Earticle

종래의 게임에서, 널리 사용되고 있는 난수는 게임 서비스 제공자에 의하여 일방적으로 제공되기 때문에, 게 임 이용자가 제공받은 난수가 어떠한 개입이나 조작이 있었는지를 검증하는 것은 어렵다. 본 논문은 상호 참 여형 난수 발생기인 TogetheRand를 제안한다. 제안된 방법은 이더리움 블록체인 시스템 위에서 작동되는 스 마트 컨트랙트이다. 제안된 방법의 난수성을 Dieharder tests를 이용하여 테스트하였다. 제안된 방법은 많은 사람들이 난수 생성에 참여할 수 있고, 모든 입력값을 확인할 수 있으며, 블록체인 시스템으로 인하여 정상 작동 여부가 보장되기 때문에 게임 이용자와 제공자 모두가 신뢰할 수 있는 방법이다. 제안된 방법은 게임 등의 신뢰성 있는 난수가 필요한 응용 분야에서 두루 적용될 수 있을 것이다. 본 논문에서 사용된 코드는 ht tps://github.com/TyeolRik/TogetheRand 에 공개되어 있다.

Pseudorandom is widely used in video games. Loot box, a set of game items, which can be received by a chance factor including specific type, effect, or performance etc, which is bought for real mone y by a user, is one of its use cases in video games and used as an important part of game business model. In existing games, since pseudorandom number is provided one-sidedly by game provider, it is hard for user to verify provided random number, whether there is game provider’s intervention or no t. In this paper, we propose a collaborative pseudorandom number generator named as TogetheRand. TogetheRand is a smart contract which is executed on blockchain network of Ethereum. Proposed alg orithm collects inputs which is provided by participants, initializes pseudorandom number generator us ing Keccak256 hash function, and generates sequence of pseudorandom numbers using WELL512a ge nerator. Randomness of proposed algorithm is tested using Dieharder tests. Proposed algorithm let bot h user and game provider trust the outputs, generated pseudorandom numbers, since users contribute r andom number, users can check all inputs, and correct execution of pseudorandom number generator i s ensured by blockchain network due to smart contract. Proposed algorithm can be used widely in ap plications, where secure and reliable pseudorandom number is needed. Code is publicly available at ht tps://github.com/TyeolRik/TogetheRand

본고는 한국과 미국의 물가상승률 예측에 랜덤 포레스트 모형을 적용할 때, Stationary Bootstrap이나 Moving Block Bootstrap 등 Block Bootstrap을 사용하는 것이 통상적인 독립 부트스트랩(Independent Bootstrap)을 사용하는 것에 비해 통계적으로 유의한 수준으로 예측력을 개선하지는 못한다는 것을 보인다. 그리고 FRED-MD를 참고한 총 93개의 관련 국내외 거시경제/금융 변수들을 사용하고, XGBoost, LSTM 등 다양한 머신러닝 방법을 활용하여 한국의 물가상승률을예측하고 분석한다. 2004년 9월에서 2022년 3월까지의 표본을 이용하였고, 1개월에서 12개월의 예측 대상기간(Forecast Horizon)을 고려하였다. 총 13개의 모형 중 대부분의 예측 대상기간에 있어 예측력이 우수한 모형이 존재하는 것으로나타났는데, 이는 보루타 알고리즘(Boruta Algorism)을 통해 중요한 변수로 분류된 변수들만을 랜덤 포레스트에 적용하는 모형이다. Giacomini and White(2006) 와 Hansen et al.(2009)의 검정을 통해 대부분의 예측 대상기간에서 통계적으로유의하게 예측력이 우수함을 확인하였는데, 특히 경제활동인구 및 취업자 수의증가율 등 고용시장 관련 변수, 기업경기실사지수, 주택가격 변화율 등이 물가상승률 예측에 중요한 변수로 선택되는 것으로 나타났다.

