nils-herrmann/clean_scirepeval huggingface dataset:
What Strikes the Strings of Your Heart?—Feature Mining for Music Emotion Analysis

Music can convey and evoke powerful emotions. This amazing ability has not only fascinated the general public but also attracted the researchers from different fields to discover the relationship between music and emotion. Psychologists have indicated that some specific characters of rhythm, harmony, and melody can evoke certain kinds of emotions. Those hypotheses are based on real life experience and proved by psychological paradigms on human beings. Aiming at the same target, this paper intends to design a systematic and quantitative framework, and answer three widely interested questions: 1) what are the intrinsic features embedded in music signal that essentially evoke human emotions; 2) to what extent these features influence human emotions; and 3) whether the findings from computational models are consistent with the existing research results from psychology. We formulate these tasks as a multi-label dimensionality reduction problem and propose an algorithm called multi-emotion similarity preserving embedding (ME-SPE). To adapt to the second-order music signals, we extend ME-SPE to its bilinear version. The proposed techniques show good performance in two standard music emotion datasets. Moreover, they demonstrate some interesting results for further research in this interdisciplinary topic.
Refocusing Through Building Walls Using Synthetic Aperture Radar

Through-wall imaging/sensing using a synthetic aperture array technique is studied by employing ultrawideband antennas and for wide incidence angles. The propagation through building walls, such as brick and poured concrete in response to point sources near the walls, is simulated by using high-frequency methods. Reciprocity is used to find the responses of point targets behind walls, which are then used to simulate the synthetic aperture radar (SAR) imaging through the walls. The effect of building walls on the target-image distortions is investigated by simulations and measurements. It is shown that by using the idea of match filtering, the effect of the wall can be compensated for, and the point target response can be reconstructed, provided that the wall parameters are known. An optimization method based on minimization of squared error in the SAR image domain within an area confined within the expected point-spread function is used to estimate the wall parameters and sharpen the image simultaneously. A controlled experiment within the laboratory environment is performed to verify the methods presented. It is shown that for an ultrawideband system operating over a frequency band of 1-3 GHz, highly distorted images of two point targets in close proximity of each other behind a wall can be resolved after refocusing. A dual-frequency synthetic method is also presented that can improve the cross-range resolution of the refocused image.
Lessons from Building a Large-scale Commercial IR-based Chatbot for an Emerging Market

In this work, we highlight some interesting challenges faced when trying to build a large-scale commercial IR-based chatbot, Ruuh, for an emerging market like India which has unique characteristics such as high linguistic and cultural diversity, large section of young population and the second largest mobile market in the world. We set out to build a "human-like" AI agent which aspires to become the trusted friend of every Indian youth. To meet this objective, we realised that we need to think beyond the utilitarian notion of merely generating "relevant" responses and enable the agent to comprehend and meet a wider range of user social needs, like expressing happiness when user's favourite team wins, sharing a cute comment on showing the pictures of the user's pet and so on. The agent should also be well-versed with the informal language of the urban Indian youth which often includes slang and code-mixing across two or more languages (English and their native language). Finally, in order to be their trusted friend, the agent has to communicate with respect without offending their sentiments and emotions. Some of the above objectives pose significant research challenges in the areas of NLP, IR and AI. We take the audience through our journey of how we tackled some of the above challenges while building a large-scale commercial IR-based conversational agent. Our attempts to solve some of the above challenges have also resulted in some interesting research contributions in the form of publications and patents in the above areas. Our chat-bot currently has more than 1M users who have engaged in more than 70M conversations.
Boosting Algorithms for Parallel and Distributed Learning

The growing amount of available information and its distributed and heterogeneous nature has a major impact on the field of data mining. In this paper, we propose a framework for parallel and distributed boosting algorithms intended for efficient integrating specialized classifiers learned over very large, distributed and possibly heterogeneous databases that cannot fit into main computer memory. Boosting is a popular technique for constructing highly accurate classifier ensembles, where the classifiers are trained serially, with the weights on the training instances adaptively set according to the performance of previous classifiers. Our parallel boosting algorithm is designed for tightly coupled shared memory systems with a small number of processors, with an objective of achieving the maximal prediction accuracy in fewer iterations than boosting on a single processor. After all processors learn classifiers in parallel at each boosting round, they are combined according to the confidence of their prediction. Our distributed boosting algorithm is proposed primarily for learning from several disjoint data sites when the data cannot be merged together, although it can also be used for parallel learning where a massive data set is partitioned into several disjoint subsets for a more efficient analysis. At each boosting round, the proposed method combines classifiers from all sites and creates a classifier ensemble on each site. The final classifier is constructed as an ensemble of all classifier ensembles built on disjoint data sets. The new proposed methods applied to several data sets have shown that parallel boosting can achieve the same or even better prediction accuracy considerably faster than the standard sequential boosting. Results from the experiments also indicate that distributed boosting has comparable or slightly improved classification accuracy over standard boosting, while requiring much less memory and computational time since it uses smaller data sets.
The distance function effect on k-nearest neighbor classification for medical datasets

