bluuebunny/crossref_metadata_2025 huggingface dataset:
The Parameterized Complexity of Clustering Incomplete Data
Authors: Eiben Eduard , Ganian Robert , Kanj Iyad , Ordyniak Sebastian , Szeider Stefan
Published: September 2022

We study fundamental clustering problems for incomplete data. Specifically, given a set of incomplete d-dimensional vectors (representing rows of a matrix), the goal is to complete the missing vector entries in a way that admits a partitioning of the vectors into at most k clusters with radius or diameter at most r. We give tight characterizations of the parameterized complexity of these problems with respect to the parameters k, r, and the minimum number of rows and columns needed to cover all the missing entries. We show that the considered problems are fixed-parameter tractable when parameterized by the three parameters combined, and that dropping any of the three parameters results in parameterized intractability. A byproduct of our results is that, for the complete data setting, all problems under consideration are fixed-parameter tractable parameterized by k+r.
Learning Prediction Intervals for Model Performance
Authors: Elder Benjamin , Arnold Matthew , Murthi Anupama , Navrátil Jiří
Published: September 2022

Understanding model performance on unlabeled data is a fundamental challenge of developing, deploying, and maintaining AI systems. Model performance is typically evaluated using test sets or periodic manual quality assessments, both of which require laborious manual data labeling. Automated performance prediction techniques aim to mitigate this burden, but potential inaccuracy and a lack of trust in their predictions has prevented their widespread adoption. We address this core problem of performance prediction uncertainty with a method to compute prediction intervals for model performance. Our methodology uses transfer learning to train an uncertainty model to estimate the uncertainty of model performance predictions. We evaluate our approach across a wide range of drift conditions and show substantial improvement over competitive baselines. We believe this result makes prediction intervals, and performance prediction in general, significantly more practical for real-world use.
Learning to Reweight with Deep Interactions
Authors: Fan Yang , Xia Yingce , Wu Lijun , Xie Shufang , Liu Weiqing , Bian Jiang , Qin Tao , Li Xiang-Yang
Published: September 2022

Recently the concept of teaching has been introduced into machine learning, in which a teacher model is used to guide the training of a student model (which will be used in real tasks) through data selection, loss function design, etc. Learning to reweight, which is a specific kind of teaching that reweights training data using a teacher model, receives much attention due to its simplicity and effectiveness. In existing learning to reweight works, the teacher model only utilizes shallow/surface information such as training iteration number and loss/accuracy of the student model from training/validation sets, but ignores the internal states of the student model, which limits the potential of learning to reweight. In this work, we propose an improved data reweighting algorithm, in which the student model provides its internal states to the teacher model, and the teacher model returns adaptive weights of training samples to enhance the training of the student model. The teacher model is jointly trained with the student model using meta gradients propagated from a validation set. Experiments on image classification with clean/noisy labels and neural machine translation empirically demonstrate that our algorithm makes significant improvement over previous methods.
Collaborative Group Learning
Authors: Feng Shaoxiong , Chen Hongshen , Ren Xuancheng , Ding Zhuoye , Li Kan , Sun Xu
Published: September 2022

Collaborative learning has successfully applied knowledge transfer to guide a pool of small student networks towards robust local minima. However, previous approaches typically struggle with drastically aggravated student homogenization when the number of students rises. In this paper, we propose Collaborative Group Learning, an efficient framework that aims to diversify the feature representation and conduct an effective regularization. Intuitively, similar to the human group study mechanism, we induce students to learn and exchange different parts of course knowledge as collaborative groups. First, each student is established by randomly routing on a modular neural network, which facilitates flexible knowledge communication between students due to random levels of representation sharing and branching. Second, to resist the student homogenization, students first compose diverse feature sets by exploiting the inductive bias from sub-sets of training data, and then aggregate and distill different complementary knowledge by imitating a random sub-group of students at each time step. Overall, the above mechanisms are beneficial for maximizing the student population to further improve the model generalization without sacrificing computational efficiency. Empirical evaluations on both image and text tasks indicate that our method significantly outperforms various state-of-the-art collaborative approaches whilst enhancing computational efficiency.
Few-Shot One-Class Classification via Meta-Learning
Authors: Frikha Ahmed , Krompaß Denis , Köpken Hans-Georg , Tresp Volker
Published: September 2022

Although few-shot learning and one-class classification (OCC), i.e., learning a binary classifier with data from only one class, have been separately well studied, their intersection remains rather unexplored. Our work addresses the few-shot OCC problem and presents a method to modify the episodic data sampling strategy of the model-agnostic meta-learning (MAML) algorithm to learn a model initialization particularly suited for learning few-shot OCC tasks. This is done by explicitly optimizing for an initialization which only requires few gradient steps with one-class minibatches to yield a performance increase on class-balanced test data. We provide a theoretical analysis that explains why our approach works in the few-shot OCC scenario, while other meta-learning algorithms fail, including the unmodified MAML. Our experiments on eight datasets from the image and time-series domains show that our method leads to better results than classical OCC and few-shot classification approaches, and demonstrate the ability to learn unseen tasks from only few normal class samples. Moreover, we successfully train anomaly detectors for a real-world application on sensor readings recorded during industrial manufacturing of workpieces with a CNC milling machine, by using few normal examples. Finally, we empirically demonstrate that the proposed data sampling technique increases the performance of more recent meta-learning algorithms in few-shot OCC and yields state-of-the-art results in this problem setting.
HiGAN: Handwriting Imitation Conditioned on Arbitrary-Length Texts and Disentangled Styles
Authors: Gan Ji , Wang Weiqiang
Published: September 2022

