• The Journal of Information Systems
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• v.31 no.4
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• pp.01-23
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• 2022

Purpose This study aims to propose a deep learning-based fake review detection model by combining authors' linguistic features and semantic information of reviews. Design/methodology/approach This study used 358,071 review data of Yelp to develop fake review detection model. We employed linguistic inquiry and word count (LIWC) to extract 24 linguistic features of authors. Then we used deep learning architectures such as multilayer perceptron(MLP), long short-term memory(LSTM) and transformer to learn linguistic features and semantic features for fake review detection. Findings The results of our study show that detection models using both linguistic and semantic features outperformed other models using single type of features. In addition, this study confirmed that differences in linguistic features between fake reviewer and authentic reviewer are significant. That is, we found that linguistic features complement semantic information of reviews and further enhance predictive power of fake detection model.

• Speech Sciences
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• v.15 no.2
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• pp.111-118
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• 2008

We identified the characteristic impairmants of linguistic semantic memory in patients with prodromal Alzheimer's disease(AD) and mild AD. To elucidate the earliest changes of semantic language function in subjects with AD, performances on confrontation naming test and verbal fluency task were compared among patients with AD patients (n=20), mild AD patients (n=27) and healthy elderly controls (n=20). Tasks in this study included the confrontation naming test of Test of Lexical Processing in Aphasia(TLPA/Japanese) and one-minute verbal fluency task (semantic/ phonetic categories). The results were as follows: 1) Performances of the prodromal AD group showed the comparable to those of the control group on the confrontation naming test, 2) In the semantic/phonetic verbal fluency tasks, the performances of the control group were better than those of the prodromal AD and mild AD groups, but no significant differences were shown between the prodromal AD and the mild AD group.

• Korean Journal of Cognitive Science
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• v.7 no.3
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• pp.61-80
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• 1996

Recent studies on emotion as memory,and effects of emotion on memory were reviewied. The main issues discussed were : memory of emotional events,relation between implicit memory and emotion, and the effect of emotion on autobigraphical memory. The theoretical possibility and implications that emotion is not stored as some lower level node information in semantic network but as some higher level and inclusive information were descussed.

• ETRI Journal
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• v.44 no.3
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• pp.450-461
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• 2022

The Gen-Z protocol is a memory semantic protocol between the memory and CPU used in computer architectures with large memory pools. This study presents the implementation of the Gen-Z hardware system configured using Gen-Z specification 1.0 and reports its performance. A hardware prototype of a DDR4 Gen-Z memory pool with an optimized character, a block device driver, and a file system for the Gen-Z hardware was designed. The Gen-Z IP was targeted to the FPGA, and a 512 GB Gen-Z memory pool was configured on an ×86 server. In the experiments, the latency and throughput of the Gen-Z memory were measured and compared with those of the local memory, SATA SSD, and NVMe using character or block device interfaces. The Gen-Z hardware exhibited superior throughput and latency performance compared with SATA SSD and NVMe at block sizes under 4 kB. The MySQL and File IO benchmark of Gen-Z showed good write performance in all block sizes and threads. Besides, it showed low latency in RocksDB's fillseq dbbench using the ext4 direct access filesystem.

• Korean Journal of Cognitive Science
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• v.28 no.4
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• pp.193-221
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• 2017

Grouping episodes into semantically related categories is necessary for better mnemonic structure. However, the effect of grouping on memory of subordinate details was not clearly understood. In an fMRI study, we tested whether attending superordinate during semantic association disrupts or enhances subordinate episodic details. In each cycle of the experiment, five cue words were presented sequentially with two related detail words placed underneath for each cue. Participants were asked whether they could imagine a category that includes the previously shown cue words in each cycle, and their confidence on retrieval was rated. Participants were asked to perform cued recall tests on presented detail words after the session. Behavioral data showed that reaction times for categorization tasks decreased and confidence levels increased in the third trial of each cycle, thus this trial was considered to be an important insight where a semantic category was believed to be successfully established. Critically, the accuracy of recalling detail words presented immediately prior to third trials was lower than those of followed trials, indicating that subordinate details were disrupted during categorization. General linear model analysis of the trial immediately prior to the completion of categorization, specifically the second trial, revealed significant activation in the temporal gyrus and inferior frontal gyrus, areas of semantic memory networks. Representative Similarity Analysis revealed that the activation patterns of the third trials were more consistent than those of the second trials in the temporal gyrus, inferior frontal gyrus, and hippocampus. Our research demonstrates that semantic grouping can cause memories of subordinate details to fade, suggesting that semantic retrieval during categorization affects the quality of related episodic memory.

