scholarly journals Comparing the Effect of Product-Based Metrics on the Translation Process

2021 ◽  
Vol 12 ◽  
Author(s):  
Bram Vanroy ◽  
Moritz Schaeffer ◽  
Lieve Macken
Keyword(s):  
Eye Tracking ◽  
Cognitive Processing ◽  
Word Order ◽  
Semantic Information ◽  
Coarse Grained ◽  
Translation Process ◽  
Fine Grained ◽  
Total Reading Time ◽  
Positive Effect ◽  
Activity Information

Characteristics of the translation product are often used in translation process research as predictors for cognitive load, and by extension translation difficulty. In the last decade, user-activity information such as eye-tracking data has been increasingly employed as an experimental tool for that purpose. In this paper, we take a similar approach. We look for significant effects that different predictors may have on three different eye-tracking measures: First Fixation Duration (duration of first fixation on a token), Eye-Key Span (duration between first fixation on a token and the first keystroke contributing to its translation), and Total Reading Time on source tokens (sum of fixations on a token). As predictors we make use of a set of established metrics involving (lexico)semantics and word order, while also investigating the effect of more recent ones concerning syntax, semantics or both. Our results show a, particularly late, positive effect of many of the proposed predictors, suggesting that both fine-grained metrics of syntactic phenomena (such as word reordering) as well as coarse-grained ones (encapsulating both syntactic and semantic information) contribute to translation difficulties. The effect on especially late measures may indicate that the linguistic phenomena that our metrics capture (e.g., word reordering) are resolved in later stages during cognitive processing such as problem-solving and revision.

scholarly journals Information Structure and Word Order Canonicity in the Comprehension of Spanish Texts: An Eye-Tracking Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Carolina A. Gattei ◽  
Luis A. París ◽  
Diego E. Shalom
Keyword(s):  
Eye Tracking ◽  
Eye Movement ◽  
Word Order ◽  
Information Structure ◽  
Sentence Comprehension ◽  
Response Times ◽  
Semantics And Pragmatics ◽  
Total Reading Time ◽  
Pragmatic Motivation

Word order alternation has been described as one of the most productive information structure markers and discourse organizers across languages. Psycholinguistic evidence has shown that word order is a crucial cue for argument interpretation. Previous studies about Spanish sentence comprehension have shown greater difficulty to parse sentences that present a word order that does not respect the order of participants of the verb's lexico-semantic structure, irrespective to whether the sentences follow the canonical word order of the language or not. This difficulty has been accounted as the cognitive cost related to the miscomputation of prominence status of the argument that precedes the verb. Nonetheless, the authors only analyzed the use of alternative word orders in isolated sentences, leaving aside the pragmatic motivation of word order alternation. By means of an eye-tracking task, the current study provides further evidence about the role of information structure for the comprehension of sentences with alternative word order and verb type, and sheds light on the interaction between syntax, semantics and pragmatics. We analyzed both “early” and “late” eye-movement measures as well as accuracy and response times to comprehension questions. Results showed an overall influence of information structure reflected in a modulation of late eye-movement measures as well as offline measures like total reading time and questions response time. However, effects related to the miscomputation of prominence status did not fade away when sentences were preceded by a context that led to non-canonical word order of constituents, showing that prominence computation is a core mechanism for argument interpretation, even in sentences preceded by context.

scholarly journals Constructing high-fidelity phenotype knowledge graphs with a fine-grained semantic information model (Preprint)

2021 ◽  
Author(s):  
Lizong Deng ◽  
Luming Chen ◽  
Tao Yang ◽  
Mi Liu ◽  
Shicheng Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Infectious Diseases ◽  
Clinical Guidelines ◽  
Semantic Information ◽  
Information Model ◽  
Coarse Grained ◽  
Hybrid Strategy ◽  
Fine Grained ◽  
Knowledge Graphs ◽  
Attribute Value

