nils-herrmann/clean_scirepeval huggingface dataset:
Robust Transfer Metric Learning for Image Classification

Metric learning has attracted increasing attention due to its critical role in image analysis and classification. Conventional metric learning always assumes that the training and test data are sampled from the same or similar distribution. However, to build an effective distance metric, we need abundant supervised knowledge (i.e., side/label information), which is generally inaccessible in practice, because of the expensive labeling cost. In this paper, we develop a robust transfer metric learning (RTML) framework to effectively assist the unlabeled target learning by transferring the knowledge from the well-labeled source domain. Specifically, RTML exploits knowledge transfer to mitigate the domain shift in two directions, i.e., sample space and feature space. In the sample space, domain-wise and class-wise adaption schemes are adopted to bridge the gap of marginal and conditional distribution disparities across two domains. In the feature space, our metric is built in a marginalized denoising fashion and low-rank constraint, which make it more robust to tackle noisy data in reality. Furthermore, we design an explicit rank constraint regularizer to replace the rank minimization NP-hard problem to guide the low-rank metric learning. Experimental results on several standard benchmarks demonstrate the effectiveness of our proposed RTML by comparing it with the state-of-the-art transfer learning and metric learning algorithms.
Fast, Scalable and Secure Onloading of Edge Functions Using AirBox

This paper argues for the utility of back-end driven onloading to the edge as a way to address bandwidth use and latency challenges for future device-cloud interactions. Supporting such edge functions (EFs) requires solutions that can provide (i) fast and scalable EF provisioning and (ii) strong guarantees for the integrity of the EF execution and confidentiality of the state stored at the edge. In response to these goals, we (i) present a detailed design space exploration of the current technologies that can be leveraged in the design of edge function platforms (EFPs), (ii) develop a solution to address security concerns of EFs that leverages emerging hardware support for OS agnostic trusted execution environments such as Intel SGX enclaves, and (iii) propose and evaluate AirBox, a platform for fast, scalable and secure onloading of edge functions.
Multi-task Deep Neural Network for Joint Face Recognition and Facial Attribute Prediction

Deep neural networks have significantly improved the performance of face recognition and facial attribute prediction, which however are still very challenging on the million scale dataset, i.e. MegaFace. In this paper, we for the first time, advocate a multi-task deep neural network for jointly learning face recognition and facial attribute prediction tasks. Extensive experimental evaluation clearly demonstrates the effectiveness of our architecture. Remarkably, on the largest face recognition benchmark -- MegaFace dataset, our networks can achieve the Rank-1 identication accuracy of 77.74% and face verication accuracy 79.24% TAR at 10-6 FAR, which are the best performance on the small protocol among all the publicly released methods.
Current Research and Open Problems in Attribute-Based Access Control

Attribute-based access control (ABAC) is a promising alternative to traditional models of access control (i.e., discretionary access control (DAC), mandatory access control (MAC), and role-based access control (RBAC)) that is drawing attention in both recent academic literature and industry application. However, formalization of a foundational model of ABAC and large scale adoption is still in its infancy. The relatively recent emergence of ABAC still leaves a number of problems unexplored. Issues like delegation, administration, auditability, scalability, hierarchical representations, and the like, have been largely ignored or left to future work.
 This article provides a basic introduction to ABAC and a comprehensive review of recent research efforts toward developing formal models of ABAC. A taxonomy of ABAC research is presented and used to categorize and evaluate surveyed articles. Open problems are identified based on the shortcomings of the reviewed works and potential solutions discussed.
A robust automatic crack detection method from noisy concrete surfaces

In maintenance of concrete structures, crack detection is important for the inspection and diagnosis of concrete structures. However, it is difficult to detect cracks automatically. In this paper, we propose a robust automatic crack-detection method from noisy concrete surface images. The proposed method includes two preprocessing steps and two detection steps. The first preprocessing step is a subtraction process using the median filter to remove slight variations like shadings from concrete surface images; only an original image is used in the preprocessing. In the second preprocessing step, a multi-scale line filter with the Hessian matrix is used both to emphasize cracks against blebs or stains and to adapt the width variation of cracks. After the preprocessing, probabilistic relaxation is used to detect cracks coarsely and to prevent noises. It is unnecessary to optimize any parameters in probabilistic relaxation. Finally, using the results from the relaxation process, a locally adaptive thresholding is performed to detect cracks more finely. We evaluate robustness and accuracy of the proposed method quantitatively using 60 actual noisy concrete surface images.
Various emotional expressions with emotion expression humanoid robot WE-4RII

