nils-herrmann/clean_scirepeval huggingface dataset:
Recommender System Based on Product Taxonomy in E-Commerce Sites

This article describes a novel and fast recommender system for websites based on product taxonomy and user click patterns. The proposed system consists of the following four steps. First, a product-preference matrix for each customer is estimated through a linear combination of the click, basket placement, and purchase statuses. Second, the preference matrix of the genre and that of the specific type are constructed by computing the ratios of the number of clicks, basket placements, and purchases of a product to the totals. Third, cluster analysis is performed using the genre preference matrix, and a neighborhood formation process is conducted using the specific-type preference matrix. Finally, data are generated for prediction, in which a customer’s preference for specific types is greater than a given threshold value. Using these data sets, computational burden and processing time are greatly reduced. The effectiveness of the proposed approach was assessed by applying the F1 metric to an experimental e-commerce website. The proposed method outperformed conventional methods.
Depth enhanced visual-inertial odometry based on Multi-State Constraint Kalman Filter

There have been increasing demands for developing robotic system combining camera and inertial measurement unit in navigation task, due to their low-cost, lightweight and complementary properties. In this paper, we present a Visual Inertial Odometry (VIO) system which can utilize sparse depth to estimate 6D pose in GPS-denied and unstructured environments. The system is based on Multi-State Constraint Kalman Filter (MSCKF), which benefits from low computation load when compared to optimization-based method, especially on resource-constrained platform. Features are enhanced with depth information forming 3D landmark position measurements in space, which reduces uncertainty of position estimate. And we derivate measurement model to access compatibility with both 2D and 3D measurements. In experiments, we evaluate the performance of the system in different in-flight scenarios, both cluttered room and industry environment. The results suggest that the estimator is consistent, substantially improves the accuracy compared with original monocular-based MSKCF and achieves competitive accuracy with other research.
Skin lesions on yellowfin tuna Thunnus albacares from Gulf of Mexico outer continental shelf: Morphological, molecular, and histological diagnosis of infection by a capsalid monogenoid.

We characterize lesion-associated capsaline infections on yellowfin tuna, Thunnus albacares, in the Gulf of Mexico by comparing our specimens with published descriptions and museum specimens ascribed to Capsala biparasiticum and its synonyms: vouchers of C. biparasiticum from parasitic copepods; the holotype of Capsala neothunni; and vouchers of Capsala abidjani. Those from parasitic copepods differed by having a small, rounded body, large anterior attachment organs, closely spaced dorsomarginal body sclerites, small testes, and a short and wide testicular field. No morphometric feature in the holotype of C. neothunni ranged outside of that reported for the newly-collected specimens, indicating conspecificity of our specimens. The specimens of C. abidjani differed by having a large anterior attachment organ, few and dendritic testes, and a short, wide testicular field. Large subunit ribosomal DNA (28S) sequences grouped our specimens and Capsala sp. as sister taxa and indicated a phylogenetic affinity of Nasicola klawei. The haptoral attachment site comprised a crater-like depression surrounded by a blackish-colored halo of extensively rugose skin, with abundant pockmarked-like, irregularly-shaped oblong or semi-circular epidermal pits surrounding these attachment sites. Histology confirmed extensive folding of epidermis and underlying stratum laxum, likely epidermal hyperplasia, foci of weak cell-to-cell adhesions among apical malpighian cells as well as that between stratum germinativum and stratum laxum, myriad goblet cells in epidermis, rodlet cells in apical layer of epidermis, and lymphocytic infiltrates and melanin in dermis. The present study comprises (i) the first published report of this parasite from yellowfin tuna captured in the Gulf of Mexico-NW Atlantic Ocean Basin, (ii) confirmation of its infection on the skin (rather than on a parasitic copepod), (iii) the first molecular data for this capsaline, and (iv) the first observations of histopathological changes associated with a capsalid infection on a wild-caught epipelagic fish.
Broadband Capacitively Coupled Stacked Patch Antenna for GNSS Applications

