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自動偵測機器所產生之文章

為了解決deep learning ian go的問題，作者陳玉芳 這樣論述：

近年來網路上出現了許多所謂的文章產生器軟體，讓使用者只要輸入主題或某些關鍵字，就可以自動產生一篇文章。這些文章產生器所生成的機器文章乍看之下就像人類所寫的真文章，甚至許多內容看似有憑有據且引經據典，但若仔細閱讀這些機器生成的假文章則很容易發現其內容缺乏邏輯性且無中心思想，甚至發生前後不連貫的情形。這往往浪費讀者的時間，尤其是學生可能以機器產生之文章蒙混繳交，投機取巧。有鑒於此，本論文嘗試以人工智慧的機器學習可否自動偵測這類的假文章，使用多種機器學習的方法來辨識機器所生成的假文章與學生所寫作的真文章兩者。實驗結果顯示，BayesNet分類正確率為100%；而NaiveBayes、Logisti

c、SMO、SGD、RandomForest、SimpleLogistic、LMT、IBk (K值為11和9) 則都有達到95%以上的分類正確率。再觀察機器學習相關的效能評估指標，前述所有分類模型的Kappa statistic和MCC除了BayesNet兩者值皆為1, 其餘分類模型的Kappa statistic和MCC皆有0.90以上；同時可見F-Measure也都高於0.95以上 (BayesNet F-Measure 值亦為1)，這些數據皆反映出前述的演算法分類模型都有極佳的真假文章辨識效能的表現。另外，又透過測試集的驗證實驗，NaiveBayes、Logistic、SMO、SGD、R

andomForest、SimpleLogistic、LMT、IBk (K值為11和9)以及LogitBoost這些演算法分類模型的測試集驗證實驗結果顯示出分類模型在測試集的真假文章的分類準確率至少皆有95%或以上的分類成功辨識率，其中又以NaiveBayes、Logistic、SMO、SGD這四種演算法分類模型在測試集的驗證實驗是達到百分百的正確分類辨識率。綜合以上各種實驗數據的分析結果，我們可以得知人工智慧機器學習是有極佳的辨識能力可以成功偵測並分類機器文章產生器所生成之機器文章。

Impact of information transfer in Blattodea group dynamics and decision-making

為了解決deep learning ian go的問題，作者鄒智文 這樣論述：

Optimal foraging theory, Selfish Herd, and several top-down models fall short of explaining the factors that determine group movement and decision-making for animals that do not follow rigid hierarchies or centralised control. These models define intergroup relations as immutable rules based on pro

ximity, without considering an animal’s sensory system or information gained from conspecifics. Thus, animal groups are modelled as an aggregation of particles, disregarding new properties thatemerge as members cooperate or compete.The American cockroach (Periplaneta Americana) does not exhibit rigi

d patterns of social hierarchy or task allocation. They seem to rely on ‘collective wisdom’ and show a preference for aggregating with peers rather than venturing alone into unbeknownst territories in search of food. The simplicity and horizontality of their social interactions make them an ideal or

ganism for studying information dissemination and its impact on system-wide behavioural patterns.This study draws on models and techniques used in network and information theory and trajectory forecasting to understand the factors that modulate information exchange and group decision-making in movin

g insects. It contrasts cockroach groupmovement in an open space, where they can interact freely, with their dynamics in a maze that restricts their interactions. The objective of this study is to describe and explain collective movement using mathematical and computational tools. For that, it uses

prediction models and mutual information to identify the most useful features to forecast individual and collective movements. It seeks to determine the impact of information transfer in insect groups by contrasting group behaviour against the non-interacting aggregation of individual behaviours.