The authors have been developing humanoid robots in order to develop new mechanisms and functions for a humanoid robot that has the ability to communicate naturally with a human by expressing human-like emotion. In 2004, we developed the emotion expression humanoid robot WE-4RII (Waseda Eye No.4 Refined II) by integrating the new humanoid robot hands RCH-I (RoboCasa Hand No.1) into the emotion expression humanoid robot WE-4R. We confirmed that WE-4RII can effectively express its emotion.
Evaluation of Machine Translation

1. I N T R O D U C T I O N Despite the long history of machine translation projects, and the well-known effects that evaluations such as the ALPAC Report (Pierce et al., 1966) have had on that history, optimal MT evaluation methodologies remain elusive. This is perhaps due in part to the subjectivity inherent in judging the quality of any translation output (human or machine). The difficulty also lies in the heterogeneity of MT language pairs, computational approaches, and intended end-use. The DARPA machine translation initiative is faced with all of these issues in evaluation, and so requires a suite of evaluation methodologies which minimize subjectivity and transcend the heterogeneity problems. At the same time, the initiative seeks to formulate this suite in such a way that it is economical to administer and portable to other MT development initiatives. This paper describes an evaluation of three research MT systems along with benchmark haman and external MT outputs. Two sets of evaluations were performed, one using a relatively complex suite of methodologies, and the other using a simpler set on the same data. The test procedure is described, along The authors would like to express their gratitude to Michael Naber for his assistance in compiling, expressing and interpreting data. with a comparison of the results of the different methodologies.
Layouts From Panoramic Images With Geometry and Deep Learning

In this letter, we propose a novel procedure for three-dimensional layout recovery of indoor scenes from single 360 $^\circ$ panoramic images. With such images, all scene is seen at once, allowing us to recover closed geometries. Our method combines strategically the accuracy provided by geometric reasoning (lines and vanishing points) with the higher level of data abstraction and pattern recognition achieved by deep learning techniques (edge and normal maps). Thus, we extract structural corners from which we generate layout hypotheses of the room assuming Manhattan world. The best layout model is selected, achieving good performance on both simple rooms (box-type), and complex shaped rooms (with more than four walls). Experiments of the proposed approach are conducted within two public datasets, SUN360 and Stanford (2D-3D-S) demonstrating the advantages of estimating layouts by combining geometry and deep learning and the effectiveness of our proposal with respect to the state of the art.
Beam-Steering SIW Leaky-Wave Subarray With Flat-Topped Footprint for 5G Applications

A novel double-layered vertically stacked substrate integrated waveguide leaky-wave antenna (SIW LWA) is presented. An array of vias on the narrow wall produces leakage through excitation of TE10 fast-wave mode of the waveguide. Attenuation and phase constants of the leaky mode are controlled independently to obtain desired pattern in the elevation. In the azimuth, top and bottom layers radiate independently, producing symmetrically located beams on both sides of broadside. A new near-field analysis of single LWA is performed to determine wavenumbers and as a way to anticipate radiation characteristics of the dual layer antenna. In addition to frequency beam steering in the elevation plane, this novel topology also offers flexibility for multispot illumination of the azimuth plane with flat-topped beams at every  ${\varphi }$ -cut through excitation of each layer separately or both antennas simultaneously. It is shown that the proposed antenna solution is a qualified candidate for 5G base station antenna (BSA) applications due to its capability of interference mitigation and latency reduction. Moreover, from the point of view of highly reliable connectivity, users can enjoy seamless mobility through the provided spatial diversity. A 15-GHz prototype has been fabricated and tested. Measured results are in good agreement with those of simulations.
Narcissism , Shame , Masochism , and Object Relations : An Exploratory Correlational Study

A correlational study (N == 701) examined three measures of narcissism, a measure of shame, two measures of masochism, a measure of object relations, and a measure of social desirability. Moderate correlations were found for a core of constructs which have been described in the clinical and theoretical literature. These are narcissism, shame, object relations, and masochism. Narcissism seemed to divide into two different styles, a "phallic," grandiose style and a narcissistically vulnerable style. Shame primarily accounted for the differences in these styles, correlating negatively with the grandiose style, positively with the more vulnerable style. The narcissistically vulnerable style correlated more with the core pathology measures; that is, object relations and masochism. Social desirability did not mediate the relationship between grandiose narcissism and shame. Only small univariate gender differences were found, but masochism was a better predictor of shame in women than was narcissism whereas there was little difference between masochism and narcissism for predicting shame in men.
How musical training affects cognitive development: rhythm, reward and other modulating variables

