nils-herrmann/clean_scirepeval huggingface dataset:
Exploiting Lists of Names for Named Entity Identification of Financial Institutions from Unstructured Documents

There is a wealth of information about financial systems that is embedded in document collections. In this paper, we focus on a specialized text extraction task for this domain. The objective is to extract mentions of names of financial institutions, or FI names, from financial prospectus documents, and to identify the corresponding real world entities, e.g., by matching against a corpus of such entities. The tasks are Named Entity Recognition (NER) and Entity Resolution (ER); both are well studied in the literature. Our contribution is to develop a rule-based approach that will exploit lists of FI names for both tasks; our solution is labeled Dict-based NER and Rank-based ER. Since the FI names are typically represented by a root, and a suffix that modifies the root, we use these lists of FI names to create specialized root and suffix dictionaries. To evaluate the effectiveness of our specialized solution for extracting FI names, we compare Dict-based NER with a general purpose rule-based NER solution, ORG NER. Our evaluation highlights the benefits and limitations of specialized versus general purpose approaches, and presents additional suggestions for tuning and customization for FI name extraction. To our knowledge, our proposed solutions, Dict-based NER and Rank-based ER, and the root and suffix dictionaries, are the first attempt to exploit specialized knowledge, i.e., lists of FI names, for rule-based NER and ER. CCS Concepts: rInformation systems→ Extraction, transformation and loading; rComputing methodologies→ Information extraction;
Children use syntax to learn verb meanings.

Verb learning is clearly a function of observation of real-world contingencies; however, it is argued that such observational information is insufficient to account fully for vocabulary acquisition. This paper provides an experimental validation of Landau & Gleitman's (1985) syntactic bootstrapping procedure; namely, that children may use syntactic information to learn new verbs. Pairs of actions were presented simultaneously with a nonsense verb in one of two syntactic structures. The actions were subsequently separated, and the children (MA = 2;1) were asked to select which action was the referent for the verb. The children's choice of referent was found to be a function of the syntactic structure in which the verb had appeared.
Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition

Convolutional Neural Networks (CNN) have showed success in achieving translation invariance for many image processing tasks. The success is largely attributed to the use of local filtering and max-pooling in the CNN architecture. In this paper, we propose to apply CNN to speech recognition within the framework of hybrid NN-HMM model. We propose to use local filtering and max-pooling in frequency domain to normalize speaker variance to achieve higher multi-speaker speech recognition performance. In our method, a pair of local filtering layer and max-pooling layer is added at the lowest end of neural network (NN) to normalize spectral variations of speech signals. In our experiments, the proposed CNN architecture is evaluated in a speaker independent speech recognition task using the standard TIMIT data sets. Experimental results show that the proposed CNN method can achieve over 10% relative error reduction in the core TIMIT test sets when comparing with a regular NN using the same number of hidden layers and weights. Our results also show that the best result of the proposed CNN model is better than previously published results on the same TIMIT test sets that use a pre-trained deep NN model.
The Economy as an Evolving Complex System II

How does one instigate a scientific revolution, or more modestly, a shift of scientific paradigm? This must have been on the minds of the organizers of the two conferences "The Economy as an Evolving Complex System, I and II" and the research program in economics at the Santa Fe Institute documented in the present volume and its predecessor of ten years ago.(1) Their strategy might be reconstructed as follows. First, the stranglehold of neoclassical economics on the Anglo-Saxon academic community since World War II is at least partly due to the ascendancy of mathematical rigor as the touchstone of serious economic theorizing. Thus if one could beat the prevailing paradigm at its own game one would immediately have a better footing in the community than the heretics, mostly from the left or one of the variousìnstitu-tional' camps, who had been sniping at it from the sidelines all the while but were never above the suspicion of not being mathematically up to comprehending it in the first place. Second, one could enlist both prominent representatives and path-breaking methods from the natural sciences to legitimize the introduction of (to economists) fresh and in some ways disturbing approaches to the subject. This was particularly the tack taken in 1987, where roughly equal numbers of scientists and economists were brought together in an extensive brain storming session. Physics has always been the role model for other aspiring`hard' sciences, and physicists seem to have succeeded in institutional-izing a `permanent revolution' in their own methodology , i.e., they are relatively less dogmatic and willing to be more eclectic in the interests of getting results. The fact that, with the exception of a brief chapter by Philip Anderson in the present volume, physicists as representatives of their discipline are no longer present, presumably indicates that their services can now be dispensed with in this enterprise.(2) Finally, one should sponsor research of the highest caliber, always laudable in itself, and make judicious use of key personalities. Care should also be taken that the work is of a form and style which, rather than explicitly provoking the profession, makes it appear as if it were the natural generalization of previous mainstream research and thus reasonably amenable to inclusion in the canon. This while tacitly encouraging and profiting from a wave of publicity in the popular media , a difficult line to tread if one does not want to appear …
1-V power supply high-speed digital circuit technology with multithreshold-voltage CMOS

