nils-herrmann/clean_scirepeval huggingface dataset:
Low-resource grapheme-to-phoneme conversion using recurrent neural networks

Grapheme-to-phoneme (G2P) conversion is an important problem for many speech and language processing applications. G2P models are particularly useful for low-resource languages that do not have well-developed pronunciation lexicons. Prominent G2P paradigms are based on initial alignments between grapheme and phoneme sequences. In this work, we devise new alignment strategies that work effectively with recurrent neural network based models when only a small number of pronunciations are available to train the models. In a small data setting, we build G2P models for Pashto, Tagalog and Lithuanian that significantly outperform a joint sequence model and a baseline recurrent neural network based model, giving up to 14% and 9% relative reductions in phone and word error rates when trained on a dataset of 250 words.
Ethical leadership : A social learning perspective for construct development and testing

Leaders should be a key source of ethical guidance for employees. Yet, little empirical research focuses on an ethical dimension of leadership. We propose social learning theory as a theoretical basis for understanding ethical leadership and offer a constitutive definition of the ethical leadership construct. In seven interlocking studies, we investigate the viability and importance of this construct. We develop and test a new instrument to measure ethical leadership, examine the proposed connections of ethical leadership with other constructs in a nomological network, and demonstrate its predictive validity for important employee outcomes. Specifically, ethical leadership is related to consideration behavior, honesty, trust in the leader, interactional fairness, socialized charismatic leadership (as measured by the idealized influence dimension of transformational leadership), and abusive supervision, but is not subsumed by any of a b Purchase Export Previous article Next article Check if you have access through your login credentials or your institution.
Object permanence in infancy : challenges to the Piagetian account . The impact of Jean Piaget ’ s account of cognitive development

The impact of Jean Piaget’s account of cognitive development (e.g. Piaget, 1954; Piaget & Inhelder, 2000) on developmental psychology is practically incalculable (Flavell, 1996; Miller, 1993; Slater, Hocking & Loose, 2003) but the latter part of the 20 th Century saw his theories under attack from several quarters. What follows is a discussion of challenges to his account of the development of the object concept.
Inverse perspective mapping based Urban road markings detection

A robust road markings detection algorithm is a fundamental component of intelligent vehicles' autonomous navigation in urban environment. This paper presents an algorithm for detecting road markings including zebra crossings, stop lines and lane markings to provide road information for intelligent vehicles. First, to eliminate the impact of the perspective effect, an Inverse Perspective Mapping (IPM) transformation is applied to the images grabbed by the camera; the region of interest (ROI) was extracted from IPM image by a low level processing. Then, different algorithms are adopted to extract zebra crossings, stop lines and lane markings. The experiments on a large number of street scenes in different conditions demonstrate the effectiveness of the proposed algorithm.
DP-ADMM: ADMM-based Distributed Learning with Differential Privacy

Privacy-preserving distributed machine learning has become more important than ever due to the high demand of large-scale data processing. This paper focuses on a class of machine learning problems that can be formulated as regularized empirical risk minimization, and develops a privacy-preserving learning approach to such problems. We use Alternating Direction Method of Multipliers (ADMM) to decentralize the learning algorithm, and apply Gaussian mechanisms to provide differential privacy guarantee. However, simply combining ADMM and local randomization mechanisms would result in a nonconvergent algorithm with poor performance even under moderate privacy guarantees. Besides, this intuitive approach requires a strong assumption that the objective functions of the learning problems should be differentiable and strongly convex. To address these concerns, we propose an improved ADMMbased Differentially Private distributed learning algorithm, DPADMM, where an approximate augmented Lagrangian function and Gaussian mechanisms with time-varying variance are utilized. We also apply the moments accountant method to bound the total privacy loss. Our theoretical analysis shows that DPADMM can be applied to a general class of convex learning problems, provides differential privacy guarantee, and achieves a convergence rate of O(1/ √ t), where t is the number of iterations. Our evaluations demonstrate that our approach can achieve good convergence and accuracy with moderate privacy guarantee.
Simultaneous genital ulcer and meningitis: a case of EBV infection

The Epstein-Barr virus (EBV) is associated with a broad spectrum of diseases, mainly because of its genomic characteristics, which result in different latency patterns in immune cells and infective mechanisms. The patient described in this report is a previously healthy young man who presented to the emergency department with clinical features consistent with meningitis and genital ulcers, which raised concern that the herpes simplex virus was the causative agent. However, the polymerase chain reaction of cerebral spinal fluid was positive for EBV. The authors highlight the importance of this infection among the differential diagnosis of central nervous system involvement and genital ulceration.
A Novel PCCM Boost PFC Converter With Fast Dynamic Response