This paper first investigates whether adopting the stationary bootstrap or the moving block bootstrap, instead of the usual independent bootstrap, in the random forest method improves forecasting of stationary time series. It is shown that the block bootstrap procedures adopted in the random forest method do not make any statistically significant improvement in Korean or US inflation forecasting. Secondly, we consider inflation forecasting in Korea using 93 macroeconomic/financial variables and various machine learning methods. The samples are from September 2004 to March 2022. Comparing total 13 models, one model outperforms the rest models for most forecast horizons, which is a simplified method of the model proposed by Kim and Han (2022). The method consists of the following two steps: 1) Select important variables based on the Boruta algorithm, 2) Using only those selected variables, implement the random forest and produce a forecast. The tests by Giacomini and White (2006) and Hansen et al. (2009) show that the model provides significantly better forecasts for most forest horizons. In particular, the Boruta algorithm selected total economically active population, total employed persons, BSI, house price as important variables for Korean inflation forecasting.

For fast access to the cell, we propose an enhanced random access scheme exploiting multiple transmissions of random access preambles and random access responses. The proposed random access scheme decreases the latency of the random access procedure, by avoiding random access retrials from the beginning of the random access procedure due to the expiry of a random access response window. We also provide a model and analysis of random access latency with the average delay and the failure probability of random access procedure. The model of random access latency considers the collisions of random access preambles transmitted by multiple mobile stations to a base station as well as the error of random access response corresponding to the received random access preamble transmitted by the base station to the mobile station. Our numerical results in accordance with the model show that the proposed scheme provides lower latency.

Due to the fully-connected complex structure of Artificial Neural Networks (ANNs), systems based on ANN may consume much computational time, energy and space. Therefore, intense research has been recently centered on changing the topology and design of ANNs to obtain high performance. To explore the influence of network structure on ANNs complex systems topologies have been applied in these networks to have more efficient and less complex structures while they are more similar to biological systems at the same time. In this paper, the methodology and results of some recent papers are summarized and discussed in which the authors investigated the efficacy of random complex networks on the performance of Hopfield associative memory and multi-layer ANNs compared with ANNs with small-world, scale-free and regular structures.

Nowadays, the ransomware became a serious threat challenge the computing world that requires an immediate consideration to avoid financial and moral blackmail. So, there is a real need for a new method that can detect and stop this type of attack. Most of the previous detection methods followed a dynamic analysis technique which involves a complicated process. The present study proposes a novel method based on static analysis to detect ransomware. The significant characteristic of proposed method is dispensing of disassemble process by direct extraction of features from raw byte with the use of frequent pattern mining which remarkably increases the detection speed. The Gain Ratio technique was used for feature selection which exhibited that 1000 features was the optimal number for detection process. The current study involved using random forest classifier with a comprehensive analysis to the effect of both tree and seed numbers on the ransomware detection. The results showed that tree numbers of 100 with seed number of 1 achieved best results in terms of time-consuming and accuracy. The experimental evaluation revealed that the proposed method could achieve a high accuracy of 97.74% for detection ransomware.

Detection of nodes disseminating false data is a prerequisite for effective deployment of Internet of Vehicles (IoV) services. This work proposed a novel hyper-tuned ensemble Random Forest (Ens. RF) algorithm to detect false basic safety messages in IoV. Performance evaluation was done using the Vehicular Reference Misbehavior (VeReMi) dataset comprising data-centric misbehavior evaluation for vehicular networks. For validation, a comparative analysis of the performance of the proposed “Ens. RF” model, five machine learning algorithms implemented in this work, and state-of-the-art ML models from related literature was presented. The performance metrics considered are time efficiency and validation accuracy for overall misbehavior classification. Also, the results confirmed the irrelevance of data balancing in real-life scenarios. Finally, we assess the performance of our proposed system for detecting each falsification scenario using precision and recall. The result shows that the proposed algorithm outperformed others with a validation accuracy of 99.60% and a negligible 604 misclassifications out of 153,730 points.