Abstruct1-V power supply high-speed low-power digital circuit technology with 0.5-pm multithreshold-voltage CMOS (MTCMOS) is proposed. This technology features both lowthreshold voltage and high-threshold voltage MOSFET’s in a single LSI. The low-threshold voltage MOSFET’s enhance speed Performance at a low supply voltage of 1 V or less, while the high-threshold voltage MOSFET’s suppress the stand-by leakage current during the sleep period. This technology has brought about logic gate characteristics of a 1.7-11s propagation delay time and 0.3-pW/MHz/gate power dissipation with a standard load. In addition, an MTCMOS standard cell library has been developed so that conventional CAD tools can be used to lay out low-voltage LSI’s. To demonstrate MTCMOS’s effectiveness, a PLL LSI based on standard cells was designed as a carrying vehicle. 18-MHz operation at 1 V was achieved using a 0.5-pm CMOS process.
Introduction to Probabilistic Topic Models

Probabilistic topic models are a suite of algorithms whose aim is to discover the hidden thematic structure in large archives of documents. In this article, we review the main ideas of this field, survey the current state-of-the-art, and describe some promising future directions. We first describe latent Dirichlet allocation (LDA) , which is the simplest kind of topic model. We discuss its connections to probabilistic modeling, and describe two kinds of algorithms for topic discovery. We then survey the growing body of research that extends and applies topic models in interesting ways. These extensions have been developed by relaxing some of the statistical assumptions of LDA, incorporating meta-data into the analysis of the documents, and using similar kinds of models on a diversity of data types such as social networks, images and genetics. Finally, we give our thoughts as to some of the important unexplored directions for topic modeling. These include rigorous methods for checking models built for data exploration, new approaches to visualizing text and other high dimensional data, and moving beyond traditional information engineering applications towards using topic models for more scientific ends.
Dissociable roles of prefrontal and anterior cingulate cortices in deception.

Recent neuroimaging studies have shown the importance of the prefrontal and anterior cingulate cortices in deception. However, little is known about the role of each of these regions during deception. Using positron emission tomography (PET), we measured brain activation while participants told truths or lies about two types of real-world events: experienced and unexperienced. The imaging data revealed that activity of the dorsolateral, ventrolateral and medial prefrontal cortices was commonly associated with both types of deception (pretending to know and pretending not to know), whereas activity of the anterior cingulate cortex (ACC) was only associated with pretending not to know. Regional cerebral blood flow (rCBF) increase in the ACC was positively correlated with that in the dorsolateral prefrontal cortex only during pretending not to know. These results suggest that the lateral and medial prefrontal cortices have general roles in deception, whereas the ACC contributes specifically to pretending not to know.
Machine Learning Attacks on PolyPUF, OB-PUF, RPUF, and PUF-FSM

A physically unclonable function (PUF) is a circuit of which the input– output behavior is designed to be sensitive to the random variations of its manufacturing process. This building block hence facilitates the authentication of any given device in a population of identically laid-out silicon chips, similar to the biometric authentication of a human. The focus and novelty of this work is the development of efficient impersonation attacks on the following four PUF-based authentication protocols: (1) the so-called PolyPUF protocol of Konigsmark, Chen, and Wong, as published in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems in 2016, (2) the so-called OB-PUF protocol of Gao, Li, Ma, Al-Sarawi, Kavehei, Abbott, and Ranasinghe, as presented at the IEEE conference PerCom 2016, (3) the so-called RPUF protocol of Ye, Hu, and Li, as presented at the IEEE conference AsianHOST 2016, and (4) the so-called PUF–FSM protocol of Gao, Ma, Al-Sarawi, Abbott, and Ranasinghe, as published in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems in 2017. The common flaw of all four designs is that the use of lightweight obfuscation logic provides insufficient protection against machine learning attacks.
Impact Of Mobile Phone Usage On Academic Performance Among Secondary School Students In Taraba State , Nigeria

