Search results for: semantic memory

Authors: D. W. M. S. Samarathunga, Isuru Dharmarathne

Abstract:

In aphasia, various levels of symbolic language processing (semantics) are affected. It is shown that Persons with Aphasia (PWA) often experience more problems comprehending some categories of words than others. The study aimed to determine lexical semantic deficits seen in Auditory Comprehension (AC) and to describe lexical-semantic deficits across six selected word categories. Thirteen (n =13) persons diagnosed with post-stroke aphasia (PSA) were recruited to perform an AC task. Foods, objects, clothes, vehicles, body parts and animals were selected as the six categories. As the test stimuli, black and white line drawings were adapted from a picture set developed for semantic studies by Snodgrass and Vanderwart. A pilot study was conducted with five (n=5) healthy nonbrain damaged Sinhala speaking adults to decide familiarity and applicability of the test material. In the main study, participants were scored based on the accuracy and number of errors shown. The results indicate similar trends of lexical semantic deficits identified in the literature confirming ‘animals’ to be the easiest category to comprehend. Mann-Whitney U test was performed to determine the association between the selected variables and the participants’ performance on AC task. No statistical significance was found between the errors and the type of aphasia reflecting similar patterns described in aphasia literature in other languages. The current study indicates the presence of selectivity of lexical semantic deficits in AC and a hierarchy was developed based on the complexity of the categories to comprehend by Sinhala speaking PWA, which might be clinically beneficial when improving language skills of Sinhala speaking persons with post-stroke aphasia. However, further studies on aphasia should be conducted with larger samples for a longer period to study deficits in Sinhala and other Sri Lankan languages (Tamil and Malay).

Keywords: aphasia, auditory comprehension, selective lexical-semantic deficits, semantic categories

Procedia PDF Downloads 255

Authors: Douglas Rossi Ramos

Abstract:

The current digital conjuncture, called by some authors as 'Internet of Things' (IoT), 'Web 2.0' or even 'Web 3.0', consists of a network that encompasses any communication of objects and entities, such as data, information, technologies, and people. At this juncture, especially characterized by an "object socialization," communication can no longer be represented as a simple informational flow of messages from a sender, crossing a channel or medium, reaching a receiver. The idea of communication must, therefore, be thought of more broadly in which it is possible to analyze the process communicative from interactions between humans and nonhumans. To think about this complexity, a communicative process that encompasses both humans and other beings or entities communicating (objects and things), it is necessary to constitute a new epistemology of communication to rethink concepts and notions commonly attributed to humans such as 'memory.' This research aims to contribute to this epistemological constitution from the discussion about the notion of memory according to the complex ontology of Henri Bergson. Among the results (the notion of memory in Bergson presents itself as a conceptual tool for the analysis of posthumanism and the anthropomorphic conjuncture of the new advent of digital), there was the need to think about an ontological memory, analyzed as a being itself (being itself of memory), as a strategy for understanding the forms of interaction and communication that constitute the new digital conjuncture, in which communicating beings or entities tend to interact with each other. Rethinking the idea of communication beyond the dimension of transmission in informative sequences paves the way for an ecological perspective of the digital dwelling condition.

Keywords: communication, digital, Henri Bergson, memory

Procedia PDF Downloads 165

Authors: Kriuk Boris, Kriuk Fedor

Abstract:

This paper presents a comparative study of fundamental similarity functions for Siamese networks in semantic textual similarity (STS) tasks. We evaluate various similarity functions using the STS Benchmark dataset, analyzing their performance and stability. Additionally, we introduce a multi-objective approach for optimal threshold selection. Our findings provide insights into the effectiveness of different similarity functions and offer a straightforward method for threshold selection optimization, contributing to the advancement of Siamese network architectures in STS applications.

Keywords: siamese networks, semantic textual similarity, similarity functions, STS benchmark dataset, threshold selection

Procedia PDF Downloads 40

Authors: Derssie Mebratu

Abstract:

As a large variety of micro services, web services, social graphic applications, and media applications are continuously developed, it is substantially vital to design and build a reliable, efficient, and faster memory tiering system. Despite limited design, implementation, and deployment in the last few years, several techniques are currently developed to improve a memory tiering system in a cloud. Some of these techniques are to develop an optimal scanning frequency; improve and track pages movement; identify pages that recently accessed; store pages across each tiering, and then identify pages as a hot, warm, and cold so that hot pages can store in the first tiering Dynamic Random Access Memory (DRAM) and warm pages store in the second tiering Compute Express Link(CXL) and cold pages store in the third tiering Non-Volatile Memory (NVM). Apart from the current proposal and implementation, we also develop a new technique based on a machine learning algorithm in that the throughput produced 25% improved performance compared to the performance produced by the baseline as well as the latency produced 95% improved performance compared to the performance produced by the baseline.

