Journal of Information Science Theory and Practice
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v.11
no.4
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pp.14-39
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2023
Cybercrime is a significant threat to Internet users, involving crimes committed using computers or computer networks. The landscape of cyberspace presents a complex terrain, making the task of tracing the origins of sensitive data a formidable and often elusive endeavor. However, tracing the source of sensitive data in online cyberspace is critically challenging, and detecting cyber-criminals on the other hand remains a time-consuming process, especially in social networks. Cyber-criminals target individuals for financial gain or to cause harm to their assets, resulting in the loss or theft of millions of user data over the past few decades. Forensic professionals play a vital role in conducting successful investigations and acquiring legally acceptable evidence admissible in court proceedings using modern techniques. This study aims to provide an overview of forensic investigation methods for extracting digital evidence from computer systems and mobile devices to combat persistent cybercrime. It also discusses current cybercrime issues and mitigation procedures.
Journal of the Korea Institute of Military Science and Technology
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v.27
no.4
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pp.474-484
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2024
Reinforcement learning, which are also studied in the field of defense, face the problem of sample efficiency, which requires a large amount of data to train. Transfer learning has been introduced to address this problem, but its effectiveness is sometimes marginal because the model does not effectively leverage prior knowledge. In this study, we propose a stochastic initial state randomization(SISR) method to enable robust knowledge transfer that promote generalized and sufficient knowledge transfer. We developed a simulation environment involving a cooperative robot transportation task. Experimental results show that successful tasks are achieved when SISR is applied, while tasks fail when SISR is not applied. We also analyzed how the amount of state information collected by the agents changes with the application of SISR.
The purpose of this study was to investigate whether preschoolers' peer interaction type and level differ depending on pair compositon and task type. Preschoolers' peer interaction type and level were compared among three kinds of pair compositions(with a Younger peer, with a same-age peer, and with an older peer) as well as between two types of tasks(tasks involving real objects, and tasks involving pictures). Subjects were seventy-eight four-year-old children recruited from six child-care centers in Seoul and Gyeonggi Province. The preschoolers were randomly assigned to one of the three kinds of pair compositions. Interaction processes of the pairs were recorded by a video camera, and the scenes were transcribed for content analyses. The traniscribed peer interactions were coded according to the categories that the researcher had generated. Statistical methods used for the data analysis were frequencies, percentiles, means, standard deviations, repeated measures ANOVA, paired t-tests, one-way ANOVA. Major findings of this study were as follows: There were significant differences preschoolers' in interaction type depending on the pair compositions and the types of tasks. The preschoolers paired with the older peer used abstract collaborative explanations more frequently than in two other types of pair composition. The preschoolers paired with the younger peer used explanations without collaboration and parallel behaviors more frequently than in two other types of pair composition.
Objective: The purpose of this study was to explore the effect of repetitive wrist extension task training with electromyography (EMG)-triggered neuromuscular electrical stimulation (NMES) for wrist extensor muscle recovery in patients with stroke. Design: Randomized controlled trial. Methods: Fifteen subjects who had suffered a stroke were randomly assigned to an EMG-triggered NMES group (n=8) or control group (n=7); subjects in both groups received conventional therapy as usual. Subjects in the experimental group received application of EMG-triggered NMES to the wrist extensor muscles for 20 minutes, twice per day, five days per week, for a period of four weeks, and were given a task to make a touch alarm go off by activity involving extension of their wrist. In the control group, subjects performed wrist self-exercises for the same duration and frequency as those in the experimental group. Outcome measures included muscle reaction time and spectrum analysis. Assessments were performed during the pre- and post-treatment periods. Results: In the EMG-triggered NMES group, faster muscle reaction time was observed, and median frequency also showed improvement, from 68.2 to 75.3 Hz, after training (p<0.05). Muscle reaction time was significantly faster, and median frequency was significantly higher in the experimental group than in the experimental group after training. Conclusions: EMG-triggered NMES is beneficial for patients with hemiparetic stroke in recovery of upper extremity function.
Journal of Korean Institute of Industrial Engineers
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v.41
no.1
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pp.43-49
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2015
The aim of this study was to model unusual association with declarative knowledge by positive affect using ACT-R cognitive architecture. Existing research related with cognitive modeling tends to pay a lot of attention to strong and negative cognitive moderator. Mild positive affect, however, has far-reaching effects on problem solving and decision making. Typically, subjects with positive affect were more likely to respond to unusual associates in a word association task than subjects with neutral affect. In this study, a cognitive model using ACT-R cognitive architecture was developed to show the effect of positive affect on the cognitive organization related with memory. First, we organized the memory structure of stimulus word 'palm' based on published results in a word association task. Then, we decreased an ACT-R parameter that reflects the amount of weighting given to the dissimilarity between the stimulus word and the associate word to represent reorganized memory structure of the model by positive affect. As a result, no significant associate probability difference between model prediction and existing empirical data was found. The ACT-R cognitive architecture could be used to model the effect of positive affect on the unusual association by decreasing (manipulating) the weight of the dissimilarity. This study is useful in conducting model-based evaluation of the effects of positive affect in complex tasks involving memory, such as creative problem solving.