Given limited handwriting scripts, humans can easily visualize (or imagine) what the handwritten words/texts would look like with other arbitrary textual contents. Moreover, a person also is able to imitate the handwriting styles of provided reference samples. Humans can do such hallucinations, perhaps because they can learn to disentangle the calligraphic styles and textual contents from given handwriting scripts. However, computers cannot study to do such flexible handwriting imitation with existing techniques. In this paper, we propose a novel handwriting imitation generative adversarial network (HiGAN) to mimic such hallucinations. Specifically, HiGAN can generate variable-length handwritten words/texts conditioned on arbitrary textual contents, which are unconstrained to any predefined corpus or out-of-vocabulary words. Moreover, HiGAN can flexibly control the handwriting styles of synthetic images by disentangling calligraphic styles from the reference samples. Experiments on handwriting benchmarks validate our superiority in terms of visual quality and scalability when comparing to the state-of-the-art methods for handwritten word/text synthesis. The code and pre-trained models can be found at https://github.com/ganji15/HiGAN.
Diffusion Network Inference from Partial Observations
Authors: Gan Ting , Han Keqi , Huang Hao , Ying Shi , Gao Yunjun , Li Zongpeng
Published: September 2022

To infer the structure of a diffusion network from observed diffusion results, existing approaches customarily assume that observed data are complete and contain the final infection status of each node, as well as precise timestamps of node infections. Due to high cost and uncertainties in the monitoring of node infections, exact timestamps are often unavailable in practice, and even the final infection statuses of nodes are sometimes missing. In this work, we study how to carry out diffusion network inference without infection timestamps, using only partial observations of the final infection statuses of nodes. To this end, we iteratively infer the structure of the target diffusion network with observed data and imputed values for missing data, and learn the most likely infection transmission probabilities between nodes w.r.t. current inferred structure, which then help us update the imputation of missing data in turn. Extensive experimental results on both synthetic and real-world networks show that our approach can properly handle missing data and accurately uncover diffusion network structures.
Storage Fit Learning with Feature Evolvable Streams
Authors: Hou Bo-Jian , Yan Yu-Hu , Zhao Peng , Zhou Zhi-Hua
Published: September 2022

Feature evolvable learning has been widely studied in recent years where old features will vanish and new features will emerge when learning with streams. Conventional methods usually assume that a label will be revealed after prediction at each time step. However, in practice, this assumption may not hold whereas no label will be given at most time steps. A good solution is to leverage the technique of manifold regularization to utilize the previous similar data to assist the refinement of the online model. Nevertheless, this approach needs to store all previous data which is impossible in learning with streams that arrive sequentially in large volume. Thus we need a buffer to store part of them. Considering that different devices may have different storage budgets, the learning approaches should be flexible subject to the storage budget limit. In this paper, we propose a new setting: Storage-Fit Feature-Evolvable streaming Learning (SF2EL) which incorporates the issue of rarely-provided labels into feature evolution. Our framework is able to fit its behavior for different storage budgets when learning with feature evolvable streams with unlabeled data. Besides, both theoretical and empirical results validate that our approach can preserve the merit of the original feature evolvable learning i.e., can always track the best baseline and thus perform well at any time step.
Learning to Reweight Imaginary Transitions for Model-Based Reinforcement Learning
Authors: Huang Wenzhen , Yin Qiyue , Zhang Junge , Huang Kaiqi
Published: September 2022

Model-based reinforcement learning (RL) is more sample efficient than model-free RL by using imaginary trajectories generated by the learned dynamics model. When the model is inaccurate or biased, imaginary trajectories may be deleterious for training the action-value and policy functions. To alleviate such problem, this paper proposes to adaptively reweight the imaginary transitions, so as to reduce the negative effects of poorly generated trajectories. More specifically, we evaluate the effect of an imaginary transition by calculating the change of the loss computed on the real samples when we use the transition to train the action-value and policy functions. Based on this evaluation criterion, we construct the idea of reweighting each imaginary transition by a well-designed meta-gradient algorithm. Extensive experimental results demonstrate that our method outperforms state-of-the-art model-based and model-free RL algorithms on multiple tasks. Visualization of our changing weights further validates the necessity of utilizing reweight scheme.
Personalized Cross-Silo Federated Learning on Non-IID Data
Authors: Huang Yutao , Chu Lingyang , Zhou Zirui , Wang Lanjun , Liu Jiangchuan , Pei Jian , Zhang Yong
Published: September 2022

