nils-herrmann/clean_scirepeval huggingface dataset:
Determination of bit-rate adaptation thresholds for the Opus codec for VoIP services

In this paper, we present an experimental evaluation of the recently standardized Opus codec used in a VoIP context. Opus operates in both narrow and wideband modes, similar to Adaptive Multi-Rate (AMR). Through the use of the Wideband Perceptual  metric, we have conducted an extensive set of experiments using multiple audio samples encoded at different bit-rates, to investigate the impact of packet loss on resulting speech quality. Using these results, fitting functions for each bit-rate were computed to provide a straightforward manner of evaluating speech quality when given a specified packet loss rate. Using ns-2, a simulation analysis was conducted to evaluate the effect of background traffic on transmitted Opus streams. We observed that, when using different levels of background traffic, the observed packet loss rates varied heavily depending on the stream bit-rate. By correlating this information with the fitting functions derived previously, we were able to define switching thresholds. These are points where the speech quality of a lower bit-rate stream is greater than that of a higher bit-rate stream for the same levels of link bandwidth saturation.
A Robot Finger Design Using a Dual-Mode Twisting Mechanism to Achieve High-Speed Motion and Large Grasping Force

A dual-mode robot finger is proposed to achieve a high-speed motion and large grasping force with a single motor. The robot finger has two actuator modes, which consist of the speed mode and the force mode. Based on the geometric analysis of each mode, the main design parameters of the proposed robot finger are derived, and their effectiveness is verified by simulations. In addition, using experiments with a prototype of a robot finger, the validity of the proposed approach is demonstrated.
A review of European research on consumer response to nutrition information on food labels

The aim of this study was to review research conducted in 2003–2006 in the EU-15 countries on how consumers perceive, understand, like and use nutrition information on food labels. Based on a search of databases on academic publications, Google-based search, and enquiries directed to a range of food retailers, food companies, consumer associations and government agencies, a total of 58 studies were identified. These studies were summarised using a standard format guided by a model of consumer information processing, and these summaries were subsequently processed using the MAXqda software in order to identify key findings and common themes across the studies. The studies show widespread consumer interest in nutrition information on food packages, though this interest varies across situations and products. Consumers like the idea of simplified front of pack information but differ in their liking for the various formats. Differences can be related to conflicting preferences for ease of use, being fully informed and not being pressurised into behaving in a particular way. Most consumers understand the most common signposting formats in the sense that they themselves believe that they understand them and they can replay key information presented to them in an experimental situation. There is, however, virtually no insight into how labelling information is, or will be, used in a real-world shopping situation, and how it will affect consumers’ dietary patterns. Results are largely in line with an earlier review by Cowburn and Stockley (Public Health Nutr 8:21–28, 2005), covering research up to 2002, but provide new insights into consumer liking and understanding of simplified front of pack signposting formats. There is an urgent need for more research studying consumer use of nutritional information on food labels in a real-world setting.
An Efficient Power Scheduling Scheme for Residential Load Management in Smart Homes

In this paper, we propose mathematical optimization models of household energy units to optimally control the major residential energy loads while preserving the user preferences. User comfort is modelled in a simple way, which considers appliance class, user preferences and weather conditions. The wind-driven optimization (WDO) algorithm with the objective function of comfort maximization along with minimum electricity cost is defined and implemented. On the other hand, for maximum electricity bill and peak reduction, min-max regret-based knapsack problem (K-WDO) algorithm is used. To validate the effectiveness of the proposed algorithms, extensive simulations are conducted for several scenarios. The simulations show that the proposed algorithms provide with the best optimal results with a fast convergence rate, as compared to the existing techniques.  2015, 5 1135
Propose but verify: Fast mapping meets cross-situational word learning

We report three eyetracking experiments that examine the learning procedure used by adults as they pair novel words and visually presented referents over a sequence of referentially ambiguous trials. Successful learning under such conditions has been argued to be the product of a learning procedure in which participants provisionally pair each novel word with several possible referents and use a statistical-associative learning mechanism to gradually converge on a single mapping across learning instances [e.g., Yu, C., & Smith, L. B. (2007). Rapid word learning under uncertainty via cross-situational statistics. Psychological Science, 18(5), 414-420]. We argue here that successful learning in this setting is instead the product of a one-trial procedure in which a single hypothesized word-referent pairing is retained across learning instances, abandoned only if the subsequent instance fails to confirm the pairing--more a 'fast mapping' procedure than a gradual statistical one. We provide experimental evidence for this propose-but-verify learning procedure via three experiments in which adult participants attempted to learn the meanings of nonce words cross-situationally under varying degrees of referential uncertainty. The findings, using both explicit (referent selection) and implicit (eye movement) measures, show that even in these artificial learning contexts, which are far simpler than those encountered by a language learner in a natural environment, participants do not retain multiple meaning hypotheses across learning instances. As we discuss, these findings challenge 'gradualist' accounts of word learning and are consistent with the known rapid course of vocabulary learning in a first language.
Thermally Stable Enhancement-Mode GaN Metal-Isolator-Semiconductor High-Electron-Mobility Transistor With Partially Recessed Fluorine-Implanted Barrier