Musical training has recently gained additional interest in education as increasing neuroscientific research demonstrates its positive effects on brain development. Neuroimaging revealed plastic changes in the brains of adult musicians but it is still unclear to what extent they are the product of intensive music training rather than of other factors, such as preexisting biological markers of musicality. In this review, we synthesize a large body of studies demonstrating that benefits of musical training extend beyond the skills it directly aims to train and last well into adulthood. For example, children who undergo musical training have better verbal memory, second language pronunciation accuracy, reading ability and executive functions. Learning to play an instrument as a child may even predict academic performance and IQ in young adulthood. The degree of observed structural and functional adaptation in the brain correlates with intensity and duration of practice. Importantly, the effects on cognitive development depend on the timing of musical initiation due to sensitive periods during development, as well as on several other modulating variables. Notably, we point to motivation, reward and social context of musical education, which are important yet neglected factors affecting the long-term benefits of musical training. Further, we introduce the notion of rhythmic entrainment and suggest that it may represent a mechanism supporting learning and development of executive functions. It also hones temporal processing and orienting of attention in time that may underlie enhancements observed in reading and verbal memory. We conclude that musical training uniquely engenders near and far transfer effects, preparing a foundation for a range of skills, and thus fostering cognitive development.
Brain Tumor Detection Using Image Processing

ARTICLE INFO Tumor is the one of the most common brain diesease and this is the reason for the diagnosis & treatment of the brain tumor has vital importance. MRI is the technique used to produce computerised image of internal body tissues. Cells are growing in uncontrollable manner this results in mass of unwanted tissue which is called as tumor. CT-Scan and MRI image which are diagnostic technique are used to detect brain tumor and classifies in types malignant & benign. This is difficult due to variations hence techniques like image preprocessing, feature extraction are used, there are many methods developed but they have different results. In this paper we are going to discuss the methods for detection of brain tumor and evaluate them.
Application of multi-criteria decision making to sustainable energy planning — A review

Multi-Criteria Decision Making (MCDM) techniques are gaining popularity in sustainable energy management. The techniques provide solutions to the problems involving conflicting and multiple objectives. Several methods based on weighted averages, priority setting, outranking, fuzzy principles and their combinations are employed for energy planning decisions. A review of more than 90 published papers is presented here to analyze the applicability of various methods discussed. A classification on application areas and the year of application is presented to highlight the trends. It is observed that Analytical Hierarchy Process is the most popular technique followed by outranking techniques PROMETHEE and ELECTRE. Validation of results with multiple methods, development of interactive decision support systems and application of fuzzy methods to tackle uncertainties in the data is observed in the published literature.
Low-dose CT denoising with convolutional neural network

To reduce the potential radiation risk, low-dose CT has attracted much attention. However, simply lowering the radiation dose will lead to significant deterioration of the image quality. In this paper, we propose a noise reduction method for low-dose CT via deep neural network without accessing original projection data. A deep convolutional neural network is trained to transform low-dose CT images towards normal-dose CT images, patch by patch. Visual and quantitative evaluation demonstrates a competing performance of the proposed method.
An AC Input Switching-Converter-Free LED Driver With Low-Frequency-Flicker Reduction

This paper presents a novel switching-converter-free ac–dc light-emitting diode (LED) driver with low-frequency-flicker reduction for general lighting applications. The proposed driving solution can minimize the system size as it enables the monolithic integration of the controller and power transistors while both the bulky off-chip electrolytic capacitors and magnetics are eliminated. Moreover, the driver can effectively reduce the harmful optical flicker at the double-line-frequency by employing a novel quasi-constant power control scheme while maintaining high efficiency and a good power factor (PF). The proposed driver is implemented with a single controller integrated circuit chip, which includes the controller and high-voltage power transistors, and the off-chip diode bridge and valley-fill circuit. The chip is fabricated with a 0.35- $\mu \text{m}$  120-V high-voltage CMOS process and occupies 1.85 mm2. The driver can provide up to 7.8-W power to the LED and achieves 87.6% peak efficiency and an over 0.925 PF with only 17.3% flicker from a 110-Vac 60-Hz input.
Religion , Social Networks , and Life Satisfaction

Although the positive association between religiosity and life satisfaction is well documented, much theoretical and empirical controversy surrounds the question of how religion actually shapes life satisfaction. Using a new panel dataset, this study offers strong evidence for social and participatory mechanisms shaping religion’s impact on life satisfaction. Our findings suggest that religious people are more satisfied with their lives because they regularly attend religious services and build social networks in their congregations. The effect of within-congregation friendship is contingent, however, on the presence of a strong religious identity. We find little evidence that other private or subjective aspects of religiosity affect life satisfaction independent of attendance and congregational friendship.
A full system x86 simulator for teaching computer organization

