nils-herrmann/clean_scirepeval huggingface dataset:
Fingerprint classification using one-vs-all support vector machines dynamically ordered with naive Bayes classifiers

Fingerprint classification reduces the number of possible matches in automated fingerprint identification systems by categorizing fingerprints into predefined classes. Support vector machines (SVMs) are widely used in pattern classification and have produced high accuracy when performing fingerprint classification. In order to effectively apply SVMs to multi-class fingerprint classification systems, we propose a novel method in which the SVMs are generated with the one-vs-all (OVA) scheme and dynamically ordered with naïve Bayes classifiers. This is necessary to break the ties that frequently occur when working with multi-class classification systems that use OVA SVMs. More specifically, it uses representative fingerprint features as the FingerCode, singularities and pseudo ridges to train the OVA SVMs and naïve Bayes classifiers. The proposed method has been validated on the NIST-4 database and produced a classification accuracy of 90.8% for five-class classification with the statistical significance. The results show the benefits of integrating different fingerprint features as well as the usefulness of the proposed method in multi-class fingerprint classification.
Global optimization and simulated annealing

In this paper we are concerned with global optimization, which can be defined as the problem of finding points on a bounded subset of IRn in which some real valued functionf assumes its optimal (i.e. maximal or minimal) value. We present a stochastic approach which is based on the simulated annealing algorithm. The approach closely follows the formulation of the simulated annealing algorithm as originally given for discrete optimization problems. The mathematic formulation is extended to continuous optimization problems and we prove asymptotic convergence to the set of global optima. Furthermore, we discuss an implementation of the algorithm and compare its performance with other well known algorithms. The performance evaluation is carried out for a standard set of test functions from the literature.  global optimization, continuous variables, simulated annealing.
Religious coping and psychological adjustment to stress: a meta-analysis.

A growing body of literature suggests that people often turn to religion when coping with stressful events. However, studies on the efficacy of religious coping for people dealing with stressful situations have yielded mixed results. No published studies to date have attempted to quantitatively synthesize the research on religious coping and psychological adjustment to stress. The purpose of the current study was to synthesize the research on situation-specific religious coping methods and quantitatively determine their efficacy for people dealing with stressful situations. A meta-analysis of 49 relevant studies with a total of 105 effect sizes was conducted in order to quantitatively examine the relationship between religious coping and psychological adjustment to stress. Four types of relationships were investigated: positive religious coping with positive psychological adjustment, positive religious coping with negative psychological adjustment, negative religious coping with positive psychological adjustment, and negative religious coping with negative psychological adjustment. The results of the study generally supported the hypotheses that positive and negative forms of religious coping are related to positive and negative psychological adjustment to stress, respectively. Implications of the findings and their limitations are discussed.
BOOM v 2 : an open-source out-of-order RISC-V core

Machine first presented in . The design exploration was performed through synthesis, place and route using the foundry-provided standard-cell library and the memory compiler in the TSMC 28 nm HPM process (high performance mobile).  processor that implements the RV64G RISC-V Instruction Set Architecture (ISA). Like most contemporary high-performance cores, BOOM is superscalar (able to execute multiple instructions per cycle) and out-oforder (able to execute instructions as their dependencies are resolved and not restricted to their program order). BOOM is implemented as a parameterizable generator written using the Chisel hardware construction language  that can used to generate synthesizable implementations targeting both FPGAs and ASICs. BOOMv2 is an update in which the design effort has been informed by analysis of synthesized, placed and routed data provided by a contemporary industrial tool flow. We also had access to standard singleand dual-ported memory compilers provided by the foundry, allowing us to explore design trade-offs using different SRAM memories and comparing against synthesized flip-flop arrays. The main distinguishing features of BOOMv2 include an updated 3-stage front-end design with a bigger set-associative Branch Target Buffer (BTB); a pipelined register rename stage; split floating point and integer register files; a dedicated floating point pipeline; separate issue windows for floating point, integer, and memory micro-operations; and separate stages for issue-select and register read. Managing the complexity of the register file was the largest obstacle to improving BOOM’s clock frequency. We spent considerable effort on placing-and-routing a semi-custom 9port register file to explore the potential improvements over a fully synthesized design, in conjunction with microarchitectural techniques to reduce the size and port count of the register file. BOOMv2 has a 37 fanout-of-four (FO4) inverter delay after synthesis and 50 FO4 after place-and-route, a 24% reduction from BOOMv1’s 65 FO4 after place-and-route. Unfortunately, instruction per cycle (IPC) performance drops up to 20%, mostly due to the extra latency between load instructions and dependent instructions. However, the new BOOMv2 physical design paves the way for IPC recovery later. BOOMv1-2f3i int/idiv/fdiv
Acoustic evidence for the emergence of tonal contrast in contemporary Korean

