Automatic generation of a large dictionary with concreteness/abstractness ratings based on a small human dictionary

2021 ◽  
pp. 1-9
Author(s):  
Vladimir Ivanov ◽  
Valery Solovyev
Keyword(s):  
State Of The Art ◽  
Research Question ◽  
Automatic Generation ◽  
Expert Assessment ◽  
High Quality ◽  
Test Set ◽  
Expert Assessments ◽  
Abstract Words ◽  
Expert Ratings

Concrete/abstract words are used in a growing number of psychological and neurophysiological research. For a few languages, large dictionaries have been created manually. This is a very time-consuming and costly process. To generate large high-quality dictionaries of concrete/abstract words automatically one needs extrapolating the expert assessments obtained on smaller samples. The research question that arises is how small such samples should be to do a good enough extrapolation. In this paper, we present a method for automatic ranking concreteness of words and propose an approach to significantly decrease amount of expert assessment. The method has been evaluated on a large test set for English. The quality of the constructed dictionaries is comparable to the expert ones. The correlation between predicted and expert ratings is higher comparing to the state-of-the-art methods.

UBAR

2021 ◽  
Vol 20 (3) ◽  
pp. 1-25
Author(s):  
Elham Shamsa ◽  
Alma Pröbstl ◽  
Nima TaheriNejad ◽  
Anil Kanduri ◽  
Samarjit Chakraborty ◽  
...  
Keyword(s):  
Resource Management ◽  
Quality Of Experience ◽  
State Of The Art ◽  
State Of Charge ◽  
User Preference ◽  
Management Approach ◽  
High Quality ◽  
Trade Off ◽  
Run Time

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.

Thermal Laser Separation – A Novel Dicing Technology Fulfilling the Demands of Volume Manufacturing of 4H-SiC Devices

2015 ◽  
Vol 821-823 ◽  
pp. 528-532 ◽  
Author(s):  
Dirk Lewke ◽  
Karl Otto Dohnke ◽  
Hans Ulrich Zühlke ◽  
Mercedes Cerezuela Barret ◽  
Martin Schellenberger ◽  
...  
Keyword(s):  
Experimental Data ◽  
Process Control ◽  
Tool Wear ◽  
Large Scale ◽  
State Of The Art ◽  
Scale Production ◽  
High Quality ◽  
Large Scale Production ◽  
Laser Separation

One challenge for volume manufacturing of 4H-SiC devices is the state-of-the-art wafer dicing technology – the mechanical blade dicing which suffers from high tool wear and low feed rates. In this paper we discuss Thermal Laser Separation (TLS) as a novel dicing technology for large scale production of SiC devices. We compare the latest TLS experimental data resulting from fully processed 4H-SiC wafers with results obtained by mechanical dicing technology. Especially typical product relevant features like process control monitoring (PCM) structures and backside metallization, quality of diced SiC-devices as well as productivity are considered. It could be shown that with feed rates up to two orders of magnitude higher than state-of-the-art, no tool wear and high quality of diced chips, TLS has a very promising potential to fulfill the demands of volume manufacturing of 4H-SiC devices.

scholarly journals SCFont: Structure-Guided Chinese Font Generation via Deep Stacked Networks

2019 ◽  
Vol 33 ◽  
pp. 4015-4022 ◽  
Author(s):  
Yue Jiang ◽  
Zhouhui Lian ◽  
Yingmin Tang ◽  
Jianguo Xiao
Keyword(s):  
Deep Learning ◽  
Domain Knowledge ◽  
State Of The Art ◽  
Automatic Generation ◽  
Chinese Characters ◽  
Generation System ◽  
Model Learning ◽  
High Quality ◽  
Large Numbers ◽  
Quantitative Assessments

Automatic generation of Chinese fonts that consist of large numbers of glyphs with complicated structures is now still a challenging and ongoing problem in areas of AI and Computer Graphics (CG). Traditional CG-based methods typically rely heavily on manual interventions, while recentlypopularized deep learning-based end-to-end approaches often obtain synthesis results with incorrect structures and/or serious artifacts. To address those problems, this paper proposes a structure-guided Chinese font generation system, SCFont, by using deep stacked networks. The key idea is to integrate the domain knowledge of Chinese characters with deep generative networks to ensure that high-quality glyphs with correct structures can be synthesized. More specifically, we first apply a CNN model to learn how to transfer the writing trajectories with separated strokes in the reference font style into those in the target style. Then, we train another CNN model learning how to recover shape details on the contour for synthesized writing trajectories. Experimental results validate the superiority of the proposed SCFont compared to the state of the art in both visual and quantitative assessments.