This study employed survey design in investigating the influence of mobile phone usage on academic performance among secondary school student in Jalingo, Taraba State, Nigeria. The sample for the study was 300 respondents selected from the total population of 6,482 respondents. Stratified sampling technique was employed to select the sample. The Mobile Phone Usage Questionnaire (MPUQ) adapted from Twum (2011), Mathematic Achievement Test (MAT) and English Language Achievement test (ELAT), were the instrument used for this study. Data collected were analyzed using mean, standard deviation, t-test and ANOVA to answer the research questions and hypotheses. The finding of this study, revealed that mobile phone usage significantly influence academic performance among male and female senior secondary school students (t = 6.113, P = 0.02), age difference was not a significant factor in mobile phone usage on academic performance among senior secondary school students (f = 6.431, P = 0.022), parent’s occupation was not a significant factor in mobile phone usage on academic performance among senior secondary school students (f = 9.005, p= 0.031) and that the frequency of mobile phone usage does not significantly influence academic performance among male and female senior secondary school students (t = 8.131, p = 0.02). It was recommended that, School psychologists, teachers, school administrators, parents and students should be sensitized on the influence of mobile phone usage on academic performance among secondary school students irrespective of gender and age differences.
Learning Word Embeddings: Unsupervised Methods for Fixed-size Representations of Variable-length Speech Segments

Fixed-length embeddings of words are very useful for a variety of tasks in speech and language processing. Here we systematically explore two methods of computing fixed-length embeddings for variable-length sequences. We evaluate their susceptibility to phonetic and speaker-specific variability on English, a high resource language, and Xitsonga, a low resource language, using two evaluation metrics: ABX word discrimination and ROC-AUC on same-different phoneme n-grams. We show that a simple downsampling method supplemented with length information can be competitive with the variable-length input feature representation on both evaluations. Recurrent autoencoders trained without supervision can yield even better results at the expense of increased computational complexity.
Deep Learning Approximation for Stochastic Control Problems

Many real world stochastic control problems suffer from the “curse of dimensionality”. To overcome this difficulty, we develop a deep learning approach that directly solves high-dimensional stochastic control problems based on Monte-Carlo sampling. We approximate the time-dependent controls as feedforward neural networks and stack these networks together through model dynamics. The objective function for the control problem plays the role of the loss function for the deep neural network. We test this approach using examples from the areas of optimal trading and energy storage. Our results suggest that the algorithm presented here achieves satisfactory accuracy and at the same time, can handle rather high dimensional problems.
Attention-based Semantic Priming for Slot-filling

The problem of sequence labelling in language understanding would benefit from approaches inspired by semantic priming phenomena. We propose that an attentionbased RNN architecture can be used to simulate semantic priming for sequence labelling. Specifically, we employ pretrained word embeddings to characterize the semantic relationship between utterances and labels. We validate the approach using varying sizes of the ATIS and MEDIA datasets, and show up to 1.4-1.9% improvement in F1 score. The developed framework can enable more explainable and generalizable spoken language understanding systems.
Assessing and improving removable partial denture learning outcome by using proficiency tests results.

Feedback of the weak areas of knowledge in RPD using continuous competency or other test forms is very essential to develop the student knowledge and the syllabus as well. This act should be a regular practice.  To use the outcome of competency test and the objectives structured clinical examination of removable partial denture as a reliable measure to provide a continuous feedback to the teaching system.  This sectional study was performed on sixty eight, fifth year students for the period from 2009 to 2010. The experiment was divided into two parts: continuous assessment and the final examination. In the first essay; some basic removable partial denture knowledge, surveying technique, and designing of the metal framework were used to estimate the learning outcome. While in the second essay, some components of the objectives structured clinical examination were compared to the competency test to see the difference in learning outcome.  The students’ performance was improved in the final assessment just in some aspects of removable partial denture. However, for the surveying, the students faced some problems.  the continuous and final tests can provide a simple tool to advice the teachers for more effective teaching of the RPD. So that the weakness in specific aspects of the RPD syllabus can be detected and corrected continuously from the beginning, during and at the end of the course.
Anatomy of the credit score

This paper addresses the question of what determines a poor credit score. We compare estimated credit scores with measures of impulsivity, time preference, risk attitude, and trustworthiness, in an effort to determine the preferences that underlie credit behavior. Data is collected using an incentivized decision-making lab experiment, together with financial and psychological surveys. Credit scores are estimated using an online FICO credit score estimator based on survey data supplied by the participants. Preferences are assessed using a survey measure of impulsivity, with experimental measures of time and risk preferences, as well as trustworthiness. Controlling for income differences, we find that the credit score is correlated with measures of impulsivity, time preference, and trustworthiness.
Optimal planning and design of a renewable energy based supply system for microgrids

