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Developmental Differences in Children and Adults' Enforcement of Explore Versus Exploit Search Strategies in the United States and Turkey
No full textAcross development, as children acquire a deeper understanding of their environment, they explore less and take advantage, or "exploit," what they already know. Here, we test whether children also enforce exploration-oriented search behaviors onto others. Specifically, we ask whether children are more likely to encourage a search agent to explore versus exploit their environment, and whether this pattern varies across childhood (between 3 and 6 years). We also ask whether this pattern differs between children and adults, and generalizes across two different sociocultural contexts-Turkey and the United States-that differ on dimensions that might relate to children's decisions about exploration (e.g., curiosity-focused educational practices, attitudes toward uncertainty avoidance). Participants (N = 358) watched an agent search for rewards and were asked at various points whether the agent should "stay" (exploit) in their current location, or "go" (explore) to a new location. At all points in the experiment, children enforced exploration significantly more often than adults. Early in the agent's search, children in the US enforced exploration more often than children in Turkey; later in the search, younger children (from both sociocultural contexts) were more likely to continue enforcing exploration compared to older children. These findings highlight that children are not only highly exploratory themselves, but also enforce exploration onto others-underscoring the central role that exploration plays in driving early cognitive development across diverse sociocultural contexts.Research Highlights The current study examined developmental and cross-cultural differences in children and adults' enforcement of explore-exploit search strategies. Children in the US and Turkey enforced exploration more than adults, who enforced exploitation more often; results were generally consistent across cultures with small differences. Mirroring developmental changes in children's own search behavior; the tendency to enforce exploration decreased between 3- to 6-years of age. Findings underscore the central role of an "exploration mindset" in children's early decision-making-even when exploration has no direct benefits to the child themselves.National Science Foundation, Grant/Award Number: BCS2047194; MEF University, Grant/Award Number: AP012National Science Foundation [BCS 2047194]; MEF University; [AP012
Consumer Responses Toward Smart Technology: a Systematic Review, Synthesis, and Future Research Agenda
No full textThis article is a comprehensive review of the literature on smart technology in consumer studies from 1996 to 2023. While the paper provides information about the development of the field by identifying important publications and authors, it employs topic modeling to pinpoint key topics in papers published in marketing and business journals. These topics are then grouped into three research streams and evaluated concerning theoretical, contextual, and methodological perspectives. While doing so, specific gaps were identified. By revealing gaps in the literature, the study suggests promising avenues for further research. Finally, this article advances our comprehension of the smart technology literature in marketing and business journals and informs future inquiry in this rapidly evolving domain.This work has been supported by Y ; imath;ld ; imath;z Technical University Scientific Research Projects Coordination Unit under project number 4565.Yimath;ldimath;z Technical University Scientific Research Projects Coordination [4565]; Yimath;ldimath;z Technical University Scientific Research Projects Coordination Uni
Temsilde Eşitlik ve Türkiye'de Kadınların Siyasal Temsili
No full textBireylerin toplum içinde eşit ve adil bir düzen sağlama istekleri, demokrasinin temelini oluşturmuştur. Tarih boyunca bu sistem içerisinde, birey olarak varlığını kabul ettirmek için mücadele eden kadınların, hak arama mücadelesi günümüzde de devam etmektedir. Kadınların, toplum tarafından kendilerine biçilen rollere, kendilerini adamaları beklenirken birey olarak varoluş mücadeleleri, eril kurallar üzerine kurulu Dünya'da Fransız Devriminden, Kurtuluş Mücadelemize farklı olaylarda kendini göstermiştir. Bu bağlamda demokrasinin temel kriteri olan kadın erkek eşitliğinin, hala sağlanamadığı ve uygar Dünya olma yolunda gidilecek yolun çok olduğunu söylemek mümkündür. Bu çalışmada temsil kavramına, demokrasi ve kadının insan hakları bağlamında yaklaşılmaktadır. Kavramsal açıklamaların ardından Dünya'da ve Türkiye'de kadınların hak mücadelesi tarihsel olarak ele alınmaktadır. Çalışmada Türkiye Büyük Millet Meclisi'ne seçilen kadın milletvekillerine sorular yöneltilerek, temsil meselesine farklı bir pencereden yaklaşılmaya çalışılmıştır. Bu çerçevede bir kadın devrimi olan Cumhuriyet'in 100. yılında, demokrasinin temelindeki kadın erkek eşitliği tablosunun ve kadının insan hakları mücadelesinin analizinin yapılması amaçlanmıştır.The claim of individuals to ensure an equal and fair demand in society has formed the basis of democracy. The struggle of women, who have struggled throughout history to have their existence accepted as individuals within this system, continues today. While women are expected to devote themselves to the roles assigned to them by society, their struggle for existence as individuals has manifested itself in different events, from the French Revolution to our War of Independence, in a world based on masculine rules. In this thesis, it is possible to say that equality between men and women, which is the basic criterion of democracy, has still not been achieved and there is still a long way to go towards becoming a civilized world. In this study, the concept of representation is approached in the context of democracy and women's human rights. Following conceptual explanations, women's struggle for rights in the world and in Turkey is discussed historically. In the study, the issue of representation was approached from a different perspective by asking questions to female members of parliament elected to the Turkish Grand National Assembly. In this thesis, it is aimed to analyze the equality between men and women on the basis of democracy and the struggle for women's human rights on the 100th anniversary of the Republic, which was a women's revolution
Does Prompt Engineering Help Turkish Named Entity Recognition?
