Academic literature on the topic 'Attention Network Test'
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Journal articles on the topic "Attention Network Test"
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Urbanek, Carsten, Nicholetta Weinges-Evers, Judith Bellmann-Strobl, Markus Bock, Jan Dörr, Eric Hahn, Andres H. Neuhaus, et al. "Attention Network Test reveals alerting network dysfunction in multiple sclerosis." Multiple Sclerosis Journal 16, no. 1 (December 7, 2009): 93–99. http://dx.doi.org/10.1177/1352458509350308.
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Attention is one of the cognitive domains typically affected in multiple sclerosis. The Attention Network Test was developed to measure the function of the three distinct attentional networks, alerting, orienting, and executive control. The Attention Network Test has been performed in various neuropsychiatric conditions, but not in multiple sclerosis. Our objective was to investigate functions of attentional networks in multiple sclerosis by means of the Attention Network Test. Patients with relapsing—remitting multiple sclerosis (n = 57) and healthy controls (n = 57) matched for age, sex, and education performed the Attention Network Test. Significant differences between patients and controls were detected in the alerting network (p = 0.003), in contrast to the orienting (p = 0.696) and the conflict (p = 0.114) network of visual attention. Mean reaction time in the Attention Network Test was significantly longer in multiple sclerosis patients than in controls (p = 0.032), Multiple sclerosis patients benefited less from alerting cues for conflict resolution compared with healthy controls. The Attention Network Test revealed specific alterations of the attention network in multiple sclerosis patients which were not explained by an overall cognitive slowing.
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Duan, Keyi, Songyun Xie, Xin Zhang, Xinzhou Xie, Yujie Cui, Ruizhen Liu, and Jian Xu. "Exploring the Temporal Patterns of Dynamic Information Flow during Attention Network Test (ANT)." Brain Sciences 13, no. 2 (January 31, 2023): 247. http://dx.doi.org/10.3390/brainsci13020247.
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The attentional processes are conceptualized as a system of anatomical brain areas involving three specialized networks of alerting, orienting and executive control, each of which has been proven to have a relation with specified time-frequency oscillations through electrophysiological techniques. Nevertheless, at present, it is still unclear how the idea of these three independent attention networks is reflected in the specific short-time topology propagation of the brain, assembled with complexity and precision. In this study, we investigated the temporal patterns of dynamic information flow in each attention network via electroencephalograph (EEG)-based analysis. A modified version of the attention network test (ANT) with an EEG recording was adopted to probe the dynamic topology propagation in the three attention networks. First, the event-related potentials (ERP) analysis was used to extract sub-stage networks corresponding to the role of each attention network. Then, the dynamic network model of each attention network was constructed by post hoc test between conditions followed by the short-time-windows fitting model and brain network construction. We found that the alerting involved long-range interaction among the prefrontal cortex and posterior cortex of brain. The orienting elicited more sparse information flow after the target onset in the frequency band 1–30 Hz, and the executive control contained complex top-down control originating from the frontal cortex of the brain. Moreover, the switch of the activated regions in the associated time courses was elicited in attention networks contributing to diverse processing stages, which further extends our knowledge of the mechanism of attention networks.
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Cohen, Noga, Avishai Henik, and Nilly Mor. "Can Emotion Modulate Attention? Evidence for Reciprocal Links in the Attentional Network Test." Experimental Psychology 58, no. 3 (November 1, 2011): 171–79. http://dx.doi.org/10.1027/1618-3169/a000083.
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Evolution theory suggests that adaptive behavior depends on our ability to give preferential attention to emotional information when it is necessary for our survival, and to down-regulate irrelevant emotional influence. However, empirical work has shown that the interaction between emotion and attention varies, based on the attentional network in question. The aim of the current research was to examine the influence of stimulus emotionality on attention in three attentional networks: alerting, orienting, and executive functions. In two studies, using negative and neutral cues in a modified version of the Attention Network Test, it was found that negative cues impaired task performance in the absence of executive conflict, but not when executive processes were activated. Moreover, it was found that the influence of negative cues on task performance in a given trial was attenuated following activation of executive processes in the previous trial. These results suggest that when executive resources are required, inhibitory mechanisms are recruited to decrease the disruptive effect of emotional stimuli. More importantly, these findings indicate that the effect of emotional stimuli on attention is down-regulated both during cognitive conflict and after the conflict has already ended.
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Hahn, Eric, Thi Minh Tam Ta, Constanze Hahn, Linn K. Kuehl, Claudia Ruehl, Andres H. Neuhaus, and Michael Dettling. "Test–retest reliability of Attention Network Test measures in schizophrenia." Schizophrenia Research 133, no. 1-3 (December 2011): 218–22. http://dx.doi.org/10.1016/j.schres.2011.09.026.
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Weaver, Bruce, Michel Bédard, Jim McAuliffe, and Marie Parkkari. "Using the Attention Network Test to predict driving test scores." Accident Analysis & Prevention 41, no. 1 (January 2009): 76–83. http://dx.doi.org/10.1016/j.aap.2008.09.006.
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Fu, Jia, Guoming Yu, and Lun Zhao. "Effect of aging on visual attention: Evidence from the Attention Network Test." Social Behavior and Personality: an international journal 49, no. 3 (March 10, 2021): 1–8. http://dx.doi.org/10.2224/sbp.9806.
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We investigated the effects of aging on attentional functions using the Attention Network Test (ANT), which enables simultaneous testing of alerting, orienting, and executive networks, and their interactions. Participants were 38 young adults (Mage = 21.35 years) and 36 older adults (Mage = 71.17 years). Although the older adults exhibited a slower overall response, the three attentional functions showed different modulation according to age group and the trial block being completed. Older adults exhibited significant impairment in the alerting function, regardless of whether they were completing the first or second block of trials, whereas their executive function decreased significantly only in Block 2 owing to cognitive fatigue. Both age groups performed similarly for the orienting function. Future researchers should seek to further clarify the specificity of attention function with people aged over 70 years to address their attention disturbance.
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Wang, Bin, Jingjing Zhao, Zheng Wu, Wei Shang, Jie Xiang, Rui Cao, Haifang Li, Junjie Chen, Hui Zhang, and Ting Yan. "Eccentricity Effects on the Efficiency of Attentional Networks: Evidence From a Modified Attention Network Test." Perception 45, no. 12 (July 11, 2016): 1375–86. http://dx.doi.org/10.1177/0301006616658307.
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Westlye, Lars T., Håkon Grydeland, Kristine B. Walhovd, and Anders M. Fjell. "Associations between Regional Cortical Thickness and Attentional Networks as Measured by the Attention Network Test." Cerebral Cortex 21, no. 2 (June 4, 2010): 345–56. http://dx.doi.org/10.1093/cercor/bhq101.