Non-IID data present a tough challenge for federated learning. In this paper, we explore a novel idea of facilitating pairwise collaborations between clients with similar data. We propose FedAMP, a new method employing federated attentive message passing to facilitate similar clients to collaborate more. We establish the convergence of FedAMP for both convex and non-convex models, and propose a heuristic method to further improve the performance of FedAMP when clients adopt deep neural networks as personalized models. Our extensive experiments on benchmark data sets demonstrate the superior performance of the proposed methods.
Variance Penalized On-Policy and Off-Policy Actor-Critic
Authors: Jain Arushi , Patil Gandharv , Jain Ayush , Khetarpal Khimya , Precup Doina
Published: September 2022

Reinforcement learning algorithms are typically geared towards optimizing the expected return of an agent. However, in many practical applications, low variance in the return is desired to ensure the reliability of an algorithm. In this paper, we propose on-policy and off-policy actor-critic algorithms that optimize a performance criterion involving both mean and variance in the return. Previous work uses the second moment of return to estimate the variance indirectly. Instead, we use a much simpler recently proposed direct variance estimator which updates the estimates incrementally using temporal difference methods. Using the variance-penalized criterion, we guarantee the convergence of our algorithm to locally optimal policies for finite state action Markov decision processes. We demonstrate the utility of our algorithm in tabular and continuous MuJoCo domains. Our approach not only performs on par with actor-critic and prior variance-penalization baselines in terms of expected return, but also generates trajectories which have lower variance in the return.
Norm-Based Generalisation Bounds for Deep Multi-Class Convolutional Neural Networks
Authors: Ledent Antoine , Mustafa Waleed , Lei Yunwen , Kloft Marius
Published: September 2022

We show generalisation error bounds for deep learning with two main improvements over the state of the art. (1) Our bounds have no explicit dependence on the number of classes except for logarithmic factors. This holds even when formulating the bounds in terms of the Frobenius-norm of the weight matrices, where previous bounds exhibit at least a square-root dependence on the number of classes.
(2) We adapt the classic Rademacher analysis of DNNs to incorporate weight sharing---a task of fundamental theoretical importance which was previously attempted only under very restrictive assumptions. In our results, each convolutional filter contributes only once to the bound, regardless of how many times it is applied. Further improvements exploiting pooling and sparse connections are provided.
The presented bounds scale as the norms of the parameter matrices, rather than the number of parameters. In particular, contrary to bounds based on parameter counting, they are asymptotically tight (up to log factors) when the weights approach initialisation, making them suitable as a basic ingredient in bounds sensitive to the optimisation procedure. We also show how to adapt the recent technique of loss function augmentation to replace spectral norms by empirical analogues whilst maintaining the advantages of our approach.
Memory and Computation-Efficient Kernel SVM via Binary Embedding and Ternary Model Coefficients
Authors: Lei Zijian , Lan Liang
Published: September 2022

Kernel approximation is widely used to scale up kernel SVM training and prediction. However, the memory and computation costs of kernel approximation models are still too large if we want to deploy them on memory-limited devices such as mobile phones, smart watches and IoT devices. To address this challenge, we propose a novel memory and computation-efficient kernel SVM model by using both binary embedding and binary model coefficients. First, we propose an efficient way to generate compact binary embedding of the data which can preserve the kernel similarity. Second, we propose a simple but effective algorithm to learn a linear classification model with binary coefficients which can support different types of loss function and regularizer. Our algorithm can achieve better generalization accuracy than existing works on learning binary coefficients since we allow coefficient to be -1, 0 or 1 during the training stage and coefficient 0 can be removed during model inference. Moreover, we provide detailed analysis on the convergence of our algorithm and the inference complexity of our model. The analysis shows that the convergence to a local optimum is guaranteed and the inference complexity of our model is much lower than other competing methods. Our experimental results on five large real-world datasets have demonstrated that our proposed method can build accurate nonlinear SVM model with memory cost less than 30KB.
High Fidelity GAN Inversion via Prior Multi-Subspace Feature Composition
Authors: Li Guanyue , Jiao Qianfen , Qian Sheng , Wu Si , Wong Hau-San
Published: September 2022

Generative Adversarial Networks (GANs) have shown impressive gains in image synthesis. GAN inversion was recently studied to understand and utilize the knowledge it learns, where a real image is inverted back to a latent code and can thus be reconstructed by the generator. Although increasing the number of latent codes can improve inversion quality to a certain extent, we find that important details may still be neglected when performing feature composition over all the intermediate feature channels. To address this issue, we propose a Prior multi-Subspace Feature Composition (PmSFC) approach for high-fidelity inversion. Considering that the intermediate features are highly correlated with each other, we incorporate a self-expressive layer in the generator to discover meaningful subspaces. In this case, the features at a channel can be expressed as a linear combination of those at other channels in the same subspace. We perform feature composition separately in the subspaces. The semantic differences between them benefit the inversion quality, since the inversion process is regularized based on different aspects of semantics. In the experiments, the superior performance of PmSFC demonstrates the effectiveness of prior subspaces in facilitating GAN inversion together with extended applications in visual manipulation.
Using a historical memoir to improve curriculum coherence in teacher education: The case of Trevor Noah's Born a Crime
Authors: Carolin A , Bennett T
Published: September 2022