No full textThe extraction of entity mentions in a text (named entity recognition) has been traditionally formulated as a sequence labeling problem. In recent years, this approach has evolved from recognizing entities to answering formulated questions related to entity types. The questions, constructed as prompts, are used to elicit desired entity mentions and their types from large language models. In this work, we investigated prompt engineering in Turkish named entity recognition and studied two prompting strategies to guide pretrained language models toward correctly identifying mentions. In particular, we examined the impact of zero-shot and few-shot prompting on the recognition of Turkish named entities by conducting experiments on two large language models. Our evaluations using different prompt templates revealed promising results and demonstrated that carefully constructed prompts can achieve high accuracy on entity recognition, even in languages with complex morphology. © 2024 IEEE
Benders Decomposition Algorithms for Minimizing the Spread of Harmful Contagions in Networks
No full textThe COVID-19 pandemic has been a recent example for the spread of a harmful contagion in large populations. Moreover, the spread of harmful contagions is not only restricted to an infectious disease, but is also relevant to computer viruses and malware in computer networks. Furthermore, the spread of fake news and propaganda in online social networks is also of major concern. In this study, we introduce the measure -based spread minimization problem (MBSMP), which can help policy makers in minimizing the spread of harmful contagions in large networks. We develop exact solution methods based on branch -and -Benders -cut algorithms that make use of the application of Benders decomposition method to two different mixed -integer programming formulations of the MBSMP: an arc -based formulation and a path -based formulation. We show that for both formulations the Benders optimality cuts can be generated using a combinatorial procedure rather than solving the dual subproblems using linear programming. Additional improvements such as using scenario -dependent extended seed sets, initial cuts, and a starting heuristic are also incorporated into our branch -and -Benderscut algorithms. We investigate the contribution of various components of the solution algorithms to the performance on the basis of computational results obtained on a set of instances derived from existing ones in the literature.This research was funded in whole, or in part, by the Austrian Science Fund (FWF) [P 35160-N] . For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.Austrian Science Fund (FWF) [P 35160-N
Integrating Genre-Based Writing and Critical Thinking in Developing Writing Skills of Preservice Language Teachers
No full text[No Abstract Available
Adapting public collections and libraries through 21st century strategies and solutions: Adapt, manage or perish
No full textThe presentation builds on three key pillars, focusing on the evolving role of librarians in the era of artificial intelligence (AI). The first pillar emphasises the importance of identifying effective strategies for navigating this transformative period. These strategies include digital transformation, digitisation and accessibility, intelligent cataloguing and search, community engagement, and the integration of advanced technologies into library operations. The second pillar highlights the critical roles and tasks that librarians are expected to undertake in this context. These tasks encompass staff training to enhance digital and AI competencies, upgrading infrastructure to support technological advancements, and implementing outreach programmes to engage communities and promote digital literacy. Practical recommendations are also provided, such as advocating for increased financial support through grant applications and lobbying efforts. Privacy and security emerge as significant concerns, necessitating measures to protect patron data. 54 Abstracts Recommended actions include the development of transparent policies, implementing threat detection and prevention systems, utilising data encryption and anonymisation techniques, and ensuring robust access control and authentication mechanisms. Continuous data monitoring, reporting, automatic updates, and patch management further enhance security protocols. The presentation also stresses the need for user-centred design in library services. This involves incorporating feedback mechanisms and