This paper describes a new graphical computer simulator developed for computer organization students. Unlike other teaching simulators, our simulator faithfully models a complete personal computer, including an i386 processor, physical memory, I/O ports, floppy and hard disks, interrupts, timers, and a serial port. It is capable of running PC software such as FreeDOS, Windows, and Minix, and can run as a Java applet. Graphical user interfaces allow students to view and modify the processor, memory, disks, and hardware devices at runtime. The simulator includes a processor development utility that allows students to design their own datapath and control units, and run their custom processor alongside the x86 processor. The paper describes labs where students use the simulator to write x86 assembly programs, device drivers, hardware controllers; and design both simple and pipelined processors.
It Takes Three to Tango: Triangulation Approach to Answer Ranking in Community Question Answering

We address the problem of answering new questions in community forums, by selecting suitable answers to already asked questions. We approach the task as an answer ranking problem, adopting a pairwise neural network architecture that selects which of two competing answers is better. We focus on the utility of the three types of similarities occurring in the triangle formed by the original question, the related question, and an answer to the related comment, which we call relevance, relatedness, and appropriateness. Our proposed neural network models the interactions among all input components using syntactic and semantic embeddings, lexical matching, and domain-specific features. It achieves state-of-the-art results, showing that the three similarities are important and need to be modeled together. Our experiments demonstrate that all feature types are relevant, but the most important ones are the lexical similarity features, the domain-specific features, and the syntactic and semantic embeddings.
Modeling the Spatial Reach of the LFP

The local field potential (LFP) reflects activity of many neurons in the vicinity of the recording electrode and is therefore useful for studying local network dynamics. Much of the nature of the LFP is, however, still unknown. There are, for instance, contradicting reports on the spatial extent of the region generating the LFP. Here, we use a detailed biophysical modeling approach to investigate the size of the contributing region by simulating the LFP from a large number of neurons around the electrode. We find that the size of the generating region depends on the neuron morphology, the synapse distribution, and the correlation in synaptic activity. For uncorrelated activity, the LFP represents cells in a small region (within a radius of a few hundred micrometers). If the LFP contributions from different cells are correlated, the size of the generating region is determined by the spatial extent of the correlated activity.
Genetic Algorithms and Machine Learning

There is no a priori reason why machine learning must borrow from nature. A field could exist, complete with well-defined algorithms, data structures, and theories of learning, without once referring to organisms, cognitive or genetic structures, and psychological or evolutionary theories. Yet at the end of the day, with the position papers written, the computers plugged in, and the programs debugged, a learning edifice devoid of natural metaphor would lack something. It would ignore the fact that all these creations have become possible only after three billion years of evolution on this planet. It would miss the point that the very ideas of adaptation and learning are concepts invented by the most recent representatives of the species Homo sapiens from the careful observation of themselves and life around them. It would miss the point that natural examples of learning and adaptation are treasure troves of robust procedures and structures. Fortunately, the field of machine learning does rely upon nature's bounty for both inspiration and mechanism. Many machine learning systems now borrow heavily from current thinking in cognitive science, and rekindled interest in neural networks and connectionism is evidence of serious mechanistic and philosophical currents running through the field. Another area where natural example has been tapped is in work on genetic algorithms (GAs) and genetics-based machine learning. Rooted in the early cybernetics movement (Holland, 1962), progress has been made in both theory (Holland, 1975; Holland, Holyoak, Nisbett, & Thagard, 1986) and application (Goldberg, 1989; Grefenstette, 1985, 1987) to the point where genetics-based systems are finding their way into everyday commercial use (Davis & Coombs, 1987; Fourman, 1985).
Coronary plaque classification with intravascular ultrasound radiofrequency data analysis.

Atherosclerotic plaque stability is related to histological composition. However, current diagnostic tools do not allow adequate in vivo identification and characterization of plaques. Spectral analysis of backscattered intravascular ultrasound (IVUS) data has potential for real-time in vivo plaque classification.



Eighty-eight plaques from 51 left anterior descending coronary arteries were imaged ex vivo at physiological pressure with the use of 30-MHz IVUS transducers. After IVUS imaging, the arteries were pressure-fixed and corresponding histology was collected in matched images. Regions of interest, selected from histology, were 101 fibrous, 56 fibrolipidic, 50 calcified, and 70 calcified-necrotic regions. Classification schemes for model building were computed for autoregressive and classic Fourier spectra by using 75% of the data. The remaining data were used for validation. Autoregressive classification schemes performed better than those from classic Fourier spectra with accuracies of 90.4% for fibrous, 92.8% for fibrolipidic, 90.9% for calcified, and 89.5% for calcified-necrotic regions in the training data set and 79.7%, 81.2%, 92.8%, and 85.5% in the test data, respectively. Tissue maps were reconstructed with the use of accurate predictions of plaque composition from the autoregressive classification scheme.



Coronary plaque composition can be predicted through the use of IVUS radiofrequency data analysis. Autoregressive classification schemes performed better than classic Fourier methods. These techniques allow real-time analysis of IVUS data, enabling in vivo plaque characterization.