Keywords: machine learning, bayesian optimization, memory tiering, CXL, DRAM

Procedia PDF Downloads 96

Authors: Jinny Hong, Jeanne C. Watson

Abstract:

Three types of autobiographical memory recall -specific, episodic, and generic- were examined in relation to the quality of descriptions and in-session levels of emotional arousal. Correlational analyses and general estimating equation were conducted to test the relationships between 1) quality of descriptions and type of memory, 2) type of memory and emotional arousal, and 3) quality of descriptions and emotional arousal. The data was transcripts drawn from an archival randomized-control study comparing cognitive-behavioral therapy and emotion-focused therapy in a 16-week treatment for depression. Autobiographical memory recall segments were identified and sorted into three categories: specific, episodic, and generic. Quality of descriptions of these segments was then operationalized and measured using the Referential Activity Scale, and each memory segment was rated on four dimensions: concreteness, specificity, clarity, and overall imagery. Clients’ level of emotional arousal for each recall was measured using the Client’s Expression Emotion Scale. Contrary to the predictions, generic memories are associated with higher emotional arousal ratings and descriptive language ratings compared to specific memories. However, a positive relationship emerged between the quality of descriptions and expressed emotional arousal, indicating that the quality of descriptions in which memories are described in sessions is more important than the type of memory recalled in predicting clients’ level of emotional arousal. The results from this study provide a clearer understanding of the role of memory recall types and use of language in activating emotional arousal in psychotherapy sessions in a depressed sample.

Keywords: autobiographical memory recall, emotional arousal, psychotherapy for depression, quality of descriptions, referential activity

Procedia PDF Downloads 163

Authors: Mary Dorothy Roxas, Jeian Mae Dungca, Reginald Agor, Beatriz Figueroa, Lennon Andre Patricio, Honey Joy Cabahug

Abstract:

Parentification occurs when children are expected to provide instrumental or emotional caregiving within the family. It was found that parentification has the latter effect on adolescents’ cognitive and emotional vulnerability. Attachment theory helps clarify the process of parentification as it involves the relationship between the child and the parent. Carandang theory of “taga-salo” helps explain parentification in the Philippines setting. The present study examined the potential risk of parentification on adolescent’s memory retention by hypothesizing that there is a correlation between the two. The research was conducted with 249 female adolescents ages 12-24, residing in Valenzuela City. Results indicated that there is a significant inverse correlation between parentification and memory retention.

Keywords: memory retention, neurocognitive, parentification, stress

Procedia PDF Downloads 686

Authors: Tobias Strübing, Fatemeh Ziaeetabar

Abstract:

Humans usually manipulate objects with their hands. To represent these actions in a simple and understandable way, we need to use a semantic framework. For this purpose, the Semantic Event Chain (SEC) method has already been presented which is done by consideration of touching and non-touching relations between manipulated objects in a scene. This method was improved by a computational model, the so-called enriched Semantic Event Chain (eSEC), which incorporates the information of static (e.g. top, bottom) and dynamic spatial relations (e.g. moving apart, getting closer) between objects in an action scene. This leads to a better action prediction as well as the ability to distinguish between more actions. Each eSEC manipulation descriptor is a huge matrix with thirty rows and a massive set of the spatial relations between each pair of manipulated objects. The current eSEC framework has so far only been used in the category of manipulation actions, which eventually involve two hands. Here, we would like to extend this approach to a whole body action descriptor and make a conjoint activity representation structure. For this purpose, we need to do a statistical analysis to modify the current eSEC by summarizing while preserving its features, and introduce a new version called Enhanced eSEC or (e2SEC). This summarization can be done from two points of the view: 1) reducing the number of rows in an eSEC matrix, 2) shrinking the set of possible semantic spatial relations. To achieve these, we computed the importance of each matrix row in an statistical way, to see if it is possible to remove a particular one while all manipulations are still distinguishable from each other. On the other hand, we examined which semantic spatial relations can be merged without compromising the unity of the predefined manipulation actions. Therefore by performing the above analyses, we made the new e2SEC framework which has 20% fewer rows, 16.7% less static spatial and 11.1% less dynamic spatial relations. This simplification, while preserving the salient features of a semantic structure in representing actions, has a tremendous impact on the recognition and prediction of complex actions, as well as the interactions between humans and robots. It also creates a comprehensive platform to integrate with the body limbs descriptors and dramatically increases system performance, especially in complex real time applications such as human-robot interaction prediction.

Keywords: enriched semantic event chain, semantic action representation, spatial relations, statistical analysis

Procedia PDF Downloads 126

Authors: Huiling Feng, Xiaoshuang Jia, Jihong Liang, Li Niu

Abstract:

Beijing, formerly romanized as Peking, is the capital of the People's Republic of China and the world's second most populous city proper and most populous capital city. Beijing is a noted historical and cultural whose city history dates back three millennia which is extremely rich in terms of cultural heritage. In 2012, a digital memory project led by Humanistic Beijing Studies Center in Renmin University of China started with the goal to build a total digital collection of knowledge assets about Beijing and represent Beijing memories in new fresh ways. The title of the entire project is ‘Peking Memory Project(PMP)’. The main goal is for safeguarding the documentary heritage and intellectual memory of Beijing, more specifically speaking, from the perspective of historical humanities and public participation, PMP will comprehensively applied digital technologies like digital capture, digital storage, digital process, digital presentation and digital communication to transform different kinds of cultural heritage of Beijing into digital formats that can be stored, re-organized and shared. These digital memories can be interpreted with a new perspective, be organized with a new theme, be presented in a new way and be utilized with a new need. Taking social memory as theoretical basis and digital technologies as tools, PMP is framed with ‘Two Sites and A Repository’. Two sites mean the special website(s) characterized by ‘professional’ and an interactive website characterized by ‘crowdsourcing’. A Repository means the storage pool used for safety long-time preservation of the digital memories. The work of PMP has ultimately helped to highlight the important role in safeguarding the documentary heritage and intellectual memory of Beijing.

