nils-herrmann/clean_scirepeval huggingface dataset:
The Berlin brain-computer interface: EEG-based communication without subject training

The Berlin Brain-Computer Interface (BBCI) project develops a noninvasive BCI system whose key features are 1) the use of well-established motor competences as control paradigms, 2) high-dimensional features from 128-channel electroencephalogram (EEG), and 3) advanced machine learning techniques. As reported earlier, our experiments demonstrate that very high information transfer rates can be achieved using the readiness potential (RP) when predicting the laterality of upcoming left- versus right-hand movements in healthy subjects. A more recent study showed that the RP similarly accompanies phantom movements in arm amputees, but the signal strength decreases with longer loss of the limb. In a complementary approach, oscillatory features are used to discriminate imagined movements (left hand versus right hand versus foot). In a recent feedback study with six healthy subjects with no or very little experience with BCI control, three subjects achieved an information transfer rate above 35 bits per minute (bpm), and further two subjects above 24 and 15 bpm, while one subject could not achieve any BCI control. These results are encouraging for an EEG-based BCI system in untrained subjects that is independent of peripheral nervous system activity and does not rely on evoked potentials even when compared to results with very well-trained subjects operating other BCI systems.
Articulated shape matching using Laplacian eigenfunctions and unsupervised point registration

Matching articulated shapes represented by voxel-sets reduces to maximal sub-graph isomorphism when each set is described by a weighted graph. Spectral graph theory can be used to map these graphs onto lower dimensional spaces and match shapes by aligning their embeddings in virtue of their invariance to change of pose. Classical graph isomorphism schemes relying on the ordering of the eigenvalues to align the eigenspaces fail when handling large data-sets or noisy data. We derive a new formulation that finds the best alignment between two congruent K-dimensional sets of points by selecting the best subset of eigenfunctions of the Laplacian matrix. The selection is done by matching eigenfunction signatures built with histograms, and the retained set provides a smart initialization for the alignment problem with a considerable impact on the overall performance. Dense shape matching casted into graph matching reduces then, to point registration of embeddings under orthogonal transformations; the registration is solved using the framework of unsupervised clustering and the EM algorithm. Maximal subset matching of non identical shapes is handled by defining an appropriate outlier class. Experimental results on challenging examples show how the algorithm naturally treats changes of topology, shape variations and different sampling densities.
Mining Contracts for Business Events and Temporal Constraints in Service Engagements

Contracts are legally binding descriptions of business service engagements. In particular, we consider business events as elements of a service engagement. Business events such as purchase, delivery, bill payment, and bank interest accrual not only correspond to essential processes but are also inherently temporally constrained. Identifying and understanding the events and their temporal relationships can help a business partner determine what to deliver and what to expect from others as it participates in the service engagement specified by a contract. However, contracts are expressed in unstructured text and their insights are buried therein. Our contributions are threefold. We develop a novel approach employing a hybrid of surface patterns, parsing, and classification to extract 1) business events and 2) their temporal constraints from contract text. We use topic modeling to 3) automatically organize the event terms into clusters. An evaluation on a real-life contract dataset demonstrates the viability and promise of our hybrid approach, yielding an F-measure of 0.89 in event extraction and 0.90 in temporal constraints extraction. The topic model yields event term clusters with an average match of 85 percent between two independent human annotations and an expert-assigned set of class labels for the clusters.
A Local/Global Approach to Mesh Parameterization

We present a novel approach to parameterize a mesh with disk topology to the plane in a shape-preserving manner. Our key contribution is a local/global algorithm, which combines a local mapping of each 3D triangle to the plane, using transformations taken from a restricted set, with a global "stitch" operation of all triangles, involving a sparse linear system. The local transformations can be taken from a variety of families, e.g. similarities or rotations, generating different types of parameterizations. In the first case, the parameterization tries to force each 2D triangle to be an as-similar-as-possible version of its 3D counterpart. This is shown to yield results identical to those of the LSCM algorithm. In the second case, the parameterization tries to force each 2D triangle to be an as-rigid-as-possible version of its 3D counterpart. This approach preserves shape as much as possible. It is simple, effective, and fast, due to pre-factoring of the linear system involved in the global phase. Experimental results show that our approach provides almost isometric parameterizations and obtains more shape-preserving results than other state-of-the-art approaches. We present also a more general "hybrid" parameterization model which provides a continuous spectrum of possibilities, controlled by a single parameter. The two cases described above lie at the two ends of the spectrum. We generalize our local/global algorithm to compute these parameterizations. The local phase may also be accelerated by parallelizing the independent computations per triangle.
Learning-Based View Synthesis for Light Field Cameras

