nils-herrmann/clean_scirepeval huggingface dataset:
Transfer learning for children's speech recognition

Children's speech processing is more challenging than that of adults due to lacking of large scale children's speech corpora. With the developing of the physical speech organ, high inter speaker and intra speaker variabilities are observed in children's speech. On the other hand, data collection on children is difficult as children usually have short attention span and their language proficiency is limited. In this paper, we propose to improve children's automatic speech recognition performance with transfer learning technique. We compare two transfer learning approaches in enhancing children's speech recognition performance with adults' data. The first method is to perform acoustic model adaptation on the pre-trained adult model. The second is to train acoustic model with deep neural network based multi-task learning approach: the adults' and children's acoustic characteristics are learnt jointly in the shared hidden layers, while the output layers are optimized with different speaker groups. Our experiment results show that both transfer learning approaches are effective in transferring rich phonetic and acoustic information from adults' model to children model. The multi-task learning approach outperforms the acoustic adaptation approach. We further show that the speakers' acoustic characteristics in languages can also benefit the target language under the multi-task learning framework.
Identification of phenylpropanoids in fig (Ficus carica L.) leaves.

In this study, the phenylpropanoid composition and antioxidant activity of identified components in fig (Ficus carica L.) leaves were examined. Known polyphenols rutin, isoschaftoside, isoquercetin, and chlorogenic acid were identified. Furthermore, caffeoylmalic acid (CMA) was the most abundant polyphenol and was identified for the first time. CMA exhibited antioxidant activity similar to that of vitamin C or catechin. Psoralen and bergapten were identified as known furanocoumarins, with psoralen being the most abundant. Moreover, psoralic acid glucoside (PAG) was identified for the first time. As a precursor of psoralen, PAG content was equivalent to the psoralen content in moles. Notably, the content of these compounds varied between the five fig varieties, and the furanocoumarin and PAG contents varied more than that of the polyphenols. Further investigations concerning the influence of CMA and PAG on human health are necessary to elucidate functionalities of fig leaves.
New technology trends in education: Seven years of forecasts and convergence

Each year since 2004, a new Horizon Report has been released. Each edition attempts to forecast the most promising technologies likely to impact on education along three horizons: the short term (the year of the report), the mid-term (the next 2 years) and the long term (the next 4 years). This paper analyzes the evolution of technology trends from 2004 to 2014 that correspond to the long-term predictions of the most recent Horizon Report. The study analyzes through bibliometric analysis which technologies were successful and became a regular part of education systems, which ones failed to have the predicted impact and why, and the shape of technology flows in recent years. The study also shows how the evolution and maturity of some technologies allowed the revival of expectations for others. The analysis here, which focuses on educational applications, offers guidelines that may be helpful to those seeking to invest in new research areas.
Contribution of obesity and abdominal fat mass to risk of stroke and transient ischemic attacks.

Waist circumference has been shown to be a better predictor of cardiovascular risk than body mass index (BMI). Our case-control study aimed to evaluate the contribution of obesity and abdominal fat mass to the risk of stroke and transient ischemic attacks (TIA).



We recruited 1137 participants: 379 cases with stroke/TIA and 758 regional controls matched for age and sex. Associations between different markers of obesity (BMI, waist-to-hip ratio, waist circumference and waist-to-stature ratio) and risk of stroke/TIA were assessed by using conditional logistic regression adjusted for other risk factors.



BMI showed a positive association with cerebrovascular risk which became nonsignificant after adjustment for physical inactivity, smoking, hypertension, and diabetes (odds ratio 1.18; 95% CI, 0.77 to 1.79, top tertile versus bottom tertile). Markers of abdominal adiposity were strongly associated with the risk of stroke/TIA. For the waist-to-hip ratio, adjusted odds ratios for every successive tertile were greater than that of the previous one (2nd tertile: 2.78, 1.57 to 4.91; 3rd tertile: 7.69, 4.53 to 13.03). Significant associations with the risk of stroke/TIA were also found for waist circumference and waist-to-stature ratio (odds ratio 4.25, 2.65 to 6.84 and odds ratio 4.67, 2.82 to 7.73, top versus bottom tertile after risk adjustment, respectively).