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Greene, Deanna J., Anat Barnea, Kristin Herzberg, Anat Rassis, Maital Neta, Amir Raz, and Eran Zaidel. "Measuring attention in the hemispheres: The lateralized attention network test (LANT)." Brain and Cognition 66, no. 1 (February 2008): 21–31. http://dx.doi.org/10.1016/j.bandc.2007.05.003.
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Pauletti, Caterina, Daniela Mannarelli, Maria Caterina De Lucia, Nicoletta Locuratolo, Antonio Currà, Paolo Missori, Lucio Marinelli, and Francesco Fattapposta. "Selective attentional deficit in essential tremor: Evidence from the attention network test." Parkinsonism & Related Disorders 21, no. 11 (November 2015): 1306–11. http://dx.doi.org/10.1016/j.parkreldis.2015.08.035.
Full textDissertations / Theses on the topic "Attention Network Test"
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Ta, Thi Minh Tam [Verfasser]. "Test-Retest Reliabilität des Attention-Network-Test bei Schizophrenie / Thi Minh Tam Ta." Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2013. http://d-nb.info/1037725654/34.
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Lehtonen, Sanna Elina. "Self-reported Inattention and Hyperactivity-impulsivity as Predictors of Attention Network Efficiency." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/psych_diss/34.
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Previous research has shown that individuals endorsing inattention and hyperactivity-impulsivity have deficient performance on tasks tapping different aspects of attention. Although there is empirical evidence suggesting that the behavioral domains of inattention and hyperactivity-impulsivity are linked to functioning of independent and separate brain areas and neurotransmitter systems, cognitive characterization of adults presenting with problems within these domains is not complete. The aim for this study was to identify the cognitive correlates of the core behavioral domains that define the diagnosis of AD/HD (i.e., inattention and hyperactivity-impulsivity) in a sample of college students, utilizing a computerized attention task, the Attention Network Test (ANT). Different ANT task components have been found to activate separate brain areas linked to the functioning of alerting, orienting and executive attention, and have the potential to provide an indication of the efficiency of these brain networks. In addition to completing the ANT, the participants filled out questionnaires covering common symptoms of adult AD/HD, anxiety and depression. Hierarchical regression analyses revealed that there were no reliable relationships between self-reported symptoms of current inattention and hyperactivity-impulsivity and ANT performance. Further, self-reported depression and/or anxiety did not seem to impact the efficiency of attention networks to a significant degree in this study sample. Gender proved to be the most consistent predictor of ANT performance. Female gender was related to poorer executive attention efficiency. An exploratory ANCOVA revealed that individuals reporting high levels of impulsivity and emotional lability had poorer executive attention efficiency in comparison to those reporting these behaviors and problems to a lesser extent. Future research is needed in order to further explore the relationship between ANT performance and behavioral expressions of adult AD/HD and other neurological and psychiatric conditions.
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Dimitriadis, Spyridon. "Multi-task regression QSAR/QSPR prediction utilizing text-based Transformer Neural Network and single-task using feature-based models." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177186.
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With the recent advantages of machine learning in cheminformatics, the drug discovery process has been accelerated; providing a high impact in the field of medicine and public health. Molecular property and activity prediction are key elements in the early stages of drug discovery by helping prioritize the experiments and reduce the experimental work. In this thesis, a novel approach for multi-task regression using a text-based Transformer model is introduced and thoroughly explored for training on a number of properties or activities simultaneously. This multi-task regression with Transformer based model is inspired by the field of Natural Language Processing (NLP) which uses prefix tokens to distinguish between each task. In order to investigate our architecture two data categories are used; 133 biological activities from ExCAPE database and three physical chemistry properties from MoleculeNet benchmark datasets. The Transformer model consists of the embedding layer with positional encoding, a number of encoder layers, and a Feedforward Neural Network (FNN) to turn it into a regression problem. The molecules are represented as a string of characters using the Simplified Molecular-Input Line-Entry System (SMILES) which is a ’chemistry language’ with its own syntax. In addition, the effect of Transfer Learning is explored by experimenting with two pretrained Transformer models, pretrained on 1.5 million and on 100 million molecules. The text-base Transformer models are compared with a feature-based Support Vector Regression (SVR) with the Tanimoto kernel where the input molecules are encoded as Extended Connectivity Fingerprint (ECFP), which are calculated features. The results have shown that Transfer Learning is crucial for improving the performance on both property and activity predictions. On bioactivity tasks, the larger pretrained Transformer on 100 million molecules achieved comparable performance to the feature-based SVR model; however, overall SVR performed better on the majority of the bioactivity tasks. On the other hand, on physicochemistry property tasks, the larger pretrained Transformer outperformed SVR on all three tasks. Concluding, the multi-task regression architecture with the prefix token had comparable performance with the traditional feature-based approach on predicting different molecular properties or activities. Lastly, using the larger pretrained models trained on a wide chemical space can play a key role in improving the performance of Transformer models on these tasks.
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Tu, Guoyun. "Image Captioning On General Data And Fashion Data : An Attribute-Image-Combined Attention-Based Network for Image Captioning on Mutli-Object Images and Single-Object Images." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-282925.
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Image captioning is a crucial field across computer vision and natural language processing. It could be widely applied to high-volume web images, such as conveying image content to visually impaired users. Many methods are adopted in this area such as attention-based methods, semantic-concept based models. These achieve excellent performance on general image datasets such as the MS COCO dataset. However, it is still left unexplored on single-object images.In this paper, we propose a new attribute-information-combined attention- based network (AIC-AB Net). At each time step, attribute information is added as a supplementary of visual information. For sequential word generation, spatial attention determines specific regions of images to pass the decoder. The sentinel gate decides whether to attend to the image or to the visual sentinel (what the decoder already knows, including the attribute information). Text attribute information is synchronously fed in to help image recognition and reduce uncertainty.We build a new fashion dataset consisting of fashion images to establish a benchmark for single-object images. This fashion dataset consists of 144,422 images from 24,649 fashion products, with one description sentence for each image. Our method is tested on the MS COCO dataset and the proposed Fashion dataset. The results show the superior performance of the proposed model on both multi-object images and single-object images. Our AIC-AB net outperforms the state-of-the-art network, Adaptive Attention Network by 0.017, 0.095, and 0.095 (CIDEr Score) on the COCO dataset, Fashion dataset (Bestsellers), and Fashion dataset (all vendors), respectively. The results also reveal the complement of attention architecture and attribute information.Bildtextning är ett avgörande fält för datorsyn och behandling av naturligt språk. Det kan tillämpas i stor utsträckning på högvolyms webbbilder, som att överföra bildinnehåll till synskadade användare. Många metoder antas inom detta område såsom uppmärksamhetsbaserade metoder, semantiska konceptbaserade modeller. Dessa uppnår utmärkt prestanda på allmänna bilddatamängder som MS COCO-dataset. Det lämnas dock fortfarande outforskat på bilder med ett objekt.I denna uppsats föreslår vi ett nytt attribut-information-kombinerat uppmärksamhetsbaserat nätverk (AIC-AB Net). I varje tidsteg läggs attributinformation till som ett komplement till visuell information. För sekventiell ordgenerering bestämmer rumslig uppmärksamhet specifika regioner av bilder som ska passera avkodaren. Sentinelgrinden bestämmer om den ska ta hand om bilden eller den visuella vaktposten (vad avkodaren redan vet, inklusive attributinformation). Text attributinformation matas synkront för att hjälpa bildigenkänning och minska osäkerheten.Vi bygger en ny modedataset bestående av modebilder för att skapa ett riktmärke för bilder med en objekt. Denna modedataset består av 144 422 bilder från 24 649 modeprodukter, med en beskrivningsmening för varje bild. Vår metod testas på MS COCO dataset och den föreslagna Fashion dataset. Resultaten visar den överlägsna prestandan hos den föreslagna modellen på både bilder med flera objekt och enbildsbilder. Vårt AIC-AB-nät överträffar det senaste nätverket Adaptive Attention Network med 0,017, 0,095 och 0,095 (CIDEr Score) i COCO-datasetet, modedataset (bästsäljare) respektive modedatasetet (alla leverantörer). Resultaten avslöjar också komplementet till uppmärksamhetsarkitektur och attributinformation.