A novel pseudo-continuous conduction mode (PCCM) boost power-factor-correction (PFC) converter and its corresponding control strategy are proposed in this paper. Connecting a power switch in parallel with the inductor makes the boost converter operate in PCCM, which provides an additional degree of control freedom to realize PFC control. Therefore, a simple and fast voltage control loop of the PCCM boost PFC converter can be designed to realize output-voltage regulation. The additional degree of control freedom introduced by inductor current freewheeling operation of the PCCM boost PFC converter has been exploited through the dead-zone control technique, which can be dynamically adjusted in accordance with the output-voltage ripple. Compared with continuous conduction mode (CCM) boost PFC converter, the controller of the proposed PCCM boost PFC converter is much simpler. Moreover, the PCCM boost PFC converter benefits with reduced inductor-current ripple and improved power factor compared with boost PFC converter operating in discontinuous conduction mode (DCM). Analysis, simulation, and a 400-W prototype of the PCCM boost PFC converter have been presented and compared with those of boost PFC converters operating in conventional CCM and DCM. The results show that the dynamic response of the PCCM boost PFC converter is significantly faster than the existing boost PFC converter.
Improved neural network based language modelling and adaptation

Neural network language models (NNLM) have become an increasingly popular choice for large vocabulary continuous speech recognition (LVCSR) tasks, due to their inherent generalisation and discriminative power. This paper present two techniques to improve performance of standard NNLMs. First, the form of NNLM is modelled by introduction an additional output layer node to model the probability mass of out-of-shortlist (OOS) words. An associated probability normalisation scheme is explicitly derived. Second, a novel NNLM adaptation method using a cascaded network is proposed. Consistent WER reductions were obtained on a state-of-the-art Arabic LVCSR task over conventional NNLMs. Further performance gains were also observed after NNLM adaptation.
A Framework of a Personalized Location-based Traveler Recommendation System in Mobile Application

In this era of evolving technology, there are various channels and platforms through which travelers can find tour information and share their tour experience. These include tourism websites, social network sites, blogs, forums, and various search engines such as Google, Yahoo, etc. However, information found in this way is not filtered based on travelers’ preferences. Hence, travelers face an information overflow problem.. There is also increasing demand for more information on local area attractions, such as local food, shopping spots, places of interest and so on during the tour. The goal of this research is to propose a suitable recommendation method for use in a Personalized Location-based Traveler Recommender System (PLTRS) to provide personalized tourism information to its users. A comparative study of available recommender systems and location-based services (LBS) is conducted to explore the different approaches to recommender systems and LBS technology. The effectiveness of the system based on the proposed framework is tested using various scenarios which might be faced by users.
net Wireless Fetal Monitoring

Fetal mortality rate is considered a good measure of the quality of health care in a country or a medical facility. If we look at the current scenario, we find that we have focused more on child mortality rate than on fetus mortality. Even it is a same situation in developed country. Our aim is to provide technological solution to help decrease the fetal mortality rate. Also if we consider pregnant women, they have to come to hospital 2-3 times a week for their regular checkups. It becomes a problem for working women and women having diabetes or other disease. For these reasons it would be very helpful if they can do this by themselves at home. This will reduce the frequency of their visit to the hospital at same time cause no compromise in the wellbeing of both the mother and the child. The end to end system consists of wearable sensors, built into a fabric belt, that collects and sends vital signs of patients via bluetooth to smart mobile phones for further processing and made available to required personnel allowing efficient monitoring and alerting when attention is required in often challenging and chaotic scenarios.
RSR2015: Database for Text-Dependent Speaker Verification using Multiple Pass-Phrases

This paper describes a new speech corpus, the RSR2015 database designed for text-dependent speaker recognition with scenario based on fixed pass-phrases. This database consists of over 71 hours of speech recorded from English speakers covering the diversity of accents spoken in Singapore. Acquisition has been done using a set of six portable devices including smart phones and tablets. The pool of speakers consists of 300 participants (143 female and 157 male speakers) from 17 to 42 years old. We propose a protocol for the case of user-dependent pass-phrases in text-dependent speaker recognition and we also report speaker recognition experiments on RSR2015 database.
Comparison of Nutritional Quality of the Vegan, Vegetarian, Semi-Vegetarian, Pesco-Vegetarian and Omnivorous Diet

