nils-herrmann/clean_scirepeval huggingface dataset:
Using VTLN for broadcast news transcription

Vocal tract length normalisation (VTLN) is a commonly used speaker normalisation approach. It is attractive compared to many normalisation schemes as it is typically dependent on only a single parameter, allowing the warp factors to be robustly calculated on little data. However, the scheme normally requires explicitly coding the data at multiple warp factors. Furthermore, it is only possible to approximate the Jacobian associated with the VTLN transformation. A new, simple, linear approximation to VTLN is described in this paper. This linear approximation allows the Jacobian to be exactly computed. It can also be highly efficient in terms of warp factor estimation and application of the warp factors. Both the linear and standard CUED VTLN schemes were evaluated in the 2003 BNE evaluation framework and found to yield similar performance. When used in system combination both VTLN schemes yielded slight gains over the baseline system.
Detection of obstructive sleep apnea through ECG signal features

Obstructive sleep apnea (OSA) is a common disorder in which individuals stop breathing during their sleep. Most of sleep apnea cases are currently undiagnosed because of expenses and practicality limitations of overnight polysomnography (PSG) at sleep labs, where an expert human observer is needed to work over night. New techniques for sleep apnea classification are being developed by bioengineers for most comfortable and timely detection. In this paper, an automated classification algorithm is presented which processes short duration epochs of the electrocardiogram (ECG) data. The automated classification algorithm is based on support vector machines (SVM) and has been trained and tested on sleep apnea recordings from subjects with and without OSA. The results show that our automated classification system can recognize epochs of sleep disorders with a high degree of accuracy, approximately 96.5%. Moreover, the system we developed can be used as a basis for future development of a tool for OSA screening.
Mathematical problem solving by analogy.

We report the results of 2 experiments and a verbal protocol study examining the component processes of solving mathematical word problems by analogy. College students first studied a problem and its solution, which provided a potential source for analogical transfer. Then they attempted to solve several analogous problems. For some problems, subjects received one of a variety of hints designed to reduce or eliminate the difficulty of some of the major processes hypothesized to be involved in analogical transfer. Our studies yielded 4 major findings. First, the process of mapping the features of the source and target problems and the process of adapting the source solution procedure for use in solving the target problem were clearly distinguished: (a) Successful mapping was found to be insufficient for successful transfer and (b) adaptation was found to be a major source of transfer difficulty. Second, we obtained direct evidence that schema induction is a natural consequence of analogical transfer. The schema was found to co-exist with the problems from which it was induced, and both the schema and the individual problems facilitated later transfer. Third, for our multiple-solution problems, the relation between analogical transfer and solution accuracy was mediated by the degree of time pressure exerted for the test problems. Finally, mathematical expertise was a significant predictor of analogical transfer, but general analogical reasoning ability was not. The implications of the results for models of analogical transfer and for instruction were considered.
Modeling and Simulation of Lithium-Ion Batteries from a Systems Engineering Perspective

The lithium-ion battery is an ideal candidate for a wide variety of applications due to its high energy/power density and operating voltage. Some limitations of existing lithium-ion battery technology include underutilization, stress-induced material damage, capacity fade, and the potential for thermal runaway. This paper reviews efforts in the modeling and simulation of lithium-ion batteries and their use in the design of better batteries. Likely future directions in battery modeling and design including promising research opportunities are outlined.
Adaptive Binary Search Trees Jonathan Carlyle Derryberry

A ubiquitous problem in the field of algorithms and data structures is that of searching for an element from an ordered universe. The simple yet powerful binary search tree (BST) model provides a rich family of solutions to this problem. Although BSTs require Ω(lg n) time per operation in the worst case, various adaptive BST algorithms are capable of exploiting patterns in the sequence of queries to achieve tighter, input-sensitive, bounds that can be o(lg n) in many cases. This thesis furthers our understanding of what is achievable in the BST model along two directions. First, we make progress in improving instance-specific lower bounds in the BST model. In particular, we introduce a framework for generating lower bounds on the cost that any BST algorithm must pay to execute a query sequence, and we show that this framework generalizes previous lower bounds. This suggests that the optimal lower bound in the framework is a good candidate for being tight to within a constant factor of the optimal BST algorithm for each input. Further, we show that lower bounds in this framework are also valid lower bounds for instances of the partial-sums problem in a restricted model of computation, which suggests that augmented BSTs may be the most efficient way of maintaining sums over ranges of an array when the entries of the array can be updated throughout time. Second, we improve the input-sensitive upper bounds that are known to be achievable in the BST model by introducing two new BST algorithms, skip-splay and cache-splay. These two algorithms are the first BSTs that are known to have running times that have nontrivial competitiveness to Iacono’s Unified Bound, which is a generalization of the dynamic finger and working set bounds. Skip-splay is a simple algorithm that is nearly identical to splaying, and it achieves a running time that is within additive O(lg lg n) per operation of the Unified Bound. Cache-splay is a slightly more complicated splay-based algorithm that is the first BST to achieve the Unified Bound to within a constant factor.
Correlation-based Feature Selection for Machine Learning