Keywords: digital memory, cultural heritage, digital technologies, peking memory project

Procedia PDF Downloads 177

Authors: Motonobu Hattori

Abstract:

In neural networks, when new patters are learned by a network, they radically interfere with previously stored patterns. This drawback is called catastrophic forgetting. We have already proposed a biologically inspired dual-network memory model which can much reduce this forgetting for static patterns. In this model, information is first stored in the hippocampal network, and thereafter, it is transferred to the neocortical network using pseudo patterns. Because, temporal sequence learning is more important than static pattern learning in the real world, in this study, we improve our conventional dual-network memory model so that it can deal with temporal sequences without catastrophic forgetting. The computer simulation results show the effectiveness of the proposed dual-network memory model.

Keywords: catastrophic forgetting, dual-network, temporal sequences, hippocampal

Procedia PDF Downloads 273

Authors: Hope Amba Neji

Abstract:

The study adopted a quasi-experimental design. Two schools were used for the experimental study, while one school was used for the control. The experimental groups were subjected to treatment for four weeks with chemistry museum artifacts and a visit as made to the museum so that learners would have real-life learning experiences with museum resources, while the control group was taught with the conventional method. The instrument for the study was a 20-item Chemistry Memory Test (CMT) and a 10-item Chemistry Interest Questionnaire (CIQ). The reliability was ascertained using (KR-20) and alpha reliability coefficient, which yielded a reliability coefficient of .83 and .81, respectively. Data obtained was analyzed using Analysis of Covariance (ANCOVA) and Analysis of variance (ANOVA) at 0.05 level of significance. Findings revealed that museum artifacts have a significant effect on students’ memory enhancement and interest in chemistry. It was recommended chemistry learning should be enhanced, motivating and real with museum artifacts, which significantly aid memory enhancement and interest in chemistry.

Keywords: museum artifacts, memory, chemistry, atitude

Procedia PDF Downloads 77

Authors: Kirolos Gerges Yakoub Gerges

Abstract:

Self-sustaining agricultural machines act in stochastic surroundings and therefore, should be capable of perceive the surroundings in real time. This notion can be done using image sensors blended with superior device learning, mainly Deep mastering. Deep convolutional neural networks excel in labeling and perceiving colour pix and since the fee of RGB-cameras is low, the hardware cost of accurate notion relies upon heavily on memory and computation power. This paper investigates the opportunity of designing lightweight convolutional neural networks for semantic segmentation (pixel clever class) with reduced hardware requirements, to allow for embedded usage in self-reliant agricultural machines. The usage of compression techniques, a lightweight convolutional neural community is designed to carry out actual-time semantic segmentation on an embedded platform. The community is skilled on two big datasets, ImageNet and Pascal Context, to apprehend as much as four hundred man or woman instructions. The 400 training are remapped into agricultural superclasses (e.g. human, animal, sky, road, area, shelterbelt and impediment) and the capacity to provide correct actual-time perception of agricultural environment is studied. The network is carried out to the case of self-sufficient grass mowing the usage of the NVIDIA Tegra X1 embedded platform. Feeding case-unique pics to the community consequences in a fully segmented map of the superclasses within the picture. As the network remains being designed and optimized, handiest a qualitative analysis of the technique is entire on the abstract submission deadline. intending this cut-off date, the finalized layout is quantitatively evaluated on 20 annotated grass mowing pictures. Light-weight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show aggressive performance on the subject of accuracy and speed. It’s miles viable to offer value-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: centrifuge pump, hydraulic energy, agricultural applications, irrigationaxial flux machines, axial flux applications, coreless machines, PM machinesautonomous agricultural machines, deep learning, safety, visual perception

Procedia PDF Downloads 28

Authors: Chhavi Saxena

Abstract:

FinFETs can be a replacement for bulk-CMOS transistors in many different designs. Its low leakage/standby power property makes FinFETs a desirable option for memory sub-systems. Memory modules are widely used in most digital and computer systems. Leakage power is very important in memory cells since most memory applications access only one or very few memory rows at a given time. As technology scales down, the importance of leakage current and power analysis for memory design is increasing. In this paper, we discover an option for low power interconnect synthesis at the 32nm node and beyond, using Fin-type Field-Effect Transistors (FinFETs) which are a promising substitute for bulk CMOS at the considered gate lengths. We consider a mechanism for improving FinFETs efficiency, called variable supply voltage schemes. In this paper, we’ve illustrated the design and implementation of FinFET based 4x4 SRAM cell array by means of one bit 7T SRAM. FinFET based 7T SRAM has been designed and analysis have been carried out for leakage current, dynamic power and delay. For the validation of our design approach, the output of FinFET SRAM array have been compared with standard CMOS SRAM and significant improvements are obtained in proposed model.