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Martínez-García, Marina. "Statistical analysis of neural correlates in decision-making." Doctoral thesis, Universitat Pompeu Fabra, 2014. http://hdl.handle.net/10803/283111.
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We investigated the neuronal processes which occur during a decision-
making task based on a perceptual classi cation judgment.
For this purpose we have analysed three di erent experimental paradigms
(somatosensory, visual, and auditory) in two di erent species (monkey and
rat), with the common goal of shedding light into the information carried by
neurons.
In particular, we focused on how the information content is preserved in
the underlying neuronal activity over time. Furthermore we considered how
the decision, the stimuli, and the con dence are encoded in memory and,
when the experimental paradigm allowed it, how the attention modulates
these features. Finally, we went one step further, and we investigated the
interactions between brain areas that arise during the process of decision-
making.Durant aquesta tesi hem investigat els processos neuronals que es pro- dueixen durant tasques de presa de decisions, tasques basades en un ju- dici l ogic de classi caci o perceptual. Per a aquest prop osit hem analitzat tres paradigmes experimentals diferents (somatosensorial, visual i auditiu) en dues espcies diferents (micos i rates), amb l'objectiu d'il.lustrar com les neurones codi quen informaci on referents a les t asques. En particular, ens hem centrat en com certes informacions estan cod- i cades en l'activitat neuronal al llarg del temps. Concretament, com la informaci o sobre: la decisi o comportamental, els factors externs, i la con- ana en la resposta, b e codi cada en la mem oria. A m es a m es, quan el paradigma experimental ens ho va permetre, com l'atenci o modula aquests aspectes. Finalment, hem anat un pas m es enll a, i hem analitzat la comu- nicaci o entre les diferents arees corticals, mentre els subjectes resolien una tasca de presa de decisions.
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Ishigami, Yoko. "THE ATTENTION NETWORK TEST (ANT): INDIVIDUAL DIFFERENCES? AND ?COMPONENTS OF ATTENTION ACROSS THE LIFE SPAN." 2011. http://hdl.handle.net/10222/13307.
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Using orthogonal subtractions of performance in selected conditions the attentional network test (ANT) measures the efficacy of three isolable components of attention: alerting, orienting, and executive control. This dissertation evaluated: 1) the relationship between these attention networks and absentmindedness measured by the Cognitive Failures Questionnaire (CFQ) and 2) stability, isolability, robustness, and reliability of the two versions of the ANT (Fan et al., 2002 and Callejas et al., 2005) with young adults and older adults and of the child version of the ANT (Rueda et al., 2004) with young children when tested over 10 sessions. A greater degree of absentmindedness as measured with CFQ was associated with a greater alerting network score in RT and with a greater orienting network scores in error rate when the ANT-I was used. However, a greater degree of absentmindedness was associated with a smaller orienting network score in error rate when the ANT was used. These results suggest that the alerting and the orienting networks are related to absentmindedness. However, the orienting networks in the two ANTs were related to absentmindedness differently which supports the proposal (Klein, 2009) that there are fundamental differences between attention when controlled endogenously (ANT) as opposed to exogenously (ANT-I). For young adults and older adults, all network scores in RT remained robust even after nine previous sessions despite some practice effects especially in the executive network both with the ANT and the ANT-I. There was some evidence that the networks do not operate independently in all situations. As expected, reliability increased as more data are added. For young children, only the alerting network scores remained robust over time. Learning effects were observed only with the executive network. The reliability was poor even when more data were added. This made it difficult to assess the isolability of the network scores. The ANT and the ANT-I were associated to the CFQ scores in a limited way. The ANT and the ANT-I can be used for applications requiring repeated testing, but the child ANT may not be suitable for such purpose.This is a thesis that is manuscript-based.
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Calvo, Alberto Lema. "Transcranial Random Noise Stimulation (tRNS) on Attention Network Test (ANT): an exploratory study." Master's thesis, 2017. http://hdl.handle.net/1822/48135.
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Dissertação de mestrado integrado em PsicologiaSpeed-accuracy tradeoff (SAT) refers to the adjustment between response speed and its accuracy, a process mediated through cortical and subcortical circuits. However, it remains unclear whether SAT can be modulated by the effects of tRNS and/or tDCS. In order to explore this effect, we used the attention network test (ANT). This tasks allowed us to test three independent networks related to both subcortical activity (alerting and orienting) and cortical level (executive). We hypothesized that performance would increase for alerting and orienting networks with tRNS whereas tDCS would increase performance in executive network. Moreover, to make the SAT effect more prominent, instructions that emphasized speed and accuracy were also introduced. Eighteen healthy students undertook three experimental sessions of tRNS, anodal tDCS and sham over the left dlPFC while performing the ANT. Results showed that no significant effect was found for stimulation. Instructions, however, showed a significant difference between speed condition and standard condition. Thus, SAT was successfully manipulated by instruction condition but not by stimulation.
O Speed-accuracy tradeoff (SAT) é definido como o ajustamento entre a velocidade e a exatidão de uma determinada resposta. Este processo é mediado por vias corticais e subcorticais suceptíveis de serem alteradas através de métodos de estimulação elétrica transcraniana. No presente estudo, dois tipos de estimulação, ETRN e ETCC, foram combinados com uma tarefa atencional (ANT) com o intuito de avaliar os seus efeitos nas vias corticais e subcorticais associadas ao SAT. Era esperado que a ETRN aumentasse o desempenho em vias atencionais subcorticais (de alerta e de orientação) enquanto que a ETCC melhorasse o desempenho na via atencional cortical (executiva). De igual modo, as instruções da tarefa também foram alteradas para manipular o SAT, favorecendo assim a velocidade ou a exatidão da resposta. Dezoito participantes saudáveis receberam de forma aleatorizada três sessões de estimulação (ETRN, ETCC, sham) unilateral sobre o CPFDL esquerdo. Os resultados mostram que a estimulação ETRN e ETCC não obteveram efeitos nas vias atentionais. As instruções, no entanto, revelam que quando o ênfase é colocado na velocidade da resposta, a via atencional executiva apresenta menor eficiência. Desta forma, O SAT apenas foi manipulado com sucesso através das intruções e não da estimulação.
