personalization data的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列特價商品、必買資訊和推薦清單

Soft Computing for Security Applications: Proceedings of Icscs 2021

為了解決personalization data的問題，作者 這樣論述：

Dr. G. Ranganathan working as a Professor and Head in Gnanamani college of Engineering and Technology, Namakal, India. He has done his PhD in the Faculty of Information and Communication Engineering from Anna University, Chennai in the year 2013. His research thesis was in the area of Bio Medical Si

gnal Processing. He has total of 29+ years of experience both in industry, teaching and research. He has guided several project works for many UG and PG Students in the areas of Bio Medical Signal Processing. He has published more than 35 research papers in International and National Journals and Co

nferences. He has also co-authored many books in electrical and electronics subjects. He has served as Referee for many reputed International Journals published by Elsevier, Springer, Taylor and Francis, etc. He has membership in various professional bodies like ISTE, IAENG etc., and has actively in

volved himself in organizing various international and national level conferences, symposiums, seminars etc.Dr. Xavier Fernando is the Director of Ryerson Communications Lab that has received total research funding of ＄3,185,000.00 since 2008 from industry and government (including joint grants). He

has (co-)authored over 200 research articles, three books (one translated to Mandarin), several book chapters and holds few patents. The present and past members and affiliates of this lab can be found at this LinkedIn group and Facebook page. He was an IEEE Communications Society Distinguished Lec

turer and has delivered over 50 invited talks and keynote presentations all over the world. He was a member in the IEEE Communications Society (COMSOC) Education Board Working Group on Wireless Communications. He was the Chair of IEEE Canada Humanitarian Initiatives Committee 2017-18. He was also th

e Chair of the IEEE Toronto Section and IEEE Canada Central Area.Dr. Fuqian Shi, was graduated from College of Computer Science and Technology, Zhejiang University and got his PhD on Engineering, and was a visiting Associate Professor at Department of Industrial Engineering and Management System, Un

iversity of Central Florida, USA from 2012 to 2014. Dr. Shi is currently serving for Rutgers Cancer Institute of New Jersey, USA. Dr. Shi is a Senior Member of IEEE, Membership of ACM, and sever as over 30 committee board membership of international conferences; Dr. Shi also serve as associate edito

rs of International Journal of Ambient Computing and Intelligence (IJACI), International Journal of Rough Sets and Data Analysis (IJRSDA), and special issue editors of IEEE Access. He published over 100 journal papers and conference proceedings, his research interests include fuzzy inference system,

artificial neuro networks, and bio-mechanical engineering.Dr. Youssouf EL ALLIOUI, got his PhD in 2012 from Sidi Mohamed Ben Abdellah University, Fez, Morocco. Currently he is a professor at the department of computer science, University of Sultan Moulay Slimane, Beni Mellal, Morocco. He works in a

multidisciplinary environment involving machine learning, educational data mining (EDM), semantic web, knowledge management, Web Personalization and Recommender Systems, E-learning and semantic E-learning. His research focus is to study those research fields to come up with solutions to various rea

l-world problems. He published several research papers at international journals and conferences. He served as a member of the technical committees of several international conferences and workshops.

personalization data進入發燒排行的影片

應用於物件偵測與關鍵字辨識之強健記憶體內運算設計

為了解決personalization data的問題，作者江宇翔 這樣論述：

近年來，由於不同的應用都能夠藉由和深度學習的結合而達到更好的結果，像是物件偵測、自然語言處理以及圖像辨識，深度學習在終端設備上的發展越來越廣泛。為了應付深度學習模型的龐大資料搬移量，記憶體內運算的技術也在近年來蓬勃發展，不同於傳統的范紐曼架構，記憶體內運算使用類比域的計算使儲存設備也同樣具備運算的能力。儘管記憶體內運算具有降低資料搬移量的優點，比起純數位的設計，在類比域進行計算容易受到非理想效應的影響，包括元件本身或是周邊電路的誤差，這會造成模型災難性的失敗。此篇論文在兩種不同的應用領域針對記憶體內運算進行強健的模型設計及硬體實現。在電阻式記憶體內運算的物件偵測應用當中，我們將重點放在改善模