Acoustic evidence suggests that contemporary Seoul Korean may be developing a tonal system, which is arising in the context of a nearly completed change in how speakers use voice onset time (VOT) to mark the language’s distinction among tense, lax and aspirated stops.Data from 36 native speakers of varying ages indicate that while VOT for tense stops has not changed since the 1960s, VOT differences between lax and aspirated stops have decreased, in some cases to the point of complete overlap. Concurrently, the mean F0 for words beginning with lax stops is significantly lower than the mean F0 for comparable words beginning with tense or aspirated stops. Hence the underlying contrast between lax and aspirated stops is maintained by younger speakers, but is phonetically manifested in terms of differentiated tonal melodies: laryngeally unmarked (lax) stops trigger the introduction of a default L tone, while laryngeally marked stops (aspirated and tense) introduce H, triggered by a feature specification for [stiff].
Big Data Infrastructure for analyzing data generated by Wireless Sensor Networks

The rapid pace of technological advances in recentyears has enabled a significant evolution and deployment ofWireless Sensor Networks (WSN). These networks are a keyplayer in the so-called Internet of Things, generating exponentiallyincreasing amounts of data. Nonetheless, there are veryfew documented works that tackle the challenges related with thecollection, manipulation and exploitation of the data generated bythese networks. This paper presents a proposal for integrating BigData tools (in rest and in motion) for the gathering, storage andanalysis of data generated by a WSN that monitors air pollutionlevels in a city. We provide a proof of concept that combinesHadoop and Storm for data processing, storage and analysis,and Arduino-based kits for constructing our sensor prototypes.
Open Set Domain Adaptation

When the training and the test data belong to different domains, the accuracy of an object classifier is significantly reduced. Therefore, several algorithms have been proposed in the last years to diminish the so called domain shift between datasets. However, all available evaluation protocols for domain adaptation describe a closed set recognition task, where both domains, namely source and target, contain exactly the same object classes. In this work, we also explore the field of domain adaptation in open sets, which is a more realistic scenario where only a few categories of interest are shared between source and target data. Therefore, we propose a method that fits in both closed and open set scenarios. The approach learns a mapping from the source to the target domain by jointly solving an assignment problem that labels those target instances that potentially belong to the categories of interest present in the source dataset. A thorough evaluation shows that our approach outperforms the state-of-the-art.
Neural Network Adaptive Beamforming for Robust Multichannel Speech Recognition

Joint multichannel enhancement and acoustic modeling using neural networks has shown promise over the past few years. However, one shortcoming of previous work [1, 2, 3] is that the filters learned during training are fixed for decoding, potentially limiting the ability of these models to adapt to previously unseen or changing conditions. In this paper we explore a neural network adaptive beamforming (NAB) technique to address this issue. Specifically, we use LSTM layers to predict time domain beamforming filter coefficients at each input frame. These filters are convolved with the framed time domain input signal and summed across channels, essentially performing FIR filter-andsum beamforming using the dynamically adapted filter. The beamformer output is passed into a waveform CLDNN acoustic model [4] which is trained jointly with the filter prediction LSTM layers. We find that the proposed NAB model achieves a 12.7% relative improvement in WER over a single channel model  and reaches similar performance to a “factored” model architecture which utilizes several fixed spatial filters  on a 2,000-hour Voice Search task, with a 17.9% decrease in computational cost.
Possibilistic linear programming: a brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem

In this paper, we review some fuzzy linear programming methods and techniques from a practical point of view. In the rst part, the general history and the approach of fuzzy mathematical programming are introduced. Using a numerical example, some models of fuzzy linear programming are described. In the second part of the paper, fuzzy mathematical programming approaches are compared to stochastic programming ones. The advantages and disadvantages of fuzzy mathematical programming approaches are exempli ed in the setting of an optimal portfolio selection problem. Finally, some newly developed ideas and techniques in fuzzy mathematical programming are brie y reviewed.
Compiling machine learning programs via high-level tracing

We describe JAX, a domain-specific tracing JIT compiler for generating high-performance accelerator code from pure Python and Numpy machine learning programs. JAX uses the XLA compiler infrastructure to generate optimized code for the program subroutines that are most favorable for acceleration, and these optimized subroutines can be called and orchestrated by arbitrary Python. Because the system is fully compatible with Autograd, it allows forwardand reverse-mode automatic differentiation of Python functions to arbitrary order. Because JAX supports structured control flow, it can generate code for sophisticated machine learning algorithms while maintaining high performance. We show that by combining JAX with Autograd and Numpy we get an easily programmable and highly performant ML system that targets CPUs, GPUs, and TPUs, capable of scaling to multi-core Cloud TPUs.
Affectively intelligent and adaptive car interfaces