A broadband stacked circular patch antenna is presented in this letter. The broadband characteristic is achieved by employing capacitively coupled feed structure. The antenna is fed by a wideband four-output-ports feed network with equal magnitude and consistent 90° phase shift. The final antenna provides very good circularly polarized radiation for Global Navigation Satellite System applications including GPS, GLONASS, Galileo, and Compass.
Emotions , Partisanship , and Misperceptions : How Anger and Anxiety Moderate the Effect of Partisan Bias on Susceptibility to Political Misinformation

Citizens are frequently misinformed about political issues and candidates but the circumstances under which inaccurate beliefs emerge are not fully understood. This experimental study demonstrates that the independent experience of two emotions, anger and anxiety, in part determines whether citizens consider misinformation in a partisan or open-minded fashion. Anger encourages partisan, motivated evaluation of uncorrected misinformation that results in beliefs consistent with the supported political party, while anxiety at times promotes initial beliefs based less on partisanship and more on the information environment. However, exposure to corrections improves belief accuracy, regardless of emotion or partisanship. The results indicate that the unique experience of anger and anxiety can affect the accuracy of political beliefs by strengthening or attenuating the influence of partisanship.
The relationship between materialism and personal well-being: A meta-analysis.

This meta-analysis investigates the relationship between individuals' materialistic orientation and their personal well-being. Theoretical approaches in psychology agree that prioritizing money and associated aims is negatively associated with individuals' well-being but differ in their implications for whether this is invariably the case. To address these and other questions, we examined 753 effect sizes from 259 independent samples. Materialism was associated with significantly lower well-being for the most widely used, multifaceted measures (materialist values and beliefs, r = -.19, ρ = -.24; relative importance of materialist goals, r = -.16, ρ = -.21), more than for measures assessing emphasis on money alone (rs = -.08 to -.11, ρs = -.09 to -.14). The relationship also depended on type of well-being outcome, with largest effects for risky health and consumer behaviors and for negative self-appraisals (rs = -.28 to -.44, ρs = -.32 to -.53) and weakest effects for life satisfaction and negative affect (rs = -.13 to -.15, ρs = -.17 to -.18). Moderator analyses revealed that the strength of the effect depended on certain demographic factors (gender and age), on value context (study/work environments that support materialistic values and cultures that emphasize affective autonomy), and on cultural economic indicators (economic growth and wealth differentials). Mediation analyses suggested that the negative link may be explained by poor psychological need satisfaction. We discuss implications for the measurement of materialist values and the need for theoretical and empirical advances to explore underlying processes, which likely will require more experimental, longitudinal, and developmental research.
A Machine Learning Approach for Better Understanding the Neuromodulation of Locomotion

Motor intent and control rely on complex high-level and spinal networks. Until now, little is known about this system’s organization and mechanisms. While cognitive abilities play an essential role in planning movements, learning and memorizing, their involvement during stereotyped tasks execution, as locomotion, is still controversial. Recently, the relationship between cognitive functions and gait has received increasing attention. Here, a machine learning approach is used to investigate the engagement of di↵erent cortical areas during motor activity. In particular, data coming from three subjects with implanted electrodes have been analyzed in the frequency domain to predict their tasks’ state. The choice of intracortical data has allowed to elude motion artifacts’ presence and exploitation concern. Good and satisfactory results have been achieved in the case of not highly stereotyped activity. During ambulation, an evidence of an engagement of the brain has been shown even if with lower classification performances. Moreover, the cortical areas that have emerged in this analysis seem to be in line with the relative functionality hypothesized in literature.
An All-in-One Network for Dehazing and Beyond

