pwc-archive/papers-with-abstracts huggingface dataset:
Germany's Tax Revenue and its Total Administrative Cost
Authors: Christopher Mantzaris, Ajda Fošner
Date: 2024-11-19 00:00:00

Tax administrative cost reduction is an economically and socially desirable goal for public policy. This article proposes total administrative cost as percentage of total tax revenue as a vivid measurand, also useful for cross-jurisdiction comparisons. Statistical data, surveys and a novel approach demonstrate: Germany's 2021 tax administrative costs likely exceeded 20% of total tax revenue, indicating need for improvement of Germany's taxation system - and for the many jurisdictions with similar tax regimes. In addition, this article outlines possible reasons for and implications of the seemingly high tax administrative burden as well as solutions.
The Hertz contact problem in couple-stress elasticity
Authors: P.A. Gourgiotis a, Th. Zisis b, A.E. Giannakopoulos b, H.G. Georgiadis
Date: 2019-08-15 00:00:00
Proceeding: journal/international-journal-of-solids-and-structures 2019 8

The classical three-dimensional Hertz contact problem is re-examined in the present work within the context of couple-stress elasticity. This theory introduces characteristic material length scales that emerge from the underlying microstructure and has proved to be very effective yet rather simple for modeling complex microstructured materials. An exact solution of the axisymmetric contact problem is obtained through Hankel integral transforms and singular integral equations. The goal is to examine to what extend this gradient theory captures the experimentally observed indentation response and the related size effects in materials with microstructure. Furthermore, the present work extends the classical Hertz contact solution in the framework of a generalized continuum theory providing thus a useful theoretical background for the interpretation of spherical indentation tests of microstructured materials. The results obtained in the present work can be used in order to model nano/micro indentation experiments of several materials, such as polymers, ceramics, composites, cellular materials, foams, masonry, and bone tissues, of which their macroscopic response is influenced by the microstructural characteristic lengths.
Fruit Maturity Recognition from Agricultural, Market and Automation Perspectives
Authors: Koteswar Rao Jerripothula, Sarvesh Kumar Shukla, Samyak Jain, Shudhanshu Singh
Date: 2021-11-10 00:00:00
Proceeding: Annual Conference of the IEEE Industrial Electronics Society (IECON) 2021 11
Tasks: Fruit-type + Maturity-state Prediction (Multi-label Classification), image-classification, Raw vs Ripe (Generic)

Motivated by the potential reduction in the required manual efforts in the fruit industry, this paper attempts to automate fruit maturity recognition. We study the problem from the agricultural, market, and automation perspectives, often taken at different points in the supply chain. Since different maturity states have different visual characteristics, an image classification technology can certainly help here. To develop fruit image classifiers, we need a feature extraction method and a learning algorithm. We use different pre-trained neural networks for effective feature extraction and employ different machine learning algorithms while carrying out bias/variance analysis of
the learned models. The analysis helps us select the best ones for each perspective under consideration. We achieve 96%, 94%, and
86% accuracies on our novel dataset named RipeRaw from the agricultural, market, and automation perspectives, respectively.
Privacy-Enhanced Data Sharing Systems from Hierarchical ID-Based Puncturable Functional Encryption with Inner Product Predicates
Authors: Cheng-Yi Lee, Zi-Yuan Liu, Masahiro Mambo, Raylin Tso
Date: 2024-09-28 00:00:00
Proceeding: IET Information Security 2024 9
Tasks: Cloud Computing

