nils-herrmann/clean_scirepeval huggingface dataset:
LIDAR and vision-based pedestrian detection system

A perception system for pedestrian detection in urban scenarios using information from a LIDAR and a single camera is presented. Two sensor fusion architectures are described, a centralized and a decentralized one. In the former, the fusion process occurs at the feature level, i.e., features from LIDAR and vision spaces are combined in a single vector for posterior classification using a single classifier. In the latter, two classifiers are employed, one per sensor-feature space, which were offline selected based on information theory and fused by a trainable fusion method applied over the likelihoods provided by the component classifiers. The proposed schemes for sensor combination, and more specifically the trainable fusion method, lead to enhanced detection performance and, in addition, maintenance of false-alarms under tolerable values in comparison with singlebased classifiers. Experimental results highlight the performance and effectiveness of the proposed pedestrian detection system and the related sensor data combination strategies.
Personality and intelligence as predictors of academic achievement : A cross-sectional study from elementary to secondary school

General intelligence and personality traits from the Five-Factor model were studied as predictors of academic achievement in a large sample of Estonian schoolchildren from elementary to secondary school. A total of 3618 students (1746 boys and 1872 girls) from all over Estonia attending Grades 2, 3, 4, 6, 8, 10, and 12 participated in this study. Intelligence, as measured by the Raven’s Standard Progressive Matrices, was found to be the best predictor of students’ grade point average (GPA) in all grades. Among personality traits (measured by self-reports on the Estonian Big Five Questionnaire for Children in Grades 2 to 4 and by the NEO Five Factor Inventory in Grades 6 to 12), Openness, Agreeableness, and Conscientiousness correlated positively and Neuroticism correlated negatively with GPA in almost every grade. When all measured variables were entered together into a regression model, intelligence was still the strongest predictor of GPA, being followed by Agreeableness in Grades 2 to 4 and Conscientiousness in Grades 6 to 12. Interactions between predictor variables and age accounted for only a small percentage of variance in GPA, suggesting that academic achievement relies basically on the same mechanisms through the school years.
Stock price movement prediction using representative prototypes of financial reports

Stock price movement prediction is an appealing topic not only for research but also for commercial applications. Most of prior research separately analyzes the meanings of the qualitative or quantitative features, and does not consider the categorical information when clustering financial reports. Since quantitative or qualitative features contain only partial information, there may be no synergy by considering them individually. It is more appropriate to predict stock price movements by simultaneously taking both quantitative and qualitative features into account. Therefore, in this study, we utilize a weighting scheme to combine both qualitative and quantitative features of financial reports together, and propose a method to predict short-term stock price movements. The proposed method employs the categorical information to localize the clusters and improve the purity of each resultant cluster. We gathered 26,255 reports of companies listed in the S&P 500 index from the EDGAR database and conducted the GICS (Global Industrial Classification System) experiments based on the industry sectors. The empirical evaluation results show that the proposed method outperforms the SVM, naïve Bayes, and PFHC methods in terms of accuracy and average profit.
Distinct representations of eye gaze and identity in the distributed human neural system for face perception

Face perception requires representation of invariant aspects that underlie identity recognition as well as representation of changeable aspects, such as eye gaze and expression, that facilitate social communication. Using functional magnetic resonance imaging (fMRI), we investigated the perception of face identity and eye gaze in the human brain. Perception of face identity was mediated more by regions in the inferior occipital and fusiform gyri, and perception of eye gaze was mediated more by regions in the superior temporal sulci. Eye-gaze perception also seemed to recruit the spatial cognition system in the intraparietal sulcus to encode the direction of another's gaze and to focus attention in that direction.
Llama: leveraging columnar storage for scalable join processing in the MapReduce framework

To achieve high reliability and scalability, most large-scale data warehouse systems have adopted the cluster-based architecture. In this paper, we propose the design of a new cluster-based data warehouse system, LLama, a hybrid data management system which combines the features of row-wise and column-wise database systems. In Llama, columns are formed into correlation groups to provide the basis for the vertical partitioning of tables. Llama employs a distributed file system (DFS) to disseminate data among cluster nodes. Above the DFS, a MapReduce-based query engine is supported. We design a new join algorithm to facilitate fast join processing. We present a performance study on TPC-H dataset and compare Llama with Hive, a data warehouse infrastructure built on top of Hadoop. The experiment is conducted on EC2. The results show that Llama has an excellent load performance and its query performance is significantly better than the traditional MapReduce framework based on row-wise storage.
FAST VEHICLE DETECTION AND TRACKING IN AERIAL IMAGE BURSTS