K-nearest neighbor (k-NN) classification is conventional non-parametric classifier, which has been used as the baseline classifier in many pattern classification problems. It is based on measuring the distances between the test data and each of the training data to decide the final classification output.



Since the Euclidean distance function is the most widely used distance metric in k-NN, no study examines the classification performance of k-NN by different distance functions, especially for various medical domain problems. Therefore, the aim of this paper is to investigate whether the distance function can affect the k-NN performance over different medical datasets. Our experiments are based on three different types of medical datasets containing categorical, numerical, and mixed types of data and four different distance functions including Euclidean, cosine, Chi square, and Minkowsky are used during k-NN classification individually.



The experimental results show that using the Chi square distance function is the best choice for the three different types of datasets. However, using the cosine and Euclidean (and Minkowsky) distance function perform the worst over the mixed type of datasets.



In this paper, we demonstrate that the chosen distance function can affect the classification accuracy of the k-NN classifier. For the medical domain datasets including the categorical, numerical, and mixed types of data, K-NN based on the Chi square distance function performs the best.
Optimal Precoders for Tracking the AoD and AoA of a mmWave Path

In millimeter-wave channels, most of the received energy is carried by a few paths. Traditional precoders sweep the angle-of-departure (AoD) and angle-of-arrival (AoA) space with directional precoders to identify directions with largest power. Such precoders are heuristic and lead to suboptimal AoD/AoA estimation. We derive optimal precoders, minimizing the Cramér–Rao bound (CRB) of the AoD/AoA under a given uncertainty range, assuming a fully digital architecture at the transmitter and spatial filtering of a single path. The precoders are found by solving a suitable convex optimization problem. We demonstrate that the accuracy can be improved by at least a factor of two over traditional precoders, and show that there is an optimal number of distinct precoders beyond which the CRB does not improve.
Dual-Polarized Slot-Coupled Microstrip Antenna Array With Stable Active Element Pattern

A dual-polarized slot-coupled microstrip antenna array with stable active element pattern is investigated. The antenna element of the array is a slot-coupled microstrip antenna with a reflector. The effect of the ground size on the radiation pattern of the antenna element is analyzed based on the electric field and current distributions. The method of adding copper pillars around the antenna element is proposed to mitigate the influence of the ground plane. A prototype of 1 × 3 microstrip antenna array is fabricated and measured. Its measured impedance bandwidth for VSWR <; 2 is 520 MHz (1.69-2.21 GHz). Within the operating band, the performances of low mutual coupling, low cross polarization, and stable active element pattern are achieved.
Mobile robot localization and object pose estimation using optical encoder, vision and laser sensors

A key problem of a mobile robot system is how to localize itself and detect objects in the workspace. In this paper, a multiple sensor based robot localization and object pose estimation method is presented. First, optical encoders and odometry model are utilized to determine the pose of the mobile robot in the workspace, with respect to the global coordinate system. Next, a CCD camera is used as a passive sensor to find an object (a box) in the environment, including the specific vertical surfaces of the box. By identifying and tracking color blobs which are attached to the center of each vertical surface of the box, the robot rotates and adjusts its base pose to move the color blob into the center of the camera view in order to make sure that the box is in the range of the laser scanner. Finally, a laser range finder, which is mounted on the top of the mobile robot, is activated to detect and compute the distances and angles between the laser source and laser contact surface on the box. Based on the information acquired in this manner, the global pose of the robot and the box can be represented using the homogeneous transformation matrix. This approach is validated using the Microsoft Robotics Studio simulation environment.
Frequency Response Analysis and Bandwidth Extension of the Doherty Amplifier