The emergence of cloud computing enables users to upload data to remote clouds and compute them. This drastically reduces computing and storage costs for users. Considering secure computing for multilevel users in enterprises, the notion of hierarchical identity-based inner product functional encryption (HIB-IPFE) is proposed. In this cryptosystem, a sender can encrypt a vector mathematical equation into a ciphertext with a hierarchical identity, while a receiver who possesses a secret key corresponding to the same hierarchical identity and a vector mathematical equation can decrypt the ciphertext and obtain the inner product mathematical equation. However, HIB-IPFE is not sufficient to capture flexible data sharing and forward security. In this study, we present a notion of hierarchical identity-based puncturable HIBP-IPFE. Furthermore, we present a formal definition and security model of HIBP-IPFE to guarantee data confidentiality and receiver anonymity. Compared with HIB-IPFE, our proposed scheme enables users to puncture keys on specific tags ensuring that the punctured keys cannot be used to decrypt the ciphertexts associated with those tags. The proposed scheme is provably secure under d-DBDHE assumption in the standard model. The experimental results indicate that our scheme is more practical in cloud computing, with superior functionality.
Modelling financial returns with mixtures of generalized normal distributions
Authors: Pierdomenico Duttilo
Date: 2024-10-21 00:00:00
Tasks: Financial Analysis, parameter estimation

This PhD Thesis presents an investigation into the analysis of financial returns using mixture models, focusing on mixtures of generalized normal distributions (MGND) and their extensions. The study addresses several critical issues encountered in the estimation process and proposes innovative solutions to enhance accuracy and efficiency. In Chapter 2, the focus lies on the MGND model and its estimation via expectation conditional maximization (ECM) and generalized expectation maximization (GEM) algorithms. A thorough exploration reveals a degeneracy issue when estimating the shape parameter. Several algorithms are proposed to overcome this critical issue. Chapter 3 extends the theoretical perspective by applying the MGND model on several stock market indices. A two-step approach is proposed for identifying turmoil days and estimating returns and volatility. Chapter 4 introduces constrained mixture of generalized normal distributions (CMGND), enhancing interpretability and efficiency by imposing constraints on parameters. Simulation results highlight the benefits of constrained parameter estimation. Finally, Chapter 5 introduces generalized normal distribution-hidden Markov models (GND-HMMs) able to capture the dynamic nature of financial returns. This manuscript contributes to the statistical modelling of financial returns by offering flexible, parsimonious, and interpretable frameworks. The proposed mixture models capture complex patterns in financial data, thereby facilitating more informed decision-making in financial analysis and risk management.
Do You Remember . . . the Future? Weak-to-Strong generalization in 3D Object Detection
Authors: Alexander Gambashidze, Aleksandr Dadukin, Maxim Golyadkin, Maria Razzhivina, Ilya Makarov
Date: 2024-08-03 00:00:00
Proceeding: IJCAI 2024 8
Tasks: 3D Object Detection, Knowledge Distillation, Object, object-detection, Object Detection

This paper demonstrates a novel method for
LiDAR-based 3D object detection, addressing ma-
jor field challenges: sparsity and occlusion. Our
approach leverages temporal point cloud sequences
to generate frames that provide comprehensive
views of objects from multiple angles. To address
the challenge of generating these frames in real-
time, we employ Knowledge Distillation within
a Teacher-Student framework, allowing the Stu-
dent model to emulate the Teacher’s advanced per-
ception. We pioneered the application of weak-
to-strong generalization in computer vision by
training our Teacher model on enriched, object-
complete data. In this demo, we showcase the ex-
ceptional quality of labels produced by the X-Ray
Teacher on object-complete frames, showing our
method distilling its knowledge to enhance object
3D detection models.
BiFA: Remote Sensing Image Change Detection With Bitemporal Feature Alignment
Authors: Haotian Zhang, Hao Chen, Chenyao Zhou, Keyan Chen, Chenyang Liu, Zhengxia Zou, Zhenwei Shi
Date: 2024-03-18 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2024 3
Tasks: Change Detection

