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Checks and Balances and Institutional Gridlock: Implications for Authoritarianism

Governance 2025年第2期38卷

作者： Adiguzel, Fatih Serkant Faculty of Arts and Social Sciences Sabancı University İstanbul Turkey Center of Excellence in Data Analytics Sabancı University İstanbul Turkey

Recently, the world saw a wave of elected leaders attack democracy. Why do people support leaders who remove checks and balances? I argue that aspiring autocrats gain more popular support when they present these institutions as obstacles to getting things done. In doing so, they exploit a critical tension between the possibility of gridlock and the abuse of power, which is inherent in democratic institutions. Using cross-national data and leveraging an original survey experiment from Turkey, I show that effective checks and balances decrease democracy satisfaction and that aspiring autocrats gain more popular support when they present these institutions as obstacles. More interestingly, respondents perceive the aspiring autocrats' gridlock justification to dismantle checks and balances as a pro-democratic attempt to remove the obstacles to a policy-responsive regime. These results show that aspiring autocrats exploit the tension in democracies, making it harder for citizens to perceive the threat they face. © 2025 Wiley Periodicals LLC.

关键词： abuse of power democratic backsliding gridlock

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AgriLLM:Harnessing Transformers for Framer Queries 3

AgriLLM:Harnessing Transformers for Framer Queries

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3rd Workshop on NLP for Positive Impact, NLP4PI 2024, held in conjunction with the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Didwania, Krish Seth, Pratinav Kasliwal, Aditya Agarwal, Amit Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Academy of Higher Education Manipal India Department of Data Science & Computer Applications Manipal Academy of Higher Education Manipal India Enterprise Analytics & Data Science Artificial Intelligence - Center of Excellence Wells Fargo International Solutions Private Limited Bangalore India

ISBN: (纸本)9798891761759

Agriculture, vital for global sustenance, necessitates innovative solutions due to a lack of organized domain experts, particularly in developing countries where many farmers are impoverished and cannot afford expert consulting. Initiatives like Farmers Helpline play a crucial role in such countries, yet challenges such as high operational costs persist. Automating query resolution can alleviate the burden on traditional call centers, providing farmers with immediate and contextually relevant information. The integration of Agriculture and Artificial Intelligence (AI) offers a transformative opportunity to empower farmers and bridge information gaps. Language models like transformers, the rising stars of AI, possess remarkable language understanding capabilities, making them ideal for addressing information gaps in agriculture. This work 1 explores and demonstrates the transformative potential of Large Language Models (LLMs) in automating query resolution for agricultural farmers, leveraging their expertise in deciphering natural language and understanding context. Using a subset of a vast dataset of real-world farmer queries collected in India, our study focuses on approximately 4 million queries from the state of Tamil Nadu, spanning various sectors, seasonal crops, and query types. © 2024 Association for Computational Linguistics.

关键词： Distribution transformers

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Investigation of Weight Initialization Using Fibonacci Sequence on the Performance of Neural Networks 4

Investigation of Weight Initialization Using Fibonacci Seque...

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4th IEEE Pune Section International Conference, PuneCon 2021

作者： Mukherjee, Dipanwita Sinha Yeri, Naveen Gururaja Enterprise Analytics and Data Science Artificial Intelligence - Center of Excellence Wells Fargo International Solutions Private Limited Bangalore India Head of Enterprise Analytics and Data Science Artificial Intelligence - Center of Excellence Wells Fargo International Solutions Private Limited Bangalore India

ISBN: (纸本)9781665444798

Initializing weights are important for fast convergence and performance improvement of Artificial Neural Network models. This study proposes a heuristic method to initialize weights for Neural Network with Fibonacci sequence. Experiments have been carried out with different network structures and datasets and results have been compared with other initialization techniques such as Zero, Random, Xavier and He. It has been observed that for small sized datasets, Fibonacci initialization technique reports 94% of test accuracy which is better than Random (85%) and close to Xavier (93%) and He (96%) initialization methods. Also, for medium sized dataset, we have noted that performance of Fibonacci weight initialization method is comparable with the same for Random, Xavier and He initialization techniques. © 2021 IEEE.