This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net). It is designed based on a reformulated atmospheric scattering model. Instead of estimating the transmission matrix and the atmospheric light separately as most previous models did, AOD-Net directly generates the clean image through a light-weight CNN. Such a novel endto-end design makes it easy to embed AOD-Net into other deep models, e.g., Faster R-CNN, for improving high-level task performance on hazy images. Experimental results on both synthesized and natural hazy image datasets demonstrate our superior performance than the state-of-the-art in terms of PSNR, SSIM and the subjective visual quality. Furthermore, when concatenating AOD-Net with Faster R-CNN and training the joint pipeline from end to end, we witness a large improvement of the object detection performance on hazy images.
A flexible and low-cost tactile sensor for robotic applications

For humans, the sense of touch is essential for interactions with the environment. With robots slowly starting to emerge as a human-centric technology, tactile information becomes increasingly important. Tactile sensors enable robots to gain information about contacts with the environment, which is required for safe interaction with humans or tactile exploration. Many sensor designs for the application on robots have been presented in literature so far. However, most of them are complex in their design and require high-tech tools for their manufacturing. In this paper, we present a novel design for a tactile sensor that can be built with low-cost, widely available materials, and low effort. The sensor is flexible, may be cut to arbitrary shapes and may have a customized spatial resolution. Both pressure distribution and absolute pressure on the sensor are detected. An experimental evaluation of our design shows low detection thresholds as well as high sensor accuracy. We seek to accelerate research on tactile feedback methods with this easy to replicate design. We consider our design a starting point for the integration of multiple sensor units to a large-scale tactile skin for robots.
Introduction to Nonlinear Optimization - Theory, Algorithms, and Applications with MATLAB

introduction to nonlinear optimization theory algorithms introduction to nonlinear optimization theory algorithms introduction to nonlinear optimization theory algorithms introduction to nonlinear optimization theory algorithms chapter 16: introduction to nonlinear programming nonlinear programming: concepts, algorithms and applications theory, algorithms, and applications with matlab introduction to nonlinear optimization epubsam course: math 657|optimization theory time: spring 2016, 5 convex optimization: modeling and algorithms nonlinear programming 13 mit massachusetts institute an introduction to nonlinear optimization theory ebook nonlinear optimization lecture notes for the course mat a and trends in optimization with e applications solving optimization problems using the matlab siam bestsellers 2000–2015 order author title code 2015 optimization an introduction imperial college london [review published in siam review, vol. 58, issue 1, pp. 164.] nonlinear optimization and applications springer convex analysis and nonlinear optimization theory and examples appm 4720/5720 advanced topics in convex optimization lecture slides on nonlinear programming based on lectures convex optimization — boyd & vandenberghe
Term-Weighting Approaches in Automatic Text Retrieval

The experimental evidence accumulated over the past 20 years indicates that text indexing systems based on the assignment of appropriately weighted single terms produce retrieval results that are superior to those obtainable with other more elaborate text representations. These results depend crucially on the choice of effective termweighting systems. This article summarizes the insights gained in automatic term weighting, and provides baseline single-term-indexing models with which other more elaborate content analysis procedures can be compared. 1. AUTOMATIC TEXT ANALYSIS In the late 195Os, Luhn [l] first suggested that automatic text retrieval systems could be designed based on a comparison of content identifiers attached both to the stored texts and to the users’ information queries. Typically, certain words extracted from the texts of documents and queries would be used for content identification; alternatively, the content representations could be chosen manually by trained indexers familiar with the subject areas under consideration and with the contents of the document collections. In either case, the documents would be represented by term vectors of the form D= (ti,tj,...ytp) (1) where each tk identifies a content term assigned to some sample document D. Analogously, the information requests, or queries, would be represented either in vector form, or in the form of Boolean statements. Thus, a typical query Q might be formulated as Q = (qa,qbr.. . ,4r) (2)
Application of Computer Vision Technique on Sorting and Grading of Fruits and Vegetables