Background: In the food manufacturing industry, exposure to inhalable aerosols contributes to respiratory illnesses such as occupational asthma and rhinitis. However, there is a lack of comprehensive exposure assessment studies. This study evaluated occupational exposure to inhalable aerosols in an instant powdered food manufacturing plant during work operations involving dried food and powders. Methods: In total, 50 workers from an instant powdered food manufacturing plant were recruited. Personal inhalable aerosol exposure measurements were taken for both full-shift and task-based activities. The concentrations of inhalable aerosols were analyzed to identify any variation within and across departments, as well as between seasons, handedness, and sex. Results: In total, 134 personal air samples were collected, and the particulate mass was determined gravimetrically. The concentrations of inhalable aerosols ranged from 0.1 to 27 mg/m3 for full-shift exposure measurements and 3.1 to 73 mg/m3 for task-based measurements. Statistically significant differences in mean aerosol concentrations were found across departments (A:B p < 0.001, A:C p < 0.05, B:C p < 0.001) and between seasons (p < 0.001). Conclusion: This study revealed high exposure to inhalable aerosols among workers, particularly those involved in manual weighing, mixing, and adding powders. The significant differences between departments highlight the specific activities contributing to increased inhalable aerosol concentrations. Seasonal variations were also evident, with autumn showing higher concentrations of inhalable aerosols in all departments compared with summer. These findings emphasize the importance of understanding the distribution of aerosol concentrations across different work tasks and departments, particularly during different seasons.
In this article, the task of evaluating the official documents that were created and issued by the Joseon Governor General office during the Japanese occupation period, with new perspectives based upon the Macro-Appraisal approaches developed by the Canadian scholars and personnel, will be attempted. Recently, the Canadian people and the authorities have been showing a tendency of evaluating the meaning and importance of a particular document with perspectives considering the historical situation and background conditions that gave birth to that document to be a more important factor, even than considering the quality and condition of that very document. Such approach requires the archivists to determine whether they should preserve a certain document or not based upon the meaning, functions and status of the entity that produced the document or the meaning of the documentation practice itself, rather than the actual document. With regard to the task of evaluating the official documents created and issued by the Joseon Governor General office and involved the city plans devised by the office back then, this author established total of 4 primary tasks that would prove crucial in the process of determining whether or not a particular theme, or event, or an ideology should be selected and documents involving those themes, events and ideologies should be preserved as important sources of information regarding the Korean history of the Japanese occupation period. Those four tasks are as follow: First, the archivists should study the current and past trends of historical researches. The archivists, who are usually not in the position of having comprehensive access to historical details, must consult the historians' studies and also the trends mirrored in such studies, in their efforts of selecting important historical events and themes. Second, the archivists should determine the level of importance of the officials who worked inside the Joseon Governor General office as they were the entities that produced the very documents. It is only natural to assume that the level of importance of a particular document must have been determined by the level of importance(in terms of official functions) of the official who authorized the document and ordered it to be released. Third, the archivists should be made well aware of the inner structure and official functions of the Joseon Governor General office, so that they can have more appropriate analyses. Fourth, in order to collect historically important documents that involved the Koreans(the Joseon people), the archivists should analyze not only the functions of the Joseon Governor General office in general but also certain areas of the Office's business in which the Japanese officials and the Koreans would have interacted with each other. The act of analyzing the documents only based upon their respective levels of apparent importance might lead the archivists to miss certain documents that reflected the Koreans' situation or were related to the general interest of the Korean people. This kind of evaluation should provide data that are required in appraising how well the Joseon Governor General office's function of devising city plans were documented back then, and how well they are preserved today, utilizing a comparative study involving the Joseon Governor General office's own evaluations of its documentations and the current status of documents that are in custody of the National Archive. The task would also end up proposing a specialized strategy of collecting data and documents that is direly needed in establishing a well-designed comprehensive archives. We should establish a plan regarding certain documents that were documented by the Joseon Governor General office but do not remain today, and devise a task model for the job of primary collecting that would take place in the future.
Many reaearchers have reproted that poor body postures are associated with pains or symptoms of musculoskeletal dissoders. Therefore, the ergonomic evaluation of postural stresses as well as biomechanical stresses is important when a job such as automobile assembly tasks involves highly repetitive and/or prolonged poor body postures. A macropostural classification shema was developed to characterise various body postures occurring in automobile assembly tasks in the study. To specify a postural code and stress level to each body posture, perceived joint discomforts were subjectively evaluated in the lab experiments for the full range of motion in five human body joints. Based on the reaults, a postural classification scheme was developed where the full range of motion in each body joint was classified into several codes repressenting different stress levels. The automobile tasks were clustered into 12 types based on the result walk-in-surveillance and the possible posture codes for each task type are defined. I was exposed that the poor postural problems in automobile assembly tasks were concerned in most part with arms, trunk and neck. Application of te developed schema to seven operations in automobile assembly tasks showed that the schema can be used as a tool to identify the operations and tasks involving highly stressful body postures. The schema can also be utilised as a basis to prioritise the candidate assembly operations for redesign of work methods.
Pramchoo, Walaiporn;Geater, Alan F.;Jamulitrat, Silom;Geater, Sarayut L.;Tangtrakulwanich, Boonsin
Safety and Health at Work
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v.8
no.3
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pp.250-257
/
2017
Background: Tasks involved in traditional charcoal production expose workers to various levels of charcoal dust and wood smoke. This study aimed to identify specific tasks influencing lung function and respiratory symptoms. Methods: Interviews, direct observation, and task/symptom checklists were used to collect data from 50 charcoal-production workers on 3 nonwork days followed by 11 workdays. The peak expiratory flow rate (PEFR) was measured four times per day. Results: The PEFR was reduced and the prevalence of respiratory symptoms increased over the first 6-7 workdays. The PEFR increased until evening on nonwork days but not on workdays. Loading the kiln and collecting charcoal from within the kiln markedly reduced the PEFR and increased the odds of respiratory symptoms. Conclusion: Tasks involving entry into the kiln were strongly associated with a short-term drop in the PEFR and the occurrence of respiratory symptoms, suggesting a need for the use of protective equipment and/or the operation of an effective kiln ventilation system.
The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.
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