关键词： Heuristic methods

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Integrating semantic features in fruit recognition based on perceptual color and semantic template

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Information Processing in Agriculture 2022年第2期9卷 316-334页

作者： Ema Rachmawati Iping Supriana Masayu Leylia Khodra Fauzan Firdaus School of Computing Universitas TelkomBandung 40257Indonesia School of Electrical Engineering and Informatics Institut Teknologi BandungBandung 40132Indonesia University Center of Excellence on Artificial Intelligence for Vision Natural Language Processing&Big Data Analytics(U-CoE AI-VLB)Institut Teknologi BandungBandung 40132Indonesia

Many fruit recognition works have applied statistical approaches to make an exact correlation between low-level visual feature information and high-level semantic concepts givenby predefined text caption or keywords. Two common fruit recognition models include bagof-features (BoF) and convolutional neural network (ConvNet), which achieve highperformance results. In most cases, the overfitting problem is unavoidable. This problemmakes it difficult to generalize new instances with only a slightly different appearance,although belonging to the same category. This article proposes a new fruit recognitionmodel by associating an object’s low-level features in an image with a high-level *** define a perceptual color for each fruit species to construct a relationship between fruitcolor and semantic color name. Furthermore, we develop our model by integrating the perceptual color and semantic template concept to solve the overfitting problem. The semantic template concept as a mapping between the high-level concept and the low-level visualfeature is adopted in this model. The experiment was conducted on three different fruitimage datasets, with one dataset as train data and the two others as test data. The experimental results demonstrate that the proposed model, called perceptual color on semantictemplate (PCoST), is significantly better than the BoF and ConvNet models in reducing theoverfitting problem.

关键词： Multi-class fruit recognition Perceptual color Semantic template Bag-of-features

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MVLA-Net:A Multi-View Lesion Attention Network for Advanced Diagnosis and Grading of Diabetic Retinopathy

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Computers, Materials & Continua 2025年第4期83卷 1173-1193页

作者： Tariq Mahmood Tanzila Saba Faten S.Alamri Alishba Tahir Noor Ayesha Artificial Intelligence and Data Analytics(AIDA)Lab College of Computer&Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Sciences University of EducationVehari CampusVehari61100Pakistan Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Shifa College of Medicine Shifa Tameer-E-Millat UniversityIslamabad44000Pakistan Center of Excellence in Cyber Security(CYBEX) Prince Sultan UniversityRiyadh11586Saudi Arabia

Innovation in learning algorithms has made retinal vessel segmentation and automatic grading tech-niques crucial for clinical diagnosis and prevention of diabetic *** traditional methods struggle with accuracy and reliability due to multi-scale variations in retinal blood vessels and the complex pathological relationship in fundus images associated with diabetic *** the single-modal diabetic retinopathy grading network addresses class imbalance challenges and lesion representation in fundus image data,dual-modal diabetic retinopathy grading methods offer superior ***,the scarcity of dual-modal data and the lack of effective feature fusion methods limit their potential due to multi-scale *** paper addresses these issues by focusing on multi-scale retinal vessel segmentation,dual feature fusion,data augmentation,and attention-based *** proposed model aims to improve comprehensive segmentation for retinal images with varying vessel *** employs a dual-branch parallel architecture that integrates a transformer encoder with a convolutional neural network encoder to extract local and global information for synergistic saliency *** that,the model uses residual structures and attention modules to extract critical lesions,enhancing the accuracy and reliability of diabetic retinopathy *** evaluate the efficacy of the proposed approach,this study compared it with other pre-trained publicly open models,ResNet152V2,ConvNext,Efficient Net,DenseNet,and Swin Transform,with the same developmental *** models achieved approximately 85%accuracy with the same image preparation ***,the proposed approach outperforms and optimizes existing models by achieving an accuracy of 99.17%,99.04%,and 99.24%,on Kaggle APTOS19,IDRiD,and EyePACS datasets,*** results support the model’s utility in helping ophthalmologists diagnose diabetic retinopathy more rapidly and accurat

关键词： Diabetic retinopathy grading retinal vessel segmentation dual-modal deep learning attention mecha-nism health risks

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Recommendation Engines: Traditional vs Deep Learning Approaches 6

Recommendation Engines: Traditional vs Deep Learning Approac...