A central problem in machine learning is identifying a repre sentative set of features from which to construct a classification model for a particular ta sk. This thesis addresses the problem of feature selection for machine learning through a correlation based approach. The central hypothesis is that good feature sets contain fea tures that are highly correlated with the class, yet uncorrelated with each other. A feature e valuation formula, based on ideas from test theory, provides an operational definitio of this hypothesis. CFS (Correlation based Feature Selection) is an algorithm that couples this evaluation formula with an appropriate correlation measure and a heuristic sea rch strategy. CFS was evaluated by experiments on artificial and natural da t sets. Three machine learning algorithms were used: C4.5 (a decision tree learner), IB 1 (an instance based learner), and naive Bayes. Experiments on artificial datasets showed t hat CFS quickly identifies and screens irrelevant, redundant, and noisy features, and ide tifies relevant features as long as their relevance does not strongly depend on other fea tures. On natural domains, CFS typically eliminated well over half the features. In mos t cases, classification accuracy using the reduced feature set equaled or bettered accuracy u sing the complete feature set. Feature selection degraded machine learning performance i n ases where some features were eliminated which were highly predictive of very small a re s of the instance space. Further experiments compared CFS with a wrapper—a well know n approach to feature selection that employs the target learning algorithm to eva luate feature sets. In many cases CFS gave comparable results to the wrapper, and in general, o utperformed the wrapper on small datasets. CFS executes many times faster than the wr apper, which allows it to scale to larger datasets. Two methods of extending CFS to handle feature interaction a re presented and experimentally evaluated. The first considers pairs of features a nd the second incorporates iii feature weights calculated by the RELIEF algorithm. Experi m nts on artificial domains showed that both methods were able to identify interacting f eatures. On natural domains, the pairwise method gave more reliable results than using we i hts provided by RELIEF.
Multimodal Machine Learning: A Survey and Taxonomy

Our experience of the world is multimodal - we see objects, hear sounds, feel texture, smell odors, and taste flavors. Modality refers to the way in which something happens or is experienced and a research problem is characterized as multimodal when it includes multiple such modalities. In order for Artificial Intelligence to make progress in understanding the world around us, it needs to be able to interpret such multimodal signals together. Multimodal machine learning aims to build models that can process and relate information from multiple modalities. It is a vibrant multi-disciplinary field of increasing importance and with extraordinary potential. Instead of focusing on specific multimodal applications, this paper surveys the recent advances in multimodal machine learning itself and presents them in a common taxonomy. We go beyond the typical early and late fusion categorization and identify broader challenges that are faced by multimodal machine learning, namely: representation, translation, alignment, fusion, and co-learning. This new taxonomy will enable researchers to better understand the state of the field and identify directions for future research.
Back to the Future: Current-Mode Processor in the Era of Deeply Scaled CMOS

This paper explores the use of MOS current-mode logic (MCML) as a fast and low noise alternative to static CMOS circuits in microprocessors, thereby improving the performance, energy efficiency, and signal integrity of future computer systems. The power and ground noise generated by an MCML circuit is typically 10-100× smaller than the noise generated by a static CMOS circuit. Unlike static CMOS, whose dominant dynamic power is proportional to the frequency, MCML circuits dissipate a constant power independent of clock frequency. Although these traits make MCML highly energy efficient when operating at high speeds, the constant static power of MCML poses a challenge for a microarchitecture that operates at the modest clock rate and with a low activity factor. To address this challenge, a single-core microarchitecture for MCML is explored that exploits the C-slow retiming technique, and operates at a high frequency with low complexity to save energy. This design principle contrasts with the contemporary multicore design paradigm for static CMOS that relies on a large number of gates operating in parallel at the modest speeds. The proposed architecture generates 10-40× lower power and ground noise, and operates within 13% of the performance (i.e., 1/ExecutionTime) of a conventional, eight-core static CMOS processor while exhibiting 1.6× lower energy and 9% less area. Moreover, the operation of an MCML processor is robust under both systematic and random variations in transistor threshold voltage and effective channel length.
Output impedance design of parallel-connected UPS inverters with wireless load-sharing control