型對於非理想效應的容忍度。首先，為了降低元件誤差的影響，我們將原本的二值化權重網路改變為三值化權重網路以提高電阻式記憶體中高阻態元件的數量，同時能夠直接使用正權重及負權重位元線上的電流值進行比較而不使用參考位元線作為基準。其次，為了避免使用高精度的正規化偏差值以及所導致的大量低阻態元件佈署，我們選擇將網路中的批次正規化層移除。最後，我們將運算從分次的電流累加運算改為一次性的運算，這能夠將電路中非線性的影響降到最低同時避免使用類比域的累加器。相較於之前的模型會受到這些非理想效應的嚴重影響導致模型無法運作，我們在考慮完整的元件特性誤差，周邊電路誤差以及硬體限制之下，於IVS 3cls中做測試，能夠

將平均精確度下降控制在7.06\%，在重新訓練模型後能更進一步將平均精確度下降的值降低到3.85\%。在靜態隨機存取記憶體內運算的關鍵字辨識應用當中，雖然非理想效應的影響相對較小，但是仍然需要針對周邊電路的誤差進行偏壓佈署補償，在經過補償及微調訓練後，在Google Speech Command Dataset上能夠將準確率下降控制在1.07\%。另外，由於語音訊號會因為不同使用者的資料而有大量的差異，我們提出了在終端設備上進行模型的個人化訓練以提高模型在小部分使用者的準確率，在終端設備的模型訓練需要考量到硬體精度的問題，我們針對這些問題進行誤差縮放和小梯度累積以達到和理想的模型訓練相當的結果

。在後佈局模擬的結果中，這個設計在推論方面相較於現有的成果能夠有更高的能源效率，達到68TOPS/W，同時也因為模型個人化的功能而有更廣泛的應用。

Enlightened Marketing in Challenging Times: Proceedings of the 2019 AMS World Marketing Congress (WMC)

為了解決personalization data的問題，作者 這樣論述：

Chapter1. Applying Evolutionary Psychology in Navigating the Standardization/Adaptation Debate in International Advertising.- Chapter2. Truth, Half-Truth or Little White Lie? Exploring Public Sentiment toward Advertising through Cartoon Analysis.- Chapter3. The Impact of Culture on Humorous Ads:

An Abstract.- Chapter4. Marketing Web Trends: An Algorithm and Brand Equity Nowcasting Application: An Abstract.- Chapter5. Privacy or Personalization? Drivers, Deterrents and Moderators of Consumers’ Willingness to Disclose Personal Data.- Chapter6. Big Data Analytics in Product Innovation Context:

An Abstract.- Chapter7. How Big Data Utilization Affect Firms Marketing-related Strategies and Performance: An Abstract.- Chapter8. The Impact of Different Aspects of Perceived Authenticity on the Tourist-Destination Relationship Quality: An Abstract.- Chapter9. Deceptive Use of the ’Regionality’ C

oncept in Product Labelling: An Abstract.- Chapter10. Brand Heritage vs. Brand Nostalgia: Same Same, but Different?.- Chapter11. I Feel Good: The Impact of Atmospherics General Interior Variables on Shopper Behavior: An Abstract.- Chapter12. Expectations vs. Reality: Can Sensory Incongruence Generat

e Positive Emotion and Behaviour in the Online Shopping Context? An Abstract.- Chapter13. Influencing Consumers’ Buying Behavior through Smell: An Abstract.- Chapter14. Thinking Creatively through Hands.- Chapter15. An Abstract: Social Service Robots: What do we Know and Where do we Go Next?.- Chapt

er16. The Impacts of Restaurant Technology on Customer Dining Experiences: The Moderating Role of Need for Interaction: An Abstract.- Chapter17. The Impacts of Restaurant Technology on Customer Dining Experiences: The Moderating Role of Need for Interaction: An Abstract.- Chapter18. How can Firms St

op Negative Word-of-Mouth? A Typology of Online Complainants: An Abstract.- Chapter19. Has Luxury Consumption Something to do with Fear and Love?.- Chapter20. Millennial Consumer’s on Instagram: Implications for Luxury Brands vs. Celebrity Influencers: An Abstract.- Chapter21. The Elephant in the Ro

om: An Abstract.- Chapter22. Components of Customer Experience and their Impact on Co-created Value: An Abstract.- Chapter23. Can Someone Become Like Someone Else by Eating his or her Leftovers? Analyzing the Perceived Transmission of Personality Traits through Leftovers: An Abstract.- Chapter24. An