In this article, we describe a new approach to enhance driving safety via multi-media technologies by recognizing and adapting to drivers’ emotions with multi-modal intelligent car interfaces. The primary objective of this research was to build an affectively intelligent and adaptive car interface that could facilitate a natural communication with its user (i.e., the driver). This objective was achieved by recognizing drivers’ affective states (i.e., emotions experienced by the drivers) and by responding to those emotions by adapting to the current situation via an affective user model created for each individual driver. A controlled experiment was designed and conducted in a virtual reality environment to collect physiological data signals (galvanic skin response, heart rate, and temperature) from participants who experienced driving-related emotions and states (neutrality, panic/fear, frustration/anger, and boredom/sleepiness). k-Nearest Neighbor (KNN), Marquardt-Backpropagation (MBP), and Resilient Backpropagation (RBP) Algorithms were implemented to analyze the collected data signals and to find unique physiological patterns of emotions. RBP was the best classifier of these three emotions with 82.6% accuracy, followed by MBP with 73.26% and by KNN with 65.33%. Adaptation of the interface was designed to provide multi-modal feedback to the users about their current affective state and to respond to users’ negative emotional states in order to decrease the possible negative impacts of those emotions. Bayesian Belief Networks formalization was employed to develop the user model to enable the intelligent system to appropriately adapt to the current context and situation by considering user-dependent factors, such as personality traits and preferences.
Temporal Lift by Galeapexy: A Review of 270 Cases

The purpose of this technique is to correct the ptotic tail of the brow and crow’s feet as well as to redrape the premalar skin to allow us to make a greater skin resection in the lower lid. The purpose of this presentation is to expose the advantages, the inconveniences, and the complications of this technique.  We reviewed the charts of 270 patients who were operated on between June 1996 and January 2002. The average age of the patients was 45 years old. Temporal lifts were performed during a frontotemporal lift in 92 cases, and during a three-stage lift (frontal, facial, and cervical) in 170 cases. In eight cases, an isolated temporal lift was performed. An upper blepharoplasty was performed in 252 cases and transconjunctival fat pad excision was performed in 188 cases. Finally, a lower lid skin resection was done following a galeapexy in 241 cases.  The surgical approach consists of a temporal incision, followed by a subgaleal dissection, and then a subcutaneous dissection that allows the cephalic galea, previously incised, to be anchored to the temporal aponeurosis. This allows good cutaneous redraping that raises the brow tail and a detachment of the orbicularis fibers. The suturing of the scalp is done without tension and without eliminating hair.  The results of the technique with a follow-up period of more than five years for the earlier cases and six months for the last cases, were evaluated with three criteria: (1) the distance between the brow and the eyelashes, (2) the reduction of crow’s feet, (3) the reduction of wrinkles at the level of the lower lid and the malar region. Overall satisfaction was also rated. Of 270 patients, 225 had ratings of very good to good, 13 had average results, and 13 had unsatisfactory results. As far as complications, five had unilateral hematoma requiring an evacuation, four patients had temporary alopecia, and eight patients had temporary frontal muscle weakness that resolved within one to three months.  This technique, performed under local anesthesia, is very efficient, reproducible, and simple. The incidence of complications can be reduced when one is meticulous during dissection.
Adaptive fuzzy inference neural network

An adaptive fuzzy inference neural network (AFINN) is proposed in this paper. It has self-construction ability, parameter estimation ability and rule extraction ability. The structure of AFINN is formed by the following four phases: (1) initial rule creation, (2) selection of important input elements, (3) identification of the network structure and (4) parameter estimation using LMS (least-mean square) algorithm. When the number of input dimension is large, the conventional fuzzy systems often cannot handle the task correctly because the degree of each rule becomes too small. AFINN solves such a problem by modification of the learning and inference algorithm.
Morphological Analysis for Japanese Noisy Text based on Character-level and Word-level Normalization

Social media texts are often written in a non-standard style and include many lexical variants such as insertions, phonetic substitutions, abbreviations that mimic spoken language. The normalization of such a variety of non-standard tokens is one promising solution for handling noisy text. A normalization task is very difficult to conduct in Japanese morphological analysis because there are no explicit boundaries between words. To address this issue, in this paper we propose a novel method for normalizing and morphologically analyzing Japanese noisy text. We generate both character-level and word-level normalization candidates and use discriminative methods to formulate a cost function. Experimental results show that the proposed method achieves acceptable levels in both accuracy and recall for word segmentation, POS tagging, and normalization. These levels exceed those achieved with the conventional rule-based system.
Optical character recognition for self-service banking