Caused by the rising interest in traffic surveillance for simulations and decision management many publications concentrate on automatic vehicle detection or tracking. Quantities and velocities of different car classes form the data basis for almost every traffic model. Especially during mass events or disasters a wide-area traffic monitoring on demand is needed which can only be provided by airborne systems. This means a massive amount of image information to be handled. In this paper we present a combination of vehicle detection and tracking which is adapted to the special restrictions given on image size and flow but nevertheless yields reliable information about the traffic situation. Combining a set of modified edge filters it is possible to detect cars of different sizes and orientations with minimum computing effort, if some a priori information about the street network is used. The found vehicles are tracked between two consecutive images by an algorithm using Singular Value Decomposition. Concerning their distance and correlation the features are assigned pairwise with respect to their global positioning among each other. Choosing only the best correlating assignments it is possible to compute reliable values for the average velocities.
A Neural Architecture Mimicking Humans End-to-End for Natural Language Inference

In this work we use the recent advances in representation learning to propose a neural architecture for the problem of natural language inference. Our approach is aligned to mimic how a human does the natural language inference process given two statements. The model uses variants of Long Short Term Memory (LSTM), attention mechanism and composable neural networks, to carry out the task. Each part of our model can be mapped to a clear functionality humans do for carrying out the overall task of natural language inference. The model is end-to-end differentiable enabling training by stochastic gradient descent. On Stanford Natural Language Inference(SNLI) dataset, the proposed model achieves better accuracy numbers than all published models in literature.
Evading Machine Learning Malware Detection

Machine learning is a popular approach to signatureless malware detection because it can generalize to never-beforeseen malware families and polymorphic strains. This has resulted in its practical use for either primary detection engines or supplementary heuristic detections by anti-malware vendors. Recent work in adversarial machine learning has shown that models are susceptible to gradient-based and other attacks. In this whitepaper, we summarize the various attacks that have been proposed for machine learning models in information security, each which require the adversary to have some degree of knowledge about the model under attack. Importantly, even when applied to attacking machine learning malware classifier based on static features for Windows portable executable (PE) files, these attacks, previous attack methodologies may break the format or functionality of the malware. We investigate a more general framework for attacking static PE anti-malware engines based on reinforcement learning, which models more realistic attacker conditions, and subsequently has provides much more modest evasion rates. A reinforcement learning (RL) agent is equipped with a set of functionality-preserving operations that it may perform on the PE file. It learns through a series of games played against the anti-malware engine which sequence of operations is most likely to result in evasion for a given malware sample. Given the general framework, it is not surprising that the evasion rates are modest. However, the resulting RL agent can succinctly summarize blind spots of the anti-malware model. Additionally, evasive variants generated by the agent may be used to harden machine learning anti-malware engine via adversarial training.
A sub-1-volt nanoelectromechanical switching device.

Nanoelectromechanical (NEM) switches have received widespread attention as promising candidates in the drive to surmount the physical limitations currently faced by complementary metal oxide semiconductor technology. The NEM switch has demonstrated superior characteristics including quasi-zero leakage behaviour, excellent density capability and operation in harsh environments. However, an unacceptably high operating voltage (4-20 V) has posed a major obstacle in the practical use of the NEM switch in low-power integrated circuits. To utilize the NEM switch widely as a core device component in ultralow power applications, the operation voltage needs to be reduced to 1 V or below. However, sub-1 V actuation has not yet been demonstrated because of fabrication difficulties and irreversible switching failure caused by surface adhesion. Here, we report the sub-1 V operation of a NEM switch through the introduction of a novel pipe clip device structure and an effective air gap fabrication technique. This achievement is primarily attributed to the incorporation of a 4-nm-thick air gap, which is the smallest reported so far for a NEM switch generated using a 'top-down' approach. Our structure and process can potentially be utilized in various nanogap-related applications, including NEM switch-based ultralow-power integrated circuits, NEM resonators, nanogap electrodes for scientific research and sensors.
Tactical Cloudlets: Moving Cloud Computing to the Edge

Soldiers and front-line personnel operating in tactical environments increasingly make use of handheld devices to help with tasks such as face recognition, language translation, decision-making, and mission planning. These resource constrained edge environments are characterized by dynamic context, limited computing resources, high levels of stress, and intermittent network connectivity. Cyber-foraging is the leverage of external resource-rich surrogates to augment the capabilities of resource-limited devices. In cloudlet-based cyber-foraging, resource-intensive computation and data is offloaded to cloudlets. Forward-deployed, discoverable, virtual-machine-based tactical cloudlets can be hosted on vehicles or other platforms to provide infrastructure to offload computation, provide forward data staging for a mission, perform data filtering to remove unnecessary data from streams intended for dismounted users, and serve as collection points for data heading for enterprise repositories. This paper describes tactical cloudlets and presents experimentation results for five different cloudlet provisioning mechanisms. The goal is to demonstrate that cyber-foraging in tactical environments is possible by moving cloud computing concepts and technologies closer to the edge so that tactical cloudlets, even if disconnected from the enterprise, can provide capabilities that can lead to enhanced situational awareness and decision making at the edge.
Design of Conical Transmission Line Power Combiners Using Tapered Line Matching Sections