The number of studies comparing nutritional quality of restrictive diets is limited. Data on vegan subjects are especially lacking. It was the aim of the present study to compare the quality and the contributing components of vegan, vegetarian, semi-vegetarian, pesco-vegetarian and omnivorous diets. Dietary intake was estimated using a cross-sectional online survey with a 52-items food frequency questionnaire (FFQ). Healthy Eating Index 2010 (HEI-2010) and the Mediterranean Diet Score (MDS) were calculated as indicators for diet quality. After analysis of the diet questionnaire and the FFQ, 1475 participants were classified as vegans (n = 104), vegetarians (n = 573), semi-vegetarians (n = 498), pesco-vegetarians (n = 145), and omnivores (n = 155). The most restricted diet, i.e., the vegan diet, had the lowest total energy intake, better fat intake profile, lowest protein and highest dietary fiber intake in contrast to the omnivorous diet. Calcium intake was lowest for the vegans and below national dietary recommendations. The vegan diet received the highest index values and the omnivorous the lowest for HEI-2010 and MDS. Typical aspects of a vegan diet (high fruit and vegetable intake, low sodium intake, and low intake of saturated fat) contributed substantially to the total score, independent of the indexing system used. The score for the more prudent diets (vegetarians, semi-vegetarians and pesco-vegetarians) differed as a function of the used indexing system but they were mostly better in terms of nutrient quality than the omnivores.
A Very Short-Term Load forecasting using Kalman filter for Load Frequency Control with Economic Load Dispatch

In this paper we have proposed a control technique for the automatic generation control of multi generating power unit of the interconnected power system. This technique established the relationship between the economic load dispatch and load forecasting mechanism to the classical concepts of the load frequency control (LFC). The LFC system monitors to keep the power system frequency at nominal value, generator output according to the load demand and net interchange scheduled tie line power flows within prescribed limit among the different control area of the power system. Due to relatively fast area load demand fluctuations and accordingly slow response of instantaneous estimate of area control error (ACE), we need some load forecasting technique for better dynamic system response as well as improved & effective load frequency control to the power system. Load prediction technique has been accomplished using the klaman filter prediction recursive algorithms and a bank of hourly predicted load data is obtained and then the concepts of 5 minute look ahead forecasting technique is applied and finally total load is shared among the different generating units according to the calculation of economic load dispatch via participation factor’s. Results and Discussion section of this paper of simulated interconnected system’s graphs support this new technique wisely.
Two Failures to Replicate High-Performance-Goal Priming Effects

Bargh et al. (2001) reported two experiments in which people were exposed to words related to achievement (e.g., strive, attain) or to neutral words, and then performed a demanding cognitive task. Performance on the task was enhanced after exposure to the achievement related words. Bargh and colleagues concluded that better performance was due to the achievement words having activated a "high-performance goal". Because the paper has been cited well over 1100 times, an attempt to replicate its findings would seem warranted. Two direct replication attempts were performed. Results from the first experiment (n = 98) found no effect of priming, and the means were in the opposite direction from those reported by Bargh and colleagues. The second experiment followed up on the observation by Bargh et al. (2001) that high-performance-goal priming was enhanced by a 5-minute delay between priming and test. Adding such a delay, we still found no evidence for high-performance-goal priming (n = 66). These failures to replicate, along with other recent results, suggest that the literature on goal priming requires some skeptical scrutiny.
Preoperative optimization of the vascular surgery patient

It is well known that patients who suffer from peripheral (noncardiac) vascular disease often have coexisting atherosclerotic diseases of the heart. This may leave the patients susceptible to major adverse cardiac events, including death, myocardial infarction, unstable angina, and pulmonary edema, during the perioperative time period, in addition to the many other complications they may sustain as they undergo vascular surgery procedures, regardless of whether the procedure is performed as an open or endovascular modality. As these patients are at particularly high risk, up to 16% in published studies, for postoperative cardiac complications, many proposals and algorithms for perioperative optimization have been suggested and studied in the literature. Moreover, in patients with recent coronary stents, the risk of non-cardiac surgery on adverse cardiac events is incremental in the first 6 months following stent implantation. Just as postoperative management of patients is vital to the outcome of a patient, preoperative assessment and optimization may reduce, and possibly completely alleviate, the risks of major postoperative complications, as well as assist in the decision-making process regarding the appropriate surgical and anesthetic management. This review article addresses several tools and therapies that treating physicians may employ to medically optimize a patient before they undergo noncardiac vascular surgery.
Deep learning based basketball video analysis for intelligent arena application