Self-service automatic teller machines (ATMs) have dramatically altered the ways in which customers interact with banks. ATMs provide the convenience of completing some banking transactions remotely and at any time. AT&T Global Information Solutions (GIS) is the world's leading provider of ATMs. These machines support such familiar services as cash withdrawals and balance inquiries. Further technological development has extended the utility and convenience of ATMs produced by GIS by facilitating check cashing and depositing, as well as direct bill payment, using an on-line system. These enhanced services, discussed in this paper, are made possible primarily through sophisticated optical character recognition (OCR) technology. Developed by an AT&T team that included GIS, AT&T Bell Laboratories Quality, Engineering, Software, and Technologies (QUEST), and AT&T Bell Laboratories Research, OCR technology was crucial to the development of these advanced ATMs.
URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors

We introduce the URIEL knowledge base for massively multilingual NLP and the lang2vec utility, which provides information-rich vector identifications of languages drawn from typological, geographical, and phylogenetic databases that are normalized to have straightforward and consistent formats, naming, and semantics. The goal of URIEL and lang2vec is to enable multilingual NLP, especially on less-resourced languages and make possible types of experiments (especially but not exclusively related to NLP tasks) that are otherwise difficult or impossible due to the sparsity and incommensurability of the data sources. lang2vec vectors have been shown to reduce perplexity in multilingual language modeling, when compared to one-hot language identification vectors.
LibN3L: A Lightweight Package for Neural NLP

We present a light-weight machine learning tool for NLP research. The package supports operations on both discrete and dense vectors, facilitating implementation of linear models as well as neural models. It provides several basic layers which mainly aims for single-layer linear and non-linear transformations. By using these layers, we can conveniently implement linear models and simple neural models. Besides, this package also integrates several complex layers by composing those basic layers, such as RNN, Attention Pooling, LSTM and gated RNN. Those complex layers can be used to implement deep neural models directly.
Summarizing Microblogs Automatically

In this paper, we focus on a recent Web trend called microblogging, and in particular a site called Twitter. The content of such a site is an extraordinarily large number of small textual messages, posted by millions of users, at random or in response to perceived events or situations. We have developed an algorithm that takes a trending phrase or any phrase specified by a user, collects a large number of posts containing the phrase, and provides an automatically created summary of the posts related to the term. We present examples of summaries we produce along with initial evaluation.
Detecting collusive spammers in online review communities

In this paper, we first define our research problem as to detect collusive spammers in online review communities. Next we present our current progress on this topic, in which we have spotted anomalies by evaluating 15 behavioral features proposed in the state-of-the-art approaches. Then we propose a novel hybrid classification/clustering method to detect colluders in our dataset based on selected informative features. Experimental results show that our method promisingly improve the performance of traditional classifiers by incorporating clustering for the smoothing. Finally, possible extensions of our current work and challenges in achieving them are discussed as our future directions.
Low Voltage and Current Stress ZVZCS Full Bridge DC–DC Converter Using Center Tapped Rectifier Reset

An improved zero-voltage and zero-current-switching (ZVZCS) full bridge dc-dc converter is proposed based on phase shift control. With an auxiliary center tapped rectifier at the secondary side, an auxiliary voltage source is applied to reset the primary current of the transformer winding. Therefore, zero-voltage switching for the leading leg switches and zero-current switching for the lagging leg switches can be achieved, respectively, without any increase of current and voltage stresses. Since the primary current in the circulating interval for the phase shift full bridge converter is eliminated, the conduction loss in primary switches is reduced. A 1 kW prototype is made to verify the theoretical analysis.
Ensemble Learning Using Multi-Objective Evolutionary Algorithms

Multi-objective evolutionary algorithms for the construction of neural ensembles is a relatively new area of research. We recently proposed an ensemble learning algorithm called DIVACE (DIVerse and ACcurate Ensemble learning algorithm). It was shown that DIVACE tries to find an optimal trade-off between diversity and accuracy as it searches for an ensemble for some particular pattern recognition task by treating these two objectives explicitly separately. A detailed discussion of DIVACE together with further experimental studies form the essence of this paper. A new diversity measure which we call Pairwise Failure Crediting (PFC) is proposed. This measure forms one of the two evolutionary pressures being exerted explicitly in DIVACE. Experiments with this diversity measure as well as comparisons with previously studied approaches are hence considered. Detailed analysis of the results show that DIVACE, as a concept, has promise.