With the introduction of consumer light field cameras, light field imaging has recently become widespread. However, there is an inherent trade-off between the angular and spatial resolution, and thus, these cameras often sparsely sample in either spatial or angular domain. In this paper, we use machine learning to mitigate this trade-off. Specifically, we propose a novel learning-based approach to synthesize new views from a sparse set of input views. We build upon existing view synthesis techniques and break down the process into disparity and color estimation components. We use two sequential convolutional neural networks to model these two components and train both networks simultaneously by minimizing the error between the synthesized and ground truth images. We show the performance of our approach using only four corner sub-aperture views from the light fields captured by the Lytro Illum camera. Experimental results show that our approach synthesizes high-quality images that are superior to the state-of-the-art techniques on a variety of challenging real-world scenes. We believe our method could potentially decrease the required angular resolution of consumer light field cameras, which allows their spatial resolution to increase.
Velocity kinematics based control of rocker-bogie type planetary rover

Kinematics of space rovers has been studied by several past researchers. However most studies assume that all the wheels of the rover is always in contact with the terrain. As the lunar terrain is very uneven and contains ash, dust, rocks, etc this assumption need not always be valid. Hence in this study we have analyzed the kinematics of a rover in which one wheel can loose contact with the ground or slide. The model considered in this paper is a bicycle model of the rover with an assumption that the yaw angle would be negligible and thus the results give us a fair insight of 3-D modeling of rovers. A quasi static analysis of the robot was used to develop a slip control chart that guides the amount of slip expected while traversing different terrains.
Adaptive Batch Mode Active Learning

Active learning techniques have gained popularity to reduce human effort in labeling data instances for inducing a classifier. When faced with large amounts of unlabeled data, such algorithms automatically identify the exemplar and representative instances to be selected for manual annotation. More recently, there have been attempts toward a batch mode form of active learning, where a batch of data points is simultaneously selected from an unlabeled set. Real-world applications require adaptive approaches for batch selection in active learning, depending on the complexity of the data stream in question. However, the existing work in this field has primarily focused on static or heuristic batch size selection. In this paper, we propose two novel optimization-based frameworks for adaptive batch mode active learning (BMAL), where the batch size as well as the selection criteria are combined in a single formulation. We exploit gradient-descent-based optimization strategies as well as properties of submodular functions to derive the adaptive BMAL algorithms. The solution procedures have the same computational complexity as existing state-of-the-art static BMAL techniques. Our empirical results on the widely used VidTIMIT and the mobile biometric (MOBIO) data sets portray the efficacy of the proposed frameworks and also certify the potential of these approaches in being used for real-world biometric recognition applications.
An Image Preprocessing Algorithm for Illumination Invariant Face Recognition

Face recognition algorithms have to deal with significant amounts of illumination variations between gallery and probe images. State-of-the-art commercial face recognition algorithms still struggle with this problem. We propose a new image preprocessing algorithm that compensates for illumination variations in images. From a single brightness image the algorithm first estimates the illumination field and then compensates for it to mostly recover the scene reflectance. Unlike previously proposed approaches for illumination compensation, our algorithm does not require any training steps, knowledge of 3D face models or reflective surface models. We apply the algorithm to face images prior to recognition. We demonstrate large performance improvements with several standard face recognition algorithms across multiple, publicly available face databases.
An introduction to Docker for reproducible research

As computational work becomes more and more integral to many aspects of scientific research, computational reproducibility has become an issue of increasing importance to computer systems researchers and domain scientists alike. Though computational reproducibility seems more straight forward than replicating physical experiments, the complex and rapidly changing nature of computer environments makes being able to reproduce and extend such work a serious challenge. In this paper, I explore common reasons that code developed for one research project cannot be successfully executed or extended by subsequent researchers. I review current approaches to these issues, including virtual machines and workflow systems, and their limitations. I then examine how the popular emerging technology Docker combines several areas from systems research - such as operating system virtualization, cross-platform portability, modular re-usable elements, versioning, and a 'DevOps' philosophy, to address these challenges. I illustrate this with several examples of Docker use with a focus on the R statistical environment.
Seasonal subsidence and rebound in Las Vegas Valley , Nevada , observed by synthetic aperture radar interferometry