Keywords: FinFET, 7T SRAM cell, leakage current, delay

Procedia PDF Downloads 455

Authors: Alinka Molnár-Tóth, Tímea Tánczos, Regina Barna, Katalin Jakab, Péter Klivényi

Abstract:

Supported by neuroscientific findings, the so-called Hub-and-Spoke model of the human semantic system is based on two subcomponents of semantic cognition, namely the semantic control process and semantic representation. Our semantic knowledge is multimodal in nature, as the knowledge system stored in relation to a conception is extensive and broad, while different aspects of the conception may be relevant depending on the purpose. The motivation of our research is to develop a new diagnostic measurement procedure based on the preservation of semantic representation, which is appropriate to the specificities of the Hungarian language and which can be used to compare the non-verbal semantic knowledge of healthy and aphasic persons. The development of the test will broaden the Hungarian clinical diagnostic toolkit, which will allow for more specific therapy planning. The sample of healthy persons (n=480) was determined by the last census data for the representativeness of the sample. Based on the concept of the Pyramids and Palm Tree Test, and according to the characteristics of the Hungarian language, we have elaborated a test based on different types of semantic information, in which the subjects are presented with three pictures: they have to choose the one that best fits the target word above from the two lower options, based on the semantic relation defined. We have measured 5 types of semantic knowledge representations: associative relations, taxonomy, motional representations, concrete as well as abstract verbs. As the first step in our data analysis, we examined the normal distribution of our results, and since it was not normally distributed (p < 0.05), we used nonparametric statistics further into the analysis. Using descriptive statistics, we could determine the frequency of the correct and incorrect responses, and with this knowledge, we could later adjust and remove the items of questionable reliability. The reliability was tested using Cronbach’s α, and it can be safely said that all the results were in an acceptable range of reliability (α = 0.6-0.8). We then tested for the potential gender differences using the Mann Whitney-U test, however, we found no difference between the two (p < 0.05). Likewise, we didn’t see that the age had any effect on the results using one-way ANOVA (p < 0.05), however, the level of education did influence the results (p > 0.05). The relationships between the subtests were observed by the nonparametric Spearman’s rho correlation matrix, showing statistically significant correlation between the subtests (p > 0.05), signifying a linear relationship between the measured semantic functions. A margin of error of 5% was used in all cases. The research will contribute to the expansion of the clinical diagnostic toolkit and will be relevant for the individualised therapeutic design of treatment procedures. The use of a non-verbal test procedure will allow an early assessment of the most severe language conditions, which is a priority in the differential diagnosis. The measurement of reaction time is expected to advance prodrome research, as the tests can be easily conducted in the subclinical phase.

Keywords: communication disorders, diagnostic toolkit, neurorehabilitation, semantic knowlegde

Procedia PDF Downloads 104

Authors: Yi-Hsiu Lai

Abstract:

The present study aimed to elucidate the nature of semantic processing in Chinese. Language and cognition related to the issue of aging are examined from the perspective of picture naming and category fluency tasks. Twenty Chinese-speaking adults (ranging from 25 to 45 years old) and twenty Chinese-speaking seniors (ranging from 65 to 75 years old) in Taiwan participated in this study. Each of them individually completed two tasks: a picture naming task and a category fluency task. Instruments for the naming task were sixty black-and-white pictures: thirty-five object and twenty-five action pictures. Category fluency task also consisted of two semantic categories – objects (or nouns) and actions (or verbs). Participants were asked to report as many items within a category as possible in one minute. Scores of action fluency and of object fluency were a summation of correct responses in these two categories. Category effects (actions vs. objects) and age effects were examined in these tasks. Objects were further divided into two major types: living objects and non-living objects. Actions were also categorized into two major types: action verbs and process verbs. Reaction time to each picture/question was additionally calculated and analyzed. Results of the category fluency task indicated that the content of information in Chinese seniors was comparatively deteriorated, thus producing smaller number of semantic-lexical items. Significant group difference was also found in the results of reaction time. Category Effect was significant for both Chinese adults and seniors in the semantic fluency task. Findings in the present study helped characterize the nature of semantic processing in Chinese-speaking adults and seniors and contributed to the issue of language and aging.

