization is built based on this numerical information, for example to classify textures. An application is proposed to classify cells nuclei in order to diagnose patients affected by the Progeria disease.
People reidentification in surveillance and forensics: A survey

The field of surveillance and forensics research is currently shifting focus and is now showing an ever increasing interest in the task of people reidentification. This is the task of assigning the same identifier to all instances of a particular individual captured in a series of images or videos, even after the occurrence of significant gaps over time or space. People reidentification can be a useful tool for people analysis in security as a data association method for long-term tracking in surveillance. However, current identification techniques being utilized present many difficulties and shortcomings. For instance, they rely solely on the exploitation of visual cues such as color, texture, and the object’s shape. Despite the many advances in this field, reidentification is still an open problem. This survey aims to tackle all the issues and challenging aspects of people reidentification while simultaneously describing the previously proposed solutions for the encountered problems. This begins with the first attempts of holistic descriptors and progresses to the more recently adopted 2D and 3D model-based approaches. The survey also includes an exhaustive treatise of all the aspects of people reidentification, including available datasets, evaluation metrics, and benchmarking.
Multi-model semantic interaction for text analytics

Semantic interaction offers an intuitive communication mechanism between human users and complex statistical models. By shielding the users from manipulating model parameters, they focus instead on directly manipulating the spatialization, thus remaining in their cognitive zone. However, this technique is not inherently scalable past hundreds of text documents. To remedy this, we present the concept of multi-model semantic interaction, where semantic interactions can be used to steer multiple models at multiple levels of data scale, enabling users to tackle larger data problems. We also present an updated visualization pipeline model for generalized multi-model semantic interaction. To demonstrate multi-model semantic interaction, we introduce StarSPIRE, a visual text analytics prototype that transforms user interactions on documents into both small-scale display layout updates as well as large-scale relevancy-based document selection.
Mobile Banking Adoption : An Examination of Technology Acceptance Model and Theory of Planned Behavior

This study aims at extending our understanding regarding the adoption of mobile banking through integrating Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). Analyzing survey data from 119 respondents yielded important findings that partially support research hypotheses. The results indicated a significant positive impact of attitude toward mobile banking and subjective norm on mobile banking adoption. Surprisingly, the effects of behavioral control and usefulness on mobile banking adoption were insignificant. Furthermore, the regression results indicated a significant impact of perceived usefulness on attitude toward mobile banking while the effect of perceived ease of use on attitude toward mobile banking was not supported. The paper concludes with a discussion of research results and draws several implications for future research.
Low-Latency Encryption - Is "Lightweight = Light + Wait"?

The processing time required by a cryptographic primitive implemented in hardware is an important metric for its performance but it has not received much attention in recent publications on lightweight cryptography. Nevertheless, there are important applications for cost effective low-latency encryption. As the first step in the field, this paper explores the lowlatency behavior of hardware implementations of a set of block ciphers. The latency of the implementations is investigated as well as the trade-offs with other metrics such as circuit area, time-area product, power, and energy consumption. The obtained results are related back to the properties of the underlying cipher algorithm and, as it turns out, the number of rounds, their complexity, and the similarity of encryption and decryption procedures have a strong impact on the results. We provide a qualitative description and conclude with a set of recommendations for aspiring low-latency block cipher designers.
A novel motion control design approach based on active disturbance rejection

A new digital control solution to motion control problems is proposed. It is based on a unique active disturbance rejection concept, where the disturbances are estimated using an extended state observer (ESO) and compensated in each sampling period. The dynamic compensation reduces the motion system to approximately a double integrator which can be easily controlled using a nonlinear proportional-derivative controller. The proposed Active Disturbance-Rejection Controller (ADRC) consists of the ESO and the nonlinear PD controller and is designed without an explicit mathematical model of the plant. Hence the controller is inherently robust against plant variations. Through simulation, frequency response analysis, and hardware tests, it is shown that the proposed approach is superior to the current PID based technology. It stands out especially in handling set point change, large inertia and friction variations, and external torque disturbance, all of which is seen as “disturbance” by ADRC and is actively compensated. The improvement in transient response and steady state error is also quite evident.
Substrate Integrated Waveguide H-Plane Horn Antenna With Improved Front-to-Back Ratio and Reduced Sidelobe Level