Purpose: This study aimed to identify predictive factors affecting adolescents’ subjective happiness using data from the 2023 Korea Youth Risk Behavior Survey. A random forest model was applied to determine the strongest predictive factors, and its predictive per- formance was compared with traditional regression models. Methods: Responses from a total of 44,320 students from grades 7 to 12 were analyzed. Data pre-processing involved handling missing values and selecting variables to con- struct an optimal dataset. The random forest model was employed for prediction, and SHAP (Shapley Additive Explanations) analysis was used to assess variable importance. Results: The random forest model demonstrated a stable predictive performance, with an R of .37. Mental and physical health factors were found to significantly affect subjec-2tive happiness. Adolescents’ subjective happiness was most strongly influenced by per- ceived stress, perceived health, experiences of loneliness, generalized anxiety disorder, suicidal ideation, economic status, fatigue recovery from sleep, and academic perfor- mance. Conclusion: This study highlights the utility of machine learning in identifying factors influencing adolescents’ subjective happiness, addressing limitations of traditional re- gression approaches. These findings underscore the need for multidimensional interven- tions to improve mental and physical health, reduce stress and loneliness, and provide integrated support from schools and communities to enhance adolescents’ subjective happiness.

Cellular networks will play an important role in realizing the newly emerging Internet-of-Everything (IoE). One of the challenging issues is to support the quality of service (QoS) during the access phase, while accommodating a massive number of machine nodes. In this paper, we show a new paradigm of multiple access priorities in random access (RA) procedure and propose a novel root-index based prioritized random access (RIPRA) scheme that implicitly embeds the access priority in the root index of the RA preambles. The performance evaluation shows that the proposed RIPRA scheme can successfully support differentiated performance for different access priority levels, even though there exist a massive number of machine nodes. Keywords: Internet-of-Everything; M2M; Random access; Zadoff?Chu sequence; Access priority

In this paper, we investigate the performance of a downlink non-orthogonal multiple access (NOMA) based visible light communication (VLC) system. We consider that the users are randomly located following a binary Poisson point (BPP) Process. The performance of the considered system is evaluated by deriving novel closed-form expressions of the bit error ratio (BER) and ergodic sum rate. The impact of an error due to the successive interference cancellation (SIC) is also taken into consideration in the BER analysis. The obtained results with respect to different system parameters are instrumental to provide useful insights into the system performance.

Global navigation satellite systems (GNSS) are the main navigation and control systems in unmanned aerial vehicles (UAVs) and their ground control stations. Without the GNSS signals, the UAV and its ground control stations cannot follow the waypoints of the desired path in jamming environments. In this paper, two new methods for detection of GNSS signal jamming attack for UAV ground control station are proposed based on random matrix theory. By using limiting distribution of mean vector and asymptotic behavior of the defined test statistic, a hypothesis test is introduced and evaluated to detect presence of jamming signal. Simulation results show that the proposed methods have significant performance in terms of detection and false alarm probabilities. Compared to existing methods, at low jamming-to-signal ratio (JSR), more than 2.5 dB improvement is achieved.

In this article, we propose a non-orthogonal resource scheduling (NORS) scheme with an enhanced preamble detection (PD) method for cellular random access (RA) systems. The enhanced PD method is designed based on a deep learning technique that can classify three PD statuses: idle, collision-free, and collision. Because of this collision resolution capability, the base station (BS) can exploit radio resources non-orthogonally in contention-based RA protocols. In addition, we mathematically analyze the performance of our proposed NORS scheme in terms of RA success probability and resource utilization efficiency and conduct a comparison with the baseline orthogonal resource scheduling (ORS) scheme. Through simulations, we verify the validity of our mathematical analysis. As a result, our results will be a useful guideline for resource scheduling and provide support for massive connectivity.