Despite the success of deep learning-based change detection (CD) methods, their existing insufficiency in temporal (channel and spatial) and multiscale alignment has rendered them insufficient capability in mitigating external factors (illumination changes and perspective differences) arising from different imaging conditions during CD. In this article, a bitemporal feature alignment (BiFA) model is proposed to produce a precise CD map in a lightweight manner by reducing the impact of irrelevant factors. Specifically, for the temporal alignment, the bitemporal interaction (BI) module is proposed to realize the alignment of the bitemporal image channel level. Our intuition is introducing the BI in the feature extraction stage may benefit suppressing the interference, such as illumination changes. Simultaneously, the alignment module based on differential flow field (ADFF) is proposed to explicitly estimate the offset of the bitemporal image and realize their spatial level alignment to mitigate the inadequate registration resulting from different perspectives. Furthermore, for the multiscale alignment, we introduce the implicit neural alignment decoder (IND) to produce more refined prediction maps achieving precise alignment of multiscale features by learning continuous image representations in coordinate space. Our BiFA outperforms other state-of-the-art methods on six datasets (such as the F1-score (F1)/intersection over union (IoU) scores are improved by 2.70%/3.91% and 2.01%/2.94% on Learning, VIsion, and Remote sensing (LEVIR)+-CD and Sun Yat-sen University (SYSU)-CD, respectively) and displays greater robustness in cross-resolution CD. Our code is available at https://github.com/zmoka-zht/BiFA.
Using data mining techniques for bike sharing demand prediction in metropolitan city
Authors: Sathishkumar V E, Jangwoo Park, Yongyun Cho
Date: 2020-03-01 00:00:00
Proceeding: Computer Communications 2020 3
Tasks: Prediction

Currently Rental bikes are introduced in many urban cities for the enhancement of mobility comfort. It is important to make the rental bike available and accessible to the public at the right time as it lessens the waiting time. Eventually, providing the city with a stable supply of rental bikes becomes a major concern. The crucial part is the prediction of bike count required at each hour for the stable supply of rental bikes. A Data mining technique is employed for overcoming the hurdles for the prediction of hourly rental bike demand. This paper discusses the models for hourly rental bike demand prediction. Data used include weather information (Temperature, Humidity, Windspeed, Visibility, Dewpoint, Solar radiation, Snowfall, Rainfall), the number of bikes rented per hour and date information. The paper also explores an filtering of features approach to eliminate the parameters which are not predictive and ranks the features based on its prediction performance. Five Statistical regression models were trained with their best hyperparameters  using repeated cross-validation and the performance is evaluated using a testing set: (a) Linear Regression (b) Gradient Boosting Machine (c) Support Vector Machine (Radial Basis Function Kernel) (d) Boosted Trees, and (e) Extreme Gradient Boosting Trees. When all the predictors are employed, the best model Gradient Boosting Machine can give the best and highest R2 value of 0.96 in the training set and 0.92 in the test set. Furthermore, several analyzes are carried out in Gradient Boosting Machine with different combinations of predictors to identify the most significant predictors and the relationships between them.
Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city
Authors: Sathishkumar V E, Changsun Shin, Yongyun Cho
Date: 2020-08-09 00:00:00
Proceeding: Building Research & Information 2020 8
Tasks: Prediction, regression

The fast development of urban advancement in the past decade requires reasonable and realistic solutions for transport, building infrastructure, natural conditions, and personal satisfaction in smart cities. This paper presents and explores predictive energy consumption models based on data-mining techniques for a smart small-scale steel industry in South Korea. Energy consumption data is collected using IoT based systems and used for prediction. Data used include the lagging and leading current reactive power, the lagging and leading current power factor, carbon dioxide emissions, and load types. Five statistical algorithms are used for energy consumption prediction:(a) General linear regression, (b) Classification and regression trees, (c) Support vector machine with a radial basis kernel, (d) K nearest neighbours, (e) CUBIST. Root mean squared error, Mean absolute error and Coefficient of variation are used to measure the prediction efficiency of the models. The results show that CUBIST model provides best results with lower error values and this model can be used for the development of energy efficient structural design which helps to optimize the energy consumption and policy making in smart cities.
Seoul bike trip duration prediction using data mining techniques
Authors: Sathishkumar V E, Jangwoo Park, Yongyun Cho
Date: 2020-11-01 00:00:00
Proceeding: IET Intelligent Transport Systems 2020 11
Tasks: Feature Engineering, Prediction