Given the tremendous growth of sport fans, the “Intelligent Arena”, which can greatly improve the fun of traditional sports, becomes one of the new-emerging applications and research topics. The development of multimedia computing and artificial intelligence technologies support intelligent sport video analysis to add live video broadcast, score detection, highlight video generation, and online sharing functions to the intelligent arena applications. In this paper, we have proposed a deep learning based video analysis scheme for intelligent basketball arena applications. First of all, with multiple cameras or mobile devices capturing the activities in arena, the proposed scheme can automatically select the camera to give high-quality broadcast in real-time. Furthermore, with basketball energy image based deep conventional neural network, we can detect the scoring clips as the highlight video reels to support the wonderful actions replay and online sharing functions. Finally, evaluations on a built real-world basketball match dataset demonstrate that the proposed system can obtain 94.59% accuracy with only less than 45m s processing time (i.e., 10m s broadcast camera selection, and 35m s for scoring detection) for each frame. As the outstanding performance, the proposed deep learning based basketball video analysis scheme is implemented into a commercial intelligent basketball arena application named “Standz Basketball”. Although the application had been only released for one month, it achieves the 85t h day download ranking place in the sport category of Chinese iTunes market.
Diferulates as structural components in soluble and insoluble cereal dietary fibre

Diferulates as structural components in soluble and insoluble cereal dietary fibre Mirko Bunzel, John Ralph, Jane M Marita, Ronald D Hatfield and Hans Steinhart Institute of Biochemistry and Food Chemistry, Department of Food Chemistry, University of Hamburg, Grindelallee 117, D-20146 Hamburg, Germany US Dairy Forage Research Center, USDA-Agricultural Research Service, Madison, WI 53706, USA Department of Forestry, University of Wisconsin-Madison, Madison, WI 53706, USA
General factorization framework for context-aware recommendations

Context-aware recommendation algorithms focus on refining recommendations by considering additional information, available to the system. This topic has gained a lot of attention recently. Among others, several factorization methods were proposed to solve the problem, although most of them assume explicit feedback which strongly limits their real-world applicability. While these algorithms apply various loss functions and optimization strategies, the preference modeling under context is less explored due to the lack of tools allowing for easy experimentation with various models. As context dimensions are introduced beyond users and items, the space of possible preference models and the importance of proper modeling largely increases. In this paper we propose a general factorization framework (GFF), a single flexible algorithm that takes the preference model as an input and computes latent feature matrices for the input dimensions. GFF allows us to easily experiment with various linear models on any context-aware recommendation task, be it explicit or implicit feedback based. The scaling properties makes it usable under real life circumstances as well. We demonstrate the framework’s potential by exploring various preference models on a 4-dimensional context-aware problem with contexts that are available for almost any real life datasets. We show in our experiments—performed on five real life, implicit feedback datasets—that proper preference modelling significantly increases recommendation accuracy, and previously unused models outperform the traditional ones. Novel models in GFF also outperform state-of-the-art factorization algorithms. We also extend the method to be fully compliant to the Multidimensional Dataspace Model, one of the most extensive data models of context-enriched data. Extended GFF allows the seamless incorporation of information into the factorization framework beyond context, like item metadata, social networks, session information, etc. Preliminary experiments show great potential of this capability.
Queriosity: Automated Data Exploration

Curiosity, a fundamental drive amongst higher living organisms, is what enables exploration, learning and creativity. In our increasingly data-driven world, data exploration, i.e., Making sense of mounting haystacks of data, is akin to intelligence for science, business and individuals. However, modern data systems -- designed for data retrieval rather than exploration -- only let us retrieve data and ask if it is interesting. This makes knowledge discovery a game of hit-and-trial which can only be orchestrated by expert data scientists. We present the vision toward Queriosity, an automated and personalized data exploration system. Designed on the principles of autonomy, learning and usability, Queriosity envisions a paradigm shift in data exploration and aims to become a a personalized "data robot" that provides a direct answer to what is interesting in a user's data set, instead of just retrieving data. Queriosity autonomously and continuously navigates toward interesting findings based on trends, statistical properties and interactive user feedback.
Non-rigid Object Tracking via Deformable Patches Using Shape-Preserved KCF and Level Sets

Part-based trackers are effective in exploiting local details of the target object for robust tracking. In contrast to most existing part-based methods that divide all kinds of target objects into a number of fixed rectangular patches, in this paper, we propose a novel framework in which a set of deformable patches dynamically collaborate on tracking of non-rigid objects. In particular, we proposed a shape-preserved kernelized correlation filter (SP-KCF) which can accommodate target shape information for robust tracking. The SP-KCF is introduced into the level set framework for dynamic tracking of individual patches. In this manner, our proposed deformable patches are target-dependent, have the capability to assume complex topology, and are deformable to adapt to target variations. As these deformable patches properly capture individual target subregions, we exploit their photometric discrimination and shape variation to reveal the trackability of individual target subregions, which enables the proposed tracker to dynamically take advantage of those subregions with good trackability for target likelihood estimation. Finally the shape information of these deformable patches enables accurate object contours to be computed as the tracking output. Experimental results on the latest public sets of challenging sequences demonstrate the effectiveness of the proposed method.