Al2O3/AlGaN/GaN enhancement-mode metalisolator-semiconductor high-electron-mobility transistor (MIS-HEMT) featuring a partially recessed (Al) GaN barrier was realized by a fluorine plasma implantation/etch technique. By properly adjusting the RF power driving the fluorine plasma, the fluorine plasma is able to produce two desirable results: 1) a well-controlled slow dry etching for gate recess and 2) implanting fluorine ions into the AlGaN barrier. The fluorine ions become negatively charged in the barrier layer and induce a positive shift in the threshold voltage. The proposed MIS-HEMT exhibits a threshold voltage (VTH) of +0.6 V at a drain current of 10 μA/mm, a maximum drive current of 730 mA/mm, an ON-resistance of 7.07 Ω · mm, and an OFF-state breakdown voltage of 703 V at an OFF-state drain leakage current of 1 μA/mm. From room temperature to 200 °C, the device exhibits a small negative shift of VTH (~0.5 V) that is attributed to the high-quality dielectric/F-implanted-(Al) GaN interface and the partially recessed barrier.
Active noise control: a tutorial review

Active noise control (ANC) is achieved by introducing a canceling “antinoise” wave through an appropriate array of secondary sources. These secondary sources are interconnected through an electronic system using a specific signal processing algorithm for the particular cancellation scheme. ANC has application to a wide variety of problems in manufacturing, industrial operations, and consumer products. The emphasis of this paper is on the practical aspects of ANC systems in terms of adaptive signal processing and digital signal processing (DSP) implementation for real-world applications. In this paper, the basic adaptive algorithm for ANC is developed and analyzed based on single-channel broad-band feedforward control. This algorithm is then modified for narrow-band feedforward and adaptive feedback control. In turn, these single-channel ANC algorithms are expanded to multiple-channel cases. Various online secondary-path modeling techniques and special adaptive algorithms, such as lattice, frequency-domain, subband, and recursive-least-squares, are also introduced. Applications of these techniques to actual problems are highlighted by several examples.
A Machine Learning Approach for SQL Queries Response Time Estimation in the Cloud

Cloud computing provides on-demand services with pay-as-you-go model. In this sense, customers expect quality from providers where QoS control over DBMSs services demands decision making, usually based on performance aspects. It is essential that the system be able to assimilate the characteristics of the application in order to determine the execution time for its queries' execution. Machine learning techniques are a promising option in this context, since they o er a wide range of algorithms that build predictive models for this purpose based on the past observations of the demand and on the DBMS system behavior. In this work, we propose and evaluate the use of regression methods for modeling the SQL query execution time in the cloud. This process uses di erent techniques, such as bag-of-words, statistical lter and genetic algorithms. We use K-Fold Cross-Validation for measuring the quality of the models in each step. Experiments were carried out based on data from database systems in the cloud generated by the TPC-C benchmark.
A NOVEL OF RECONFIGURABLE PLANAR ANTENNA ARRAY ( RPAA ) WITH BEAM STEERING CONTROL

A new antenna structure is formed by combining the concept of reconfigurable planar antenna array (RPAA) with the parasitic elements to produce beam steering patterns. The antenna has been integrated with the PIN diode switches that enable the beam to be steered in the desired direction. This has been done by changing the switch state to either on or off mode. In this work, a number of parasitic elements have been applied to the antenna, namely reflectors and directors. They are placed in between the driven elements, which is aimed to improve the beam steering angle. With such configuration, the main beam radiated by the array can be tilted due to the effect of mutual coupling between the driven elements and parasitic elements (reflectors and director). The unique property of this antenna design is that instead of fabricating all together in the same plane, the antenna’s feeding network is separated from the antenna radiating elements (the patches) by an air gap distance. This allows reducing the spurious effects from the feeding line. The optimization results for the resonant Corresponding author: M. T. Ali (mizi732002@yahoo.com).
Sequential Decisions and Predictions in Natural Language Processing