Two of the recurring concerns identified in teacher education are a lack of curricular coherence and a schism between content and practice. In this article, we discuss a specific intervention that was aimed at addressing these two challenges as they relate to English and History specifically. We argue that through the use of a carefully selected historical memoir, much tighter coherence between these subjects can be articulated in ways that facilitate students' mastery of core concepts and skills across both these learning areas, as well as a richer appreciation of their implication for teaching practice. For the purposes of this article, we define curricular coherence as an experienced sense of connectedness within and across modules. Focusing on the use of Trevor Noah's memoir, Born a Crime (2016), we argue that engaging with a single historical text across multiple modules can improve curricular coherence and offer a more integrated approach to engaging with written texts and historical resources. With close reference to the Department of Higher Education and Training's Policy on the Minimum Requirements for Teacher Education Qualifications, we reflect on our experiences of integrating this memoir into an undergraduate Intermediate Phase (IP) teacher education programme at the University ofJohannesburg (UJ). We show how this memoir was integrated into four modules that form part of the second year of the degree, namely English for the Primary School, Social Sciences for the Intermediate Phase, Teaching Methodology for English, and Teaching Methodology for the Social Sciences.
Shy-Dragger Syndrome – An underdiagnosed sad reality?
Authors: Vasco Pinto Neves , Maria Pacheco , Maria Inês Silva Gonçalve , Joao Rodrigues , Inês Salvado , Jessica Fidalgo , Pedro Daniel Carneiro Ventura
Published: September 2022

Multiple System Atrophy (MSA) regards a group of neurodegenerative diseases sharing the same physiopathology. It is a rare group of diseases and often represents a diagnostic challenge for clinicians. Mild symptoms are present at the onset of the disease and are often neglected by patients. The case report describes a 62-year-old female with multiple episodes of syncope with 3 months evolutions. Anamnesis revealed sleep apnoea and urinary incontinence. In order to perform complementary exams, the patient was admitted to the medical ward. Autoimunnity results were negative discarding this etiology; 24-hour arterial pressure monitoring revealed a severe fluctuation of tensional values capable of explaining syncope. Other exams revealed no pathological alterations responsible for the patient complaints. Multiple System Atrophy diagnosis was made after exclusion of other possible aetiological causes for the patient’s symptoms. More prevalent diseases such as Parkinson’s Disease, atypical Parkinsonic syndromes, and pure autonomic failure, among others may replicate the same symptoms. Diagnosing the patient with Multiple System Atrophy represented a challenge because of its rarity and clinical complexity. Being a disease with poor outcomes and representing necessary lifestyle changes to the patients and family life, an early and exact diagnosis may provide time and more life quality, within the disease limitations. Internists are often presented with complex patients being obligated to gather all pieces, and put them together, so that de puzzle may be deciphered. Learning points - Multiple systems atrophy is a rare incapacitating disease and it is believed to be underdiagnosed; - Complex diseases often present mild nonlimiting symptoms that patients might tend to neglect.
In vitro antiviral effect of silver nanoparticles against SARS-CoV-2
Authors: Flórez-Álvarez Lizdany , Hernandez Juan C. , Zapata-Builes Wildeman , Charry-Zuluaga José Iván , Jaramillo José R. , Taborda Natalia A. , Gonzalez Juan D. , Martínez Larry L. , Yasssin-Noreña Lina Maria
Published: September 2022

Introduction: COVID-19 is an acute respiratory tract disease caused by the emerging coronavirus SARS-CoV-2. Although several options for chemoprophylaxis are under development, effective treatment for COVID-19 is not yet available.
Objective: To investigate the antiviral properties of synthesized silver nanoparticles (AgNPs) against SARS-CoV-2 using in vitro models.
Materials and methods: This work synthesized AgNPs using an electrochemical method and characterized them using physico-chemical techniques (ICP-OES, ultraviolet-visible spectroscopy, and transmission electron microscopy). AgNPs with diameter sizes ranging between 2.6 to 30 nm and an average size of 6.2 nm were obtained by the electrochemical method. The cytotoxic effect and the antiviral activity of prepared AgNPs against SARS-CoV-2 were evaluated in vitro using Vero E6 cells. Cell viability was evaluated by MTT assay in the presence of serial dilutions of AgNPs. The antiviral effect of AgNPs was evaluated before and after the infection of Vero E6 cells by plaque assay.
Results: Cytotoxic effect was observed at concentrations above 0.07 ppm. AgNPs exhibit a significant reduction of SARS-CoV-2 viral titer after a pre-post treatment strategy with inhibition of 96.5%, 64.13%, and 74.72% at 0.03, 0.017, and 0.008 ppm, respectively.
Conclusion: Our results suggest that AgNPs could reduce SARS-CoV-2 replication with a low cytotoxic effect. Still, additional in vitro and in vivo studies are required to define its potential therapeutic application in humans.
A modified voltage changes ratio (MVCR) for in-vitro volume estimation
Authors: Mosquera Leyton Víctor Hugo , Rengifo-Rodas Carlos Felipe
Published: September 2022

