nils-herrmann/clean_scirepeval huggingface dataset:
Speech recognition using neural networks

This thesis examines how artificial neural networks can benefit a large vocabulary, speaker independent, continuous speech recognition system. Currently, most speech recognition systems are based on hidden Markov models (HMMs), a statistical framework that supports both acoustic and temporal modeling. Despite their state-of-the-art performance, HMMs make a number of suboptimal modeling assumptions that limit their potential effectiveness. Neural networks avoid many of these assumptions, while they can also learn complex functions, generalize effectively, tolerate noise, and support parallelism. While neural networks can readily be applied to acoustic modeling, it is not yet clear how they can be used for temporal modeling. Therefore, we explore a class of systems called NN-HMM hybrids, in which neural networks perform acoustic modeling, and HMMs perform temporal modeling. We argue that a NN-HMM hybrid has several theoretical advantages over a pure HMM system, including better acoustic modeling accuracy, better context sensitivity, more natural discrimination, and a more economical use of parameters. These advantages are confirmed experimentally by a NN-HMM hybrid that we developed, based on context-independent phoneme models, that achieved 90.5% word accuracy on the Resource Management database, in contrast to only 86.0% accuracy achieved by a pure HMM under similar conditions. In the course of developing this system, we explored two different ways to use neural networks for acoustic modeling: prediction and classification. We found that predictive networks yield poor results because of a lack of discrimination, but classification networks gave excellent results. We verified that, in accordance with theory, the output activations of a classification network form highly accurate estimates of the posterior probabilities P(class|input), and we showed how these can easily be converted to likelihoods P(input|class) for standard HMM recognition algorithms. Finally, this thesis reports how we optimized the accuracy of our system with many natural techniques, such as expanding the input window size, normalizing the inputs, increasing the number of hidden units, converting the network’s output activations to log likelihoods, optimizing the learning rate schedule by automatic search, backpropagating error from word level outputs, and using gender dependent networks.
Visualizing the Impact of Art: An Update and Comparison of Current Psychological Models of Art Experience

The last decade has witnessed a renaissance of empirical and psychological approaches to art study, especially regarding cognitive models of art processing experience. This new emphasis on modeling has often become the basis for our theoretical understanding of human interaction with art. Models also often define areas of focus and hypotheses for new empirical research, and are increasingly important for connecting psychological theory to discussions of the brain. However, models are often made by different researchers, with quite different emphases or visual styles. Inputs and psychological outcomes may be differently considered, or can be under-reported with regards to key functional components. Thus, we may lose the major theoretical improvements and ability for comparison that can be had with models. To begin addressing this, this paper presents a theoretical assessment, comparison, and new articulation of a selection of key contemporary cognitive or information-processing-based approaches detailing the mechanisms underlying the viewing of art. We review six major models in contemporary psychological aesthetics. We in turn present redesigns of these models using a unified visual form, in some cases making additions or creating new models where none had previously existed. We also frame these approaches in respect to their targeted outputs (e.g., emotion, appraisal, physiological reaction) and their strengths within a more general framework of early, intermediate, and later processing stages. This is used as a basis for general comparison and discussion of implications and future directions for modeling, and for theoretically understanding our engagement with visual art.
How to Gauge the Relevance of Codes in Qualitative Data Analysis? - A Technique Based on Information Retrieval

Qualitative research has experienced broad acceptance in the IS discipline. Despite the merits for exploring new phenomena, qualitative methods are criticized for their subjectivity when it comes to interpretation. Therefore, research mostly emphasized the development of criteria and guidelines for good practice. I present an approach to counteract the issue of credibility and traceability in qualitative data analysis and expand the repertoire of approaches used in IS research. I draw on an existing approach from the information science discipline and adapt it to analyze coded qualitative data. The developed approach is designed to answer questions about the specific relevance of codes and aims to support the researcher in detecting hidden information in the coded material. For this reason, the paper contributes to the IS methodology with bringing new insights to current methods by enhancing them with an approach from another
A neural predictor of cultural popularity