Markers of abdominal adiposity showed a graded and significant association with risk of stroke/TIA, independent of other vascular risk factors. Waist circumference and related ratios can better predict cerebrovascular events than BMI.
A theory of multi-layer flat refractive geometry

Flat refractive geometry corresponds to a perspective camera looking through single/multiple parallel flat refractive mediums. We show that the underlying geometry of rays corresponds to an axial camera. This realization, while missing from previous works, leads us to develop a general theory of calibrating such systems using 2D-3D correspondences. The pose of 3D points is assumed to be unknown and is also recovered. Calibration can be done even using a single image of a plane. We show that the unknown orientation of the refracting layers corresponds to the underlying axis, and can be obtained independently of the number of layers, their distances from the camera and their refractive indices. Interestingly, the axis estimation can be mapped to the classical essential matrix computation and 5-point algorithm  can be used. After computing the axis, the thicknesses of layers can be obtained linearly when refractive indices are known, and we derive analytical solutions when they are unknown. We also derive the analytical forward projection (AFP) equations to compute the projection of a 3D point via multiple flat refractions, which allows non-linear refinement by minimizing the reprojection error. For two refractions, AFP is either 4th or 12th degree equation depending on the refractive indices. We analyze ambiguities due to small field of view, stability under noise, and show how a two layer system can be well approximated as a single layer system. Real experiments using a water tank validate our theory.
Packet Switching in Radio Channels: Part I - Carrier Sense Multiple-Access Modes and Their Throughput-Delay Characteristics

Radio communication is considered as a method for providing remote terminal access to computers. Digital byte streams from each terminal are partitioned into packets (blocks) and transmitted in a burst mode over a shared radio channel. When many terminals operate in this fashion, transmissions may conflict with and destroy each other. A means for controlling this is for the terminal to sense the presence of other transmissions; this leads to a new method for multiplexing in a packet radio environment: carrier sense multiple access (CSMA). Two protocols are described for CSMA and their throughput-delay characteristics are given. These results show the large advantage CSMA provides as compared to the random ALOHA access modes.
Enhancing Business Impact Analysis and Risk Assessment Applying a Risk-Aware Business Process Modeling and Simulation Methodology

Driven by the steadily growing number of natural disasters, the threat of terrorist and other criminal attacks as well as changed legislation and regulations, companies are increasingly forced to prepare against threats that endanger the survivability of crucial business activities. As a consequence, management has to pay more attention to business continuity issues including serious management commitment and more appropriate funding. Business impact analysis and risk assessment concepts enable adequate business continuity planning as they deliver essential information about the impact of resources' disruption on business. In this paper we present how these concepts can be enhanced through the application of the ROPE (Risk-Oriented Process Evaluation) methodology enabling risk-aware business process management and simulation. Moreover, we present essential extensions of the ROPE simulation capabilities leading to a more efficient and effective business continuity planning.
Removal Mechanism in Chemical Mechanical Polishing : Theory and Modeling

The abrasion mechanism in solid-solid contact mode of the chemical mechanical polishing (CMP) process is investigated in detail. Based on assumptions of plastic contact over wafer-abrasive and pad-abrasive interfaces, the normal distribution of abrasive size and an assumed periodic roughness of pad surface, a novel model is developed for material removal in CMP. The basic model is = removed, where is the density of wafer, the number of active abrasives, and removed the volume of material removed by a single abrasive. The model proposed integrates process parameters including pressure and velocity and other important input parameters including the wafer hardness, pad hardness, pad roughness, abrasive size, and abrasive geometry into the same formulation to predict the material removal rate ( ). An interface between the chemical effect and mechanical effect has been constructed through a fitting parameter , a “dynamical” hardness value of the wafer surface, in the model. It reflects the influences of chemicals on the mechanical material removal. The fluid effect in the current model is attributed to the number of active abrasives. It is found that the nonlinear down pressure dependence of material removal rate is related to a probability density function of the abrasive size and the elastic deformation of the pad. Compared with experimental results, the model accurately predicts . With further verification of the model, a better understanding of the fundamental mechanism involved in material removal in the CMP process, particularly different roles played by the consumables and their interactions, can be obtained.
Maintaining Views Incrementally

We present incremental evaluation algorithms to compute changes to materialized views in relational and deductive database systems, in response to changes (insertions, deletions, and updates) to the relations. The view definitions can be in SQL or Datalog, and may use UNION, negation, aggregation (e.g. SUM, MIN), linear recursion, and general recursion.
We first present a counting algorithm that tracks the number of alternative derivations (counts) for each derived tuple in a view. The algorithm works with both set and duplicate semantics. We present the algorithm for nonrecursive views (with negation and aggregation), and show that the count for a tuple can be computed at little or no cost above the cost of deriving the tuple. The algorithm is optimal in that it computes exactly those view tuples that are inserted or deleted. Note that we store only the number of derivations, not the derivations themselves.
We then present the Delete and Rederive algorithm, DRed, for incremental maintenance of recursive views (negation and aggregation are permitted). The algorithm works by first deleting a superset of the tuples that need to be deleted, and then rederiving some of them. The algorithm can also be used when the view definition is itself altered.
LTE for vehicular networking: a survey