Trip duration is the most fundamental measure in all modes of transportation. Hence, it is crucial to predict the trip-time precisely for the advancement of Intelligent Transport Systems and traveller information systems. To predict the trip duration, data mining techniques are employed in this study to predict the trip duration of rental bikes in Seoul Bike sharing system. The prediction is carried out with the combination of Seoul Bike data and weather data. The data used include trip duration, trip distance, pickup and dropoff latitude and longitude, temperature, precipitation, wind speed, humidity, solar radiation, snowfall, ground temperature and 1-hour average dust concentration. Feature engineering is done to extract additional features from the data. Four statistical models are used to predict the trip duration. (a) Linear regression, (b) Gradient boosting machines, (c) k nearest neighbour and (d) Random Forest (RF). Four performance metrics root mean squared error, coefficient of variance, mean absolute error and median absolute error is used to determine the efficiency of the models. In comparison with the other models, the best model RF can explain the variance of 93% in the testing set and 98% (R 2) in the training set. The outcome proves that RF is effective to be employed for the prediction of trip duration.
Utilizing Large Language Models to Synthesize Product Desirability Datasets
Authors: John D. Hastings, Sherri Weitl-Harms, Joseph Doty, Zachary J. Myers, Warren Thompson
Date: 2024-11-20 00:00:00
Tasks: Diversity

This research explores the application of large language models (LLMs) to generate synthetic datasets for Product Desirability Toolkit (PDT) testing, a key component in evaluating user sentiment and product experience. Utilizing gpt-4o-mini, a cost-effective alternative to larger commercial LLMs, three methods, Word+Review, Review+Word, and Supply-Word, were each used to synthesize 1000 product reviews. The generated datasets were assessed for sentiment alignment, textual diversity, and data generation cost. Results demonstrated high sentiment alignment across all methods, with Pearson correlations ranging from 0.93 to 0.97. Supply-Word exhibited the highest diversity and coverage of PDT terms, although with increased generation costs. Despite minor biases toward positive sentiments, in situations with limited test data, LLM-generated synthetic data offers significant advantages, including scalability, cost savings, and flexibility in dataset production.
ToolScan: A Benchmark for Characterizing Errors in Tool-Use LLMs
Authors: Shirley Kokane, Ming Zhu, Tulika Awalgaonkar, JianGuo Zhang, Thai Hoang, Akshara Prabhakar, Zuxin Liu, Tian Lan, Liangwei Yang, Juntao Tan, Rithesh Murthy, Weiran Yao, Zhiwei Liu, Juan Carlos Niebles, Huan Wang, Shelby Heinecke, Caiming Xiong, Silivo Savarese
Date: 2024-11-20 00:00:00

Evaluating Large Language Models (LLMs) is one of the most critical aspects of building a performant compound AI system. Since the output from LLMs propagate to downstream steps, identifying LLM errors is crucial to system performance. A common task for LLMs in AI systems is tool use. While there are several benchmark environments for evaluating LLMs on this task, they typically only give a success rate without any explanation of the failure cases. To solve this problem, we introduce TOOLSCAN, a new benchmark to identify error patterns in LLM output on tool-use tasks. Our benchmark data set comprises of queries from diverse environments that can be used to test for the presence of seven newly characterized error patterns. Using TOOLSCAN, we show that even the most prominent LLMs exhibit these error patterns in their outputs. Researchers can use these insights from TOOLSCAN to guide their error mitigation strategies.
BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games
Authors: Davide Paglieri, Bartłomiej Cupiał, Samuel Coward, Ulyana Piterbarg, Maciej Wolczyk, Akbir Khan, Eduardo Pignatelli, Łukasz Kuciński, Lerrel Pinto, Rob Fergus, Jakob Nicolaus Foerster, Jack Parker-Holder, Tim Rocktäschel
Date: 2024-11-20 00:00:00
Tasks: Benchmarking, NetHack, Spatial Reasoning