An axially symmetric power combiner, which utilizes a tapered conical impedance matching network to transform ten 50-Omega inputs to a central coaxial line over the X-band, is presented. The use of a conical line allows standard transverse electromagnetic design theory to be used, including tapered impedance matching networks. This, in turn, alleviates the problem of very low impedance levels at the common port of conical line combiners, which normally requires very high-precision manufacturing and assembly. The tapered conical line is joined to a tapered coaxial line for a completely smooth transmission line structure. Very few full-wave analyses are needed in the design process since circuit models are optimized to achieve a wide operating bandwidth. A ten-way prototype was developed at X-band with a 47% bandwidth, very low losses, and excellent agreement between simulated and measured results.
A GPS/GIS method for travel mode detection in New York City

0198-9715/$ see front matter 2011 ; fax E-mail address: gong@hunter.cuny.edu (H. Gong). 1 Present address: MTA Bus Company, Metropolitan Broadway, New York, NY 10004, United States. Handheld GPS provides a new technology to trace people’s daily travels and has been increasingly used for household travel surveys in major cities worldwide. However, methodologies have not been developed to successfully manage the enormous amount of data generated by GPS, especially in a complex urban environment such as New York City where urban canyon effects are significant and transportation networks are complicated. We develop a GIS algorithm that automatically processes the data from GPSbased travel surveys and detects five travel modes (walk, car, bus, subway, and commuter rail) from a multimodal transportation network in New York City. The mode detection results from the GIS algorithm are checked against the travel diaries from two small handheld GPS surveys. The combined success rate is a promising 82.6% (78.9% for one survey and 86.0% for another). Challenges we encountered in the mode detection process, ways we developed to meet these challenges, as well as possible future improvement to the GPS/GIS method are discussed in the paper, in order to provide a much-needed methodology to process GPS-based travel data for other cities.
A GaN transistor based 90W AC/DC adapter with a buck-PFC stage and an isolated Quasi-switched-capacitor DC/DC stage

This paper presents a GaN Transistor based 90W ac/dc adapter with a Buck-PFC stage and an isolated Quasi-Switched-Capacitor (QSC) dc/dc stage. In the dc/dc stage, two different QSC converters are proposed. Compared to Flyback and LLC resonant converters, the QSC converters feature: 1) reduced voltage stress on the primary-side switches to 2/3 of the input voltage; 2) reduced voltage stress on the transformer to 1/3 of the input voltage and a lower transformer turns ratio; 3) a wide range for soft-switching operation and high efficiency; 4) a simple control strategy. The operation principles and simulation results are presented. A 90 W, 85 V/19 V, 1 MHz QSC resonant converter is built, using 100 V EPC eGaN FETs for all switches. This prototype achieves: 1) a high power density of 10.5 W/cm3; 2) wide-range soft switching and a peak efficiency of 92.8% at 900 kHz in preliminary test results. A Buck-PFC evaluation module from TI is tested with a GaN HEMT and a SiC Schottky diode. The peak efficiency reached 97.1%, and the experimental results are compared with those from the Si based version.
Automatic boolean query suggestion for professional search

In professional search environments, such as patent search or legal search, search tasks have unique characteristics: 1) users interactively issue several queries for a topic, and 2) users are willing to examine many retrieval results, i.e., there is typically an emphasis on recall. Recent surveys have also verified that professional searchers continue to have a strong preference for Boolean queries because they provide a record of what documents were searched. To support this type of professional search, we propose a novel Boolean query suggestion technique. Specifically, we generate Boolean queries by exploiting decision trees learned from pseudo-labeled documents and rank the suggested queries using query quality predictors. We evaluate our algorithm in simulated patent and medical search environments. Compared with a recent effective query generation system, we demonstrate that our technique is effective and general.
Deep Domain Generalization With Structured Low-Rank Constraint

