nils-herrmann/clean_scirepeval huggingface dataset:
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction

This paper presents StereoNet, the first end-to-end deep architecture for real-time stereo matching that runs at 60fps on an NVidia Titan X, producing high-quality, edge-preserved, quantization-free disparity maps. A key insight of this paper is that the network achieves a sub-pixel matching precision than is a magnitude higher than those of traditional stereo matching approaches. This allows us to achieve realtime performance by using a very low resolution cost volume that encodes all the information needed to achieve high disparity precision. Spatial precision is achieved by employing a learned edge-aware upsampling function. Our model uses a Siamese network to extract features from the left and right image. A first estimate of the disparity is computed in a very low resolution cost volume, then hierarchically the model reintroduces high-frequency details through a learned upsampling function that uses compact pixel-to-pixel refinement networks. Leveraging color input as a guide, this function is capable of producing high-quality edgeaware output. We achieve compelling results on multiple benchmarks, showing how the proposed method offers extreme flexibility at an acceptable computational budget.
1 . 2 Sparsity : A Reasonable Assumption ?

Compressed sensing is a novel research area, which was introduced in 2006, and since then has already become a key concept in various areas of applied mathematics, computer science, and electrical engineering. It surprisingly predicts that high-dimensional signals, which allow a sparse representation by a suitable basis or, more generally, a frame, can be recovered from what was previously considered highly incomplete linear measurements by using efficient algorithms. This article shall serve as an introduction to and a survey about compressed sensing.
Implementation of flash flood monitoring system based on wireless sensor network in Bangladesh

This research involved in the design & implementation of water monitoring system that provides a solution to remotely monitoring water levels, early warning of events, sending notifications when critical water levels are reached through mobile and accessing all information via a website. It enables people to obtain readings remotely instead of having to travel to each monitoring stations to collect data and gives protection of the safety of monitoring personnel under certain dangerous circumstances. The system consists of the field sensor, Arduino Ethernet-shield, data collection software module, web console module, Mobile based monitoring module and distributed servers. The field sensor acquires the real-time data as water-level information from river stations. The data is then stored to distributed web servers and represented to website so that everyone gets the status of water levels. The proposed monitoring system presents useful characteristics as large network capacity, sensor hardware compatibility, long-range communication, low power consumption, and capable of taking challenge to natural environment threats like weather conditions and various type of water mixed up with others materials.
Joint Registration and Representation Learning for Unconstrained Face Identification

Recent advances in deep learning have resulted in human-level performances on popular unconstrained face datasets including Labeled Faces in the Wild and YouTube Faces. To further advance research, IJB-A benchmark was recently introduced with more challenges especially in the form of extreme head poses. Registration of such faces is quite demanding and often requires laborious procedures like facial landmark localization. In this paper, we propose a Convolutional Neural Networks based data-driven approach which learns to simultaneously register and represent faces. We validate the proposed scheme on template based unconstrained face identification. Here, a template contains multiple media in the form of images and video frames. Unlike existing methods which synthesize all template media information at feature level, we propose to keep the template media intact. Instead, we represent gallery templates by their trained one-vs-rest discriminative models and then employ a Bayesian strategy which optimally fuses decisions of all medias in a query template. We demonstrate the efficacy of the proposed scheme on IJB-A, YouTube Celebrities and COX datasets where our approach achieves significant relative performance boosts of 3.6%, 21.6% and 12.8% respectively.
Blind Trust Online : Experimental Evidence from Baseball Cards

Before reaping transaction cost savings from the Internet, online sellers must credibly communicate the quality of their goods or their reliability of delivery. We use baseball cards as an example to address (1) whether consumers fully understand the risk of online trading, (2) the extent to which consumers employ seller reputation and professional grading to alleviate the risk they are exposed to, and (3) the impact of online trading on the retail market and supporting industries. An experiment is carried out to obtain actual baseball cards from both online and retail markets whose quality is then professionally graded and compared to the prices paid by online buyers for goods with similar claims. Our findings indicate that some uninformed online buyers are misled by non-credible claims of quality; they pay higher prices but do not receive better quality and in fact are defrauded more often. Although a combination of professional grading and reputation mechanisms appear to have the potential to solve the problem, they have only had limited success so far. Data on structural changes in the baseball card industry suggest the problems stemming from buyer misconception may have far-reaching consequences, extending beyond the immediate losses of defrauded consumers to influencing producer and supply decisions. Our findings also suggest that empirical studies using online price data should be careful of what assumptions they are making of online users and what behaviors of buyers and sellers are likely to drive the price data. Department of Economics, University of Maryland, 3115 G Tydings, College Park, MD 20742.  We thank Seth Sanders, John List, John Shea, Dan Vincent, David Lucking-Reiley, Larry Ausubel, Peter Cramton, V. Joseph Hotz, Jimmy Chan, Vincent Crawford, attendants in Johns Hopkins UMD conference and participants of UCLA,UCSD and FTC seminars for constructive advice and suggestions. We are grateful to 8 friends who acted as our agents in purchasing baseball cards in retail markets, and numerous sports card store owners for their time and insights regarding the sports card industry. Excellent research assistance from Randy Alexander Moore and Krzysztof Fizyta is gratefully acknowledged. Any remaining errors are ours.
Clustering of multivariate time series data using particle swarm optimization