Electrical bioimpedance (EBI) is a non-invasive technique for monitoring bladder volume. The EBI approaches global impedance (GI), impedance ratio method (IRM), and voltage change ratio (VCR) have been successfully applied to detect changes in the volume of objects with constant but unknown conductivity. In this work, simulated and in-vitro experiments are used to compare the performance of these methods with respect to a new approach proposed by some authors and called modified VCR (MVCR). Experimental results indicated that GI, VCR, and MVCR indices increase when the volume of the object under study increases. On the other hand, no direct or inverse relationship between IRM and volume was observed. The coefficient of variation, defined as the interquartile range divided by the median of a set of measurements, was lower for MVCR, which indicates that the proposed approach is less sensitive to measurement noise than the other three methods.
Effect of nutrients, inoculum and co-substrates on methane potential of cattle manure
Authors: Ruiz-Bastidas Rosa Cecilia , Cadavid-Rodríguez Luz Stella
Published: September 2022

In this research, the methane potential of the codigestion of cattle manure (CM) with guinea pig manure (GPM) and dairy wastewater (DW) was evaluated. The effect of the addition of nutrients, inoculum (I) and co-substrates on methane production was studied. Later, two low-cost tubular biodigesters were implemented, at a rural farm in the cold climate municipality of Cumbal, fed with the mixture of CM and co-substrates that produced higher biogas production at the lab scale. The results evidenced that the addition of nutrients had no significant effect on methane potential. The mixture CM + GPM + DW + I, produced a theoretical biomethane potential (BMP) of 69.07%, significantly higher than the percentage of the theoretical BMP obtained individually, 43.81% and 34.49% for CM + I and DW+ I respectively. Further, it was observed that the addition of inoculum avoided problems of acidification. Finally, it was proved that this process can reduce environmental contamination and, at the same time, be a sustainable source of renewable energy for rural families in Cumbal (Nariño-Colombia).
User Studies, Mediation of Information and Information Literacy in the Contexts of Social Vulnerability: Possible Dialogues
Authors: Righetto Guilherme , De Brito Tânia Regina , Vieira Vitorino Elizete
Published: September 2022

This article seeks to trace the convergences between the sub-areas/disciplines of user studies, mediation of information and information literacy aimed at the contexts of social vulnerability, seeking to present more holistic views that contribute to the discussions of the area and for the development of social, digital and cultural protagonism of unfortunate groups. In this sense, the methodological procedures were established through the ends as a qualitative, exploratory and descriptive research, seeking to discuss the theoretical-conceptual assumptions and the possible dialogues among the themes brought. Also, the study is classified as bibliographic, since it used reading, analysis and interpretation of concepts and theories about the themes brought up, seeking to present such assumptions and dialogues around the bias of the social perspective of Information Science. As a result of the discussion, it is understood that user studies, mediation of information and information literacy can be considered interference actions, as they are concerned with the understanding and training of critical and reflective citizens in the face of socio-informational realities. The set of these actions consolidates an investigative posture of both the one who teaches as well the apprentice, as it allows empowerment and emancipation in the tangle of available information.
Uso de bibliotecas digitales solares para la enseñanza del cambio climático en comunidades rurales
Authors: Gómez Zermeño Marcela Georgina , Alemán de la Garza Lorena , Hosman Laura
Published: September 2022

Este artículo presenta una investigación educativa realizada con el objetivo de generar información sobre el uso de la biblioteca digital SolarSPELL, como una estrategia de innovación educativa para la enseñanza del cambio climático en comunidades rurales de las islas Fiyi. SolarSPELL funciona con la energía que generan sus paneles solares y brinda acceso a través de su propia red wifi; además, ofrece una variedad de recursos educativos abiertos, seleccionados por su potencial para fortalecer las prácticas educativas y el logro de los Objetivos de Desarrollo Sostenible de la Agenda 2030. Con base en la pregunta de investigación se seleccionó un estudio de caso, pues permite estudiar con intensidad un fenómeno complejo en un corto periodo de tiempo y analizar el objeto de estudio en un contexto especifico. Se aplicó el instrumento DIAPASON en las bibliotecas comunitarias o escolares que participan en el programa piloto. En los resultados se observa que la SolarSPELL coadyuva a la transformación digital de las prácticas educativas y los bibliotecarios valoran poder contar con un mayor número de recursos bibliográficos sobre el cambio climático. En la actualidad, el proyecto “Escuelas que Aprenden Post COVID-19” retomó estos resultados para realizar un piloto en comunidades rurales de Chiapas, México. En los resultados se enfatiza la necesidad de que el bibliotecario del futuro incorpore a su labor acciones enfocadas a la mejora de la calidad de la educación y que permitan potencializar el desarrollo sostenible de sus comunidades, para mejorar el conocimiento colectivo y crear conciencia sobre este fenómeno.
Effectiveness Comparison of Polysaccharides, Proteins, and Lipids as Composite Edible Coatings on the Quality of Food Products
Authors: Budianto Budianto , Suparmi Anik , Arifin Muh Jaenal , Haryani Ratna
Published: September 2022