Analyses of areal variations in the subsidence and rebound occurring over stressed aquifer systems, in conjunction with measurements of the hydraulic head fluctuations causing these displacements, can yield valuable information about the compressibility and storage properties of the aquifer system. Historically, stress-strain relationships have been derived from paired extensometer/piezometer installations, which provide only point source data. Because of the general unavailability of spatially detailed deformation data, areal stress-strain relations and their variability are not commonly considered in constraining conceptual and numerical models of aquifer systems. Interferometric synthetic aperture radar (InSAR) techniques can map ground displacements at a spatial scale of tens of meters over 100 km wide swaths. InSAR has been used previously to characterize larger magnitude, generally permanent aquifer system compaction and land subsidence at yearly and longer timescales, caused by sustained drawdown of groundwater levels that produces intergranular stresses consistently greater than the maximum historical stress. We present InSAR measurements of the typically small-magnitude, generally recoverable deformations of the Las Vegas Valley aquifer system occurring at seasonal timescales. From these we derive estimates of the elastic storage coefficient for the aquifer system at several locations in Las Vegas Valley. These high-resolution measurements offer great potential for future investigations into the mechanics of aquifer systems and the spatial heterogeneity of aquifer system structure and material properties as well as for monitoring ongoing aquifer system compaction and land subsidence.
Consolidated Tree Classifier Learning in a Car Insurance Fraud Detection Domain with Class Imbalance

This paper presents an analysis of the behaviour of Consolidated Trees, CT (classification trees induced from multiple subsamples but without loss of explaining capacity). We analyse how CT trees behave when used to solve a fraud detection problem in a car insurance company. This domain has two important characteristics: the explanation given to the classification made is critical to help investigating the received reports or claims, and besides, this is a typical example of class imbalance problem due to its skewed class distribution. In the results presented in the paper CT and C4.5 trees have been compared, from the accuracy and structural stability (explaining capacity) point of view and, for both algorithms, the best class distribution has been searched.. Due to the different associated costs of different error types (costs of investigating suspicious reports, etc.) a wider analysis of the error has also been done: precision/recall, ROC curve, etc.
Vision-Based Kidnap Recovery with SLAM for Home Cleaning Robots

Emerged as salient in the recent home appliance consumer market is a new generation of home cleaning robot featuring the capability of Simultaneous Localization and Mapping (SLAM). SLAM allows a cleaning robot not only to selfoptimize its work paths for efficiency but also to self-recover from kidnappings for user convenience. By kidnapping, we mean that a robot is displaced, in the middle of cleaning, without its SLAM aware of where it moves to. This paper presents a vision-based kidnap recovery with SLAM for home cleaning robots, the first of its kind, using a wheel drop switch and an upwardlooking camera for low-cost applications. In particular, a camera with a wide-angle lens is adopted for a kidnapped robot to be able to recover its pose on a global map with only a single image. First, the kidnapping situation is effectively detected based on a wheel drop switch. Then, for S. Lee · S. Lee (B) School of Information and Communication Engineering and Department of Interaction Science, Sungkyunkwan University, Suwon, South Korea e-mail: lsh@ece.skku.ac.kr S. Lee e-mail: seongsu.lee@lge.com S. Lee · S. Baek Future IT Laboratory, LG Electronics Inc., Seoul, South Korea e-mail: seungmin2.baek@lge.com an efficient kidnap recovery, a coarse-to-fine approach to matching the image features detected with those associated with a large number of robot poses or nodes, built as a map in graph representation, is adopted. The pose ambiguity, e.g., due to symmetry is taken care of, if any. The final robot pose is obtained with high accuracy from the fine level of the coarse-to-fine hierarchy by fusing poses estimated from a chosen set of matching nodes. The proposed method was implemented as an embedded system with an ARM11 processor on a real commercial home cleaning robot and tested extensively. Experimental results show that the proposed method works well even in the situation in which the cleaning robot is suddenly kidnapped during the map building process.
Learning and Transfer : A General Role for Analogical Encoding

Teaching by examples and cases is widely used to promote learning, but it varies widely in its effectiveness. The authors test an adaptation to case-based learning that facilitates abstracting problemsolving schemas from examples and using them to solve further problems: analogical encoding, or learning by drawing a comparison across examples. In 3 studies, the authors examined schema abstraction and transfer among novices learning negotiation strategies. Experiment 1 showed a benefit for analogical learning relative to no case study. Experiment 2 showed a marked advantage for comparing two cases over studying the 2 cases separately. Experiment 3 showed that increasing the degree of comparison support increased the rate of transfer in a face-to-face dynamic negotiation exercise.
HCL2000 - A Large-scale Handwritten Chinese Character Database for Handwritten Character Recognition