Renewable energy sources are gradually being recognized as important options in supply side planning for microgrids. This paper focuses on the optimal design, planning, sizing and operation of a hybrid, renewable energy based microgrid with the goal of minimizing the lifecycle cost, while taking into account environmental emissions. Four different cases including a diesel-only, a fully renewable-based, a diesel-renewable mixed, and an external grid-connected microgrid configurations are designed, to compare and evaluate their economics, operational performance and environmental emissions. Analysis is also carried out to determine the break-even economics for a grid-connected microgrid. The wellknown energy modeling software for hybrid renewable energy systems, HOMER is used in the studies reported in this paper.
A PDTB-Styled End-to-End Discourse Parser

Since the release of the large discourse-level annotation of the Penn Discourse Treebank (PDTB), research work has been carried out on certain subtasks of this annotation, such as disambiguating discourse connectives and classifying Explicit or Implicit relations. We see a need to construct a full parser on top of these subtasks and propose a way to evaluate the parser. In this work, we have designed and developed an end-to-end discourse parser-to-parse free texts in the PDTB style in a fully data-driven approach. The parser consists of multiple components joined in a sequential pipeline architecture, which includes a connective classifier, argument labeler, explicit classifier, non-explicit classifier, and attribution span labeler. Our trained parser first identifies all discourse and non-discourse relations, locates and labels their arguments, and then classifies the sense of the relation between each pair of arguments. For the identified relations, the parser also determines the attribution spans, if any, associated with them. We introduce novel approaches to locate and label arguments, and to identify attribution spans. We also significantly improve on the current state-of-the-art connective classifier. We propose and present a comprehensive evaluation from both component-wise and error-cascading perspectives, in which we illustrate how each component performs in isolation, as well as how the pipeline performs with errors propagated forward. The parser gives an overall system F1 score of 46.80 percent for partial matching utilizing gold standard parses, and 38.18 percent with full automation.
Path Planning Strategies for UAVS in 3D Environments

The graph-search algorithms developed between 60s and 80s were widely used in many fields, from robotics to video games. The A* algorithm shall be mentioned between some of the most important solutions explicitly oriented to motion-robotics, improving the logic of graph search with heuristic principles inside the loop. Nevertheless, one of the most important drawbacks of the A* algorithm resides in the heading constraints connected with the grid characteristics. Different solutions were developed in the last years to cope with this problem, based on postprocessing algorithms or on improvements of the graph-search algorithm itself. A very important one is Theta* that refines the graph search allowing to obtain paths with “any” heading. In the last two years, the Flight Mechanics Research Group of Politecnico di Torino studied and implemented different path planning algorithms. A L. De Filippis (B) · G. Guglieri · F. Quagliotti Dipartimento di Ingegneria Aeronautica e Spaziale, Politecnico di Torino, Corso Duca Degli Abruzzi 24, 10129 Turin, Italy e-mail: luca.defilippis@polito.it G. Guglieri e-mail: giorgio.guglieri@polito.it F. Quagliotti e-mail: fulvia.quagliotti@polito.it Matlab based planning tool was developed, collecting four separate approaches: geometric predefined trajectories, manual waypoint definition, automatic waypoint distribution (i.e. optimizing camera payload capabilities) and a comprehensive A*-based algorithm used to generate paths, minimizing risk of collision with orographic obstacles. The tool named PCube exploits Digital Elevation Maps (DEMs) to assess the risk maps and it can be used to generate waypoint sequences for UAVs autopilots. In order to improve the A*-based algorithm, the solution is extended to tri-dimensional environments implementing a more effective graph search (based on Theta*). In this paper the application of basic Theta* to tridimensional path planning will be presented. Particularly, the algorithm is applied to orographic obstacles and in urban environments, to evaluate the solution for different kinds of obstacles. Finally, a comparison with the A* algorithm will be introduced as a metric of the algorithm
Speaker-aware training of LSTM-RNNS for acoustic modelling

Long Short-Term Memory (LSTM) is a particular type of recurrent neural network (RNN) that can model long term temporal dynamics. Recently it has been shown that LSTM-RNNs can achieve higher recognition accuracy than deep feed-forword neural networks (DNNs) in acoustic modelling. However, speaker adaption for LSTM-RNN based acoustic models has not been well investigated. In this paper, we study the LSTM-RNN speaker-aware training that incorporates the speaker information during model training to normalise the speaker variability. We first present several speaker-aware training architectures, and then empirically evaluate three types of speaker representation: I-vectors, bottleneck speaker vectors and speaking rate. Furthermore, to factorize the variability in the acoustic signals caused by speakers and phonemes respectively, we investigate the speaker-aware and phone-aware joint training under the framework of multi-task learning. In AMI meeting speech transcription task, speaker-aware training of LSTM-RNNs reduces word error rates by 6.5% relative to a very strong LSTM-RNN baseline, which uses FMLLR features.