Domain adaptation nowadays attracts increasing interests in pattern recognition and computer vision field, since it is an appealing technique in fighting off weakly labeled or even totally unlabeled target data by leveraging knowledge from external well-learned sources. Conventional domain adaptation assumes that target data are still accessible in the training stage. However, we would always confront such cases in reality that the target data are totally blind in the training stage. This is extremely challenging since we have no prior knowledge of the target. In this paper, we develop a deep domain generalization framework with structured low-rank constraint to facilitate the unseen target domain evaluation by capturing consistent knowledge across multiple related source domains. Specifically, multiple domain-specific deep neural networks are built to capture the rich information within multiple sources. Meanwhile, a domain-invariant deep neural network is jointly designed to uncover most consistent and common knowledge across multiple sources so that we can generalize it to unseen target domains in the test stage. Moreover, structured low-rank constraint is exploited to align multiple domain-specific networks and the domain-invariant one in order to better transfer knowledge from multiple sources to boost the learning problem in unseen target domains. Extensive experiments are conducted on several cross-domain benchmarks and the experimental results show the superiority of our algorithm by comparing it with state-of-the-art domain generalization approaches.
Deep Photo Style Transfer

This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style. Our approach builds upon the recent work on painterly transfer that separates style from the content of an image by considering different layers of a neural network. However, as is, this approach is not suitable for photorealistic style transfer. Even when both the input and reference images are photographs, the output still exhibits distortions reminiscent of a painting. Our contribution is to constrain the transformation from the input to the output to be locally affine in colorspace, and to express this constraint as a custom fully differentiable energy term. We show that this approach successfully suppresses distortion and yields satisfying photorealistic style transfers in a broad variety of scenarios, including transfer of the time of day, weather, season, and artistic edits.
Neural-body coupling for emergent locomotion: A musculoskeletal quadruped robot with spinobulbar model

To gain a synthetic understanding of how the body and nervous system co-create animal locomotion, we propose an investigation into a quadruped musculoskeletal robot with biologically realistic morphology and a nervous system. The muscle configuration and sensory feedback of our robot are compatible with the mono- and bi-articular muscles of a quadruped animal and with its muscle spindles and Golgi tendon organs. The nervous system is designed with a biologically plausible model of the spinobulbar system with no pre-defined gait patterns such that mutual entrainment is dynamically created by exploiting the physics of the body. In computer simulations, we found that designing the body and the nervous system of the robot with the characteristics of biological systems increases information regularities in sensorimotor flows by generating complex and coordinated motor patterns. Furthermore, we found similar results in robot experiments with the generation of various coordinated locomotion patterns created in a self-organized manner. Our results demonstrate that the dynamical interaction between the physics of the body with the neural dynamics can shape behavioral patterns for adaptive locomotion in an autonomous fashion.
Improving text categorization methods for event tracking

Automated tracking of events from chronologically ordered document streams is a new challenge for statistical text classification. Existing learning techniques must be adapted or improved in order to effectively handle difficult situations where the number of positive training instances per event is extremely small, the majority of training documents are unlabelled, and most of the events have a short duration in time. We adapted several supervised text categorization methods, specifically several new variants of the k-Nearest Neighbor (kNN) algorithm and a Rocchio approach, to track events. All of these methods showed significant improvement (up to 71% reduction in weighted error rates) over the performance of the original kNN algorithm on TDT benchmark collections, making kNN among the top-performing systems in the recent TDT3 official evaluation. Furthermore, by combining these methods, we significantly reduced the variance in performance of our event tracking system over different data collections, suggesting a robust solution for parameter optimization.
A Jointly Learned Deep Architecture for Facial Attribute Analysis and Face Detection in the Wild

Facial attribute analysis in the real world scenario is very challenging mainly because of complex face variations. Existing works of analyzing face attributes are mostly based on the cropped and aligned face images. However, this result in the capability of attribute prediction heavily relies on the preprocessing of face detector. To address this problem, we present a novel jointly learned deep architecture for both facial attribute analysis and face detection. Our framework can process the natural images in the wild and our experiments on CelebA and LFWA datasets clearly show that the state-of-the-art performance is obtained.
Deep-FExt: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction

Feature extraction is a very crucial task in image and pixel (voxel) classification and regression in biomedical image modeling. In this work we present a machine learning based feature extraction scheme based on inception models for pixel classification tasks. We extract features under multi-scale and multi-layer schemes through convolutional operators. Layers of Fully Convolutional Network are later stacked on this feature extraction layers and trained end-to-end for the purpose of classification. We test our model on the DRIVE and STARE public data sets for the purpose of segmentation and centerline detection and it out performs most existing hand crafted or deterministic feature schemes found in literature. We achieve an average maximum Dice of 0.85 on the DRIVE data set which out performs the scores from the second human annotator of this data set. We also achieve an average maximum Dice of 0.85 and kappa of 0.84 on the STARE data set. Though these datasets are mainly 2-D we also propose ways of extending this feature extraction scheme to handle 3-D datasets.