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Lei, Sot-Fu, and 李述富. "Performance of ADHD in the Attention Network Test(ANT) and Stop Signal Task(SST)." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/38382634929228758136.
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碩士中山醫學大學
心理學系暨臨床心理學碩士班
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The purpose of this study is to explore and compare the performance in Attention Network test (ANT) and the stop signal task (SST) between the Attention Deficit Hyperactivity Disorder(ADHD) children and normal children. The results will be used as references in developing clinical evaluation tools of neurological functions.This study uses ANT and SST to test subjects’ performances in different tasks. This study assumes ANT and SST are able to distinguish ADHA children and normal children. Study 1’s subjects are preschool stage children who are 6-12 years old ,23 subjects are suffering from ADHA and 23 subjects are normal, 46 subjects in total. Study 1 will use ANT to test subjects’ attention network function. Study 2 ’s subjects are preschool stage children who are 6-12 years old ,23 subjects are suffering from ADHA and 23 subjects are normal, 46 subjects in total. Study 2 will use SST to test subjects’ inhibit functions. The purpose of the studies mentioned above is to test the sensitivity of cognitive impairment. ANT in Study 1 shows that there is no obvious difference in 3 ANT scores (alert, orientation,executive control) between ADHA children and normal children. SST in Study 2 shows obvious difference in Stop Signal Reaction Time (SSRT) between ADHA children and normal children. To conclude, the sensitivity of SST testing impairment of inhibit function is higher the ANT testing cognitive impairment. As a result, SST will be more able to use in developing clinical evaluation tools. A discussion focus on ANT will be developed to find out the probable reason and related suggestions.
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Brady, JJR. "The effect of mindfulness meditation on ERP measures of attention." Thesis, 2019. https://eprints.utas.edu.au/34692/1/Brady_whole_thesis.pdf.
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Mindfulness meditation refers to the non-judgmental and present focus on thoughts, feelings and experiences. Neurocognitive studies have associated processes involved in mindfulness meditation with neural networks implicit in attention. These processes are described in Attention Network Theory. The literature reflects growing support for the use of mindfulness meditation as an attentional intervention. Recent innovation has seen the development of neurofeedback devices, designed to augment the outcomes of mindfulness meditation training. However, there are few studies which have quantified these effects. The Attention Network Test is well-supported in assessing measures of attention outlined by Attention Network Theory. Accordingly, we use the Attention Network Test to compare reaction time (ms), accuracy (% correct), and N1 and P3 ERPs (μV) to quantify the effects of mindfulness meditation relative to an active electrodermal-assisted relaxation control. Results indicate no significant improvement in RT for the meditation group relative to controls, or significant corresponding N1 or P3 amplitudes. However, relating to stable reaction time measures, we find evidence of enhanced attentional network efficiency in the meditation group. These observations are paired with state and trait-based self-reports of disposition, in order to track the intervention’s manipulation. We found increased self-reported emotional regulation in both groups. Additionally, we present a case for the use of mixed models in such designs. We conclude that further research is warranted to investigate underlying mechanisms of meditation on attention, and the influence that any dispositional variation may have on training outcomes.
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Flynn, SF. "Behavioural and neural correlates of orienting and executive control in high and low spider fear groups." Thesis, 2015. https://eprints.utas.edu.au/22616/1/Shelley%20Flynn%20Thesis.pdf.
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Research suggests that attentional bias to threat in specific fear can be demonstrated as facilitated orienting effects such as the rapid automatic detection and processing of threat-related information, and/or interference effects thought to be associated with impaired executive control processes such as the inhibition of task-irrelevant information. This study examined the influence of spider fear on the behavioural (RT and accuracy) and electrophysiological correlates (P1 and N1) of facilitated orienting and executive control. Twenty-six female participants (15 high-fear, 11 low-fear) completed a novel attentional networks test consisting of an alerting condition (present/absent), a pictorial (spider/cow) orienting cue (valid/invalid), and a central target flanked by distractors (congruent/incongruent). In relation to facilitated orienting, no between-group differences were observed, suggesting that greater levels of cognitive load increased interference effects, thus masking the facilitation effect for high-fear participants. Partial support for predictions of behavioural interference effects were observed. This finding was further supported by evidence of attenuated P1 and enhanced N1 amplitude for high-fear participants for incongruent targets preceded by spider images, however these effects were modulated by interactions between the attentional networks. This is a novel finding but is consistent with a complex and interactive attentional networks model.
Books on the topic "Attention Network Test"
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Robbins, Trevor. The Neuropsycho–Pharmacology of Attention. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.028.
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Pharmacological influences on cognition and behaviour are often accompanied by effects on different aspects of attention. The actions of many psychoactive drugs (often used in the treatment of psychiatric disorders) depend on effects exerted on the classical chemical modulatory neurotransmitter systems including acetylcholine, and the monoamines, dopamine, noradrenaline and serotonin (or 5-hydroxytryptamine, 5-HT). These chemical systems originate in the reticular core of the brain and modulate attention by actions on forebrain structures including the thalamus, striatum, and the neocortex (especially the prefrontal cortex). Current research is attempting to dissect separable functions of these chemical neurotransmitters in mediating attention in relation to states of arousal and stress in comparable test paradigms in experimental animals and humans. New directions in research in this area are also identified, including the functions of the novel neurotransmitter orexin, and the role of GABA and glutamate in gamma oscillations and the network properties of the neocortex.
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Dorogovtsev, Sergey N., and José F. F. Mendes. The Nature of Complex Networks. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780199695119.001.0001.
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Abstract The researchers studying complex networks will acquire from this advanced modern book a number of new issues and ideas, not yet touched upon in other reference volumes. The book considers a wide range of networks and processes taking place on them, paying particular attention to the recently developed directions, methods, and techniques. It proposes a statistical mechanics view of random networks based on the concept of statistical ensembles, but approaches and methods of modern graph theory, concerning random graphs, overlap strongly with statistical physics. Hence mathematicians have a good chance to discover interesting things in this book, even though it does not contain mathematical proofs and trades off rigour for comprehension, brevity, and relevance. The book combines features of an advanced textbook, a reference book and a detailed review of the current state of the art. This book will be useful for undergraduate, master, and PhD students and young researchers from physics, multidisciplinary studies, computer science, and applied mathematics wishing to gain a serious insight into the principles of complex networks. The book can be used as a text in university courses on complex networks. It proposes to determined students not only a brief trip to the land of complex networks but an option to stay there forever.