Keywords: semantic processing, aging, Chinese, category effects

Procedia PDF Downloads 361

Authors: Mingaile Jurkute, Vilnius University

Abstract:

The memory of the Lithuanian partisan war (1944-1953) underwent extremely dramatic transformations. During this war, the image of the resistance and a partisan was one of the key elements of Lithuanian identity. Its importance is evidenced by the extremely large legacy of songs about partisans, no other topic has collected so much folklore in Lithuania. In the Soviet years, this resistance was practically forced to be forgotten. Terror and Soviet laws have forced people to stop talking about the events, even in the family circle. In addition, the Soviets created their own propaganda story, reinterpreting the Lithuanian partisan war, presenting partisans as bandits who brutally tortured and murdered locals. But even in the Soviet years, the memory could neither be completely suppressed, nor completely transformed into wishful shape. The analysis of fiction and cinema shows that the traumatic memory of real events rushed to the surface, thus transforming the very propagandistic narrative. After the restoration of the Republic of Lithuania in 1990, the Lithuanian partisan war was gradually returned to the central place of Lithuanian history. After 2014 the nationalist heroic narrative about Lithuanian partisans became the central narrative of modern Lithuanian history. Nevertheless, interviews I conducted in Lithuanian villages reveal that the memory of local communities and families preserves quite different experiences that do not fit into neither the Soviet narrative nor the heroic one. Such experiences include, for example, partisan violence against local families. This paper is about the efforts of two political ideologies (the Soviet and the Lithuanian patriotic) to use the history of the Lithuanian partisans for their own needs, and the attempts of small communities (mostly families) to resist these efforts. The research reveals that family memory, even when opposed to aggressive state memory policies, can preserve counter-narratives by exploiting unexpected objects beyond the control of the state, such as nature and wildlife. Basically, the paper analyses the limits of the instrumentalization of memory, even by extremely aggressive political regimes.

Keywords: collective memory, post-memory, violence, military conflict, family memory

Procedia PDF Downloads 92

Authors: Ahmad Shahin, Fadi Chakik, Walid Moudani

Abstract:

Our study is about elaborating a potential solution for a search engine that involves semantic technology to retrieve information and display it significantly. Semantic search engines are not used widely over the web as the majorities are still in Beta stage or under construction. Many problems face the current applications in semantic search, the major problem is to analyze and calculate the meaning of query in order to retrieve relevant information. Another problem is the ontology based index and its updates. Ranking results according to concept meaning and its relation with query is another challenge. In this paper, we are offering a light meta-engine (QESM) which uses Google search, and therefore Google’s index, with some adaptations to its returned results by adding multi-query expansion. The mission was to find a reliable ranking algorithm that involves semantics and uses concepts and meanings to rank results. At the beginning, the engine finds synonyms of each query term entered by the user based on a lexical database. Then, query expansion is applied to generate different semantically analogous sentences. These are generated randomly by combining the found synonyms and the original query terms. Our model suggests the use of semantic similarity measures between two sentences. Practically, we used this method to calculate semantic similarity between each query and the description of each page’s content generated by Google. The generated sentences are sent to Google engine one by one, and ranked again all together with the adapted ranking method (QESM). Finally, our system will place Google pages with higher similarities on the top of the results. We have conducted experimentations with 6 different queries. We have observed that most ranked results with QESM were altered with Google’s original generated pages. With our experimented queries, QESM generates frequently better accuracy than Google. In some worst cases, it behaves like Google.

Keywords: semantic search engine, Google indexing, query expansion, similarity measures

Procedia PDF Downloads 426

Authors: Haitham S. Hamza, Mohamed Maher, Shourok Alaa, Aya Khattab, Hadeal Ismail, Kamilia Hosny

Abstract:

Radio Frequency Identification (RFID) has become a key technology in the margining concept of Internet of Things (IoT). Naturally, business applications would require the deployment of various RFID systems that are developed by different vendors and use various data formats. This heterogeneity poses a real challenge in developing large-scale IoT systems with RFID as integration is becoming very complex and challenging. Semantic integration is a key approach to deal with this challenge. To do so, ontology for RFID systems need to be developed in order to annotated semantically RFID systems, and hence, facilitate their integration. Accordingly, in this paper, we propose ontology for RFID systems. The proposed ontology can be used to semantically enrich RFID systems, and hence, improve their usage and reasoning. The usage of the proposed ontology is explained through a simple scenario in the health care domain.

Keywords: RFID, semantic technology, ontology, sparql query language, heterogeneity

Procedia PDF Downloads 471

Authors: Xiaoshuang Jia, Huiling Feng, Li Niu, Wei Hai

Abstract:

Led by Humanistic Beijing Studies Center in Renmin University of China, My Peking Memory Project(MPMP) is a long-time digital memory project under guarantee of public participation to enable the cultural and intellectual memory of Beijing to be collected, organized, preserved and promoted for discovery and research. Taking digital memory as a new way, MPMP is an important part of Peking Memory Project(PMP) which is aimed at using digital technologies to protect and (re)present the cultural heritage in Beijing. The key outcome of MPMP is the co-building of a total digital collection of knowledge assets about Beijing. Institutional memories are central to Beijing’s collection and consist of the official published documentary content of Beijing. These have already fall under the archival collection purview. The advances in information and communication technology and the knowledge form social memory theory have allowed us to collect more comprehensively beyond institutional collections. It is now possible to engage citizens on a large scale to collect private memories through crowdsourcing in digital formats. Private memories go beyond official published content to include personal narratives, some of which are just in people’s minds until they are captured by MPMP. One aim of MPMP is to engage individuals, communities, groups or institutions who have formed memories and content about Beijing, and would like to contribute them. The project hopes to build a culture of remembering and it believes ‘Every Memory Matters’. Digital memory contribution was achieved through the development of the MPMP. In reducing barriers to digital contribution and promoting high public Participation, MPMP has taken explored the harvesting of transcribe service for digital ingestion, mobile platform and some off-line activities like holding social forum. MPMP has also cooperated with the ‘Implementation Plan of Support Plan for Growth of Talents in Renmin University of China’ to get manpower and intellectual support. After six months of operation, now MPMP have more than 2000 memories added and 7 Special Memory Collections now online. The work of MPMP has ultimately helped to highlight the important role in safeguarding the documentary heritage and intellectual memory of Beijing.