scholarly journals Bridging the gap between geographic concept and the data we have: The case of labor markets in the USA

2020 ◽  
Vol 52 (7) ◽  
pp. 1395-1414
Author(s):  
Christopher S Fowler ◽  
Leif Jensen
Keyword(s):  
Labor Market ◽  
Labor Markets ◽  
Research Question ◽  
High Quality ◽  
Regional Bias ◽  
Regional Variations ◽  
Research Outcomes ◽  
The Usa ◽  
Over Time

A broad literature has made it clear that geographic units must be selected with care or they are likely to introduce error and uncertainty into results. Nevertheless, researchers often use data “off the shelf” with the implicit assumptions that their observations are consistent with the geographical concept relevant for their research question, and that they are of uniformly high quality in capturing this geographic identity. In this paper, we consider the geographical concept of “labor market” and offer a template for both clarifying its meaning for research and testing the suitability of extant labor-market delineations. We establish a set of metrics for comparing the quality of existing labor-market delineations with respect to the diverse meanings that researchers apply to the concept. Using the fit metrics established here, researchers can explore how delineations vary geographically, how they vary over time, and how this variation may shape research outcomes. Our assessment is that the quality of the extant delineations is relatively high overall. However, we find that different delineations vary significantly in the types of labor markets they represent, and that regional variations in fit within any given delineation may introduce noise or regional bias that merits consideration in any analysis conducted with these units. More broadly, the kinds of metrics we propose here have applicability for many other geographic entities where boundaries and scale can be only imperfectly defined.

scholarly journals Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning

2019 ◽  
Vol 33 ◽  
pp. 6722-6729 ◽  
Author(s):  
Ziming Li ◽  
Julia Kiseleva ◽  
Maarten De Rijke
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Imitation Learning ◽  
Local Optimum ◽  
Inverse Reinforcement Learning ◽  
High Quality ◽  
Overall Performance

The performance of adversarial dialogue generation models relies on the quality of the reward signal produced by the discriminator. The reward signal from a poor discriminator can be very sparse and unstable, which may lead the generator to fall into a local optimum or to produce nonsense replies. To alleviate the first problem, we first extend a recently proposed adversarial dialogue generation method to an adversarial imitation learning solution. Then, in the framework of adversarial inverse reinforcement learning, we propose a new reward model for dialogue generation that can provide a more accurate and precise reward signal for generator training. We evaluate the performance of the resulting model with automatic metrics and human evaluations in two annotation settings. Our experimental results demonstrate that our model can generate more high-quality responses and achieve higher overall performance than the state-of-the-art.

scholarly journals 3D modelling and measurements of historical violins

ACTA IMEKO ◽  
2017 ◽  
Vol 6 (3) ◽  
pp. 29 ◽  
Author(s):  
Piercarlo Dondi ◽  
Luca Lombardi ◽  
Marco Malagodi ◽  
Maurizio Licchelli
Keyword(s):  
3D Models ◽  
3D Modelling ◽  
High Quality ◽  
Test Set ◽  
Restricted Access ◽  
Crucial Information ◽  
Historical Violins

Measuring historical violins provides crucial information about the morphology of the instruments, useful both for researchers and violin makers. Generally, these measures are taken manually using a calliper, but they can be repeated only occasionally due to both the restricted access to these precious instruments and the need of avoiding accidental damages to the wood or to the varnishes. In this work, we describe and assess the accuracy of a protocol for the acquisition and creation of high quality 3D models of violins, suitable for taking accurate measurements. Six historical violins of 17th – 18th centuries, kept in “Museo del Violino” in Cremona (Italy), were used as test set. The quality of the final outcomes was checked comparing measures taken on the 3D meshes with the correspondent ones taken by calliper on the original instruments. Finally, a comparison between the sound board of the instruments were performed.

scholarly journals Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints

2017 ◽  
Vol 5 ◽  
pp. 309-324 ◽  
Author(s):  
Nikola Mrkšić ◽  
Ivan Vulić ◽  
Diarmuid Ó Séaghdha ◽  
Ira Leviant ◽  
Roi Reichart ◽  
...  
Keyword(s):  
State Of The Art ◽  
Vector Spaces ◽  
Lexical Resources ◽  
High Quality ◽  
Performance Improvements ◽  
State Tracking ◽  
Cross Lingual ◽  
Semantic Transfer ◽  
Multiple Languages

We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the use of constraints from mono- and cross-lingual resources, yielding semantically specialized cross-lingual vector spaces. Our evaluation shows that the method can make use of existing cross-lingual lexicons to construct high-quality vector spaces for a plethora of different languages, facilitating semantic transfer from high- to lower-resource ones. The effectiveness of our approach is demonstrated with state-of-the-art results on semantic similarity datasets in six languages. We next show that Attract-Repel-specialized vectors boost performance in the downstream task of dialogue state tracking (DST) across multiple languages. Finally, we show that cross-lingual vector spaces produced by our algorithm facilitate the training of multilingual DST models, which brings further performance improvements.