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Scacco, Joshua M., and Kevin Coe. The Ubiquitous Presidency. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197520635.001.0001.
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American democracy is in a period of striking tumult. The clash of a rapidly changing socio-technological environment and the traditional presidency has led to an upheaval in the scope and standards of executive leadership. Research on the presidency, although abundant, has been slow to adjust to changing realities associated with digital technologies, diverse audiences, and new political practices. Meanwhile, journalists and the public continue to encounter and shape emerging presidential efforts in deeply consequential ways. This book offers a comprehensive framework for understanding contemporary presidential communication: the ubiquitous presidency. Presidents harness new opportunities in the media environment to create a nearly constant and highly visible presence in political and nonpolitical arenas. They do this by trying to achieve longstanding presidential goals, namely visibility, adaptation, and control. However, in an environment where accessibility, personalization, and pluralism are omnipresent considerations, the strategies presidents use to achieve their goals are very different from what we once knew. Using this novel framework, the book undertakes one of the most expansive analyses of presidential communication to date. A wide variety of approaches—ranging from surveys and survey-experiments, to large-scale automated content and network analyses, to qualitative textual analysis—uncover new aspects of the intricate relationship between the president, news media, and the public. Focusing on the presidency since Ronald Reagan, and devoting particular attention to the cases of Barack Obama and Donald Trump, the book uncovers remarkable shifts in communication that test the institution of the presidency and, consequently, democratic governance itself.
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Willie, Rachel. Translation. Edited by Andrew Hiscock and Helen Wilcox. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199672806.013.9.
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Transnational exchange and intellectual networks in the early modern period relied upon translation—mainly into Latin—as a way to communicate across Europe. Translation was integral to humanist education where creative engagement with the source text was admired. Yet the exegetical and socio-political considerations that underpinned biblical translation meant that the rights and wrongs of translating the Bible into the vernacular in England was hotly debated. Whereas scriptural translation drew attention to the need to translate word for word to prevent heresy and to maintain accuracy in the presentation of the Word, psalm translation and translating from other vernacular languages posed different challenges for the translator; these challenges perhaps become most apparent when translating across confessional divides. This chapter considers the relationship between translation and religion in early modern English literature and the wider European perspectives that informed the ways in which narrative was recreated in English imaginative writing.
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autores, Varios. A practical guidance on estimation of european wild ungulate population density. Ediciones de la Universidad de Castilla-La Mancha, 2022. http://dx.doi.org/10.18239/atenea_2022.41.00.
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Given the diversity of available methods and the geographical diversity of Europe, methodological harmonization is duly needed. The general aim of this guidance is to review the methods for estimating density in European wild ungula¬tes. This guidance is based in previous comprehensive reviews carried out by the ENETWILD Consortium, which propo¬sed general recommendations for practical implementation of methods to estimate wild ungulate density. We present 9 methods used in nineteen wild ruminant species and wild boar distributed across Europe, paying special attention to most practical methods for further implementation in the field to calculate reliable and accurate density estimates, allowing further comparable results over their distribution ranges. This guidance provides recommendations to select the methods to estimate the density and its implementations for ungulate populations with the aim of increasing the output quality (good accuracy and precision). The method should be used in a harmonized way: we provide detailed instructions for the design of most recommended methods, but specific protocols must be specifically adapted to local conditions. Every method on estimating reliable and comparable wild ungulate population density has some advanta¬ges and disadvantages depending on the habitat, the weather conditions and the benefit and do not discard their use if applied in a harmonized way. It arises from this guidance the need of developing a permanent network and a data platform to collect and share local density estimates, so as abundance in the EU.
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Ufimtseva, Nataliya V., Iosif A. Sternin, and Elena Yu Myagkova. Russian psycholinguistics: results and prospects (1966–2021): a research monograph. Institute of Linguistics, Russian Academy of Sciences, 2021. http://dx.doi.org/10.30982/978-5-6045633-7-3.
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The monograph reflects the problems of Russian psycholinguistics from the moment of its inception in Russia to the present day and presents its main directions that are currently developing. In addition, theoretical developments and practical results obtained in the framework of different directions and research centers are described in a concise form. The task of the book is to reflect, as far as it is possible in one edition, firstly, the history of the formation of Russian psycholinguistics; secondly, its methodology and developed methods; thirdly, the results obtained in different research centers and directions in different regions of Russia; fourthly, to outline the main directions of the further development of Russian psycholinguistics. There is no doubt that in the theoretical, methodological and applied aspects, the main problems and the results of their development by Russian psycholinguistics have no analogues in world linguistics and psycholinguistics, or are represented by completely original concepts and methods. We have tried to show this uniqueness of the problematics and the methodological equipment of Russian psycholinguistics in this book. The main role in the formation of Russian psycholinguistics was played by the Moscow psycholinguistic school of A.A. Leontyev. It still defines the main directions of Russian psycholinguistics. Russian psycholinguistics (the theory of speech activity - TSA) is based on the achievements of Russian psychology: a cultural-historical approach to the analysis of mental phenomena L.S. Vygotsky and the system-activity approach of A.N. Leontyev. Moscow is the most "psycholinguistic region" of Russia - INL RAS, Moscow State University, Moscow State Linguistic University, RUDN, Moscow State Pedagogical University, Moscow State Pedagogical University, Sechenov University, Moscow State University and other Moscow universities. Saint Petersburg psycholinguists have significant achievements, especially in the study of neurolinguistic problems, ontolinguistics. The most important feature of Russian psycholinguistics is the widespread development of psycholinguistics in the regions, the emergence of recognized psycholinguistic research centers - St. Petersburg, Tver, Saratov, Perm, Ufa, Omsk, Novosibirsk, Voronezh, Yekaterinburg, Kursk, Chelyabinsk; psycholinguistics is represented in Cherepovets, Ivanovo, Volgograd, Vyatka, Kaluga, Krasnoyarsk, Irkutsk, Vladivostok, Abakan, Maikop, Barnaul, Ulan-Ude, Yakutsk, Syktyvkar, Armavir and other cities; in Belarus - Minsk, in Ukraine - Lvov, Chernivtsi, Kharkov, in the DPR - Donetsk, in Kazakhstan - Alma-Ata, Chimkent. Our researchers work in Bulgaria, Hungary, Vietnam, China, France, Switzerland. There are Russian psycholinguists in Canada, USA, Israel, Austria and a number of other countries. All scientists from these regions and countries have contributed to the development of Russian psycholinguistics, to the development of psycholinguistic theory and methods of psycholinguistic research. Their participation has not been forgotten. We tried to present the main Russian psycholinguists in the Appendix - in the sections "Scientometrics", "Monographs and Manuals" and "Dissertations", even if there is no information about them in the Electronic Library and RSCI. The principles of including scientists in the scientometric list are presented in the Appendix. Our analysis of the content of the resulting monograph on psycholinguistic research in Russia allows us to draw preliminary conclusions about some of the distinctive features of Russian psycholinguistics: 1. cultural-historical approach to the analysis of mental phenomena of L.S.Vygotsky and the system-activity approach of A.N. Leontiev as methodological