BACKGROUND Phenotypes characterize clinical manifestations of disease, which provide important information for diagnosis. Therefore, constructing phenotype knowledge graphs of disease is valuable to the development of artificial intelligence in medicine. However, phenotype knowledge graphs in current knowledge bases such as WikiData and DBpedia are coarse-grained knowledge graphs, because they only consider core concepts of phenotypes but neglects details (attributes) associated with phenotypes. OBJECTIVE To characterize details of disease phenotypes in clinical guidelines, we proposed a fine-grained semantic information model named PhenoSSU (Semantic Structured Unit of Phenotypes). METHODS PhenoSSU is an "entity-attribute-value" model by its very nature, which aims to capture full semantics underlying phenotype descriptions with a series of attributes and values. 193 clinical guidelines of infectious diseases from Wikipedia were selected as the study corpus, and 12 attributes from SNOMED-CT were introduced into the PhenoSSU model based on co-occurrences of phenotype concepts and attribute values. The expressive power of the PhenoSSU model was evaluated by analyzing whether a PhenoSSU instance could capture full semantic underlying the corresponding phenotype description. To automatically construct fine-grained phenotype knowledge graphs, A hybrid strategy that firstly recognized phenotype concepts with the MetaMap tool and then predicted attribute values of phenotypes with machine learning classifiers was developed. RESULTS Fine-grained phenotype knowledge graphs of 193 infectious diseases were manually constructed with the BRAT annotation tool. It was found that the PhenoSSU model could precisely represent 89.5% (3757/4020) of phenotype descriptions in clinical guidelines. By comparison, other information models such as the Clinical Element Model and the HL7 FHIR model could only capture full semantics underlying 48.4% and 21.8% of phenotype descriptions, respectively. The hybrid strategy achieved an F1-score of 0.732 for the subtask of phenotype concept recognition and an average weighted accuracy of 0.776 for the subtask of attribute value prediction. CONCLUSIONS PhenoSSU is an effective information model for the precise representation of phenotype knowledge in clinical guidelines, and machine learning can be used to improve efficiency for constructing PhenoSSU-based knowledge graphs. Our work will potentially benefit knowledge-based systems for diagnosis.

Effortful text production in translation

2014 ◽  
Vol 9 (1) ◽  
pp. 25-51 ◽  
Author(s):  
Fabio Alves ◽  
Adriana Pagano ◽  
Igor Antônio Lourenço da Silva
Keyword(s):  
Eye Tracking ◽  
Qualitative Data ◽  
Professional Competence ◽  
Pause Duration ◽  
Source Text ◽  
Translation Process ◽  
Fine Grained ◽  
Text Production ◽  
Combined Use ◽  
The Impact

This article analyzes data generated by the combined use of keylogging and eye tracking to examine grammatical (de)metaphorization as a case of explicitation/implicitation (Steiner 2001). It also aims at investigating effortful text production from the perspective of automaticity and monitoring in the translation process (Tirkkonen-Condit 2005). Brazilian and German physicists and professional translators were recruited to translate one of two versions of an English (L2) source text into Brazilian Portuguese or German, respectively (L1). The versions differed in the level of grammatical metaphoricity of the sentences. Quantitative and qualitative data was analyzed to determine the impact of metaphoricity level on target text renditions as evidence of effort in the translation process. Results showed that regardless of which of the two versions was translated, most subjects opted for a particular wording from the start of their text production process; subsequent changes had to do with attempting more delicate choices in lexis rather than in grammar, evidence in favor of Tirkkonen-Condit’s claims about automatism in the translation process. Variables used to measure effort (i.e., number of renditions in microunits, pause duration, and drafting time) indicated that (de)metaphorization is an effortful procedure. Eye tracking, eliciting more fine-grained data, was instrumental in mapping instances of grammatical (de)metaphorization. The results have implications for issues related to the development of professional competence in translation, suggesting that instances of grammatical (de)metaphorization relate to higher levels of monitoring.

Correlation between dislocation, grain boundary and interface of duplex SS in stress corrosion cracking(SCC)

1990 ◽  
Vol 48 (4) ◽  
pp. 928-929
Author(s):  
Wang Zheng-fang ◽  
Z.F. Wang
Keyword(s):  
Stainless Steel ◽  
Thermal Cycle ◽  
Secondary Phase ◽  
Corrosion Cracking ◽  
Coarse Grained ◽  
Test Environment ◽  
Fine Grained ◽  
Evaluation Test ◽  
Welding Thermal Cycle ◽  
Micro Analysis