A complete frequency response analysis of the Doherty amplifier is presented with the conventional output combining network consisting of two quarter-wavelength (λ/4) transmission lines at a center frequency f0 . Expressions for output power and efficiency were derived over the whole dynamic range and at any frequency f. The analysis shows that the amount of efficiency enhancement, as well as the maximum output power, reduce as the deviation from f0 increases. For instance, the derived expressions show that a conventional Doherty amplifier has a drain efficiency of η ≥ 52.7%, which represents at least 13.4% efficiency enhancement over a class B amplifier, and up to 33.3% fractional bandwidth. A modified output combining network, using λ/4 lines with reduced impedance transformation ratio, is also analyzed, which results in a bandwidth extension of the Doherty amplifier when compared to the conventional design. To verify the derived analyses, three unsymmetrical GaN Doherty power amplifiers (DPAs) were designed and characterized; the first DPA was based on the conventional output combining network, while the second DPA was based on the proposed network. Measurements showed that the first DPA had, at 5-6-dB back-off, a drain efficiency of η ≥ 44% and over 28% fractional bandwidth (1.7-2.25 GHz), while the DPA with the proposed output combining network had a better wideband performance than the third reference conventional DPA, with a back-off drain efficiency of η ≥ 41%, and over 42% fractional bandwidth (1.7-2.6 GHz). To the best of authors' knowledge, the designed DPAs have the highest bandwidths reported thus far.
A Study of Feature Extraction Algorithms for Optical Flow Tracking

Sparse optical flow algorithms, such as the Lucas-Kanade approach, provide more robustness to noise than dense optical flow algorithms and are the preferred approach in many scenarios. Sparse optical flow algorithms estimate the displacement for a selected number of pixels in the image. These pixels can be chosen randomly. However, pixels in regions with more variance between the neighbors will produce more reliable displacement estimates. The selected pixel locations should therefore be chosen wisely. In this study, the suitability of Harris corners, Shi-Tomasi’s “Good features to track”, SIFT and SURF interest point extractors, Canny edges, and random pixel selection for the purpose of frame-by-frame tracking using a pyramidical Lucas-Kanade algorithm is investigated. The evaluation considers the important factors of processing time, feature count, and feature trackability in indoor and outdoor scenarios using ground vehicles and unmanned areal vehicles, and for the purpose of visual odometry estimation.
TeleKinect : A Telepresence Platform for Shared Experiences in a Composited Video Environment

TeleKinect is a platform for collaborate interactions at a distance in the same virtual space. The framework supports the real-time videos transport of foreground elements from remote locations and composites them into a merged virtual space. In this position paper we present two applications that utilize the framework for shared experiences at a distance: WaaZam is an application focused on supporting social engagement through creative play. It is focused on how users can collaboratively customize the environment and build sets and studios as places to be together at a distance. The project presents potential scenarios where users can do things together like broadcasting, karaoke, presentations, teaching, and watching television. InReach explores how remote collaborates can "reach into" a shared digital workspace where they can manipulate virtual objects and 3D data. The demo presents scenarios where users can manipulate simple 3D data for discussion, visualization, and online education environments for classroom purposes.
An AI Approach to Automatic Natural Music Transcription

Automatic music transcription (AMT) remains a fundamental and difficult problem in music information research, and current music transcription systems are still unable to match human performance. AMT aims to automatically generate a score representation given a polyphonic acoustical signal. In our project, we approach the AMT problem on two fronts: acoustic modeling to identify pitches from a frame of audio, and establishing a score generation model to convert exact piano roll representations of audio into more ‘natural’ sheet music. We build an end to end pipeline that aims to convert .wav classical piano audio files into a ‘natural’ score representation.
Entity Set Expansion via Knowledge Graphs

The entity set expansion problem is to expand a small set of seed entities to a more complete set of similar entities. It can be applied in applications such as web search, item recommendation and query expansion. Traditionally, people solve this problem by exploiting the co-occurrence of entities within web pages, where latent semantic correlation among seed entities cannot be revealed. We propose a novel approach to solve the problem using knowledge graphs, by considering the deficiency (e.g., incompleteness) of knowledge graphs. We design an effective ranking model based on the semantic features of seeds to retrieve the candidate entities. Extensive experiments on public datasets show that the proposed solution significantly outperforms the state-of-the-art techniques.
The Habits of Highly Effective Researchers: An Empirical Study

Interest in the habits of influential individuals cuts across domains. As researchers, we are intrigued why few attain significant eminence in their fields, whereas many operate in obscurity. An empirical examination of this question has been made possible by the recent availability of large scale publication data. In this paper, we use information from the AMiner Paper  Networks to discern factors that relate to the impact of influential researchers across five domains in the computing discipline. We propose and apply a novel algorithm to identify influential vertices in co-authorship networks built from total corpora of 1,00,000+ papers and 72,000+ authors over a span of more than 50 years. The results from our study indicate that the impact of these influential researchers relate to a variety of factors. Surprisingly, we find evidence across the domains that higher impact is associated with lower levels of collaboration, and authority.
100 top-cited scientific papers in limb prosthetics