Abstract: Sports Marketing Special Session: Youth Sport Participation and Youth Olympic Games.- Chapter25. An Abstract: Sports Marketing Special Session: Cricket in the North: The Impact of Sports Participation and Sponsorship on Immigrant Social Inclusion and Fostering Welcoming Communities.- Chap

ter26. An Abstract: Sports Marketing Special Session: Ice Hockey Consumers: Who Cares about Women Recreational Players?.- Chapter27. An Abstract: An Abstract: Sports Marketing Special Session: Enlightened Learning in Sport Marketing.- Chapter28. A Multiple-Item Dimensional Scale for Measuring Store

Equity.- Chapter29. Brand Strength’s Nomological Net and Dimensional Dynamics: An Abstract.- Chapter30. How Firms Achieve Technology Readiness for IoT Adoption? Views from Market Orientation and Adaptive Capability: An Abstract.- Chapter31. From the Stone Age to our Digital Age: A Theoretical Explic

ation of the Historical Development of International Distribution: An Abstract.- Chapter32. An Abstract: Strategic Responses to Dynamic Changes: The Roles of Agility and Ambidexterity in International Marketing.- Chapter33. An Abstract: The Underlying Sources of OI-IO’s Strategic Approaches and Inte

rnational Marketing: An Exploratory Qualitative Study.- Chapter34. The Interplay between Market, Innovativeness, Learning, and Entrepreneurial (MILE) Strategic Orientations and Export Performance: A Configurational Perspective Using fsQCA: An Abstract.- Chapter35. Startups and Country of Origin Posi

tioning Strategy: An Abstract.- Chapter36. Special Session: Relationship Intelligence

甚麽是親中？中國－馬來西亞關係近況發展的6M分析（2013年－2018年）

為了解決personalization data的問題，作者黃以樂 這樣論述：

2013年至2018年之間，中國與馬來西亞之關係可謂是達到了新高點。在此期間，中馬兩國在許多面向展開合作關係，包括軍事、經貿、教育及文化等等。雙方的合作關係甚至成為了馬來西亞2018年全國選舉的重點議題之一，當時執政者以首相納吉．拉薩（Najib Razak）為首，其發起或支持的許多中馬合作工程案備受質疑，被批評是「親中」的表現。其中一個大力批評納吉親中的群體為希望聯盟（Pakatan Harapan），而他們於2018年全國選舉中的勝利無意間也被刻畫成「反中派」的勝利。整起事件的過程中，「親中」的使用似乎是貶義用途。2019年「反對逃犯條例修訂草案運動」開始時，馬來西亞普遍華裔也高度關注此

事，而「親中」與「反中」逐漸成為了嘲諷意味極重的政治標籤。馬來西亞在2013年至2018年之間與中國的互動關係似乎也被貼上了一樣的標籤。甚麼是親中？本研究認為目前「親中」作為形容詞的用法帶有犧牲自主權，並妥協自身立場的含意。中馬關係中是否真的有如此現象？現今有關兩國互動關係的理論架構，主要以「遠近」為衡量單位，或是以國對國之反應來判斷其關係之本質，如：新現實主義中的「抗衡」（Balancing）、「扈從」（Bandwagoning）或「避險」（Hedging）。然而，由此角度並未能充分解釋「親中」，因為這些理論主要以國家行為者（state as actor）為衡量基準，缺乏了深入到社會層級互動

之考量。國家行為者制定決策的考量主要以可衡量之客觀元素，如：國家之硬實力（Hard power），但「親中」的表現似乎有意忽略此元素，以「偏好」（preference）作為制定決策之基本考量，社會行動者（societal actor）也因此是探討「親中」之定義重要的研究對象。本研究嘗試以Andrew Moravscik所提出的自由主義理論架構，結合Chia-Chien Chang及Alan H. Yang所提出的6M分析法，對中馬在2013年至2018年之間的互動過程進行分析，並以此探討「親中」之定義。馬國社會中第二大族群就是具有「中華情結」之華裔群體，馬國的「親中」表現極有可能由此開始。但本

研究發現馬國「親中」的表現除了源自於華裔社會行動者，也可能從處在執政層級之巫裔社會行動者。本研究以6M分析法歸納出2013年至2018年之間重要的「親中」事件，並總結出兩大「親中化」過程，即「由上至下」（國家行為者至社會行動者）以及「由下至上」（社會行動者至國家行為者）。