A wide variety of applications for road safety and traffic efficiency are intended to answer the urgent call for smarter, greener, and safer mobility. Although IEEE 802.11p is considered the de facto standard for on-the-road communications, stakeholders have recently started to investigate the usability of LTE to support vehicular applications. In this article, related work and running standardization activities are scanned and critically discussed; strengths and weaknesses of LTE as an enabling technology for vehicular communications are analyzed; and open issues and critical design choices are highlighted to serve as guidelines for future research in this hot topic.
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples

Adversarial perturbations of normal images are usually imperceptible to humans, but they can seriously confuse state-of-the-art machine learning models. What makes them so special in the eyes of image classifiers? In this paper, we show empirically that adversarial examples mainly lie in the low probability regions of the training distribution, regardless of attack types and targeted models. Using statistical hypothesis testing, we find that modern neural density models are surprisingly good at detecting imperceptible image perturbations. Based on this discovery, we devised PixelDefend, a new approach that purifies a maliciously perturbed image by moving it back towards the distribution seen in the training data. The purified image is then run through an unmodified classifier, making our method agnostic to both the classifier and the attacking method. As a result, PixelDefend can be used to protect already deployed models and be combined with other model-specific defenses. Experiments show that our method greatly improves resilience across a wide variety of state-of-the-art attacking methods, increasing accuracy on the strongest attack from 63% to 84% for Fashion MNIST and from 32% to 70% for CIFAR-10.
Big Data Analytics in Heart Attack Prediction

Acute myocardial infarction (heart attack) is one of the deadliest diseases patients face. The key to cardiovascular disease management is to evaluate large scores of datasets, compare and mine for information that can be used to predict, prevent, manage and treat chronic diseases such as heart attacks. Big Data analytics, known in the corporate world for its valuable use in controlling, contrasting and managing large datasets can be applied with much success to the prediction, prevention, management and treatment of cardiovascular disease. Data mining, visualization and Hadoop are technologies or tools of big data in mining the voluminous datasets for
"Cheap" Rapid Locomotion of a Quadruped Robot: Self-Stabilization of Bounding Gait

The legged animals are capable of rapid, energy efficient, and adaptive locomotion in a complex environment. Toward a comprehensive understanding of the nature of such ecologically balanced legged locomotion, in this paper, we propose a novel method to achieve a form of bounding gait for a quadruped robot by using a minimalistic approach. Although this method uses a simple sinusoidal position control with no global sensory feedback, it is shown that the rapid bounding is possible in a relatively robust manner by properly exploiting the intrinsic dynamics and the interaction with the environment. The behavioral analyses with the robot experiments show that this relatively complicated dynamic locomotion is achieved even with a simple controller mainly because of a self-stabilization mechanism. Moreover, by exploiting this mechanism of self-stabilization, we propose a unique approach to control the forward velocity of the locomotion.
What is the central feature of extraversion? Social attention versus reward sensitivity.

R. E. Lucas, E. Diener, A. Grob, E. M. Suh, and L. Shao (2000) recently argued that the core of the personality dimension of Extraversion is not sociability but a construct called reward sensitivity. This article accepts their argument that the mere preference for social interaction is not the central element of Extraversion. However, it claims that the real core of the Extraversion factor is the tendency to behave in ways that attract social attention. Data from a sample of 200 respondents were used to test the 2 hypotheses with comparisons of measures of reward sensitivity and social attention in terms of their saturation with the common variance of Extraversion measures. The results clearly showed that social attention, not reward sensitivity, represents the central feature of Extraversion.
Evaluating enterprise resource planning ( ERP ) post-implementation problems in Egypt

The aim of the research is to identify and evaluate the main problems experienced in the ERP postimplementation stage of multinational, privately-owned Egyptian and governmental organizations in Egypt. Data gathering was achieved by means of a set of interviews and online questionnaire conducted to 50 companies implementing ERP in Egypt. The paper presents a descriptive analysis of the difficulties and problems encountered by organizations in Egypt following ERP implementation and how these have contributed to unsuccessful implementation overall.
Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data