basis of Russian psycholinguistics; 2. theoretical nature of psycholinguistic research as a characteristic feature of Russian psycholinguistics. Our psycholinguistics has always built a general theory of the generation and perception of speech, mental vocabulary, linked specific research with the problems of ontogenesis, the relationship between language and thinking; 3. psycholinguistic studies of speech communication as an important subject of psycholinguistics; 4. attention to the psycholinguistic analysis of the text and the development of methods for such analysis; 5. active research into the ontogenesis of linguistic ability; 6. investigation of linguistic consciousness as one of the important subjects of psycholinguistics; 7. understanding the need to create associative dictionaries of different types as the most important practical task of psycholinguistics; 8. widespread use of psycholinguistic methods for applied purposes, active development of applied psycholinguistics. The review of the main directions of development of Russian psycholinguistics, carried out in this monograph, clearly shows that the direction associated with the study of linguistic consciousness is currently being most intensively developed in modern Russian psycholinguistics. As the practice of many years of psycholinguistic research in our country shows, the subject of study of psycholinguists is precisely linguistic consciousness - this is a part of human consciousness that is responsible for generating, understanding speech and keeping language in consciousness. Associative experiments are the core of most psycholinguistic techniques and are important both theoretically and practically. The following main areas of practical application of the results of associative experiments can be outlined. 1. Education. Associative experiments are the basis for constructing Mind Maps, one of the most promising tools for systematizing knowledge, assessing the quality, volume and nature of declarative knowledge (and using special techniques and skills). Methods based on smart maps are already widely used in teaching foreign languages, fast and deep immersion in various subject areas. 2. Information search, search optimization. The results of associative experiments can significantly improve the quality of information retrieval, its efficiency, as well as adaptability for a specific person (social group). When promoting sites (promoting them in search results), an associative experiment allows you to increase and improve the quality of the audience reached. 3. Translation studies, translation automation. An associative experiment can significantly improve the quality of translation, take into account intercultural and other social characteristics of native speakers. 4. Computational linguistics and automatic word processing. The results of associative experiments make it possible to reveal the features of a person's linguistic consciousness and contribute to the development of automatic text processing systems in a wide range of applications of natural language interfaces of computer programs and robotic solutions. 5. Advertising. The use of data on associations for specific words, slogans and texts allows you to predict and improve advertising texts. 6. Social relationships. The analysis of texts using the data of associative experiments makes it possible to assess the tonality of messages (negative / positive moods, aggression and other characteristics) based on user comments on the Internet and social networks, in the press in various projections (by individuals, events, organizations, etc.) from various social angles, to diagnose the formation of extremist ideas. 7. Content control and protection of personal data. Associative experiments improve the quality of content detection and filtering by identifying associative fields in areas subject to age restrictions, personal information, tobacco and alcohol advertising, incitement to ethnic hatred, etc. 8. Gender and individual differences. The data of associative experiments can be used to compare the reactions (and, in general, other features of thinking) between men and women, different social and age groups, representatives of different regions. The directions for the further development of Russian psycholinguistics from the standpoint of the current state of psycholinguistic science in the country are seen by us, first of all: in the development of research in various areas of linguistic consciousness, which will contribute to the development of an important concept of speech as a verbal model of non-linguistic consciousness, in which knowledge revealed by social practice and assigned by each member of society during its inculturation is consolidated for society and on its behalf; in the expansion of the problematics, which is formed under the influence of the growing intercultural communication in the world community, which inevitably involves the speech behavior of natural and artificial bilinguals in the new object area of psycholinguistics; in using the capabilities of national linguistic corpora in the interests of researchers studying the functioning of non-linguistic and linguistic consciousness in speech processes; in expanding research on the semantic perception of multimodal texts, the scope of which has greatly expanded in connection with the spread of the Internet as a means of communication in the life of modern society; in the inclusion of the problems of professional communication and professional activity in the object area of psycholinguistics in connection with the introduction of information technologies into public practice, entailing the emergence of new professions and new features of the professional ethos; in the further development of the theory of the mental lexicon (identifying the role of different types of knowledge in its formation and functioning, the role of the word as a unit of the mental lexicon in the formation of the image of the world, as well as the role of the natural / internal metalanguage and its specificity in speech activity); in the broad development of associative lexicography, which will meet the most diverse needs of society and cognitive sciences. The development of associative lexicography may lead to the emergence of such disciplines as associative typology, associative variantology, associative axiology; in expanding the spheres of applied use of psycholinguistics in social sciences, sociology, semasiology, lexicography, in the study of the brain, linguodidactics, medicine, etc. This book is a kind of summarizing result of the development of Russian psycholinguistics today. Each section provides a bibliography of studies on the relevant issue. The Appendix contains the scientometrics of leading Russian psycholinguists, basic monographs, psycholinguistic textbooks and dissertations defended in psycholinguistics. The content of the publications presented here is convincing evidence of the relevance of psycholinguistic topics and the effectiveness of the development of psycholinguistic problems in Russia.
Book chapters on the topic "Attention Network Test"
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Worden, Michael S. "Attention Network Test (ANT)." In Encyclopedia of Clinical Neuropsychology, 296–97. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-79948-3_1268.
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Worden, Michael S. "Attention Network Test (ANT)." In Encyclopedia of Clinical Neuropsychology, 1–3. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56782-2_1268-2.
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Worden, Michael S. "Attention Network Test (ANT)." In Encyclopedia of Clinical Neuropsychology, 405–7. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_1268.
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Toa, Chean Khim, Kok Swee Sim, and Shing Chiang Tan. "Emotiv Insight with Convolutional Neural Network: Visual Attention Test Classification." In Advances in Computational Collective Intelligence, 348–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88113-9_28.
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Seong, ChangMin, YoungRok Song, Jiwung Hyun, and Yun-Gyung Cheong. "Towards Building Intrusion Detection Systems for Multivariate Time-Series Data." In Silicon Valley Cybersecurity Conference, 45–56. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96057-5_4.
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AbstractRecent network intrusion detection systems have employed machine learning and deep learning algorithms to defend against dynamically evolving network attacks. While most previous studies have focused on detecting attacks which can be determined based on a single time instant, few studies have paid attention to subsequence outliers, which require inspecting consecutive points in time for detection. To address this issue, this paper applies a time-series anomaly detection method in an unsupervised learning manner. To this end, we converted the UNSW-NB15 dataset into the time-series data. We carried out a preliminary evaluation to test the performance of the anomaly detection on the created time-series network dataset as well as on a time-series dataset obtained from sensors. We analyze and discuss the results.