Coding of pictures for transmission through cables was attempted way back in 1920 (even before the advent of digital computer), but serious attempts for digital image processing commenced in late 60s and early 70s. Importance of computer vision increases due to the fact that vision is one of the most dominant human senses. The use of computer vision for the inspection of fruits and vegetables has increased during recent years. Computer vision technique has been a subject of research and application for more than four decades. This technique is used in many engineering fields such as robotics, industrial image processing, food processing and other fields. Quickness, nondestructive evaluation possibilities, easy procedures for application, quantum of output per unit time are some advantages favouring application of computer vision to engineering problems. Application of computer vision to food processing fields evolved first in 1989 for grain quality inspection .
3D Surface Reconstruction of Noisy Point Clouds Using Growing Neural Gas: 3D Object/Scene Reconstruction

With the advent of low-cost 3D sensors and 3D printers, scene and object 3D surface reconstruction has become an important research topic in the last years. In this work, we propose an automatic (unsupervised) method for 3D surface reconstruction from raw unorganized point clouds acquired using low-cost 3D sensors. We have modified the growing neural gas network, which is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation, to perform 3D surface reconstruction of different real-world objects and scenes. Some improvements have been made on the original algorithm considering colour and surface normal information of input data during the learning stage and creating complete triangular meshes instead of basic wire-frame representations. The proposed method is able to successfully create 3D faces online, whereas existing 3D reconstruction methods based on self-organizing maps required post-processing steps to close gaps and holes produced during the 3D reconstruction process. A set of quantitative and qualitative experiments were carried out to validate the proposed method. The method has been implemented and tested on real data, and has been found to be effective at reconstructing noisy point clouds obtained using low-cost 3D sensors.
NLOS Identification in Range-Based Source Localization: Statistical Approach

Least squares estimation is a widely-used technique for range-based source localization, which obtains the most probable position of mobile station. These methods cannot provide desirable accuracy in the case with a non line of sight (NLOS) path between mobile station and base stations. To circumvent this drawback, many algorithms have been proposed to identify and mitigate this error; however, they have a large run-time overhead. On the other hand, new positioning systems utilize a large set of base stations, and a practical algorithm should be fast enough to deal with them. In this paper, we propose a novel algorithm based on subspace method to identify and eliminate the NLOS error. Simulation studies show that our algorithm is faster and more accurate compared with other conventional methods, especially in the large-scale cases.
Dual-Polarized Dual-Band Microstrip Antenna With Similar-Shaped Radiation Pattern

A dual-polarized dual-band (DPDB) microstrip antenna with two orthogonal feeding ports and similar-shaped radiation pattern is studied. The working mechanism of the dual-band operation and the similar-shaped radiation pattern is analyzed based on the S-parameters and the electric field distributions, respectively. A prototype shows that the measured impedance bandwidths for VSWR <; 2 are 150 MHz (810-960 MHz, 17%) and 550 MHz (1690-2240 MHz, 28%) with mutual couplings of lower than -32 and -26 dB at the lower and higher frequency bands, respectively. The antenna covers the GSM850/GSM900/GSM1800/GSM1900/UMTS bands for 2G/3G base station applications. Within the two frequency bands, the performance of similar-shaped radiation pattern, low cross-polarization, and stable gain is achieved.
Inversion of seismic reflection data in the acoustic approximation

The nonlinear inverse problem for seismic reflection data is solved in the acoustic approximation. The method is based on the generalized least-squares criterion, and it can handle errors in the data set and a priori information on the model. Multiply reflected energy is naturally taken into account, as well as refracted energy or surface waves. The inverse problem can be solved using an iterative algorithm which gives, at each iteration, updated values of bulk modulus, density, and time source function. Each step of the iterative algorithm essentially consists of a forward propagation of the actual sources in the current model and a forward propagation (backward in time) of the data residuals. The correlation at each point of the space of the two fields thus obtained yields the corrections of the bulk modulus and density models. This shows, in particular, that the general solution of the inverse problem can be attained by methods strongly related to the methods of migration of unstacked data, and commercially competitive with them.
A study of array antenna with phase compensated technique for 60 GHz communication