Keywords: digital memory, public participation, MPMP, cultural heritage, collection

Procedia PDF Downloads 170

Authors: Lana Burmistrova

Abstract:

Introduction: The influence of visual impairments on several cognitive processes used in the music learning process is an increasingly important area in special education and cognitive musicology. Many children have several visual impairments due to the refractive errors and irreversible inhibitors. However, based on the compensatory neuroplasticity and functional reorganization, congenitally blind (CB) and early blind (EB) individuals use several areas of the occipital lobe to perceive and process auditory and tactile information. CB individuals have greater memory capacity, memory reliability, and less false memory mechanisms are used while executing several tasks, they have better working memory (WM) and short-term memory (STM). Blind individuals use several strategies while executing tactile and working memory n-back tasks: verbalization strategy (mental recall), tactile strategy (tactile recall) and combined strategies. Methods and design: The aim of the pilot study was to substantiate similar tendencies while executing attention, memory and combined auditory tasks in blind and sighted individuals constructed for this study, and to investigate attention, memory and combined mechanisms used in the music learning process. For this study eight (n=8) blind and eight (n=8) sighted individuals aged 13-20 were chosen. All respondents had more than five years music performance and music learning experience. In the attention task, all respondents had to identify pitch changes in tonal and randomized melodic pairs. The memory task was based on the mismatch negativity (MMN) proportion theory: 80 percent standard (not changed) and 20 percent deviant (changed) stimuli (sequences). Every sequence was named (na-na, ra-ra, za-za) and several items (pencil, spoon, tealight) were assigned for each sequence. Respondents had to recall the sequences, to associate them with the item and to detect possible changes. While executing the combined task, all respondents had to focus attention on the pitch changes and had to detect and describe these during the recall. Results and conclusion: The results support specific features in CB and EB, and similarities between late blind (LB) and sighted individuals. While executing attention and memory tasks, it was possible to observe the tendency in CB and EB by using more precise execution tactics and usage of more advanced periodic memory, while focusing on auditory and tactile stimuli. While executing memory and combined tasks, CB and EB individuals used passive working memory to recall standard sequences, active working memory to recall deviant sequences and combined strategies. Based on the observation results, assessment of blind respondents and recording specifics, following attention and memory correlations were identified: reflective attention and STM, reflective attention and periodic memory, auditory attention and WM, tactile attention and WM, auditory tactile attention and STM. The results and the summary of findings highlight the attention and memory features used in the music learning process in the context of blindness, and the tendency of the several attention and memory types correlated based on the task, strategy and individual features.

Keywords: attention, blindness, memory, music learning, strategy

Procedia PDF Downloads 186

Authors: S. Pesina, T. Solonchak

Abstract:

Lexical invariants, being a sort of stereotypes within the frames of ordinary consciousness, are created by the members of a language community as a result of uniform division of reality. The invariant meaning is formed in person’s mind gradually in the course of different actualizations of secondary meanings in various contexts. We understand lexical the invariant as abstract language essence containing a set of semantic components. In one of its configurations it is the basis or all or a number of the meanings making up the semantic structure of the word.

Keywords: lexical invariant, invariant theories, polysemantic word, cognitive linguistics

Procedia PDF Downloads 322

Authors: Leila Maleki, Ezatollah Ahmadi

Abstract:

The aim of this study was to investigate the effectiveness of memory training exercise on cognitive flexibility. The method of this study was experimental. The statistical population selected 40 students 14 years old, samples were chosen by available sampling method and then they were replaced in experimental (training program) group and control group randomly and answered to Wisconsin Card Sorting Test; covariance test results indicated that there were a significant in post-test scores of experimental group (p<0.005).