The main purpose of this study highlights on the evaluation of chloride SCC resistance of the material,duplex stainless steel,OOCr18Ni5Mo3Si2 (18-5Mo) and its welded coarse grained zone(CGZ).18-5Mo is a dual phases (A+F) stainless steel with yield strength:512N/mm2 .The proportion of secondary Phase(A phase) accounts for 30-35% of the total with fine grained and homogeneously distributed A and F phases(Fig.1).After being welded by a specific welding thermal cycle to the material,i.e. Tmax=1350°C and t8/5=20s,microstructure may change from fine grained morphology to coarse grained morphology and from homogeneously distributed of A phase to a concentration of A phase(Fig.2).Meanwhile,the proportion of A phase reduced from 35% to 5-10°o.For this reason it is known as welded coarse grained zone(CGZ).In association with difference of microstructure between base metal and welded CGZ,so chloride SCC resistance also differ from each other.Test procedures:Constant load tensile test(CLTT) were performed for recording Esce-t curve by which corrosion cracking growth can be described, tf,fractured time,can also be recorded by the test which is taken as a electrochemical behavior and mechanical property for SCC resistance evaluation. Test environment:143°C boiling 42%MgCl2 solution is used.Besides, micro analysis were conducted with light microscopy(LM),SEM,TEM,and Auger energy spectrum(AES) so as to reveal the correlation between the data generated by the CLTT results and micro analysis.

The Opposition of Surprisal and Semantic Similarity in the Prediction of Language Processing: Evidence from Eye-tracking Data

2020 ◽  
Author(s):  
Kun Sun
Keyword(s):  
Eye Tracking ◽  
Semantic Similarity ◽  
Cognitive Processing ◽  
Language Processing ◽  
Language Comprehension ◽  
Word Processing ◽  
Reading Time ◽  
Computational Models ◽  
Tracking Data ◽  
Dynamic Approach

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

Do you see what I mean?: Using mobile eye tracking to capture parent–child dynamics in the context of anxiety risk

2021 ◽  
pp. 1-16
Author(s):  
Leigha A. MacNeill ◽  
Xiaoxue Fu ◽  
Kristin A. Buss ◽  
Koraly Pérez-Edgar
Keyword(s):  
Eye Tracking ◽  
Positive Reinforcement ◽  
Parental Behavior ◽  
Anxiety Level ◽  
Eye Tracker ◽  
Fine Grained ◽  
Controlling Parenting ◽  
Attractor Strength ◽  
Child Age ◽  
Mobile Eye Tracking

Abstract Temperamental behavioral inhibition (BI) is a robust endophenotype for anxiety characterized by increased sensitivity to novelty. Controlling parenting can reinforce children's wariness by rewarding signs of distress. Fine-grained, dynamic measures are needed to better understand both how children perceive their parent's behaviors and the mechanisms supporting evident relations between parenting and socioemotional functioning. The current study examined dyadic attractor patterns (average mean durations) with state space grids, using children's attention patterns (captured via mobile eye tracking) and parental behavior (positive reinforcement, teaching, directives, intrusion), as functions of child BI and parent anxiety. Forty 5- to 7-year-old children and their primary caregivers completed a set of challenging puzzles, during which the child wore a head-mounted eye tracker. Child BI was positively correlated with proportion of parent's time spent teaching. Child age was negatively related, and parent anxiety level was positively related, to parent-focused/controlling parenting attractor strength. There was a significant interaction between parent anxiety level and child age predicting parent-focused/controlling parenting attractor strength. This study is a first step to examining the co-occurrence of parenting behavior and child attention in the context of child BI and parental anxiety levels.

scholarly journals Balanced Sparsity for Efficient DNN Inference on GPU

2019 ◽  
Vol 33 ◽  
pp. 5676-5683 ◽  
Author(s):  
Zhuliang Yao ◽  
Shijie Cao ◽  
Wencong Xiao ◽  
Chen Zhang ◽  
Lanshun Nie
Keyword(s):  
Deep Neural Networks ◽  
General Purpose ◽  
Coarse Grained ◽  
Efficient Computation ◽  
Model Accuracy ◽  
Sparse Model ◽  
Model Inference ◽  
Fine Grained ◽  
Practical Inference ◽  
Speed Up

In trained deep neural networks, unstructured pruning can reduce redundant weights to lower storage cost. However, it requires the customization of hardwares to speed up practical inference. Another trend accelerates sparse model inference on general-purpose hardwares by adopting coarse-grained sparsity to prune or regularize consecutive weights for efficient computation. But this method often sacrifices model accuracy. In this paper, we propose a novel fine-grained sparsity approach, Balanced Sparsity, to achieve high model accuracy with commercial hardwares efficiently. Our approach adapts to high parallelism property of GPU, showing incredible potential for sparsity in the widely deployment of deep learning services. Experiment results show that Balanced Sparsity achieves up to 3.1x practical speedup for model inference on GPU, while retains the same high model accuracy as finegrained sparsity.