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Mash, Lisa E., Raymond M. Klein, and Jeanne Townsend. "Attention Network Tests in ASD." In Encyclopedia of Autism Spectrum Disorders, 1–5. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4614-6435-8_102499-1.
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Mash, Lisa E., Raymond M. Klein, and Jeanne Townsend. "Attention Network Tests in ASD." In Encyclopedia of Autism Spectrum Disorders, 381–85. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-91280-6_102499.
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Yang, Ze, Pengfei Wang, Lei Zhang, Linjun Shou, and Wenwen Xu. "A Recurrent Attention Network for Judgment Prediction." In Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series, 253–66. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30490-4_21.
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Rizvi, Syed Zeeshan, Muhammad Umar Farooq, and Rana Hammad Raza. "Performance Comparison of Deep Residual Networks-Based Super Resolution Algorithms Using Thermal Images: Case Study of Crowd Counting." In Digital Interaction and Machine Intelligence, 75–87. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11432-8_7.
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AbstractHumans are able to perceive objects only in the visible spectrum range which limits the perception abilities in poor weather or low illumination conditions. The limitations are usually handled through technological advancements in thermographic imaging. However, thermal cameras have poor spatial resolutions compared to RGB cameras. Super-resolution (SR) techniques are commonly used to improve the overall quality of low-resolution images. There has been a major shift of research among the Computer Vision researchers towards SR techniques particularly aimed for thermal images. This paper analyzes the performance of three deep learning-based state-of-the-art SR algorithms namely Enhanced Deep Super Resolution (EDSR), Residual Channel Attention Network (RCAN) and Residual Dense Network (RDN) on thermal images. The algorithms were trained from scratch for different upscaling factors of ×2 and ×4. The dataset was generated from two different thermal imaging sequences of BU-TIV benchmark. The sequences contain both sparse and highly dense type of crowds with a far field camera view. The trained models were then used to super-resolve unseen test images. The quantitative analysis of the test images was performed using common image quality metrics such as PSNR, SSIM and LPIPS, while qualitative analysis was provided by evaluating effectiveness of the algorithms for crowd counting application. After only 54 and 51 epochs of RCAN and RDN respectively, both approaches were able to output average scores of 37.878, 0.986, 0.0098 and 30.175, 0.945, 0.0636 for PSNR, SSIM and LPIPS respectively. The EDSR algorithm took the least computation time during both training and testing because of its simple architecture. This research proves that a reasonable accuracy can be achieved with fewer training epochs when an application-specific dataset is carefully selected.
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Sun, Qiang, and Yue Wu. "A Multi-level Attention Model for Text Matching." In Artificial Neural Networks and Machine Learning – ICANN 2018, 142–53. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01418-6_15.
Full textConference papers on the topic "Attention Network Test"
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Yu, Fuxun, Chenchen Liu, Di Wang, Yanzhi Wang, and Xiang Chen. "AntiDote: Attention-based Dynamic Optimization for Neural Network Runtime Efficiency." In 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2020. http://dx.doi.org/10.23919/date48585.2020.9116416.
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Ma, Junchi, Zongtao Duan, and Lei Tang. "GATPS: An attention-based graph neural network for predicting SDC-causing instructions." In 2021 IEEE 39th VLSI Test Symposium (VTS). IEEE, 2021. http://dx.doi.org/10.1109/vts50974.2021.9441056.
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Yang, Yiding, Xinchao Wang, Mingli Song, Junsong Yuan, and Dacheng Tao. "SPAGAN: Shortest Path Graph Attention Network." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/569.
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Graph convolutional networks (GCN) have recently demonstrated their potential in analyzing non-grid structure data that can be represented as graphs. The core idea is to encode the local topology of a graph, via convolutions, into the feature of a center node. In this paper, we propose a novel GCN model, which we term as Shortest Path Graph Attention Network (SPAGAN). Unlike conventional GCN models that carry out node-based attentions, on either first-order neighbors or random higher-order ones, the proposed SPAGAN conducts path-based attention that explicitly accounts for the influence of a sequence of nodes yielding the minimum cost, or shortest path, between the center node and its higher-order neighbors. SPAGAN therefore allows for a more informative and intact exploration of the graph structure and further the more effective aggregation of information from distant neighbors, as compared to node-based GCN methods. We test SPAGAN for the downstream classification task on several standard datasets, and achieve performances superior to the state of the art.
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Wang, Yaxiong, Hao Yang, Xueming Qian, Lin Ma, Jing Lu, Biao Li, and Xin Fan. "Position Focused Attention Network for Image-Text Matching." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/526.
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Image-text matching tasks have recently attracted a lot of attention in the computer vision field. The key point of this cross-domain problem is how to accurately measure the similarity between the visual and the textual contents, which demands a fine understanding of both modalities. In this paper, we propose a novel position focused attention network (PFAN) to investigate the relation between the visual and the textual views. In this work, we integrate the object position clue to enhance the visual-text joint-embedding learning. We first split the images into blocks, by which we infer the relative position of region in the image. Then, an attention mechanism is proposed to model the relations between the image region and blocks and generate the valuable position feature, which will be further utilized to enhance the region expression and model a more reliable relationship between the visual image and the textual sentence. Experiments on the popular datasets Flickr30K and MS-COCO show the effectiveness of the proposed method. Besides the public datasets, we also conduct experiments on our collected practical news dataset (Tencent-News) to validate the practical application value of proposed method. As far as we know, this is the first attempt to test the performance on the practical application. Our method can achieve the state-of-art performance on all of these three datasets.
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Nikzad, Mohammad, Yongsheng Gao, and Jun Zhou. "Attention-based Pyramid Dilated Lattice Network for Blind Image Denoising." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/129.
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Though convolutional neural networks (CNNs) with residual and dense aggregations have obtained much attention in image denoising, they are incapable of exploiting different levels of contextual information at every convolutional unit in order to infer different levels of noise components with a single model. In this paper, to overcome this shortcoming we present a novel attention-based pyramid dilated lattice (APDL) architecture and investigate its capability for blind image denoising. The proposed framework can effectively harness the advantages of residual and dense aggregations to achieve a great trade-off between performance, parameter efficiency, and test time. It also employs a novel pyramid dilated convolution strategy to effectively capture contextual information corresponding to different noise levels through the training of a single model. Our extensive experimental investigation verifies the effectiveness and efficiency of the APDL architecture for image denoising as well as JPEG artifacts suppression tasks.
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Abbas, Fadya. "An Improved NLP for Syntactic and Semantic Matching using Bidirectional LSTM and Attention Mechanism." In 9th International Conference on Artificial Intelligence and Applications (AIAPP 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120906.