Topic modeling has been commonly used to discover topics from document collections. However, unsupervised models can generate many incoherent topics. To address this problem, several knowledge-based topic models have been proposed to incorporate prior domain knowledge from the user. This work advances this research much further and shows that without any user input, we can mine the prior knowledge automatically and dynamically from topics already found from a large number of domains. This paper first proposes a novel method to mine such prior knowledge dynamically in the modeling process, and then a new topic model to use the knowledge to guide the model inference. What is also interesting is that this approach offers a novel lifelong learning algorithm for topic discovery, which exploits the big (past) data and knowledge gained from such data for subsequent modeling. Our experimental results using product reviews from 50 domains demonstrate the effectiveness of the proposed approach.
Fault Detection, Isolation, and Service Restoration in Distribution Systems: State-of-the-Art and Future Trends

This paper surveys the conceptual aspects, as well as recent developments in fault detection, isolation, and service restoration (FDIR) following an outage in an electric distribution system. This paper starts with a discussion of the rationale for FDIR, and then investigates different areas of the FDIR problem. Recently reported approaches are compared and related to discussions on current practices. This paper then addresses some of the often-cited associated technical, environmental, and economic challenges of implementing self-healing for the distribution grid. The review concludes by pointing toward the need and directions for future research.
A Unifying View of Sparse Approximate Gaussian Process Regression

We provide a new unifying view, including all existing prope r probabilistic sparse approximations for Gaussian process regression. Our approach relies on exp ressing theeffective priorwhich the methods are using. This allows new insights to be gained, and highlights the relationship between existing methods. It also allows for a clear theoretically j ustified ranking of the closeness of the known approximations to the corresponding full GPs. Finall y we point directly to designs of new better sparse approximations, combining the best of the exi sting strategies, within attractive computational constraints.
Topic-sensitive PageRank

In the original PageRank algorithm for improving the ranking of search-query results, a single PageRank vector is computed, using the link structure of the Web, to capture the relative "importance" of Web pages, independent of any particular search query. To yield more accurate search results, we propose computing a set of PageRank vectors, biased using a set of representative topics, to capture more accurately the notion of importance with respect to a particular topic. By using these (precomputed) biased PageRank vectors to generate query-specific importance scores for pages at query time, we show that we can generate more accurate rankings than with a single, generic PageRank vector. For ordinary keyword search queries, we compute the topic-sensitive PageRank scores for pages satisfying the query using the topic of the query keywords. For searches done in context (e.g., when the search query is performed by highlighting words in a Web page), we compute the topic-sensitive PageRank scores using the topic of the context in which the query appeared.
Relations such as Hypernymy: Identifying and Exploiting Hearst Patterns in Distributional Vectors for Lexical Entailment

We consider the task of predicting lexical entailment using distributional vectors. We focus experiments on one previous classifier which was shown to only learn to detect prototypicality of a word pair. Analysis shows that the model single-mindedly learns to detect Hearst Patterns, which are well known to be predictive of lexical relations. We present a new model which exploits this Hearst Detector functionality, matching or outperforming prior work on multiple data sets.
Information systems strategy: Quo vadis?

This article is a personal retrospective which traces the evolution of information systems strategy (ISS) since it emerged as a topic in the late 1970s and considers the nature of organisations’ ISSs and how they have been influenced by the interplay of many factors over that period. In addition to responding to the rapidly evolving underlying technologies, ISS practice in organisations has had to deal with the combined effects of economic cycles and an increasingly global business context, which effect both the organisations themselves and the development of the IT industry. This article argues that the changing fortunes of the IT suppliers and their strategies are two of the most significant influences on organisations’ ISSs. The influence and contribution of academics and their research is also discussed. The study of ISS has largely followed practice and attempted to explain its nature, role and impact using contemporary theoretical paradigms but often based on relatively limited empirical data. In conclusion it is suggested that a new multi-centred, collaborative approach, involving both academic and practitioner experts to develop a comprehensive evidence base, would enable greater understanding of how the range of factors interact to determine the nature and value of ISS in 21st century organisations.
A data-driven approach to exploring similarities of tourist attractions through online reviews

The motivation for tourists to visit a city is often driven by the uniqueness of the attractions accessible within the region. The draw to these locations varies by visitor as some travelers are interested in a single specific attraction while others prefer thematic travel. Tourists today have access to detailed experiences of other visitors to these locations in the form of user-contributed text reviews, opinions, photographs, and videos, all contributed through online tourism platforms. The data available through these platforms offer a unique opportunity to examine the similarities and difference between these attractions, their cities, and the visitors that contribute the reviews. In this work we take a data-driven approach to assessing similarity through textual analysis of user-contributed reviews, uncovering nuanced differences and similarities in the ways that reviewers write about attractions and cities.