A cost-effective way of reducing sidelobe level and improving front-to-back ratio of the substrate integrated waveguide H-plane horn antennas is proposed in this letter. Metal rectangular patches and dielectric loading are integrated to the aperture of the horn antenna, resulting in an increased gain, narrow E-plane beamwidth, and reduced sidelobes and backward radiation. The overall dimensions of the fabricated antenna are 42 × 18.6 mm2 . The antenna works at 22.7 GHz with a measured gain of 10.1 dBi. A single substrate and a 3.5-mm connector with a transition pin are used to ensure the lowest cost for mass production.
Midfield Wireless Powering for Implantable Systems

Efficient wireless power transfer across tissue is highly desirable for removing bulky energy storage components. Most existing power transfer systems are conceptually based on coils linked by slowly varying magnetic fields (less than 10 MHz). These systems have many important capabilities, but are poorly suited for tiny, millimeter-scale implants where extreme asymmetry between the source and the receiver results in weak coupling. This paper first surveys the analysis of near-field power transfer and associated strategies to optimize efficiency. It then reviews analytical models that show that significantly higher efficiencies can be obtained in the electromagnetic midfield. The performance limits of such systems are explored through optimization of the source, and a numerical example of a cardiac implant demonstrates that millimeter-sized devices are feasible.
Monoaural Audio Source Separation Using Deep Convolutional Neural Networks

In this paper we introduce a low-latency monaural source separation framework using a Convolutional Neural Network (CNN). We use a CNN to estimate time-frequency soft masks which are applied for source separation. We evaluate the performance of the neural network on a database comprising of musical mixtures of three instruments: voice, drums, bass as well as other instruments which vary from song to song. The proposed architecture is compared to a Multilayer Perceptron (MLP), achieving on-par results and a significant improvement in processing time. The algorithm was submitted to source separation evaluation campaigns to test efficiency, and achieved competitive results.
Toward a Justification of Meta-learning : Is the No Free Lunch Theorem a Showstopper ?

We present a preliminary analysis of the fundamental viability of meta-learning, revisiting the No Free Lunch (NFL) theorem. The analysis shows that given some simple and very basic assumptions, the NFL theorem is of little relevance to research in Machine Learning. We augment the basic NFL framework to illustrate that the notion of an Ultimate Learning Algorithm is well defined. We show that, although cross-validation still is not a viable way to construct general-purpose learning algorithms, meta-learning offers a natural alternative. We still have to pay for our lunch, but the cost is reasonable: the necessary fundamental assumptions are ones we all make anyway.
A bias correction function for classification performance assessment in two-class imbalanced problems