Particle swarm optimization (PSO) is a practical and effective optimization approach that has been applied recently for data clustering in many applications. While various non-evolutionary optimization and clustering algorithms have been applied for clustering multivariate time series in some applications such as customer segmentation, they usually provide poor results due to their dependency on the initial values and their poor performance in manipulating multiple objectives. In this paper, a particle swarm optimization algorithm is proposed for clustering multivariate time series data. Since the time series data sometimes do not have the same length and they usually have missing data, the regular Euclidean distance and dynamic time warping can not be applied for such data to measure the similarity. Therefore, a hybrid similarity measure based on principal component analysis and Mahalanobis distance is applied in order to handle such limitations. The comparison between the results of the proposed method with the similar ones in the literature shows the superiority of the proposed method.
Active Messages: A Mechanism for Integrated Communication and Computation

The design challenge for large-scale multiprocessors is (1) to minimize communication overhead, (2) allow communication to overlap computation, and (3) coordinate the two without sacrificing processor cost/performance. We show that existing message passing multiprocessors have unnecessarily high communication costs. Research prototypes of message driven machines demonstrate low communication overhead, but poor processor costlperformance. We introduce a simple communication mechanism, Active Messages, show that it is intrinsic to both architectures, allows cost effective use of the hardware, and offers tremendous flexibility. Implementations on nCUBE/2 and CM-5 are described and evaluated using a split-phase shared-memory extension to C, Split-C. We further show that active messages are sufficient to implement the dynamically scheduled languages for which message driven machines were designed. With this mechanism, latency tolerance becomes a programming/compiling concern. Hardware support for active messages is desirable and we outline arangeof enhancements to mainstream processors.
Automatically refining the wikipedia infobox ontology

The combined efforts of human volunteers have recently extracted numerous facts from Wikipedia, storing them as machine-harvestable object-attribute-value triples in Wikipedia infoboxes. Machine learning systems, such as Kylin, use these infoboxes as training data, accurately extracting even more semantic knowledge from natural language text. But in order to realize the full power of this information, it must be situated in a cleanly-structured ontology. This paper introduces KOG, an autonomous system for refining Wikipedia's infobox-class ontology towards this end. We cast the problem of ontology refinement as a machine learning problem and solve it using both SVMs and a more powerful joint-inference approach expressed in Markov Logic Networks. We present experiments demonstrating the superiority of the joint-inference approach and evaluating other aspects of our system. Using these techniques, we build a rich ontology, integrating Wikipedia's infobox-class schemata with WordNet. We demonstrate how the resulting ontology may be used to enhance Wikipedia with improved query processing and other features.
Parsing Natural Scenes and Natural Language with Recursive Neural Networks

Recursive structure is commonly found in the inputs of different modalities such as natural scene images or natural language sentences. Discovering this recursive structure helps us to not only identify the units that an image or sentence contains but also how they interact to form a whole. We introduce a max-margin structure prediction architecture based on recursive neural networks that can successfully recover such structure both in complex scene images as well as sentences. The same algorithm can be used both to provide a competitive syntactic parser for natural language sentences from the Penn Treebank and to outperform alternative approaches for semantic scene segmentation, annotation and classification. For segmentation and annotation our algorithm obtains a new level of state-of-theart performance on the Stanford background dataset (78.1%). The features from the image parse tree outperform Gist descriptors for scene classification by 4%.
The interplay between goal intentions and implementation intentions.