offering inclusive services tailored to diverse user needs, ensuring accessibility and relevance for all patrons. The third pillar explores the International Federation of Library Associations and Institutions (IFLA) and its contributions to the intersection of AI and libraries. Key initiatives include the work of the Artificial Intelligence and Libraries Working Group, which addresses topics such as AI ethics and provides valuable publications and reports to guide libraries in adopting AI responsibly. In addition, the presentation introduces the pioneering work of MEF University and the MEF Library in Türkiye, both leaders in integrating AI within the academic and library sectors. MEF University achieved a significant milestone in November 2023 by publishing the first book on higher education and artificial intelligence applications on a global scale. It also became the first university in Türkiye to publish its AI policy, issuing two versions to address the evolving landscape
FNIRS tabanlı nöropazarlama araştırmalarında çizge sı̇nı̇r ağları ile makı̇ne öğrenimi algorı̇tmalarının karşılaştırılması
No full textFonksiyonel yakın kızılötesi spektroskopinin (fNIRS) maliyet ve taşınabilirlik açısından diğer beyin görüntüleme yöntemlerine göre bazı avantajları vardır. Bu nedenle nöropazarlama alanında kullanımı gittikçe artmaktadır. Ancak fNIRS, sağladığı avantajların yanında bazı zorlukları da beraberinde getirmektedir. Çok kanallı ölçüm ve yüksek zamansal çözünürlük gibi özellikler nedeniyle fNIRS verilerinin doğası karmaşık ve çok boyutludur [7]. Nöropazarlama araştırmacıları, bu zorlukların üstesinden gelebilmek için makine öğrenimi algoritmalarından yararlanmıştır. Bu çalışmalar incelendiğinde başarılı sonuçların ortaya çıktığı görülmüştür. Makine öğrenimi, nöropazarlama araştırmacılarının yanı sıra çizge üzerinde çalışan araştırmacıları da etkilemiştir. Böylece yapay sinir ağlarının çizge veri yapılarına uygulanmasına izin veren çizge sinir ağları ortaya çıkmıştır. Beynin fonksiyonel bağlantılar kullanılarak çizge yapısı şeklinde modellenebilmesi [14] ve fNIRS'in yüksek zamansal çözünürlüğü sayesinde [7], çizge sinir ağları ile fNIRS'in birlikte kullanıldığı nörogörüntüleme çalışmaları mevcuttur. Ancak başarılı sonuçlara rağmen bu kombinasyona yer veren nöropazarlama araştırmasına rastlanmamıştır. Bu nedenle bu çalışmada, çizge sinir ağlarının fNIRS temelli nöropazarlama alanındaki performansı incelenmiş ve bu bağlamda başarılı sonuçlar verdiği görülen makine öğrenimi algoritmaları ile karşılaştırılması yapılmıştır. Karşılaştırma için, markalara yönelik algıları belirlemek amacıyla yürütülen bir nöropazarlama deneyinin fNIRS ölçümleri kullanılmıştır. Deneyde, tüketicilerden marka logosuyla birlikte gösterilen sıfatın markaya uygun olup olmadığına dair karar vermeleri (evet/hayır) istenmiştir. Elde edilen ölçümler temizlenerek veri seti elde edilmiştir. İlk olarak bu veri setine gözetimli makine öğrenimi yaklaşımı uygulanmıştır. Veri seti birkaç veri ön işleme aşamasından geçirildikten sonra üzerinde çeşitli algoritmalar eğitilmiştir. Bunlar, K-Nearest Neighbors, Support Vector Machines, Random Forest, Naive Bayes ve XGBoost algoritmalarıdır. Sonrasında ise diğerlerine göre daha başarılı olan algoritmalardan, biri soft voting diğeri hard voting olmak üzere iki farklı voting classifier oluşturulmuştur. Makine öğrenimi yaklaşımı tamamlandıktan sonra çizge sinir ağları yaklaşımına geçilmiştir. fNIRS aracılığı ile elde edilen veriler, beyindeki fonksiyonel bağlantılar kullanılarak çizge yapısına dönüştürülmüştür. Fonksiyonel bağlantıların hesaplanmasında Pearson korelasyon katsayısı kullanılmıştır. Katılımcıların her denemesi için bir çizge oluşturulduğundan ve her çizgenin etiketi (evet/hayır) bulunduğundan, çizge seviyesinde sınıflandırma yapılmıştır. Çizgelerin sınıflandırılması için, oluşturulan çizgeler, çizge sinir ağları mimarilerine girdi olarak verilmiştir. Çalışmada kullanılan mimariler, Graph Convolutional Network, Graph Attention Network ve Graph Isomorphism Network'ten oluşmaktadır. Son olarak, bu mimarilerin bir araya getirilmesiyle bir soft voting classifier oluşturulmuştur. Tüm yöntemlerin test accuracy değerleri hesaplanmış ve bu değerlere güven aralıkları eklenmiştir. Karşılaştırma sonuçları, genel olarak makine öğrenimi algoritmalarının çizge sinir ağlarından daha iyi performans verdiğini göstermiştir. Ek olarak, topluluk öğrenimine dayalı makine öğrenimi modelleri en iyi skorlara sahiptir.Functional near-infrared spectroscopy (fNIRS) has some advantages