This paper introduces a framework that allows to mitigate the impact of class imbalance on most scalar performance measures when used to evaluate the behavior of classifiers. Formally, a correction function is defined with the aim of highlighting those classification results that present moderately higher prediction rates on the minority class. Besides, this function punishes those scenarios that are biased towards the majority class, but also those that are strongly biased to favor the minority class. This strategy assumes a typical imbalance task, in which the minority class contains the most relevant samples to the research purposes. A novel experimental framework is designed to show the advantages of our approach when compared to the standard use of well-established measures, demonstrating its consistency and validity. Most of traditional learning methods assume that the classes of the problem share similar prior probabilities and/or misclassifi-cation costs. However, in many real-world tasks the ratios of prior probabilities between classes are significantly skewed. This situation is typically known as the imbalance problem. A two-class data set is said to be imbalanced when one of the classes (the minority one) is heavily under-represented regarding the other class (the majority one) . Paradoxically, the minority class is often the most important and usually the one with the highest misclassifi-cation costs. Some typical real-life applications where this problem arises are prediction of microarray gene expression , prediction of corporate bankruptcy  and credit risk [4], fraud detection in mobile telephone communications  and text categorization . Because of examples of the minority and majority classes usually represent the presence and absence of rare cases respectively, they are also referred to as positive and negative examples. As pointed out by many authors [7–10], the use of plain accuracy and/or error rates to evaluate the performance of classifiers in imbal-anced domains might produce misleading conclusions, since they do not take misclassification costs into account, are strongly biased to favor the majority class, and are non-sensitive to class skews. A plethora of alternative scalar and graphical methods have been proposed to properly assess classification performance on imbalanced scenarios. Graphical approaches depict trade-offs between two or more evaluation perspectives, allowing a richer analysis of results but making the comparison of learning algorithms a non-trivial issue. Some well-studied examples are the Receiver Operating Characteristic (ROC) curve [11,12], the Precision–Recall (P–R) curve [13], cost curves  and the Bayesian Receiver Operating Characteristic (B-ROC) curve …
A Link-Based Approach to the Cluster Ensemble Problem

Cluster ensembles have recently emerged as a powerful alternative to standard cluster analysis, aggregating several input data clusterings to generate a single output clustering, with improved robustness and stability. From the early work, these techniques held great promise; however, most of them generate the final solution based on incomplete information of a cluster ensemble. The underlying ensemble-information matrix reflects only cluster-data point relations, while those among clusters are generally overlooked. This paper presents a new link-based approach to improve the conventional matrix. It achieves this using the similarity between clusters that are estimated from a link network model of the ensemble. In particular, three new link-based algorithms are proposed for the underlying similarity assessment. The final clustering result is generated from the refined matrix using two different consensus functions of feature-based and graph-based partitioning. This approach is the first to address and explicitly employ the relationship between input partitions, which has not been emphasized by recent studies of matrix refinement. The effectiveness of the link-based approach is empirically demonstrated over 10 data sets (synthetic and real) and three benchmark evaluation measures. The results suggest the new approach is able to efficiently extract information embedded in the input clusterings, and regularly illustrate higher clustering quality in comparison to several state-of-the-art techniques.
Quantifying Political Leaning from Tweets, Retweets, and Retweeters

The widespread use of online social networks (OSNs) to disseminate information and exchange opinions, by the general public, news media, and political actors alike, has enabled new avenues of research in computational political science. In this paper, we study the problem of quantifying and inferring the political leaning of Twitter users. We formulate political leaning inference as a convex optimization problem that incorporates two ideas: (a) users are consistent in their actions of tweeting and retweeting about political issues, and (b) similar users tend to be retweeted by similar audience. We then apply our inference technique to 119 million election-related tweets collected in seven months during the 2012 U.S. presidential election campaign. On a set of frequently retweeted sources, our technique achieves 94 percent accuracy and high rank correlation as compared with manually created labels. By studying the political leaning of 1,000 frequently retweeted sources, 232,000 ordinary users who retweeted them, and the hashtags used by these sources, our quantitative study sheds light on the political demographics of the Twitter population, and the temporal dynamics of political polarization as events unfold.
Fast Group Recommendations by Applying User Clustering

Recommendation systems have received significant attention, with most of the proposed methods focusing on personal recommendations. However, there are contexts in which the items to be suggested are not intended for a single user but for a group of people. For example, assume a group of friends or a family that is planning to watch a movie or visit a restaurant. In this paper, we propose an extensive model for group recommendations that exploits recommendations for items that similar users to the group members liked in the past. We do not exhaustively search for similar users in the whole user base, but we pre-partition users into clusters of similar ones and use the cluster members for recommendations. We efficiently aggregate the single user recommendations into group recommendations by leveraging the power of a top-k algorithm. We evaluate our approach in a real dataset of movie ratings.