Two studies tested whether action control by implementation intentions is sensitive to the activation and strength of participants' underlying goal intentions. In Study 1, participants formed implementation intentions (or did not) and their goal intentions were measured. Findings revealed a significant interaction between implementation intentions and the strength of respective goal intentions. Implementation intentions benefited the rate of goal attainment when participants had strong goal intentions but not when goal intentions were weak. Study 2 activated either a task-relevant or a neutral goal outside of participants' conscious awareness and found that implementation intentions affected performance only when the relevant goal had been activated. These findings indicate that the rate of goal attainment engendered by implementation intentions takes account of the state (strength, activation) of people's superordinate goal intentions.
Neural Program Meta-Induction

Most recently proposed methods for Neural Program Induction work under the assumption of having a large set of input/output (I/O) examples for learning any underlying input-output mapping. This paper aims to address the problem of data and computation efficiency of program induction by leveraging information from related tasks. Specifically, we propose two approaches for cross-task knowledge transfer to improve program induction in limited-data scenarios. In our first proposal, portfolio adaptation, a set of induction models is pretrained on a set of related tasks, and the best model is adapted towards the new task using transfer learning. In our second approach, meta program induction, a k-shot learning approach is used to make a model generalize to new tasks without additional training. To test the efficacy of our methods, we constructed a new benchmark of programs written in the Karel programming language . Using an extensive experimental evaluation on the Karel benchmark, we demonstrate that our proposals dramatically outperform the baseline induction method that does not use knowledge transfer. We also analyze the relative performance of the two approaches and study conditions in which they perform best. In particular, meta induction outperforms all existing approaches under extreme data sparsity (when a very small number of examples are available), i.e., fewer than ten. As the number of available I/O examples increase (i.e. a thousand or more), portfolio adapted program induction becomes the best approach. For intermediate data sizes, we demonstrate that the combined method of adapted meta program induction has the strongest performance.
Sarcasm Detection on Czech and English Twitter

This paper presents a machine learning approach to sarcasm detection on Twitter in two languages – English and Czech. Although there has been some research in sarcasm detection in languages other than English (e.g., Dutch, Italian, and Brazilian Portuguese), our work is the first attempt at sarcasm detection in the Czech language. We created a large Czech Twitter corpus consisting of 7,000 manually-labeled tweets and provide it to the community. We evaluate two classifiers with various combinations of features on both the Czech and English datasets. Furthermore, we tackle the issues of rich Czech morphology by examining different preprocessing techniques. Experiments show that our language-independent approach significantly outperforms adapted state-of-the-art methods in English (F-measure 0.947) and also represents a strong baseline for further research in Czech (F-measure 0.582).
Application in Market Basket Research Based on FP-Growth Algorithm

Market basket analysis gives us insight into the merchandise by telling us which products tend to be purchased together and which are most enable to purchase. The market basket analysis is a powerful tool especially in retailing it is essential to discover large baskets, since it deals with thousands of items. FP-growth algorithm is an efficient algorithm for mining frequent patterns. It does not need to produce the candidate sets and that is quite time consuming. It scans database only twice and frequent item set is mining by using of FP tree. In this paper, Visual C++ is applied to design the program to mine the frequent item sets using FP-growth algorithm. According to the mining result, the merchandise in the supermarket is arranged together in the same place well-suited for customer.
MeshFlow: Minimum Latency Online Video Stabilization

Many existing video stabilization methods often stabilize videos off-line, i.e. as a postprocessing tool of pre-recorded videos. Some methods can stabilize videos online, but either require additional hardware sensors (e.g., gyroscope) or adopt a single parametric motion model (e.g., affine, homography) which is problematic to represent spatiallyvariant motions. In this paper, we propose a technique for online video stabilization with only one frame latency using a novel MeshFlow motion model. The MeshFlow is a spatial smooth sparse motion field with motion vectors only at the mesh vertexes. In particular, the motion vectors on the matched feature points are transferred to their corresponding nearby mesh vertexes. The MeshFlow is produced by assigning each vertex an unique motion vector via two median filters. The path smoothing is conducted on the vertex profiles, which are motion vectors collected at the same vertex location in the MeshFlow over time. The profiles are smoothed adaptively by a novel smoothing technique, namely the Predicted Adaptive Path Smoothing (PAPS), which only uses motions from the past. In this way, the proposed method not only handles spatiallyvariant motions but also works online in real time, offering potential for a variety of intelligent applications (e.g., security systems, robotics, UAVs). The quantitative and qualitative evaluations show that our method can produce comparable results with the state-of-the-art off-line methods.
Likelihood-informed dimension reduction for nonlinear inverse problems