Purpose: Depression and frailty are common health problems that occur separately or simultaneously in later life. The two syndromes are correlated, but they need to be distinguished to promote successful aging. Previous studies have examined the reciprocal relationship between depression and frailty, but there are limitations in the methods or statistical analysis. This study aims to confirm the potential prospective bidirectional and causal relationship between depression and frailty. Methods: We used data from 887 older adults aged 70 to 84 from the Korean Frailty and Aging Cohort Study (KFACS) in 2016, 2018, and 2020 (3 waves). We separated the within-individual process from the stable between-individual differences using the random intercepts cross-lagged panel model. Results: Significant bidirectional causal effects were observed in 2 paths. Older adults with higher depression than their within-person average at T1 had a higher risk of frailty at T2 (β=.22, p=.008). Subsequently, older adults with higher-than-average frailty scores at T2 showed higher depression at T3 (β=.14, p=.010). Autoregressive effects were only significant from T2 to T3 for both constructs (Depression: β=.16, p=.044; Frailty: β=.13, p=.028). At the between-person level, the correlation was significant between the random intercepts between depression and frailty (β=.47, p<.001). Conclusions: We find that depressed older adults have an increased risk of frailty, which contributes to the onset of depression and the maintenance of frailty. Therefore, interventions for each condition may prevent the entry and worsening of the other condition, as well as prevent comorbidity.

Purpose: The goal of this study was to explore the current status and influencing factors of nurses'intention to report adverse nursing events, using a random forest model to rank and select these factors. Methods: In August 2024, a survey was conducted among nurses at a tertiary hospital in Shanghai usinga convenience sampling method. The instruments included the General Information Questionnaire, theIntention to Report Questionnaire, the Reporting of Clinical Adverse Effects Scale (RoCAES), and theReporting Barrier Questionnaire. A random forest model and stepwise multiple linear regression wereused to analyze the factors influencing nurses' intention to report nursing adverse events. Results: The median total score for the intention to report adverse events among 823 nurses was 14(range: 0 to 15). Lasso regression identified eight key factors influencing the intention to report adverseevents: professional title, whether the nurse had reported adverse events, age, punitive culture,reporting process, reporting significance, reporting purpose, and reporting environment. Stepwiseregression analysis revealed that younger nurses, those with no prior reporting experience, and thosewho perceived reporting as important had a higher intention to report adverse events. Additionally,nurses were more likely to report adverse events when the hospital's reporting process was simpler andthe culture was less punitive. Conclusions: Nurses' intention to report adverse events in nursing is moderate. Nursing managers shouldconsider these influencing factors holistically and implement targeted measures to enhance nurses'intention to report, thereby improving nursing quality and patient safety.

This paper evaluates outage probability (OP) and intercept probability (IP) of physical-layer security based MIMO networks adopting cooperative jamming (Coop-Jam). In the considered scenario, a multi-antenna source communicates with a multi-antenna destination employing transmit antenna selection (TAS)/ selection combining (SC), in presence of a multi-antenna eavesdropper using SC. One of jammers appearing near the destination is selected for generating jamming noises on the eavesdropper. Moreover, the destination supports the wireless energy for the chosen jammer, and cooperates with it to remove the jamming noises. We consider two jammer selection approaches, named RAND and SHORT. In RAND, the destination randomly selects the jammer, and in SHORT, the jammer, which is nearest to the destination, is chosen. We derive exact and asymptotic expressions of OP and IP over Rayleigh fading, and perform Monte-Carlo simulations to verify the correction of our derivation. The results present advantages of the proposed RAND and SHORT methods, as compared with the corresponding one without using Coop-Jam.