Background: This research was motivated by the complaints of tomato farmers about their crops that quickly rotted before being sold, as well as the many research results (raw materials and methods) that edible coating films could not be applied optimally. Objectives: The research was a practical recommendation by comparing the effectiveness of raw materials (polysaccharides, proteins, and lipids) with the dipping and spray methods. Materials and methods used in the comparison process were the application of Structural Equation Modeling (SEM) with the Partial Least Square (PLS) approach. Results: Dipping has a strong effect (f2 ≥ 0.35; p<0.05), while spray had a moderate effect (f2: 0.15-0.35; p<0.05). Thus, the role of dipping as a mediator was more dominant than spray. Compared to proteins and lipids, polysaccharides had the best effectiveness (β:0.460-0.584; f2: 0.15-0.35; p<0.05). Conclusion: the three ingredients improved the quality of tomatoes, and the dipping method was easier to apply by farmers than the spray method, which had many obstacles in its application.
YAYLA ALANLARINDA YAPILAŞMANIN İNCELENMESİ: BOLU ATYAYLASI ÖRNEĞİ
Authors: İBAN Muzaffer Can , KILIÇ Muhammed Eren
Published: September 2022

Bu makale, Bolu İli Merkez İlçesi’ne bağlı Atyaylası yöresindeki yapılaşmanın zamansal değişimini yüksek çözünürlüklü uzaktan algılama verilerini ve coğrafi bilgi sistemlerini kullanarak tespit etmeyi, buna bağlı olarak da yayla alanlarının yönetimi konusunda arazi politikaları üzerine bir tartışma sunmayı hedeflemiştir. Günümüzdeki fiili arazi kullanımına baktığımız zaman, yayla varlıkları üzerinde özellikle konut ve turizm tesisi olarak kullanılan çok sayıda yasal olmayan yapılaşma gözlemlenmektedir. Anayasamızın 45. maddesinde, devletin çayır ve meraların amaç dışında kullanılmasını ve tahribini önleme sorumluluğundan bahsedilmektedir. Bununla birlikte, Mera Kanunu gereğince Türkiye’de yayla varlıkları üzerinde inşaat yasağı kesindir. Bu yasal hükümlere karşın, Atyaylası yöresinin 2014, 2018 ve 2021 yılları için hazırlanan haritaları değerlendirildiğinde, yapılaşmanın ve yayla içi yol üretiminin bu süre zarfında iki kattan fazla bir oranda yükseldiği, 2018 yılında yürürlüğe konulan İmar Barışı’nın bu yapılaşmayı ivmelendirdiği sonucuna ulaşılmıştır. Yasaların güvence altına aldığı kamu orta mallarından olan yaylaların, kamu yararına ve ekolojik sürdürülebilirlik gözetilerek kullanılması gerekmektedir. Ancak bu çalışmanın bulguları, Türkiye’de bu durumun tersi yönde bir eğilimde olduğu fikrini desteklemektedir.
TÜKETİCİLERDEKİ REKLAM OKURYAZARLIĞI BOYUTLARININ RASYONEL VE DUYGUSAL REKLAMLARA YÖNELİK TUTUMA ETKİSİ ÜZERİNE BİR SAHA ARAŞTIRMASI
Authors: Becan Cihan
Published: September 2022

Markaların tüketicileri ile iletişime geçmek için geçmişten bu yana başvurdukları iletişim faaliyetlerinin başında reklam gelmektedir. Reklamın, markalar tarafından vazgeçilmez bir unsur olarak görülmesi, bireylerin gündelik hayatında bilinçli veya bilinçsiz bir şekilde sayısız mesajlara maruz kalmasına neden olmaktadır. Buna ilişkin olarak tüketicilerin bu mesajları nasıl anladığı ve yorumladığı, söz konusu reklama yönelik tutumdan, satın alma eylemine kadar yönlendirebilmektedir. Bu noktada medya okuryazarlığının bir parçası olarak reklam okuryazarlığı önemli bir rol oynamaktadır. Buradan hareketle çalışmada, tüketicilerin reklam okuryazarlık boyutları tespit edilerek, sahip oldukları reklam okuryazarlık boyutlarının rasyonel ve duygusal reklama yönelik tutumlarının ne yönde değiştiğini ölçümlemek amaçlanmaktadır. Bu amaçla elverişli örneklem yöntemiyle, çevrimiçi ortamda, 271 kişiye, Google Forms aracılığıyla oluşturulan anket tekniği uygulanmıştır. Malmelin’in (2010) dört boyutlu reklam okuryazarlığı modelinden hareketle geliştirilen araştırmanın sonucuna göre, bilgisel, retoriksel, görsel/estetik ve promosyonel olmak üzere dört temel reklam okuryazarlığı faktörü ortaya çıkarılmıştır. Rasyonel ve duygusal reklama yönelik tutum üzerinde, bilgisel ve görsel/estetik boyutun anlamlı ve pozitif yönde bir etkisi olduğu gözlemlenmiştir.
COVİD-19 DÖNEMİNDE YENİ EKOLOJİK PARADİGMANIN SOSYAL SORUMLU TÜKETİM DAVRANIŞINA ETKİSİ
Authors: Ersoy Yıldırım Ayşe
Published: September 2022