Title of dissertation: SEQUENTIAL DECISIONS AND PREDICTIONS IN NATURAL LANGUAGE PROCESSING He He, Doctor of Philosophy, 2016 Dissertation directed by: Professor Hal Daumé III Department of Computer Science Natural language processing has achieved great success in a wide range of applications, producing both commercial language services and open-source language tools. However, most methods take a static or batch approach, assuming that the model has all information it needs and makes a one-time prediction. In this dissertation, we study dynamic problems where the input comes in a sequence instead of all at once, and the output must be produced while the input is arriving. In these problems, predictions are often made based only on partial information. We see this dynamic setting in many real-time, interactive applications. These problems usually involve a trade-off between the amount of input received (cost) and the quality of the output prediction (accuracy). Therefore, the evaluation considers both objectives (e.g., plotting a Pareto curve). Our goal is to develop a formal understanding of sequential prediction and decision-making problems in natural language processing and to propose efficient solutions. Toward this end, we present meta-algorithms that take an existent batch model and produce a dynamic model to handle sequential inputs and outputs. We build our framework upon theories of Markov Decision Process (MDP), which allows learning to trade off competing objectives in a principled way. The main machine learning techniques we use are from imitation learning and reinforcement learning, and we advance current techniques to tackle problems arising in our settings. We evaluate our algorithm on a variety of applications, including dependency parsing, machine translation, and question answering. We show that our approach achieves a better cost-accuracy trade-off than the batch approach and heuristic-based decisionmaking approaches. We first propose a general framework for cost-sensitive prediction, where different parts of the input come at different costs. We formulate a decision-making process that selects pieces of the input sequentially, and the selection is adaptive to each instance. Our approach is evaluated on both standard classification tasks and a structured prediction task (dependency parsing). We show that it achieves similar prediction quality to methods that use all input, while inducing a much smaller cost. Next, we extend the framework to problems where the input is revealed incrementally in a fixed order. We study two applications: simultaneous machine translation and quiz bowl (incremental text classification). We discuss challenges in this setting and show that adding domain knowledge eases the decision-making problem. A central theme throughout the chapters is an MDP formulation of a challenging problem with sequential input/output and trade-off decisions, accompanied by a learning algorithm that solves the MDP. SEQUENTIAL DECISIONS AND PREDICTIONS IN NATURAL LANGUAGE PROCESSING
Inference rules for RDF(S) and OWL in N3Logic

This paper presents inference rules for Resource Description Framework (RDF), RDF Schema (RDFS) and Web Ontology Language (OWL). Our formalization is based on Notation 3 Logic, which extended RDF by logical symbols and created Semantic Web logic for deductive RDF graph stores. We also propose OWL-P that is a lightweight formalism of OWL and supports soft inferences by omitting complex language constructs.
Semantic annotation of unstructured and ungrammatical text

There are vast amounts of free text on the internet that are neither grammatical nor formally structured, such as item descriptions on Ebay or internet classifieds like Craig’s list. These sources of data, called “posts,” are full of useful information for agents scouring the Semantic Web, but they lack the semantic annotation to make them searchable. Annotating these posts is difficult since the text generally exhibits little formal grammar and the structure of the posts varies. However, by leveraging collections of known entities and their common attributes, called “reference sets,” we can annotate these posts despite their lack of grammar and structure. To use this reference data, we align a post to a member of the reference set, and then exploit this matched member during information extraction. We compare this extraction approach to more traditional information extraction methods that rely on structural and grammatical characteristics, and we show that our approach outperforms traditional methods on this type of data.
An investigation into language model data augmentation for low-resourced STT and KWS

This paper reports on investigations using two techniques for language model text data augmentation for low-resourced automatic speech recognition and keyword search. Lowresourced languages are characterized by limited training materials, which typically results in high out-of-vocabulary (OOV) rates and poor language model estimates. One technique makes use of recurrent neural networks (RNNs) using word or subword units. Word-based RNNs keep the same system vocabulary, so they cannot reduce the OOV, whereas subword units can reduce the OOV but generate many false combinations. A complementary technique is based on automatic machine translation, which requires parallel texts and is able to add words to the vocabulary. These methods were assessed on 10 languages in the context of the Babel program and NIST OpenKWS evaluation. Although improvements vary across languages with both methods, small gains were generally observed in terms of word error rate reduction and improved keyword search performance.
A hierarchical lstm model with multiple features for sentiment analysis of sina weibo texts