scholarly journals Submarine lava deltas of the 2018 eruption of Kīlauea volcano

2021 ◽  
Vol 83 (4) ◽  
Author(s):  
S. Adam Soule ◽  
Michael Zoeller ◽  
Carolyn Parcheta
Keyword(s):  
Grain Size ◽  
Pore Pressure ◽  
Mechanistic Model ◽  
Large Fraction ◽  
Volcanic Hazards ◽  
Lava Flows ◽  
Coarse Grained ◽  
Fine Grained ◽  
Fine Grain ◽  
Delta Formation

AbstractHawaiian and other ocean island lava flows that reach the coastline can deposit significant volumes of lava in submarine deltas. The catastrophic collapse of these deltas represents one of the most significant, but least predictable, volcanic hazards at ocean islands. The volume of lava deposited below sea level in delta-forming eruptions and the mechanisms of delta construction and destruction are rarely documented. Here, we report on bathymetric surveys and ROV observations following the Kīlauea 2018 eruption that, along with a comparison to the deltas formed at Pu‘u ‘Ō‘ō over the past decade, provide new insight into delta formation. Bathymetric differencing reveals that the 2018 deltas contain more than half of the total volume of lava erupted. In addition, we find that the 2018 deltas are comprised largely of coarse-grained volcanic breccias and intact lava flows, which contrast with those at Pu‘u ‘Ō‘ō that contain a large fraction of fine-grained hyaloclastite. We attribute this difference to less efficient fragmentation of the 2018 ‘a‘ā flows leading to fragmentation by collapse rather than hydrovolcanic explosion. We suggest a mechanistic model where the characteristic grain size influences the form and stability of the delta with fine grain size deltas (Pu‘u ‘Ō‘ō) experiencing larger landslides with greater run-out supported by increased pore pressure and with coarse grain size deltas (Kīlauea 2018) experiencing smaller landslides that quickly stop as the pore pressure rapidly dissipates. This difference, if validated for other lava deltas, would provide a means to assess potential delta stability in future eruptions.

scholarly journals A cooperative DDoS attack detection scheme based on entropy and ensemble learning in SDN

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Shanshan Yu ◽  
Jicheng Zhang ◽  
Ju Liu ◽  
Xiaoqing Zhang ◽  
Yafeng Li ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Denial Of Service ◽  
Attack Detection ◽  
Coarse Grained ◽  
Communication Overhead ◽  
Detection Scheme ◽  
Fine Grained ◽  
Ddos Attack ◽  
Network Status ◽  
Ddos Attack Detection

AbstractIn order to solve the problem of distributed denial of service (DDoS) attack detection in software-defined network, we proposed a cooperative DDoS attack detection scheme based on entropy and ensemble learning. This method sets up a coarse-grained preliminary detection module based on entropy in the edge switch to monitor the network status in real time and report to the controller if any abnormality is found. Simultaneously, a fine-grained precise attack detection module is designed in the controller, and a ensemble learning-based algorithm is utilized to further identify abnormal traffic accurately. In this framework, the idle computing capability of edge switches is fully utilized with the design idea of edge computing to offload part of the detection task from the control plane to the data plane innovatively. Simulation results of two common DDoS attack methods, ICMP and SYN, show that the system can effectively detect DDoS attacks and greatly reduce the southbound communication overhead and the burden of the controller as well as the detection delay of the attacks.

scholarly journals Microstructure Evaluation Study of Al5083 Alloy Using EBSD Technique after Processing with Different ECAP Processes and Temperatures

Crystals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 862
Author(s):  
Muneer Baig ◽  
Asiful H. Seikh ◽  
Ateekh Ur Rehman ◽  
Jabair A. Mohammed ◽  
Faraz Hussain Hashmi ◽  
...  
Keyword(s):  
Grain Refinement ◽  
Evaluation Study ◽  
Coarse Grained ◽  
Angular Pressing ◽  
Microstructure Evaluation ◽  
Microstructural Evaluation ◽  
Higher Temperature ◽  
Ebsd Technique ◽  
Number Of Passes ◽  
Positive Effect

The temperature effects on the microstructural evolution of a coarse-grained Al5083 alloy during equal channel angular pressing (ECAP), were studied at ambient and high temperatures. The microstructural evaluation was done using an EBSD (electron backscattering diffraction) process. The grain refinement occurred as the number of passes increased, which had a positive effect on its strength. Additionally, increasing the pressing temperature leads to a decrease in the new grain’s formation and an increase in the normal grain size in the third pass. This can be ascribed to the unwinding of strain similarity between the grains because of the continuous activity of dynamic recuperation and the grain limit sliding occurring at a higher temperature. The attainment of grain refinement is examined exhaustively in this study.

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