Keywords: cognitive flexibility, working memory exercises, problem solving, reaction time

Procedia PDF Downloads 426

Authors: Wambugu Naftaly, Ruisheng Wang, Zhijun Wang

Abstract:

Models based on convolutional neural networks (CNNs), in conjunction with Transformer, have excelled in semantic segmentation, a fundamental task for intelligent Earth observation using remote sensing (RS) imagery. Nonetheless, tokenization in the Transformer model undermines object structures and neglects inner-patch local information, whereas CNNs are unable to simulate global semantics due to limitations inherent in their convolutional local properties. The integration of the two methodologies facilitates effective global-local feature aggregation and interactions, potentially enhancing segmentation results. Inspired by the merits of CNNs and Transformers, we introduce a layer-level feature aggregation network (LLFA-Net) to address semantic segmentation of fine-resolution remote sensing (FRRS) images for land cover classification. The simple yet efficient system employs a transposed unit that hierarchically utilizes dense high-level semantics and sufficient spatial information from various encoder layers through a layer-level feature aggregation module (LLFAM) and models global contexts using structured Transformer blocks. Furthermore, the decoder aggregates resultant features to generate rich semantic representation. Extensive experiments on two public land cover datasets demonstrate that our proposed framework exhibits competitive performance relative to the most recent frameworks in semantic segmentation.

Keywords: land cover mapping, semantic segmentation, remote sensing, vision transformer networks, deep learning

Procedia PDF Downloads 11

Authors: Rui Xing, Baolin Ye, Nan Zhou, Guohong Wang

Abstract:

Drawing upon a perspective of cognitive interaction, this study explores the relationship between team cognitive heterogeneity and team strategic decision-making flexibility, treating the transactive memory system as a mediator and task complexity as a moderator. The hypotheses were tested in linear regression models by using data gathered from 67 strategic decision-making teams in the new-energy vehicle industry. It is found that team cognitive heterogeneity has a positive impact on strategic decision-making flexibility through the mediation of specialization and coordination of the transactive memory system, which is positively moderated by task complexity.

Keywords: strategic decision-making flexibility, team cognitive heterogeneity, transactive memory system, task complexity

Procedia PDF Downloads 80

Authors: Yuri Kitahara

Abstract:

Background: Cognitive reappraisal refers to an emotion regulation strategy in which one changes the interpretation of emotion-eliciting events. Numerous studies show that cognitive reappraisal is associated with mental health and better social functioning. However the examination of the predictive factors of adaptive emotion regulation remains as an issue. The present study examined the factors contributing to the habitual use of cognitive reappraisal, with a focus on emotional awareness and working memory. Methods: Data was collected from 30 junior high school students, using a Japanese version of the Emotion Regulation Questionnaire (ERQ), the Levels of Emotional Awareness Scale for Children (LEAS-C), and N-back task. Results: A positive correlation between emotional awareness and cognitive reappraisal was observed in the high-working-memory group (r = .54, p < .05), whereas no significant relationship was found in the low-working-memory group. In addition, the results of the analysis of variance (ANOVA) showed a significant interaction between emotional awareness and working memory capacity (F(1, 26) = 7.74, p < .05). Subsequent analysis of simple main effects confirmed that high working memory capacity significantly increases the use of cognitive reappraisal for high-emotional-awareness subjects, and significantly decreases the use of cognitive reappraisal for low-emotional-awareness subjects. Discussion: These results indicate that under the condition when one has an adequate ability for simultaneous processing of information, explicit understanding of emotion would contribute to adaptive cognitive emotion regulation. The findings are discussed along with neuroscientific claims.

Keywords: cognitive reappraisal, emotional awareness, emotion regulation, working memory

Procedia PDF Downloads 234

Authors: Chang Qin, Daham Mustafa, Abderrahmane Khiat, Pierre Bienert, Paulo Zanini

Abstract:

Nowadays, companies are facing lots of challenges in the process of digital transformation, which can be a complex and costly undertaking. Digital transformation involves the collection and analysis of large amounts of data, which can create challenges around data management and governance. Furthermore, it is also challenged to integrate data from multiple systems and technologies. Although with these pains, companies are still pursuing digitalization because by embracing advanced technologies, companies can improve efficiency, quality, decision-making, and customer experience while also creating different business models and revenue streams. In this paper, the issue that data is stored in data silos with different schema and structures is focused. The conventional approaches to addressing this issue involve utilizing data warehousing, data integration tools, data standardization, and business intelligence tools. However, these approaches primarily focus on the grammar and structure of the data and neglect the importance of semantic modeling and semantic standardization, which are essential for achieving data interoperability. In this session, the challenge of data silos in Industry 4.0 is addressed by developing a semantic modeling approach compliant with Asset Administration Shell (AAS) models as an efficient standard for communication in Industry 4.0. The paper highlights how our approach can facilitate the data mapping process and semantic lifting according to existing industry standards such as ECLASS and other industrial dictionaries. It also incorporates the Asset Administration Shell technology to model and map the company’s data and utilize a knowledge graph for data storage and exploration.