In this paper, we present a large scale off-line handwritten Chinese character database-HCL2000 which will be made public available for the research community. The database contains 3,755 frequently used simplified Chinesecharacters written by 1,000 different subjects. The writers’ information is incorporated in the database to facilitate testing on grouping writers with different background such as age, occupation, gender, and education etc. We investigate some characteristics of writing styles from different groups of writers. We evaluate HCL2000 database using three different algorithms as a baseline. We decide to publish the database along with this paper and make it free for a research purpose.
Prediction of emerging technologies based on analysis of the US patent citation network

The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (1) identifies actual clusters of patents: i.e., technological branches, and (2) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the citation vector, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at the beginning of 1997. These new tools of predictive analytics could support policy decision making processes in science and technology, and help formulate recommendations for action.
Random projections fuzzy c-means (RPFCM) for big data clustering

Many contemporary biomedical applications such as physiological monitoring, imaging, and sequencing produce large amounts of data that require new data processing and visualization algorithms. Algorithms such as principal component analysis (PCA), singular value decomposition and random projections (RP) have been proposed for dimensionality reduction. In this paper we propose a new random projection version of the fuzzy c-means (FCM) clustering algorithm denoted as RPFCM that has a different ensemble aggregation strategy than the one previously proposed, denoted as ensemble FCM (EFCM). RPFCM is more suitable than EFCM for big data sets (large number of points, n). We evaluate our method and compare it to EFCM on synthetic and real datasets.
Customer Relationship Management : Related Theories , Challenges and Application in Banking Sector

What is CRM Customer relationship Management (CRM) appears to be a simple and straightforward concept, but there are many different definitions and implementations of CRM. At present, a number of different conceptual understandings are associated with the term "Customer Relationship Management (CRM). There understanding range from IT driven programs designed to optimize customer contact to comprehensive approaches for the establishment and design of long-term relationships. The effort to establish a meaningful relationship with the customer is characteristic of this last understanding (Barnes 2003).
Efficient Hyperparameter Optimization for Deep Learning Algorithms Using Deterministic RBF Surrogates

Automatically searching for optimal hyperparameter configurations is of crucial importance for applying deep learning algorithms in practice. Recently, Bayesian optimization has been proposed for optimizing hyperparameters of various machine learning algorithms. Those methods adopt probabilistic surrogate models like Gaussian processes to approximate and minimize the validation error function of hyperparameter values. However, probabilistic surrogates require accurate estimates of sufficient statistics (e.g., covariance) of the error distribution and thus need many function evaluations with a sizeable number of hyperparameters. This makes them inefficient for optimizing hyperparameters of deep learning algorithms, which are highly expensive to evaluate. In this work, we propose a new deterministic and efficient hyperparameter optimization method that employs radial basis functions as error surrogates. The proposed mixed integer algorithm, called HORD, searches the surrogate for the most promising hyperparameter values through dynamic coordinate search and requires many fewer function evaluations. HORD does well in low dimensions but it is exceptionally better in higher dimensions. Extensive evaluations on MNIST and CIFAR-10 for four deep neural networks demonstrate HORD significantly outperforms the well-established Bayesian optimization methods such as GP, SMAC and TPE. For instance, on average, HORD is more than 6 times faster than GP-EI in obtaining the best configuration of 19 hyperparameters.
Solid-State Thermal Management for Lithium-Ion EV Batteries

Battery thermal management system (BTMS) is essential for electric-vehicle (EV) and hybrid-vehicle (HV) battery packs to operate effectively in all climates. Lithium-ion (Li-ion) batteries offer many advantages to the EV such as high power and high specific energy. However, temperature affects their performance, safety, and productive life. This paper is about the design and evaluation of a BTMS based on the Peltier effect heat pumps. The discharge efficiency of a 60-Ah prismatic Li-ion pouch cell was measured under different rates and different ambient temperature values. The obtained results were used to design a solid-state BTMS based on Peltier thermoelectric coolers (TECs). The proposed BTMS is then modeled and evaluated at constant current discharge in the laboratory. In addition, The BTMS was installed in an EV that was driven in the US06 cycle. The thermal response and the energy consumption of the proposed BTMS were satisfactory.