We use neuroimaging to predict cultural popularity — something that is popular in the broadest sense and appeals to a large number of individuals. Neuroeconomic research suggests that activity in reward-related regions of the brain, notably the orbitofrontal cortex and ventral striatum, is predictive of future purchasing decisions, but it is unknown whether the neural signals of a small group of individuals are predictive of the purchasing decisions of the population at large. For neuroimaging to be useful as a measure of widespread popularity, these neural responses would have to generalize to a much larger population that is not the direct subject of the brain imaging itself. Here, we test the possibility of using functional magnetic resonance imaging (fMRI) to predict the relative popularity of a common good: music. We used fMRI to measure the brain responses of a relatively small group of adolescents while listening to songs of largely unknown artists. As a measure of popularity, the sales of these songs were totaled for the three years following scanning, and brain responses were then correlated with these “future” earnings. Although subjective likability of the songs was not predictive of sales, activity within the ventral striatum was significantly correlated with the number of units sold. These results suggest that the neural responses to goods are not only predictive of purchase decisions for those individuals actually scanned, but such responses generalize to the population at large and may be used to predict cultural popularity.
Personalizing Dialogue Agents: I have a dog, do you have pets too?

Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the task of making chit-chat more engaging by conditioning on profile information. We collect data and train models to (i) condition on their given profile information; and (ii) information about the person they are talking to, resulting in improved dialogues, as measured by next utterance prediction. Since (ii) is initially unknown, our model is trained to engage its partner with personal topics, and we show the resulting dialogue can be used to predict profile information about the interlocutors.
Assessing Marketing Performance : Reasons for Metrics Selection

In recent years both practitioners and academics have shown an increasing interest in the assessment of marketing -performance. This paper explores the metrics that firms select and some reasons for those choices. Our data are drawn from two UK studies. The first reports practitioner usage by the main metrics categories (consumer behaviour and intermediate, trade customer, competitor, accounting and innovativeness). The second considers which individual metrics are seen as the most important and whether that differs by sector. The role of brand equity in performance assessment and top
Modeling data entry rates for ASR and alternative input methods

An often-cited advantage of automatic speech recognition (ASR) is that it is ‘fast’; it is quite easy for a person to speak at several hundred words a minute, well above the rates that are possible using other modes of data entry. However, in order to conduct a fair comparison between alternative data entry methods, it is necessary to consider not the input rate per se, but the rate at which it is possible to enter information that is fully correct. This paper describes a model for predicting the relative success of alternative method of data entry in terms of the effective ‘throughput’ that is achievable taking into account typical input data entry rates, error rates and error correction times. Results are presented for the entry of both conventional and SMS-style text.
An Analysis for Big Data and its Technologies

“Big Data” is a term encompassing the use of techniques to capture, process, analyse and visualize potentially large datasets in a reasonable timeframe not accessible to standard IT technologies. By extension, the platform, tools and software used for this purpose are collectively called “Big Data technologies”. In this paper, we provide the meaning, characteristics, models, technologies, life cycle and many other aspects of big data.  Big Data, Hadoop, Map Reduce, HDFS (Hadoop Distributed File System), Cloud Computing.
High quality MELP coding at bit-rates around 4 kb/s

Recently, a number of coding techniques have been reported to achieve near toll quality synthesized speech at bit-rates around 4 kb/s. These include variants of Code Excited Linear Prediction (CELP), Sinusoidal Transform Coding (STC) and Multi-Band Excitation (MBE). While CELP has been an effective technique for bit-rates above 6 kb/s, STC, MBE, Waveform Interpolation (WI) and Mixed Excitation Linear Prediction (MELP) [1, 2] models seem to be attractive at bit-rates below 3 kb/s. In this paper, we present a system to encode speech with high quality using MELP, a technique previously demonstrated to be effective at bit-rates of 1.6–2.4 kb/s. We have enhanced the MELP model producing significantly higher speech quality at bit-rates above 2.4 kb/s. We describe the development and testing of a high quality 4 kb/s MELP coder.
Face Recognition based on Deep Neural Network

In modern life, we see more techniques of biometric features recognition have been used to our surrounding life, especially the applications in telephones and laptops. These biometric recognition techniques contain face recognition, fingerprint recognition and iris recognition. Our work focuses on the face recognition problem and uses a deep learning method, convolutional neural network, to solve it. And we use the Sobel operator to improve our result accuracy. LFW dataset is used for training and testing which gets a considerable result. And we also test our system on other face dataset, which also has a high accuracy on the recognition.
Discovering governing equations from data by sparse identification of nonlinear dynamical systems.

Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive, as in climate science, neuroscience, ecology, finance, and epidemiology, to name only a few examples. In this work, we combine sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover governing equations from noisy measurement data. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems in an appropriate basis. In particular, we use sparse regression to determine the fewest terms in the dynamic governing equations required to accurately represent the data. This results in parsimonious models that balance accuracy with model complexity to avoid overfitting. We demonstrate the algorithm on a wide range of problems, from simple canonical systems, including linear and nonlinear oscillators and the chaotic Lorenz system, to the fluid vortex shedding behind an obstacle. The fluid example illustrates the ability of this method to discover the underlying dynamics of a system that took experts in the community nearly 30 years to resolve. We also show that this method generalizes to parameterized systems and systems that are time-varying or have external forcing.
Bootstrapping single-channel source separation via unsupervised spatial clustering on stereo mixtures

Separating an audio scene into isolated sources is a fundamental problem in computer audition, analogous to image segmentation in visual scene analysis. Source separation systems based on deep learning are currently the most successful approaches for solving the underdetermined separation problem, where there are more sources than channels. Traditionally, such systems are trained on sound mixtures where the ground truth decomposition is already known. Since most real-world recordings do not have such a decomposition available, this limits the range of mixtures one can train on, and the range of mixtures the learned models may successfully separate. In this work, we use a simple blind spatial source separation algorithm to generate estimated decompositions of stereo mixtures. These estimates, together with a weighting scheme in the time-frequency domain, based on confidence in the separation quality, are used to train a deep learning model that can be used for single-channel separation, where no source direction information is available. This demonstrates how a simple cue such as the direction of origin of source can be used to bootstrap a model for source separation that can be used in situations where that cue is not available.
Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling

Recent BIO-tagging-based neural semantic role labeling models are very high performing, but assume gold predicates as part of the input and cannot incorporate span-level features. We propose an endto-end approach for jointly predicting all predicates, arguments spans, and the relations between them. The model makes independent decisions about what relationship, if any, holds between every possible word-span pair, and learns contextualized span representations that provide rich, shared input features for each decision. Experiments demonstrate that this approach sets a new state of the art on PropBank SRL without gold
Security for Industrial Communication Systems

Modern industrial communication networks are increasingly based on open protocols and platforms that are also used in the office IT and Internet environment. This reuse facilitates development and deployment of highly connected systems, but also makes the communication system vulnerable to electronic attacks. This paper gives an overview of IT security issues in industrial automation systems which are based on open communication systems. First, security objectives, electronic attack methods, and the available countermeasures for general IT systems are described. General security objectives and best practices are listed. Particularly for the TCP/IP protocol suite, a wide range of cryptography-based secure communication protocols is available. The paper describes their principles and scope of application. Next, we focus on industrial communication systems, which have a number of security-relevant characteristics distinct from the office IT systems. Confidentiality of transmitted data may not be required; however, data and user authentication, as well as access control are crucial for the mission critical and safety critical operation of the automation system. As a result, modern industrial automation systems, if they include security measures at all, emphasize various forms of access control. The paper describes the status of relevant specifications and implementations for a number of standardized automation protocols. Finally, we illustrate the application of security concepts and tools by brief case studies describing security issues in the configuration and operation of substations, plants, or for remote access.
Understanding the requirements for developing open source software systems

This study presents an initial set of findings from an empirical study of social processes, technical system configurations, organizational contexts, and interrelationships that give rise to open software. The focus is directed at understanding the requirements for open software development efforts, and how the development of these requirements differs from those traditional to software engineering and requirements engineering. Four open software development communities are described, examined, and compared to help discover what these differences may be. Informal software descriptions and the online social discourse that surrounds these narrative descriptions are found to play a critical role in the elicitation, analysis, specification, validation, and management of requirements for developing open software systems. DRAFT – Circulated for comment only, July 2001. 2 Understanding Requirements for Developing Open Source
From in silico hit to long-acting late-stage preclinical candidate to combat HIV-1 infection.