This paper deals with the design of the output impedance of uninterruptible power system (UPS) inverters with parallel-connection capability. In order to avoid the need for any communication among modules, the power-sharing control loops are based on the P/Q droop method. Since in these systems the power-sharing accuracy is highly sensitive to the inverters output impedance, novel control loops to achieve both stable output impedance and proper power balance are proposed. In this sense, a novel wireless controller is designed by using three nested loops: 1) the inner loop is performed by using feedback linearization control techniques, providing a good quality output voltage waveform; 2) the intermediate loop enforces the output impedance of the inverter, achieving good harmonic power sharing while maintaining low output voltage total harmonic distortion; and 3) the outer loop calculates the output active and reactive powers and adjusts the output impedance value and the output voltage frequency during the load transients, obtaining excellent power sharing without deviations in either the frequency or the amplitude of the output voltage. Simulation and experimental results are reported from a parallel-connected UPS system sharing linear and nonlinear loads.
Resource Management in Clouds: Survey and Research Challenges

Resource management in a cloud environment is a hard problem, due to: the scale of modern data centers; the heterogeneity of resource types and their interdependencies; the variability and unpredictability of the load; as well as the range of objectives of the different actors in a cloud ecosystem. Consequently, both academia and industry began significant research efforts in this area. In this paper, we survey the recent literature, covering 250+ publications, and highlighting key results. We outline a conceptual framework for cloud resource management and use it to structure the state-of-the-art review. Based on our analysis, we identify five challenges for future investigation. These relate to: providing predictable performance for cloud-hosted applications; achieving global manageability for cloud systems; engineering scalable resource management systems; understanding economic behavior and cloud pricing; and developing solutions for the mobile cloud paradigm .
Recommendations in location-based social networks: a survey

Recent advances in localization techniques have fundamentally enhanced social networking services, allowing users to share their locations and location-related contents, such as geo-tagged photos and notes. We refer to these social networks as location-based social networks (LBSNs). Location data bridges the gap between the physical and digital worlds and enables a deeper understanding of users’ preferences and behavior. This addition of vast geo-spatial datasets has stimulated research into novel recommender systems that seek to facilitate users’ travels and social interactions. In this paper, we offer a systematic review of this research, summarizing the contributions of individual efforts and exploring their relations. We discuss the new properties and challenges that location brings to recommender systems for LBSNs. We present a comprehensive survey analyzing 1) the data source used, 2) the methodology employed to generate a recommendation, and 3) the objective of the recommendation. We propose three taxonomies that partition the recommender systems according to the properties listed above. First, we categorize the recommender systems by the objective of the recommendation, which can include locations, users, activities, or social media. Second, we categorize the recommender systems by the methodologies employed, including content-based, link analysis-based, and collaborative filtering-based methodologies. Third, we categorize the systems by the data sources used, including user profiles, user online histories, and user location histories. For each category, we summarize the goals and contributions of each system and highlight the representative research effort. J. Bao ( ) · M. Mokbel University of Minnesota, Minneapolis, MN, USA e-mail: baojie@cs.umn.edu M. Mokbel e-mail: mokbel@cs.umn.edu Y. Zheng Microsoft Research, Beijing, China e-mail: yuzheng@microsoft.com D. Wilkie University of North Carolina, Wilmington, NC, USA e-mail: wilkie@cs.unc.edu 526 Geoinformatica (2015) 19:525–565 Further, we provide comparative analysis of the recommender systems within each category. Finally, we discuss the available data-sets and the popular methods used to evaluate the performance of recommender systems. Finally, we point out promising research topics for future work. This article presents a panorama of the recommender systems in location-based social networks with a balanced depth, facilitating research into this important research
Software-defined control of the virtualized mobile packet core

Mobile packet core networks are undergoing major changes to meet the requirements of the future data tsunami, enhance network flexibility, and reduce both CAPEX and OPEX. In this regard, SDN and NFV technologies have gained great momentum among the teleos, with the promise of interoperability, programmability, and on-demand dynamic provisioning. In this article, we present work being developed in the Mobile Packet Core project within the ONF Wireless & Mobile Working Group regarding SDN architecture for the Mobile Packet Core. In addition, we propose an SDN-based MPC in the NFV context in order to facilitate dynamic provisioning of MPC network functions. Finally, a potential control architecture considering both SDN and NFV is proposed.
Facial Expression Recognition in the Wild Using Improved Dense Trajectories and Fisher Vector Encoding

Improved dense trajectory features have been successfully used in video-based action recognition problems, but their application to face processing is more challenging. In this paper, we propose a novel system that deals with the problem of emotion recognition in real-world videos, using improved dense trajectory, LGBP-TOP, and geometric features. In the proposed system, we detect the face and facial landmarks from each frame of a video using a combination of two recent approaches, and register faces by means of Procrustes analysis. The improved dense trajectory and geometric features are encoded using Fisher vectors and classification is achieved by extreme learning machines. We evaluate our method on the extended Cohn-Kanade (CK+) and EmotiW 2015 Challenge databases. We obtain state-of the-art results in both databases.
A cross-national profile of bullying and victimization among adolescents in 40 countries