본 연구는 임의회귀모형을 이용하여 한우 거세우 체중에 대해서 유전모수 추정을 하고 이것을 단형질 개체모형의 결과와 비교해 보고자 실시하였다. 분석에 이용한 자료는 총 1,372두의 한우 거세우의 체중 자료로, 농협중앙회 가축개량사업소에서 실시한 한우 후대검정우의 기록이다. 이차의 임의회귀 모형에 적용한 결과 유전력이 총 800일령까지의 검정기간에 대해 0.17~0.30의 범위로 나타났다. 개체모형을 통해 얻은 유전력은 0.24~0.36의 범위로 나타났다. 측정일간의 영구환경효과의 상관은 검정일령이 늘어남에 따라 함께 증가하는 경향을 보였다. 반면, 측정일간의 유전상관의 경우 검정초기에는 0.30 정도의 약한 음의 상관을 보이지만, 검정이 이뤄짐에 따라 상관이 점차 증가하여 검정이 종료될 무렵이면 거의 고정되는 것으로 나타났다. 임의회귀모형과 개체모형의 결과를 비교해보면 두가지 모형 모두 비슷한 경향을 보여 큰 차이를 보이지 않았다. 따라서, 임의회귀모형을 한우에 대한 국가유전능력평가에 사용하는 것이 가능할 것으로 사료된다.

The study aimed to estimate genetic parameters of body weights in Hanwoo steers using random regression model and compare it with single trait animal model. A total of 1,372 Hanwoo steers that belonged to progeny testing program of the Hanwoo Genetic Improvement conducted at the Livestock Improvement Main Center of the National Agricultural Cooperative Federation (LIMC-NACF) in Rep. of Korea were used. Results of the random regression model fitting quadratic function revealed heritability values from 0.17 to 0.30 for the whole testing days up to 800 days. The results of the animal model showed estimated heritability values ranged from 0.24 to 0.36. Estimates of permanent environmental correlations tended to increase with increasing test in days. Unlike in the direct genetic correlation that at early stage the estimate was slightly negative it was 0.30 then increased to approach unity at later stage of test. Comparing the results between random regression model and the animal model showed not much differences and both followed similar pattern and therefore the use of random regression model for the national genetic evaluation of Hanwoo could be implemented.

Beliefs regarding the controllability of emotions are closely associated with emotional distress, such as depression and anxiety. Most previous studies have focused on the unidirectional prediction of emotional distress based on emotion controllability beliefs. However, it is equally plausible that emotion controllability beliefs and emotional distress influence each other in a bi- directional manner. Using a random intercept cross-lagged panel model (RI-CLPM), this study aimed to elucidate the direc- tionality of the relationship between emotion controllability beliefs and emotional distress, with a focus on depression and anxiety. A total of 393 participants reported their emotion beliefs, and symptoms of depression and anxiety at time 1. They were followed up at 5 weeks (time 2), and 10 weeks (time 3). At the within-person level, higher-than-average levels of a per- son’s depression and anxiety symptoms predicted increases in beliefs that depression and anxiety are uncontrollable. In addi- tion, we found a significant bidirectional relationship between anxiety controllability beliefs and anxiety symptoms. These findings highlight the need to expand our current understanding of emotion controllability beliefs to account for the recipro- cal relationships between these beliefs and emotional distress, especially in the context of anxiety.

Background: The milk yield can be affected by the frequency of milking per day, in dairy cows. Previous studies have shown that the milk yield is increased by 6?25 % per lactation when the milking frequency is increased from 2 to 3 times per day while the somatic cell count is decreased. To investigate the effect of milking frequency (3X vs. 4X) on milk yield and it’s genetic parameters in the first and second lactations of the Iranian Holstein dairy cows, a total of 142,604 test day (TD) records of milk yield were measured on 20,762 cows. Results: Heritability estimates of milk yield were 0.25 and 0.19 for 3X milking frequency and 0.34 and 0.26 for 4X milking frequency throughout the first and second lactations, respectively. Repeatability estimates of milk yield were 0.70 and 0.71 for 3X milking frequency and 0.76 and 0.77 for 4X milking frequency, respectively. In comparison with 3X milking frequency, the milk yield of the first and second lactations was increased by 11.6 and 12.2 %, respectively when 4X was used (p < 0.01). Conclusions: Results of this research demonstrated that increasing milking frequency led to an increase in heritability and repeatability of milk yield. The current investigation provided clear evidences for the benefits of using 4X milking frequency instead of 3X in Iranian Holstein dairy cows.