Pandeminin yaşandığı ve dünyanın sarsıldığı bu dönemde, doğa ve insan yaşamının sürdürülebilirliğiyle ilgili değerlerin sahiplenilmesi gerekliliği dikkat çekerken, tüketimde farkındalığı artırmakla ilgili konular, insan-çevre ilişkisi hakkında daha yüksek düzeydeki değerleri ve içinde yaşadığımız dünya hakkındaki kapsayıcı inançları temsil eden sosyal sorumlu davranışlara odaklanılmasına sebep olmuştur. Covid-19 döneminde yeni ekolojik paradigmanın sosyal sorumlu tüketim davranışına etkisini belirlemek amacıyla bu çalışmada veri toplama aracı olarak, bir ekolojinin kuramsal yönünü ortaya koyabilmek için insanların dünya görüşlerini ölçmeyi amaçlayan yeni ekolojik paradigma ölçeği ile sosyal sorumlu tüketim davranışı ölçeği kullanılmıştır. Araştırma ilişkisel nicel araştırma niteliğindedir. Araştırmanın ana kütlesini İstanbul ilinde yaşamakta olan 479 tüketici oluşturmaktadır. Kolayda örnekleme yöntemi kullanılarak, çevrimiçi anket tekniğiyle araştırmanın verileri toplanmıştır. Araştırma kapsamındaki verilerin faktör analizi için uygunluğu belirlenmiştir. Değişkenler arasındaki ilişkilerin incelenmesi için korelasyon analizi ve ilişkilerin modellenmesi için ise regresyon analizi uygulanmıştır, Etki düzeyini ortaya koymak için de yapısal eşitlik modelinden yararlanılmıştır. Yeni ekolojik paradigma alt boyutlarından doğanın dengesi, muafiyet karşıtı ve anti- antroposentrizm bağımsız değişkenlerin anlamlı düzeylerde sosyal sorumlu tüketim davranışıyla ilişkili olduğu görülmüştür. Ekolojik sınırlar ve ekolojik kriz boyutlarının ise sosyal sorumlu tüketim davranışına etki etmediği bulunmuştur.
REFİK HALİD KARAY’IN BUGÜNÜN SARAYLISI ROMANINDA YAPI VE İZLEK
Authors: Şahin Veysel , Topdaş Çelik Fatma
Published: September 2022

Millî Edebiyat Dönemi’nin önemli sanatçılarından olan Refik Halid Karay’ın Bugünün Saraylısı adlı romanında modernleşme sürecinin bireysel ve toplumsal düzeyde yarattığı etkiler anlatılır. Bu süreç içerisinde kültürel değerlerden uzaklaşmakla meydana gelen yozlaşma ve aidiyet problemi, romanda çatışma unsurunu sağlayan temel epizotlar olarak belirir. Ayrıca romanda bireysel ve toplumsal çözülmenin yanısıra aşk, kıskançlık gibi bireysel duygu ve yönelimlerin de ele alınması izleksel kurguya farklı bir yön kazandırır. Bu temel izlekleri eleştirel bir bakış açısı ile sunan Refik Halid Karay, diğer eserlerinde olduğu gibi Bugünün Saraylısı romanında da kendi dünya görüşünü ve yaşam üslubunu yansıtır. Nitekim okuyucuya vermek ve benimsetmek istediği düşünceyi, kurguladığı karakterler, zaman ve mekân algısı ile aktaran Refik Halid Karay’ın geleneksel olanın önemini ve değerini ortaya çıkarması bireysel bakış açısının yansımalarından izler taşır. Bu çalışmada hem geleneksel hem de modern yaşama ışık tutan Bugünün Saraylısı romanı sahip olduğu bütün bu içerik düzlemi ile birlikte yapısal unsurlar bakımından da tahlil edilecektir.
College Students’ Time Management Disposition, Self-Esteem and Mobile Phone Addiction
Authors: Wu Ming , Yang Jian , Yuan Yuan , Zhang Yuan
Published: September 2022

The purpose of this study was to analyze the impact of time management disposition on mobile phone addiction,
explore the mediating role of self-esteem, and examine the differences between active and inactive college
students in this mediating role. 887 college students participated in this study with valid measures. According
to physical activity rating scale (PARS-3), students who met the recommendations of the World Health
Organization's Global recommendations on physical activity for health were categorized into active
groups while the others were inactive group. The data was subjected to mediation model testing and multi-group
comparison using Amos 21.0. The proportion of physical inactivity of college students was as high as 66.76%, there
were significant differences between active and inactive college student in time monitoring concept, time efficacy,
self-esteem, withdrawal symptoms, prominent behavior, social comfort and mood change. Each dimension of time management
disposition was significantly negatively correlated with each dimension of mobile phone addiction, and was significantly
positively correlated with self-esteem. Self-esteem was significantly negatively correlated with all dimensions of mobile
phone addiction. According to the model test, the data fitted the hypothesized model well. In the whole sample model and
active group model, self-esteem played a partial mediating role. In the inactive college student group, the exercise value
cognition played a part of mediating role. Multi-group analysis found that the moderating effect of active or not was not
significant. Self-esteem played a part of mediating role in the influence of time management disposition on mobile phone
addiction. The findings suggest that persistent participation in physical activity help students to improve time management
ability and reduce mobile phone dependence and even addiction. Future research needs to explore more influential factors for
promoting college students' physical and mental health.
Experimental Research of Body Functional Training on Promoting Physical Quality of College Students
Authors: Du Songjian , Zhang Zhendong , Huang Yingping
Published: September 2022