This paper presents phase compensated techniques for 60 GHz communication. A phase compensation method for antenna embedded in a mobile device and a planar lens for base station antenna are introduced. For the Line-of Sight (LoS) scenario, end-fire antenna arrays are adapted for mobile device. 8×1 end-fire patch antenna arrays are designed in Low temperature co-fired ceramic (LTCC) substrate. Simulated max gain of the antenna arrays is 13.03 dBi. When the antenna module is mounted, scattering problem is occurred and its max gain is severely degraded to 6.4 dBi. By using phase compensated technique, max gain of the antenna arrays are enhanced to 12.5 dBi. The beam steered gain of the arrays is also simulated and good coverage can be achieved. A planar lens antenna is also studied for high gain of the base station antenna.
Anonymous Walk Embeddings

The task of representing entire graphs has seen a surge of prominent results, mainly due to learning convolutional neural networks (CNNs) on graphstructured data. While CNNs demonstrate stateof-the-art performance in graph classification task, such methods are supervised and therefore steer away from the original problem of network representation in task-agnostic manner. Here, we coherently propose an approach for embedding entire graphs and show that our feature representations with SVM classifier increase classification accuracy of CNN algorithms and traditional graph kernels. For this we describe a recently discovered graph object, anonymous walk, on which we design task-independent algorithms for learning graph representations in explicit and distributed way. Overall, our work represents a new scalable unsupervised learning of state-of-the-art representations of entire graphs.
Design of Dual-Band Microstrip Patch Antenna with Defected Ground Plane for Modern Wireless Applications

In this paper, we design and analyze a dual-band rectangular microstrip patch antenna with defected ground plane for 2.5 GHz and 3.5 GHz communications. The design concept is to have two slots etched out from the ground plane of a microstrip patch antenna designed for 2.5 GHz operation to enable second frequency band (3.5 GHz) operation. The positions of etched slots are determined using parametric analysis to meet the objectives of large impedance bandwidth and high gains in the desired bands and mismatching in the undesired bands. The proposed antenna provides wide impedance bandwidths of 13.56% (2.3-2.7 GHz) and 10.36% (3.3-3.7 GHz) at center frequencies of 2.5 GHz and 3.5 GHz, respectively. The gains in E-Plane are 6.7 dB and 5.1 dB, and the gains in H-Plane are 6.5 dB and 4.88 dB, for 2.5 GHz and 3.5 GHz, respectively. The proposed dual-band antenna shows monopole-like radiation patterns with higher gains as compared to monopole antennas.
The BOSARIS Toolkit: Theory, Algorithms and Code for Surviving the New DCF

The change of two orders of magnitude in the new DCF of SRE’10, relative to the old DCF evaluation criterion, posed a difficult challenge for participants and evaluator alike. Initially, participants were at a loss as to how to calibrate their systems, while the evaluator underestimated the required number of evaluation trials. After the fact, it is now obvious that both calibration and evaluation require very large sets of trials. This poses the challenges of (i) how to decide what number of trials is enough, and (ii) how to process such large data sets with reasonable memory and CPU requirements. After SRE’10, at the BOSARIS Workshop, we built solutions to these problems into the freely available BOSARIS Toolkit. This paper explains the principles and algorithms behind this toolkit. The main contributions of the toolkit are: 1. The Normalized Bayes Error-Rate Plot, which analyses likelihoodratio calibration over a wide range of DCF operating points. These plots also help in judging the adequacy of the sizes of calibration and evaluation databases. 2. Efficient algorithms to compute DCF and minDCF for large score files, over the range of operating points required by these plots. 3. A new score file format, which facilitates working with very large trial lists. 4. A faster logistic regression optimizer for fusion and calibration. 5. A principled way to define equal error rate, which is of practical interest when the absolute error count is small.