(1) To compare the prevalence of bullying and victimization among boys and girls and by age in 40 countries. (2) In 6 countries, to compare rates of direct physical, direct verbal, and indirect bullying by gender, age, and country. Cross-sectional self-report surveys including items on bullying and being bullied were obtained from nationally representative samples of 11, 13 and 15 year old school children in 40 countries, N = 202,056. Six countries (N = 29,127 students) included questions about specific types of bullying (e. g., direct physical, direct verbal, indirect). Exposure to bullying varied across countries, with estimates ranging from 8.6% to 45.2% among boys, and from 4.8% to 35.8% among girls. Adolescents in Baltic countries reported higher rates of bullying and victimization, whereas northern European countries reported the lowest prevalence. Boys reported higher rates of bullying in all countries. Rates of victimization were higher for girls in 29 of 40 countries. Rates of victimization decreased by age in 30 of 40 (boys) and 25 of 39 (girls) countries. There are lessons to be learned from the current research conducted in countries where the prevalence is low that could be adapted for use in countries with higher prevalence.
Deep Neural Networks for Semantic Segmentation of Multispectral Remote Sensing Imagery

A semantic segmentation algorithm must assign a label to every pixel in an image. Recently, semantic segmentation of RGB imagery has advanced significantly due to deep learning. Because creating datasets for semantic segmentation is laborious, these datasets tend to be significantly smaller than object recognition datasets. This makes it difficult to directly train a deep neural network for semantic segmentation, because it will be prone to overfitting. To cope with this, deep learning models typically use convolutional neural networks pre-trained on large-scale image classification datasets, which are then fine-tuned for semantic segmentation. For non-RGB imagery, this is currently not possible because large-scale labeled non-RGB datasets do not exist. In this paper, we developed two deep neural networks for semantic segmentation of multispectral remote sensing imagery. Prior to training on the target dataset, we initialize the networks with large amounts of synthetic multispectral imagery. We show that this significantly improves results on real-world remote sensing imagery, and we establish a new state-of-the-art result on the challenging Hamlin Beach State Park Dataset.
Chemoinformatics and Drug Discovery

This article reviews current achievements in the field of chemoinformatics and their impact on modern drug discovery processes. The main data mining approaches used in cheminformatics, such as descriptor computations, structural similarity matrices, and classification algorithms, are outlined. The applications of cheminformatics in drug discovery, such as compound selection, virtual library generation, virtual high throughput screening, HTS data mining, and in silico ADMET are discussed. At the conclusion, future directions of chemoinformatics are suggested.
Design and implementation of SDR based QPSK modulator on FPGA

Software defined radio (SDR) technology enables implementation of wireless devices that support multiple air-interfaces and modulation formats, which is very important if consider proliferation of wireless standards. To enable such functionality SDR is using reconfigurable hardware platform such as Field Programmable Gate Array (FPGA). In this paper, we present design procedure and implementation result of SDR based QPSK modulator on Altera Cyclone IV FPGA. For design and implementation of QPSK modulator we used Altera DSP Builder Tool combined with Matlab/Simulink, Modelsim and Quartus II design tools. As reconfigurable hardware platform we used Altera DE2-115 development and education board with AD/DA daughter card. Software and Hardware-in-the-loop (HIL) simulation was conducted before hardware implementation and verification of designed system. This method of design makes implementation of SDR based modulators simpler ad faster.
Dynamic Conversion Behavior at E-Commerce Sites

T paper develops a model of conversion behavior (i.e., converting store visits into purchases) that predicts each customer’s probability of purchasing based on an observed history of visits and purchases. We offer an individual-level probability model that allows for different forms of customer heterogeneity in a very flexible manner. Specifically, we decompose an individual’s conversion behavior into two components: one for accumulating visit effects and another for purchasing threshold effects. Each component is allowed to vary across households as well as over time. Visit effects capture the notion that store visits can play different roles in the purchasing process. For example, some visits are motivated by planned purchases, while others are associated with hedonic browsing (akin to window shopping); our model is able to accommodate these (and several other) types of visit-purchase relationships in a logical, parsimonious manner. The purchasing threshold captures the psychological resistance to online purchasing that may grow or shrink as a customer gains more experience with the purchasing process at a given website. We test different versions of the model that vary in the complexity of these two key components and also compare our general framework with popular alternatives such as logistic regression. We find that the proposed model offers excellent statistical properties, including its performance in a holdout validation sample, and also provides useful managerial diagnostics about the patterns underlying online buyer behavior.