The intrinsic dimensionality of an inverse problem is affected by prior information, the accuracy and number of observations, and the smoothing properties of the forward operator. From a Bayesian perspective, changes from the prior to the posterior may, in many problems, be confined to a relatively lowdimensional subspace of the parameter space. We present a dimension reduction approach that defines and identifies such a subspace, called the “likelihoodinformed subspace” (LIS), by characterizing the relative influences of the prior and the likelihood over the support of the posterior distribution. This identification enables new and more efficient computational methods for Bayesian inference with nonlinear forward models and Gaussian priors. In particular, we approximate the posterior distribution as the product of a lower-dimensional posterior defined on the LIS and the prior distribution marginalized onto the complementary subspace. Markov chain Monte Carlo sampling can then proceed in lower dimensions, with significant gains in computational efficiency. We also introduce a Rao-Blackwellization strategy that de-randomizes Monte Carlo estimates of posterior expectations for additional variance reduction. We demonstrate the efficiency of our methods using two numerical examples: inference of permeability in a groundwater system governed by an elliptic PDE, and an atmospheric remote sensing problem based on Global Ozone Monitoring System (GOMOS) observations.
Low power 3–2 and 4–2 adder compressors implemented using ASTRAN

This paper presents two adder compressors architectures addressing high-speed and low power. Adder compressors are used to implement arithmetic circuits such as multipliers and digital signal processing units like the Fast Fourier Transform (FTT). To address the objective of high-speed and low power, it is well known that optimization efforts should be applied in all abstraction levels. In this paper are combined optimizations at logic, electrical and physical level. At the logic level, the circuit is optimized by using multiplexers instead of XOR gates to reduce delay, power and area. At the electrical level, this work presents an architecture that generate the XOR and XNOR signals simultaneously, this reduce internal glitches hence dynamic power as well. And finally at the physical level, and automatic layout generation tool (ASTRAN) is used to make the adder compressors layouts. This tool has proved to reduce power consumption and delay due to the smaller input capacitances of the complex gates generated compared to manual-designed layouts.
A taxonomy of argumentation models used for knowledge representation

Understanding argumentation and its role in human reasoning has been a continuous subject of investigation for scholars from the ancient Greek philosophers to current researchers in philosophy, logic and artificial intelligence. In recent years, argumentation models have been used in different areas such as knowledge representation, explanation, proof elaboration, commonsense reasoning, logic programming, legal reasoning, decision making, and negotiation. However, these models address quite specific needs and there is need for a conceptual framework that would organize and compare existing argumentation-based models and methods. Such a framework would be very useful especially for researchers and practitioners who want to select appropriate argumentation models or techniques to be incorporated in new software systems with argumentation capabilities. In this paper, we propose such a conceptual framework, based on taxonomy of the most important argumentation models, approaches and systems found in the literature. This framework highlights the similarities and differences between these argumentation models. As an illustration of the practical use of this framework, we present a case study which shows how we used this framework to select and enrich an argumentation model in a knowledge acquisition project which aimed at representing argumentative knowledge contained in texts critiquing military courses of action.
Verbalization: Narration of Autonomous Robot Experience

Autonomous mobile robots navigate in our spaces by planning and executing routes to destinations. When a mobile robot appears at a location, there is no clear way to understand what navigational path the robot planned and experienced just by looking at it. In this work, we address the generation of narrations of autonomous mobile robot navigation experiences. We contribute the concept of verbalization as a parallel to the well-studied concept of visualization. Through verbalizations, robots can describe through language what they experience, in particular in their paths. For every executed path, we consider many possible verbalizations that could be generated. We introduce the verbalization space that covers the variability of utterances that the robot may use to narrate its experience to different humans. We present an algorithm for segmenting a path and mapping each segment to an utterance, as a function of the desired point in the verbalization space, and demonstrate its application using our mobile service robot moving in our buildings. We believe our verbalization space and algorithm are applicable to different narrative aspects for many mobile robots, including autonomous cars.
A Broadband Folded Printed Quadrifilar Helical Antenna Employing a Novel Compact Planar Feeding Circuit

A broadband printed quadrifilar helical antenna employing a novel compact feeding circuit is proposed in this paper. This antenna presents an excellent axial ratio over a wide beamwidth, with a 29% bandwidth. A specific feeding circuit based on an aperture-coupled transition and including two 90° surface mount hybrids has been designed to be integrated with the quadrifilar antenna. Over the bandwidth, the measured reflection coefficient of the antenna fed by the wideband compact circuit has been found to be equal to or lower than -12 dB and the maximum gain varies between 1.5 and 2.7 dBic from 1.18 to 1.58 GHz. The half-power beamwidth is 150°, with an axial ratio below 3 dB over this range. The compactness of the feeding circuit allows small element spacing in array arrangements.