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6th International Conference on Computing, Communication and Security, ICCCS 2021

作者： Dhawad, Aniket Sarkar, Suranjana Agarwal, Amit Darlapudi, Rama Kishore Wells Fargo International Solutions Private Limited Enterprise Analytics and Data Science Artificial Intelligence-Center of Excellence Bangalore India

ISBN: (纸本)9781665440394

With digitlzation growing by leap and bounds, websites are now overloaded with products and information density, making it challenging for customers to choose between a variety of products. The Recommendation engine enables companies to deliver their customers more personalized and customized products or information efficiently. As a result, the recommendation engine eventually helps in boosting different business performance metrics such as revenues, Click- Through Rates (CTRs), conversions, etc., for these companies significantly. And in the long run, it also optimizes user experience, thus translating to higher customer satisfaction and retention. In this paper, we will highlight some of the contemporary algorithms used in building high-performance recommendation engines, illustrate use cases from Tech Giants that can be applied in the context of the banking and financial services domain and identify major challenges that are open for further research work. Also, in this paper, we will be using recommendation engine and recommender system alternatively as both are synonymous. © 2021 IEEE.

关键词： Recommender systems

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From Occasional to Steady: Habit Formation Insights From a Comprehensive Fitness Study

arXiv

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arXiv 2025年

作者： Demirci, Ege Tüzün, Efe Ün, Ahmet Furkan Sönmez, Taner Giray Varol, Onur Faculty of Engineering and Natural Sciences Sabanci University Istanbul34956 Turkey Mars Athletic Club Istanbul34340 Turkey Center of Excellence in Data Analytics Sabanci University Istanbul34956 Turkey

Exercising regularly is widely recognized as a cornerstone of health, yet the challenge of sustaining consistent exercise habits persists. Understanding the factors that influence the formation of these habits is crucial for developing effective interventions. This study utilizes data from Mars Athletic Club, Türkiye’s largest sports chain, to investigate the dynamics of gym attendance and habit formation. The general problem addressed by this study is identifying the critical periods and factors that contribute to the successful establishment of consistent exercise routines among gym-goers. Here we show that there are specific periods during which gym attendance is most crucial for habit formation. By developing a survival metric based on gym attendance patterns, we pinpoint these critical periods and segment members into distinct clusters based on their visit patterns. Our analysis reveals significant differences in how various subgroups respond to interventions, such as group classes, personal trainer sessions, and visiting different clubs. Using causal inference analysis, we demonstrate that personalized guidance and social dynamics are key drivers of sustained long-term engagement. By systematically examining these variables and considering the specific characteristics of different clusters, our research demonstrates the importance of a tailored, multi-dimensional approach to promoting exercise habits, which integrates social dynamics, personalized guidance, and strategic interventions to sustain long-term engagement. Copyright © 2025, The Authors. All rights reserved.

关键词： Cluster analysis

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Politics and Propaganda on Social Media: How Twitter and Meta Moderate State-Linked Information Operations

arXiv

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arXiv 2024年

作者： Mugurtay, Nihat Duygu, Umut Varol, Onur Faculty of Engineering and Natural Sciences Sabanci University Turkey Sabanci University Faculty of Arts and Social Sciences Sabanci University Turkey Center of Excellence in Data Analytics Sabanci University Turkey

Why do Social Media Corporations (SMCs) engage in state-linked information operations? Social media can significantly influence the global political landscape, allowing governments and other political entities to engage in concerted information operations, shaping or manipulating domestic and foreign political agendas. In response to state-linked political manipulation tactics on social media, Twitter and Meta carried out take-down operations against propaganda networks, accusing them of interfering foreign elections, organizing disinformation campaigns, manipulating political debates and many other issues. This research investigates the two SMCs' policy orientation to explain which factors can affect these two companies' reaction against state-linked information operations. We find that good governance indicators such as democracy are significant elements of SMCs' country-focus. This article also examines whether Meta and Twitter's attention to political regime characteristics is influenced by international political alignments. This research illuminates recent trends in SMCs' take-down operations and illuminating interplay between geopolitics and domestic regime characteristics. Copyright © 2024, The Authors. All rights reserved.