Keywords: data interoperability in industry 4.0, digital integration, industrial dictionary, semantic modeling

Procedia PDF Downloads 94

Authors: Mathew Wakefield, Matthew Mitchell, Lisa Wise, Christopher McCarthy

Abstract:

The properties of memory representations in artificial neural networks have cognitive implications. Distributed representations that encode instances as a pattern of activity across layers of nodes afford memory compression and enforce the selection of a single point in instance space. These encoding schemes also appear to distort the representational space, as well as trading off the ability to validate that input information is within the bounds of past experience. In contrast, a localist representation which encodes some meaningful information into individual nodes in a network layer affords less memory compression while retaining the integrity of the representational space. This allows the validity of an input to be determined. The validity (or familiarity) of input along with the capacity of localist representation for multiple instance selections affords a memory sampling approach that dynamically balances the bias-variance trade-off. When the input is familiar, bias may be high by referring only to the most similar instances in memory. When the input is less familiar, variance can be increased by referring to more instances that capture a broader range of features. Using this approach in a localist instance memory network, an experiment demonstrates a relationship between representational conflict, generalization performance, and memorization demand. Relatively small sampling ranges produce the best performance on a classic machine learning dataset of visual objects. Combining memory validity with conflict detection produces a reliable confidence judgement that can separate responses with high and low error rates. Confidence can also be used to signal the need for supervisory input. Using this judgement, the need for supervised learning as well as memory encoding can be substantially reduced with only a trivial detriment to classification performance.

Keywords: artificial neural networks, representation, memory, conflict monitoring, confidence

Procedia PDF Downloads 129

Authors: Khon Natalya, Kim Alla, Mukhitdinova Tansulu

Abstract:

The aim of the research was to reveal the link between mental variables, such as spatial abilities, memory, intellect and professional experience of drivers. Participants were allocated within 4 groups: no experience, inexperienced, skilled and professionals (total 85 participants). Level of ability for spatial navigation and indicator of nonverbal memory grow along the process of accumulation of driving experience. At high levels of driving experience this tendency is especially noticeable. The professionals having personal achievements in driving (racing) differ from skilled drivers in better feeling of direction which is specific for them not just in a short-term situation of an experimental task, but in life-size perspective. The level of ability of mental rotation does not grow with growth of driving experience which confirms the multiple intelligence theory according to which spatial abilities represent specific, other than logical intelligence type of intellect. The link between spatial abilities, memory, intellect, and professional experience of drivers seems to be different relating spatial navigation or mental rotation as different kinds of spatial abilities.

Keywords: memory, spatial ability, intellect, drivers

Procedia PDF Downloads 625

Authors: A. Mondaca, E. Mendoza, D. Jackson-Maldonado, A. García-Obregón

Abstract:

Deaf signers usually show lower scores in Auditory Working Memory (AWM) tasks and higher scores in Visual Working Memory (VWM) tasks than their hearing pairs. Further, Working Memory has been correlated with reading abilities and vocabulary in Deaf and Hearing individuals. The aim of the present study is to compare the performance of Mexican Deaf signers and hearing adults in VWM, reading and Vocabulary tasks and observe if the latter are correlated to the former. 15 Mexican Deaf signers were assessed using the Corsi block test for VWM, four different subtests of PROLEC (Batería de Evaluación de los Procesos Lectores) for reading abilities, and the LexTale in its Spanish version for vocabulary. T-tests show significant differences between groups for VWM and Vocabulary but not for all the PROLEC subtests. A significant Pearson correlation was found between VWM and Vocabulary but not between VWM and reading abilities. This work is part of a larger research study and results are not yet conclusive. A discussion about the use of PROLEC as a tool to explore reading abilities in a Deaf population is included.

Keywords: deaf signers, visual working memory, reading, Mexican sign language

Procedia PDF Downloads 169

Authors: Haiyan Wu, Ying Liu, Shaoyun Shi

Abstract:

Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets.

Keywords: authorship attribution, attention mechanism, syntactic feature, feature extraction

Procedia PDF Downloads 137

Authors: Yong Chen

Abstract:

To understand the detailed molecular mechanisms of memory formation in engram cells is one of the most fundamental questions in neuroscience. Recent single-cell RNA-seq (scRNA-seq) and single-nucleus RNA-seq (snRNA-seq) techniques have allowed us to explore the sparsely activated engram ensembles, enabling access to the molecular mechanisms that underlie experience-dependent memory formation and consolidation. However, the absence of specific and powerful computational methods to detect memory-related genes (modules) and their regulatory relationships in the sc/snRNA-seq datasets has strictly limited the analysis of underlying mechanisms and memory coding principles in mammalian brains. Here, we present a deep-learning method named SCENTBOX, to detect memory-related gene modules and causal regulatory relationships among themfromsc/snRNA-seq datasets. SCENTBOX first constructs codifferential expression gene network (CEGN) from case versus control sc/snRNA-seq datasets. It then detects the highly correlated modules of differential expression genes (DEGs) in CEGN. The deep network embedding and attention-based convolutional neural network strategies are employed to precisely detect regulatory relationships among DEG genes in a module. We applied them on scRNA-seq datasets of TRAP; Ai14 mouse neurons with fear memory and detected not only known memory-related genes, but also the modules and potential causal regulations. Our results provided novel regulations within an interesting module, including Arc, Bdnf, Creb, Dusp1, Rgs4, and Btg2. Overall, our methods provide a general computational tool for processing sc/snRNA-seq data from case versus control studie and a systematic investigation of fear-memory-related gene modules.

Keywords: sc/snRNA-seq, memory formation, deep learning, gene module, causal inference

Procedia PDF Downloads 120