Online Social Exergames for Seniors

2020 ◽  
pp. 1599-1631
Author(s):  
Stathis Th. Konstantinidis ◽  
Ellen Brox ◽  
Per Egil Kummervold ◽  
Josef Hallberg ◽  
Gunn Evertsen ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Physical Activity ◽  
Physical Training ◽  
State Of The Art ◽  
Social Contact ◽  
High Quality ◽  
Current State ◽  
The Future ◽  
The Impact

The population is getting older, and the resources for care will be even more limited in the future than they are now. There is thus an aim for the society that the seniors can manage themselves as long as possible, while at the same time keeping a high quality of life. Physical activity is important to stay fit, and social contact is important for the quality of life. The aim of this chapter is to provide a state-of-the-art of online social exergames for seniors, providing glimpses of senior users' opinions and games limitations. The importance of the motivational techniques is emphasized, as well as the impact that the exergames have to seniors. It contributes to the book objectives focusing on current state and practice in health games for physical training and rehabilitation and the use of gamification, exploring future opportunities and uses of gamification in eHealth and discussing the respective challenges and limitations.

scholarly journals Sensoring a Generative System to Create User-Controlled Melodies

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3201
Author(s):  
María Navarro-Cáceres ◽  
Wataru Hashimoto ◽  
Sara Rodríguez-González ◽  
Belén Pérez-Lancho ◽  
Juan Corchado
Keyword(s):  
Intelligent System ◽  
State Of The Art ◽  
Automatic Generation ◽  
Case Based Reasoning ◽  
Mechanical Device ◽  
Specific Context ◽  
Partial Control ◽  
Art Research ◽  
Generative System

The automatic generation of music is an emergent field of research that has attracted the attention of countless researchers. As a result, there is a broad spectrum of state of the art research in this field. Many systems have been designed to facilitate collaboration between humans and machines in the generation of valuable music. This research proposes an intelligent system that generates melodies under the supervision of a user, who guides the process through a mechanical device. The mechanical device is able to capture the movements of the user and translate them into a melody. The system is based on a Case-Based Reasoning (CBR) architecture, enabling it to learn from previous compositions and to improve its performance over time. The user uses a device that allows them to adapt the composition to their preferences by adjusting the pace of a melody to a specific context or generating more serious or acute notes. Additionally, the device can automatically resist some of the user’s movements, this way the user learns how they can create a good melody. Several experiments were conducted to analyze the quality of the system and the melodies it generates. According to the users’ validation, the proposed system can generate music that follows a concrete style. Most of them also believed that the partial control of the device was essential for the quality of the generated music.

scholarly journals CNN BASED RETINAL IMAGE UPSCALING USING ZERO COMPONENT ANALYSIS

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 27-31 ◽  
Author(s):  
A. Nasonov ◽  
K. Chesnakov ◽  
A. Krylov
Keyword(s):  
Medical Image ◽  
Visual Analysis ◽  
Preparation Method ◽  
State Of The Art ◽  
Medical Image Processing ◽  
Main Idea ◽  
Objective Evaluation ◽  
Component Analysis ◽  
High Quality

The aim of the paper is to obtain high quality of image upscaling for noisy images that are typical in medical image processing. A new training scenario for convolutional neural network based image upscaling method is proposed. Its main idea is a novel dataset preparation method for deep learning. The dataset contains pairs of noisy low-resolution images and corresponding noiseless highresolution images. To achieve better results at edges and textured areas, Zero Component Analysis is applied to these images.<br><br> The upscaling results are compared with other state-of-the-art methods like DCCI, SI-3 and SRCNN on noisy medical ophthalmological images. Objective evaluation of the results confirms high quality of the proposed method. Visual analysis shows that fine details and structures like blood vessels are preserved, noise level is reduced and no artifacts or non-existing details are added. These properties are essential in retinal diagnosis establishment, so the proposed algorithm is recommended to be used in real medical applications.

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