关键词： Social networking (online)

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A data-driven approach to estimating post-discovery parameters of unexplored oilfields

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Petroleum 2023年第2期9卷 285-300页

作者： Fransiscus Pratikto Sapto Indratno Kadarsah Suryadi Djoko Santoso Industrial Engineering Department Bandung Institute of TechnologyJl Ganesha 10Bandung40132Indonesia Mathematics Department Bandung Institute of TechnologyJl Ganesha 10Bandung40132Indonesia Geophysical Engineering Department Bandung Institute of TechnologyJl Ganesha 10Bandung40132Indonesia University Center of Excellence on Artificial Intelligence for Vision Institut Teknologi BandungNatural Language Processing&Big Data Analytics(U-CoE AI-VLB)Bandung 40132West JavaIndonesia

Consider a typical situation where an investor is considering acquiring an unexplored *** oilfield has undergone a preliminary geological and geophysical study in which pre-discovery data such as lithology,depth,depositional system,diagenetic overprint,structural compartmentalization,and trap type are *** this situation,investors usually estimate production rates using a volumetric approach.A more accurate estimation of production rates can be obtained using analytical methods,which require additional data such as net pay,porosity,oil formation volume factor,permeability,viscosity,and *** call these data post-discovery parameters because they are only available after discovery through exploration drilling.A data-driven approach to estimating post-discovery parameters of an unexplored oilfield is developed based on its pre-discovery data by learning from proven reservoir *** the Gaussian mixture model,and a data-driven reservoir typology based on the joint probability distribution of post-discovery parameters is *** came up with 12 reservoir ***,an artificial neural network classification model with the resilient backpropagation algorithm is used to find relationships between pre-discovery data and reservoir *** on k-fold crossvalidation with k?10,the accuracy of the classification model is stable with an average of 87.9%.With our approach,an investor considering acquiring an unexplored oilfield can classify the oilfield's reservoir into a particular type and estimate its post-discovery parameters'joint probability *** investor can incorporate this information into a valuation model to calculate the production rates more accurately,estimate the oilfield's value and risk,and make an informed acquisition decision accordingly.

关键词： data-driven Pre-discovery data Post-discovery parameters Gaussian mixture model Artificial neural network

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Optimisation-based sampling time selection for modelling the three interacting tanks

Optimisation-based sampling time selection for modelling the...

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2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023

作者： Rahadian, Helmy Burohman, Azka Muji Widyotriatmo, Augie Joelianto, Endra Institut Teknologi Bandung Engineering Physics Doctoral Program Bandung Indonesia Universitas Dian Nuswantoro Electrical Engineering Study Program Semarang Indonesia Research Center for Photonics Tangerang Selatan Indonesia Institut Teknologi Bandung Instrumentation and Control Research Group Bandung Indonesia University Center of Excellence Ai on Vision Nlp & Big Data Analytics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350309225

This paper considers parameters and sampling time identification of a three interacting tanks system. A novel tailor-made model is introduced to deal with the real plant in the laboratory. Experimental data are then exploited to perform parameter identification via particle swarm optimisation (PSO) method. In addition to the system parameters, we propose to simultaneously optimize the sampling selection in the optimisation process. Even though several well known sampling time selection are established, a more precise selection is required. It is shown in this paper that a meta-heuristic optimisation method (PSO) can be an efficient procedure for this problem. Moreover, three different error measures for performing optimisation are also discussed. © 2023 IEEE.

关键词： Heuristic methods

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