• Science of Emotion and Sensibility
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• v.21 no.3
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• pp.61-72
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• 2018

Items related to a crime that are known only to criminals and investigators can be used in the concealed information test (CIT) to assess whether the suspect is guilty of the offense. However, in many cases wherein the suspect is exposed to information about the crime, the CIT cannot be used. Although the perpetrator's memories about the details of the crime are episodic, the memories of a suspect who has inadvertently discovered the details of the crime are more likely to be semantic. The retrieval of episodic memories is associated with theta wave activity, whereas that of semantic memories is associated with alpha wave activity. Therefore, these aspects of memory retrieval can be useful in identifying the perpetrator of the crime. In this study, P300-based CITs were conducted in a guilty participant in a mock crime and an innocent participant who has been given information about the simulated offense. The results demonstrate that the difference in P300 amplitudes between the probe and the irrelevant stimulus did not differ between the guilty and innocent conditions. As expected, the lower theta band power (4-6 Hz) was higher in the probe than in the irrelevant stimulus in the guilty condition, but there was no difference in the innocent condition. Conversely, the upper alpha band power (8-10 Hz) was lower in the probe than in the irrelevant stimulus in the innocent condition, but there was no difference in the guilty condition. The possibility of using theta and alpha band powers in lie detection is discussed.

• ETRI Journal
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• v.36 no.5
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• pp.741-751
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• 2014

Smart TV is expected to bring cloud services based on virtualization technologies to the home environment with hardware and software support. Although most physical resources can be shared among virtual machines (VMs) using a time sharing approach, allocating the proper amount of memory to VMs is still challenging. In this paper, we propose a novel mechanism to dynamically balance the memory allocation among VMs in virtualized Smart TV systems. In contrast to previous studies, where a virtual machine monitor (VMM) is solely responsible for estimating the working set size, our mechanism is symbiotic. Each VM periodically reports its memory usage pattern to the VMM. The VMM then predicts the future memory demand of each VM and rebalances the memory allocation among the VMs when necessary. Experimental results show that our mechanism improves performance by up to 18.28 times and reduces expensive memory swapping by up to 99.73% with negligible overheads (0.05% on average).

• Korean Journal of Biological Psychiatry
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• v.21 no.4
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• pp.175-181
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• 2014

Objectives It was the aim to examine the association of the thyroid-related hormones with cognitive function, depression, and subjective memory impairment in community-dwelling elders with questionable dementia. Methods The sample consisted of 399 community residents with 'questionable dementia' aged 60 or over in whom serum thyroid-related hormones [thyroid stimulating hormone (TSH) and thyroxine] had been assayed. Cognitive impairment was defined using the Korean version of the Consortium Establish a Registry for Alzheimer's Disease. Depression was diagnosed using the Korean version of Geriatric Depression Scale and subjective memory complaint (SMC) was checked using the subjective memory complaints questionnaire (SMCQ). Age, gender, education, and the presence of apolipoprotein E {\varepsilon}4 were included as covariates. Results There was a significant positive association between verbal fluency test (VFT) score and serum TSH levels (p = 0.01). There was a significant negative association between SMCQ total score and word list memory test (WLMT)(p = 0.002) or word list recall test (WLRT) score (p = 0.013). Conclusions Lower serum TSH levels were associated with semantic memory (VFT), and we found that SMC was associated with episodic memory (WLMT and WLRT) in this sample.

• Journal of KIISE
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• v.42 no.9
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• pp.1109-1116
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• 2015

In this paper, we propose a query processing approach that uses the Spark functional programming and distributed memory system to solve the computational overhead of SPARQL. In the semantic web, RDF ontology data is produced at large scale, and the main challenge for the semantic web is to query and manipulate such a large ontology with a high throughput. The most existing studies on SPARQL have focused on deploying the Hadoop MapReduce framework, and although approaches based on Hadoop MapReduce have shown promising results, they achieve a low level of throughput due to the underlying distributed file processes. Therefore, in order to speed up the query processes, we suggest query- processing methods that are based on memory caching in distributed memory system. Our approach is also integrated with a clause unification method for propagating between the clauses that exploits Spark join, map and filter methods along with caching. In our experiments, we have achieved a high level of performance relative to other approaches. In particular, our performance was nearly similar to that of Sempala, which has been considered to be the fastest query processing system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3211-3229
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• 2022

To solve the problems existing in the process of Weibo disaster rumor recognition, such as lack of corpus, poor text standardization, difficult to learn semantic information, and simple semantic features of disaster rumor text, this paper takes Sina Weibo as the data source, constructs a dataset for Weibo disaster rumor recognition, and proposes a deep learning model BERT_AT_Stacked LSTM for Weibo disaster rumor recognition. First, add adversarial disturbance to the embedding vector of each word to generate adversarial samples to enhance the features of rumor text, and carry out adversarial training to solve the problem that the text features of disaster rumors are relatively single. Second, the BERT part obtains the word-level semantic information of each Weibo text and generates a hidden vector containing sentence-level feature information. Finally, the hidden complex semantic information of poorly-regulated Weibo texts is learned using a Stacked Long Short-Term Memory (Stacked LSTM) structure. The experimental results show that, compared with other comparative models, the model in this paper has more advantages in recognizing disaster rumors on Weibo, with an F1_Socre of 97.48%, and has been tested on an open general domain dataset, with an F1_Score of 94.59%, indicating that the model has better generalization.