The HIV-1 pandemic affecting over 37 million people worldwide continues, with nearly one-half of the infected population on highly active antiretroviral therapy (HAART). Major therapeutic challenges remain because of the emergence of drug-resistant HIV-1 strains, limitations because of safety and toxicity with current HIV-1 drugs, and patient compliance for lifelong, daily treatment regimens. Nonnucleoside reverse transcriptase inhibitors (NNRTIs) that target the viral polymerase have been a key component of the current HIV-1 combination drug regimens; however, these issues hamper them. Thus, the development of novel more effective NNRTIs as anti-HIV-1 agents with fewer long-term liabilities, efficacy on new drug-resistant HIV-1 strains, and less frequent dosing is crucial. Using a computational and structure-based design strategy to guide lead optimization, a 5 µM virtual screening hit was transformed to a series of very potent nanomolar to picomolar catechol diethers. One representative, compound I, was shown to have nanomolar activity in HIV-1-infected T cells, potency on clinically relevant HIV-1 drug-resistant strains, lack of cytotoxicity and off-target effects, and excellent in vivo pharmacokinetic behavior. In this report, we show the feasibility of compound I as a late-stage preclinical candidate by establishing synergistic antiviral activity with existing HIV-1 drugs and clinical candidates and efficacy in HIV-1-infected humanized [human peripheral blood lymphocyte (Hu-PBL)] mice by completely suppressing viral loads and preventing human CD4+ T-cell loss. Moreover, a long-acting nanoformulation of compound I [compound I nanoparticle (compound I-NP)] in poly(lactide-coglycolide) (PLGA) was developed that shows sustained maintenance of plasma drug concentrations and drug efficacy for almost 3 weeks after a single dose.
Diabetes Research in Iran: a Scientometric Analysis of Publications Output

In the developing countries, diabetes mellitus as a chronic diseases, have replaced infectious diseases as the main causes of morbidity and mortality. International Diabetes Federation (IDF) recently estimates 382 million people have diabetes globally and more than 34.6 million people in the Middle East Region and this number will increase to 67.9 million by 2035. The aim of this study was to analyze Iran's research performance on diabetes in national and international context.



This Scientometric analysis is based on the Iranian publication data in diabetes research retrieved from the Scopus citation database till the end of 2014. The string used to retrieve the data was developed using "diabetes" keyword in title, abstract and keywords, and finally Iran in the affiliation field was our main string.



Iran's cumulative publication output in diabetes research consisted of 4425 papers from 1968 to 2014, with an average number of 96.2 papers per year and an annual average growth rate of 25.5%. Iran ranked 25th place with 4425 papers among top 25 countries with a global share of 0.72 %. Average of Iran's publication output was 6.19 citations per paper. The average citation per paper for Iranian publications in diabetes research increased from 1.63 during 1968-1999 to 10.42 for 2014.



Although diabetic population of Iran is increasing, number of diabetes research is not remarkable. International Diabetes Federation suggested increased funding for research in diabetes in Iran for cost-effective diabetes prevention and treatment. In addition to universal and comprehensive services for diabetes care and treatment provided by Iranian health care system, Iranian policy makers should invest more on diabetes research.
Procedural Generation of Dungeons

The use of procedural content generation (PCG) techniques in game development has been mostly restricted to very specific types of game elements. PCG has seldom been deployed for generating entire game levels, a notable exception to this being dungeons: a specific type of game level often encountered in adventure and role playing games. Due to their peculiar combination of pace, gameplay, and game spaces, dungeon levels are among the most suited to showcase the benefits of PCG. This paper surveys research on procedural methods to generate dungeon game levels. We summarize common practices, discuss pros and cons of different approaches, and identify a few promising challenges ahead. In general, what current procedural dungeon generation methods are missing is not performance, but more powerful, accurate, and richer control over the generation process. Recent research results seem to indicate that gameplay-related criteria can provide this high-level control. However, this area is still in its infancy, and many research challenges still lie ahead, e.g., improving the intuitiveness and accessibility of such methods for designers. We also observe that more research is needed into generic mechanisms for automating the generation of the actual dungeon-geometric models. We conclude that the foundations for enabling gameplay-based control of dungeon-level generation are worth being researched, and that its promising results may be instrumental in bringing PCG into mainstream game development.
BinaryConnect: Training Deep Neural Networks with binary weights during propagations

Deep Neural Networks (DNN) have achieved state-of-the-art results in a wide range of tasks, with the best results obtained with large training sets and large models. In the past, GPUs enabled these breakthroughs because of their greater computational speed. In the future, faster computation at both training and test time is likely to be crucial for further progress and for consumer applications on low-power devices. As a result, there is much interest in research and development of dedicated hardware for Deep Learning (DL). Binary weights, i.e., weights which are constrained to only two possible values (e.g. -1 or 1), would bring great benefits to specialized DL hardware by replacing many multiply-accumulate operations by simple accumulations, as multipliers are the most space and powerhungry components of the digital implementation of neural networks. We introduce BinaryConnect, a method which consists in training a DNN with binary weights during the forward and backward propagations, while retaining precision of the stored weights in which gradients are accumulated. Like other dropout schemes, we show that BinaryConnect acts as regularizer and we obtain near state-of-the-art results with BinaryConnect on the permutation-invariant MNIST, CIFAR-10 and SVHN.