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Dealing with extensive amounts of textual data requires an efficient deep learning model to be adapted. However, the following reasons; the highly ambiguous and complex nature of many prosodic phrasing also enough dataset suitable for system training is always limited, cause big challenges for training the NLP models. This proposed conceptual framework aims to provide an understanding and familiarity with the elements of modern deep learning networks for NLP use. In this design, the encoder uses Bidirectional Long Short-Term Memory deep network layers, to encode the test sequences into more context-sensitive representations. Moreover, the attention mechanism is mainly used to generate a context vector that is determined from distinct alignment scores at different word positions, hence, it can focus more on a small words' subset. Hence, the attention mechanism improved the model data efficiency, and the model performance is validated using an example of data sets that show promise for a real-life application.
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Li, Xiao, Jiaxing Song, and Weidong Liu. "Label-Attentive Hierarchical Attention Network for Text Classification." In ICBDC 2020: 2020 5th International Conference on Big Data and Computing. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3404687.3404706.
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Zhang, Huai Qiang, Qing Cheng Liu, and Yu Juan Liu. "Study on Network Radiation Environmental Monitoring System." In 2013 21st International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icone21-15487.
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With the application of nuclear technology in many fields and the development of nuclear power, the people pay more and more attention to which the radioactive substances are harmful to the human, environmental pollution and nuclear security. In order to prevent the dangers of radioactive substances on the professional staff and the public, the pollution of the environment, it is particularly important to establish a scientific and perfect radiation environment monitoring system. In this paper, the radiation environmental monitoring system based on network is designed for environment radiation situation of the original enterprise, such as the uranium mining, nuclear power plant, uranium hydrometallurgical plant, nuclear fuel processing plant. The network technology is introduced in the monitoring system which links the field monitoring system, data center service system, data transmission system and client system. the worker can control the on-site monitoring points, data acquisition and transmission in any of the network interface functions, complete displaying, analyzing and processing in the remote server, meanwhile the network power supply technology is applied in the monitoring system which can supply electricity to on-site monitoring points. According to the test, the radiation environmental monitoring system which based on network technology is easy to apply and maintain, with a certain degree of practicality and economy.
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Mughaz, Dror, Michael Cohen, Sagit Mejahez, Tal Ades, and Dan Bouhnik. "From an Artificial Neural Network to Teaching [Abstract]." In InSITE 2020: Informing Science + IT Education Conferences: Online. Informing Science Institute, 2020. http://dx.doi.org/10.28945/4557.
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[This Proceedings paper was revised and published in the "Interdisciplinary Journal of e-Skills and Lifelong Learning," 16, 1-17.] Aim/Purpose: Using Artificial Intelligence with Deep Learning (DL) techniques, which mimic the action of the brain, to improve a student’s grammar learning process. Finding the subject of a sentence using DL, and learning, by way of this computer field, to analyze human learning processes and mistakes. In addition, showing Artificial Intelligence learning processes, with and without a general overview of the problem that it is under examination. Applying the idea of the general perspective that the network gets on the sentences and deriving recommendations from this for teaching processes. Background: We looked for common patterns of computer errors and human grammar mistakes. Also deducing the neural network’s learning process, deriving conclusions, and applying concepts from this process to the process of human learning. Methodology: We used DL technologies and research methods. After analysis, we built models from three types of complex neuronal networks – LSTM, Bi-LSTM, and GRU – with sequence-to-sequence architecture. After this, we combined the sequence-to- sequence architecture model with the attention mechanism that gives a general overview of the input that the network receives. Contribution: The cost of computer applications is cheaper than that of manual human effort, and the availability of a computer program is much greater than that of humans to perform the same task. Thus, using computer applications, we can get many desired examples of mistakes without having to pay humans to perform the same task. Understanding the mistakes of the machine can help us to under-stand the human mistakes, because the human brain is the model of the artificial neural network. This way, we can facilitate the student learning process by teaching students not to make mistakes that we have seen made by the artificial neural network. We hope that with the method we have developed, it will be easier for teachers to discover common mistakes in students’ work before starting to teach them. In addition, we show that a “general explanation” of the issue under study can help the teaching and learning process. Findings: We performed the test case on the Hebrew language. From the mistakes we received from the computerized neuronal networks model we built, we were able to classify common human errors. That is, we were able to find a correspondence between machine mistakes and student mistakes. Recommendations for Practitioners: Use an artificial neural network to discover mistakes, and teach students not to make those mistakes. We recommend that before the teacher begins teaching a new topic, he or she gives a general explanation of the problems this topic deals with, and how to solve them. Recommendations for Researchers: To use machines that simulate the learning processes of the human brain, and study if we can thus learn about human learning processes. Impact on Society: When the computer makes the same mistakes as a human would, it is very easy to learn from those mistakes and improve the study process. The fact that ma-chine and humans make similar mistakes is a valuable insight, especially in the field of education, Since we can generate and analyze computer system errors instead of doing a survey of humans (who make mistakes similar to those of the machine); the teaching process becomes cheaper and more efficient. Future Research: We plan to create an automatic grammar-mistakes maker (for instance, by giving the artificial neural network only a tiny data-set to learn from) and ask the students to correct the errors made. In this way, the students will practice on the material in a focused manner. We plan to apply these techniques to other education subfields and, also, to non-educational fields. As far as we know, this is the first study to go in this direction ‒ instead of looking at organisms and building machines, to look at machines and learn about organisms.
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Tay, Yi, Anh Tuan Luu, and Siu Cheung Hui. "Hermitian Co-Attention Networks for Text Matching in Asymmetrical Domains." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/615.
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Co-Attentions are highly effective attention mechanisms for text matching applications. Co-Attention enables the learning of pairwise attentions, i.e., learning to attend based on computing word-level affinity scores between two documents. However, text matching problems can exist in either symmetrical or asymmetrical domains. For example, paraphrase identification is a symmetrical task while question-answer matching and entailment classification are considered asymmetrical domains. In this paper, we argue that Co-Attention models in asymmetrical domains require different treatment as opposed to symmetrical domains, i.e., a concept of word-level directionality should be incorporated while learning word-level similarity scores. Hence, the standard inner product in real space commonly adopted in co-attention is not suitable. This paper leverages attractive properties of the complex vector space and proposes a co-attention mechanism based on the complex-valued inner product (Hermitian products). Unlike the real dot product, the dot product in complex space is asymmetric because the first item is conjugated. Aside from modeling and encoding directionality, our proposed approach also enhances the representation learning process. Extensive experiments on five text matching benchmark datasets demonstrate the effectiveness of our approach.
Reports on the topic "Attention Network Test"
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Idakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang, and Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41302.
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Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR activity classes (i.e., agonist, antagonist, inactive, and inconclusive) at the same distribution rates amongst the training, validation and test subsets. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p < 0.001, ANOVA) by 22–27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Further in-depth analyses of chemical scaffolding shed insights on structural alerts for AR agonists/antagonists and inactive/inconclusive compounds, which may aid in future drug discovery and improvement of toxicity prediction modeling.