Sentiment analysis has long been a hot topic in natural language processing. With the development of social network, sentiment analysis on social media such as Facebook, Twitter and Weibo becomes a new trend in recent years. Many different methods have been proposed for sentiment analysis, including traditional methods (SVM and NB) and deep learning methods (RNN and CNN). In addition, the latter always outperform the former. However, most of existing methods only focus on local text information and ignore the user personality and content characteristics. In this paper, we propose an improved LSTM model with considering the user-based features and content-based features. We first analysis the training dataset to extract artificial features which consists of user-based and content-based. Then we construct a hierarchical LSTM model, named LSTM-MF (a hierarchical LSTM model with multiple features), and introduce the features into the model to generate sentence and document representations. The experimental results show that our model achieves significant and consistent improvements compared to all state-of-the-art methods.
Modeling and Experiments of Untethered Quadrupedal Running with a Bounding Gait: The Scout II Robot

In this paper we compare models and experiments involving Scout II, an untethered four-legged running robot with only one actuator per compliant leg. Scout II achieves dynamically stable running of up to 1.3 m s−1 on flat ground via a bounding gait. Energetics analysis reveals a highly efficient system with a specific resistance of only 1.4. The running controller requires no task-level or body-state feedback, and relies on the passive dynamics of the mechanical system. These results contribute to the increasing evidence that apparently complex dynamically dexterous tasks may be controlled via simple control laws. We discuss general modeling issues for dynamically stable legged robots. Two simulation models are compared with experimental data to test the validity of common simplifying assumptions. The need for including motor saturation and non-rigid torque transmission characteristics in simulation models is demonstrated. Similar issues are likely to be important in other dynamically stable legged robots as well. An extensive suite of experimental results documents the robot’s performance and the validity of the proposed
Semantics and implementation of continuous sliding window queries over data streams

In recent years the processing of continuous queries over potentially infinite data streams has attracted a lot of research attention. We observed that the majority of work addresses individual stream operations and system-related issues rather than the development of a general-purpose basis for stream processing systems. Furthermore, example continuous queries are often formulated in some declarative query language without specifying the underlying semantics precisely enough. To overcome these deficiencies, this article presents a consistent and powerful operator algebra for data streams which ensures that continuous queries have well-defined, deterministic results. In analogy to traditional database systems, we distinguish between a logical and a physical operator algebra. While the logical algebra specifies the semantics of the individual operators in a descriptive but concrete way over temporal multisets, the physical algebra provides efficient implementations in the form of stream-to-stream operators. By adapting and enhancing research from temporal databases to meet the challenging requirements in streaming applications, we are able to carry over the conventional transformation rules from relational databases to stream processing. For this reason, our approach not only makes it possible to express continuous queries with a sound semantics, but also provides a solid foundation for query optimization, one of the major research topics in the stream community. Since this article seamlessly explains the steps from query formulation to query execution, it outlines the innovative features and operational functionality implemented in our state-of-the-art stream processing infrastructure.
Bayesian super-resolution of text image sequences from low resolution observations

This paper deals with the problem of reconstructing high-resolution text images from an incomplete set of undersampled, blurred, and noisy images shifted with subpixel displacement. We derive mathematical expressions for the calculation of the maximum a posterioriestimate of the high resolution image and the estimation of the parameters involved in the model. The method is tested on real text images and car plates, examining the impact of blurring and the number of available low resolution images on the final estimate.
Modeling and Generating Random Vectorswith Arbitrary Marginal Distributions and Correlation

We describe a model for representing random vectors whose component random variables have arbitrary marginal distributions and correlation matrix, and describe how to generate data based upon this model for use in a stochastic simulation. The central idea is to transform a multivariate normal random vector into the desired random vector, so we refer to these vectors as having a NORTA (NORmal To Anything) distribution. NORTA vectors are most useful when the marginal distributions of the component random variables are neither identical nor from the same family of distributions, and they are particularly valuable when the dimension of the random vector is greater than two. Several numerical examples are provided.
Classification ability of single hidden layer feedforward neural networks

Multilayer perceptrons with hard-limiting (signum) activation functions can form complex decision regions. It is well known that a three-layer perceptron (two hidden layers) can form arbitrary disjoint decision regions and a two-layer perceptron (one hidden layer) can form single convex decision regions. This paper further proves that single hidden layer feedforward neural networks (SLFN's) with any continuous bounded nonconstant activation function or any arbitrary bounded (continuous or not continuous) activation function which has unequal limits at infinities (not just perceptrons) can form disjoint decision regions with arbitrary shapes in multidimensional cases. SLFN's with some unbounded activation function can also form disjoint decision regions with arbitrary shapes.