Research has tremendously contributed to the developments in both practical and fundamental aspects of limb prosthetics. These advancements are reflected in scientific articles, particularly in the most cited papers. This article aimed to identify the 100 top-cited articles in the field of limb prosthetics and to investigate their main characteristics. Articles related to the field of limb prosthetics and published in the Web of Knowledge database of the Institute for Scientific Information (ISI) from the period of 1980 to 2012. The 100 most cited articles in limb prosthetics were selected based on the citation index report. All types of articles except for proceedings and letters were included in the study. The study design and level of evidence were determined using Sackett's initial rules of evidence. The level of evidence was categorized either as a systematic review or meta-analysis, randomized controlled trial, cohort study, case-control study, case series, expert opinion, or design and development. The top cited articles in prosthetics were published from 1980 to 2012 with a citation range of 11 to 90 times since publication. The mean citation rate was 24.43 (SD 16.7) times. Eighty-four percent of the articles were original publications and were most commonly prospective (76%) and case series studies (67%) that used human subjects (96%) providing level 4 evidence. Among the various fields, rehabilitation (47%), orthopedics (29%), and sport sciences (28%) were the most common fields of study. The study established that studies conducted in North America and were written in English had the highest citations. Top cited articles primarily dealt with lower limb prosthetics, specifically, on transtibial and transradial prosthetic limbs. Majority of the articles were experimental studies.
SERVQUAL : A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality

This paper describes the development of a 22-item instrument (called SERVQUAL) for assessing customer perceptions of service quality in service and retailing organizations. After a discussion of the conceptualization and operationalization of the service quality construct, the procedures used in constructing and refining a multiple-item scale to measure the construct are described. Evidence of the scale's reliability, factor structure, and validity on the basis of analyzing data from four independent samples is presented next. The paper concludes with a discussion of potential applications of the scale.
Evaluating the public health impact of health promotion interventions: the RE-AIM framework.

Progress in public health and community-based interventions has been hampered by the lack of a comprehensive evaluation framework appropriate to such programs. Multilevel interventions that incorporate policy, environmental, and individual components should be evaluated with measurements suited to their settings, goals, and purpose. In this commentary, the authors propose a model (termed the RE-AIM model) for evaluating public health interventions that assesses 5 dimensions: reach, efficacy, adoption, implementation, and maintenance. These dimensions occur at multiple levels (e.g., individual, clinic or organization, community) and interact to determine the public health or population-based impact of a program or policy. The authors discuss issues in evaluating each of these dimensions and combining them to determine overall public health impact. Failure to adequately evaluate programs on all 5 dimensions can lead to a waste of resources, discontinuities between stages of research, and failure to improve public health to the limits of our capacity. The authors summarize strengths and limitations of the RE-AIM model and recommend areas for future research and application.
Sampling-Based Estimation of the Number of Distinct Values of an Attribute

We provide several new sampling-based estimators of the number of distinct values of an attribute in a relation. We compare these new estimators to estimators from the database and statistical literature empirically, using a large number of attribute-value distributions drawn from a variety of real-world databases. This appears to be the first extensive comparison of distinct-value estimators in either the database or statistical literature, and is certainly the first to use highlyskewed data of the sort frequently encountered in database applications. Our experiments indicate that a new “hybrid” estimator yields the highest precision on average for a given sampling fraction. This estimator explicitly takes into account the degree of skew in the data and combines a new “smoothed jackknife” estimator with an estimator due to Shlosser. We investigate how the hybrid estimator behaves as we scale up the size of the database.
Intelligent book positioning for library using RFID and book spine matching

In library, the management of books is very complication and timing costing. The location of books could be altered by librarian, students, teachers and any one around the library. Therefore, allocating a book is not an easy task in big library. Indoor positioning is an important technology to help storage management and customer services providing. RFID provides a good wireless platform to facilitate indoor positioning. However, duo to the small width of each book spine, adopting positioning based RFID alone is not enough to locate books in a library. In this work, we combine image matching with L-GEM based RBFNN to enhance the accuracy and robustness of the book locating system. We apply this new method in a library environment to position the certain books. Experimental results show that the proposed method is highly accurate and robust to white noise of RFID signals.