After being introduced into China, body functional training has been widely applied to the training of high-level athletes
(Wang & Liu, 2014; Cui & Qiu, 2013). However, it is still in the exploratory stage in college teaching. In this research,
body functional training was designed for the physical characteristics of ordinary college students, and the physical health status
of students was monitored in order to understand the promoting effect of body functional training on college students' physical
quality. It also provides theoretical and data support for the continuous improvement of body functional training courses. The
research process was approved by the institutional review board of Zhengzhou University. Participants were 50 freshmen (non-physical
education major, 17-19 years) enrolled into a college in central China. Among them, 25 students (13 female students and 12 male
students) were selected as one group. The experimental group and the control group were given body functional training and normal
physical exercise for 12 weeks (2 times a week, 1 hour each time), while the other physical activities of each group were the same.
Physical fitness test and physical fitness item test were conducted before and after the experiment. SPSS 22.0 statistical software
was used to conduct paired T-test on the data before and after the experiment, and independent sample T-test was conducted on the data
before and after the experiment. In terms of physical health, there was no significant difference in body weight and forced vital capacity
in the control group (P > 0.05), but significant change in body weight (P < 0.05) and significant increase in FVC (P < 0.01) were observed
in the experimental group. From the perspective of physical fitness, the results of the control group showed an improvement trend but not reached
a statistical significance (P > 0.05), while the flexibility of the experimental group showed a very significant change (P < 0.01), the
strength of lower limbs and upper limbs increased significantly (P < 0.05), and the endurance improved significantly (P < 0.05). The differences
between the experimental group and the control group were statistically significant. Body functional training can improve the physical fitness of
college students and has a positive effect on improving their physical fitness. In addition, body functional training can be used as an effective
training method in college physical education. However, due to the limited of time and ability, experiments were conducted for only 12 weeks,
future researchers could make more detailed studies on this training method.
The Origin and Characteristics of Family Sports in Germany
Authors: Yuan Run
Published: September 2022

Family sports, as a breakthrough of national fitness in Germany, has gone through more than 30 years of development. This study examined the origin, management, operation, guarantee, and promotion mechanism of family sports in Germany by using the methods of literature and field investigation. It is found that family sports in Germany originated from the implementation of "Gold Plan" in Germany, which has the following characteristics: Taking the top management organization of national sports as the link, paying attention to the coordinated management of family and club; paying attention to the coordination and cooperation of multiple departments and organizations. To integrate social resources, to focus on the responsibility of sports clubs, to create a family sports guide, and to focus on family friendly development model are the mechanism driving force to promote the development of family sports. And get the following enlightenment: give full play to the guiding role of the policy, cultivate all kinds of social organizations to promote the development of family sports; based on the integration model of school, family and community, create a good environment for the development of family sports; create the family sports guide to guide the development of Court Sports in our country; adhere to the family sports development model of "family centered".
Impact of International Sport Events in Promoting International Attention of Cities
Authors: Chen Yuanxin , Li Jingyu
Published: September 2022

In order to raise the international attention and gain more competitive advantages, many Chinese cities are aiming at holding international sport events to promote urban development. However, previous studies indicated that international sport events have attracted international attention for hosting cities, but only stay at theoretical level of interpretation, empirical studies are scarce and needed. Therefore, the major purpose of this study was to investigate the impact of international sport events on urban development from the perspective of cities’ international attention, and to provide new dimensions and ideas for the relationship between sport events and urban development. This study used Google Trends corpus as the data source and used the Google Trends Index to measure five cities’ (Beijing, Shanghai, Nanjing, Chengdu, Wuhan) international attention in order to analyze the impact of international sport events on hosting cities in China. Through data mining, this study collected weekly data of international attention (time range is from January 1st, 2014 to December 31th, 2018) of five cities and compared with major media events in the corresponding period when highest international attention appeared in each city. Using Pearson Correlation, correlation analysis of international attention (all categories) and international attention (sports categories) of five cities was tested. Valuable information was obtained through the relevant keyword search function of the search volume ranking to provide more comprehensive evidence for the empirical results. There was a significant correlation between the international attention (all categories) and international attention (sports categories) in these five cities, and Nanjing's data were the strongest (P=0.987). Through empirical analysis, the peak time of international attention in Beijing, Shanghai, Nanjing, Chengdu and Wuhan in recent five years was highly consistent with the major international sport events (Beijing IAAF World Championships, 2015; ATP1000 Shanghai Masters, 2018; Nanjing Summer Youth Olympics, 2014; ATP250 Chengdu Open ,2018; Wuhan Open ,2018), but the effect was negligible as the events ended. International sport events are an important dynamic attraction for hosting cities to gain more international attention, which can produce intense impulsive impact on each city's international attention. The effect of international sport events on hosting city's international attention is relatively short and concentrated, and there is no obvious long term effect. Finally, the international attention of cities has a relatively stable level from a long time perspective.