over other brain imaging methods in terms of cost and portability. For this reason, its use in neuromarketing is increasing. However, fNIRS brings some challenges along with its advantages. Due to features such as multichannel measurement and high temporal resolution, the nature of fNIRS data is complex and multidimensional [7]. Neuromarketing researchers have utilized machine learning algorithms to overcome these challenges. When these studies are analyzed, it is seen that successful results have emerged. Machine learning has influenced researchers working on graphs as well as neuromarketing researchers. Thus, graph neural networks have emerged, which allow the application of artificial neural networks to graph data structures. Thanks to the fact that the brain can be modeled as a graph structure using functional connections [14] and the high temporal resolution of fNIRS [7], there are neuroimaging studies using graph neural networks and fNIRS together. However, despite successful results, there is no neuromarketing research using this combination. Therefore, in this study, the performance of graph neural networks in fNIRS-based neuromarketing was analyzed and compared with machine learning algorithms that have been shown to yield successful results in this context. For the comparison, fNIRS measurements of a neuromarketing experiment conducted to determine perceptions toward brands were used. In the experiment, consumers were asked to decide (yes/no) whether the adjective shown with the brand logo was appropriate for the brand. The data set was obtained by cleaning the obtained measurements. First, a supervised machine learning approach was applied to this dataset. After the dataset went through several data preprocessing stages, various algorithms were trained on it. These were K-Nearest Neighbors, Support Vector Machines, Random Forest, Naive Bayes, and XGBoost algorithms. Then, two different voting classifiers, one for soft voting and one for hard voting, were created from the algorithms that were more successful than the others. After the machine learning approach was completed, the graph neural network approach was applied. The data obtained through fNIRS was transformed into a graph structure using functional connections in the brain. The Pearson correlation coefficient was used to calculate the functional connections. Since a graph was created for each trial of the participants and each graph had a label (yes/no), classification was performed at the graph level. For graph classification, the generated graphs were given as input to graph neural network architectures. The architectures used in the study consisted of Graph Convolutional Network, Graph Attention Network, and Graph Isomorphism Network. Finally, a soft voting classifier was created by combining these architectures. Test accuracy values of all methods were calculated and binomial confidence intervals were added to these values. The comparison results showed that machine learning algorithms generally outperform graph neural networks. Additionally, machine learning models based on ensemble learning have the best scores
TALICS3 : Tape library cloud storage system simulator
No full textHigh performance computing data is surging fast into the exabyte-scale world, where tape libraries are the main platform for long-term durable data storage besides high -cost DNA. Tape libraries are extremely hard to model, but accurate modeling is critical for system administrators to obtain valid performance estimates for their designs. This research introduces a discrete- event tape simulation platform that realistically models tape library behavior in a networked cloud environment, by incorporating real -world phenomena and effects. The platform addresses several challenges, including precise estimation of data access latency, rates of robot exchange, data collocation, deduplication/compression ratio, and attainment of durability goals through replication or erasure coding. Using the proposed simulator, one can compare the single enterprise configuration with multiple commodity library configurations, making it a useful tool for system administrators and reliability engineers. This makes the simulator a valuable tool for system administrators and reliability engineers, enabling them to acquire practical and dependable performance estimates for their enduring, cost-efficient cold data storage architecture designs.Quantum Corporation, Irvine, CA, USAThis work is partially supported by Quantum Corporation, Irvine, CA, USA