Large Language Models (LLMs) and Vision Language Models (VLMs) possess extensive knowledge and exhibit promising reasoning abilities, however, they still struggle to perform well in complex, dynamic environments. Real-world tasks require handling intricate interactions, advanced spatial reasoning, long-term planning, and continuous exploration of new strategies-areas in which we lack effective methodologies for comprehensively evaluating these capabilities. To address this gap, we introduce BALROG, a novel benchmark designed to assess the agentic capabilities of LLMs and VLMs through a diverse set of challenging games. Our benchmark incorporates a range of existing reinforcement learning environments with varying levels of difficulty, including tasks that are solvable by non-expert humans in seconds to extremely challenging ones that may take years to master (e.g., the NetHack Learning Environment). We devise fine-grained metrics to measure performance and conduct an extensive evaluation of several popular open-source and closed-source LLMs and VLMs. Our findings indicate that while current models achieve partial success in the easier games, they struggle significantly with more challenging tasks. Notably, we observe severe deficiencies in vision-based decision-making, as several models perform worse when visual representations of the environments are provided. We release BALROG as an open and user-friendly benchmark to facilitate future research and development in the agentic community. Code and Leaderboard at balrogai.com.
Metacognition for Unknown Situations and Environments (MUSE)
Authors: Rodolfo Valiente, Praveen K. Pilly
Date: 2024-11-20 00:00:00

Metacognition--the awareness and regulation of one's cognitive processes--is central to human adaptability in unknown situations. In contrast, current autonomous agents often struggle in novel environments due to their limited capacity for adaptation. We hypothesize that metacognition is a critical missing ingredient in adaptive autonomous systems, equipping them with the cognitive flexibility needed to tackle unfamiliar challenges. Given the broad scope of metacognitive abilities, we focus on two key aspects: competence awareness and strategy selection for novel tasks. To this end, we propose the Metacognition for Unknown Situations and Environments (MUSE) framework, which integrates metacognitive processes--specifically self-awareness and self-regulation--into autonomous agents. We present two initial implementations of MUSE: one based on world modeling and another leveraging large language models (LLMs), both instantiating the metacognitive cycle. Our system continuously learns to assess its competence on a given task and uses this self-awareness to guide iterative cycles of strategy selection. MUSE agents show significant improvements in self-awareness and self-regulation, enabling them to solve novel, out-of-distribution tasks more effectively compared to Dreamer-v3-based reinforcement learning and purely prompt-based LLM agent approaches. This work highlights the promise of approaches inspired by cognitive and neural systems in enabling autonomous systems to adapt to new environments, overcoming the limitations of current methods that rely heavily on extensive training data.
Identity Preserving 3D Head Stylization with Multiview Score Distillation
Authors: Bahri Batuhan Bilecen, Ahmet Berke Gokmen, Furkan Guzelant, Aysegul Dundar
Date: 2024-11-20 00:00:00
Tasks: Diversity

3D head stylization transforms realistic facial features into artistic representations, enhancing user engagement across gaming and virtual reality applications. While 3D-aware generators have made significant advancements, many 3D stylization methods primarily provide near-frontal views and struggle to preserve the unique identities of original subjects, often resulting in outputs that lack diversity and individuality. This paper addresses these challenges by leveraging the PanoHead model, synthesizing images from a comprehensive 360-degree perspective. We propose a novel framework that employs negative log-likelihood distillation (LD) to enhance identity preservation and improve stylization quality. By integrating multi-view grid score and mirror gradients within the 3D GAN architecture and introducing a score rank weighing technique, our approach achieves substantial qualitative and quantitative improvements. Our findings not only advance the state of 3D head stylization but also provide valuable insights into effective distillation processes between diffusion models and GANs, focusing on the critical issue of identity preservation. Please visit the https://three-bee.github.io/head_stylization for more visuals.
Entropy Bootstrapping for Weakly Supervised Nuclei Detection
Authors: James Willoughby, Irina Voiculescu
Date: 2024-11-20 00:00:00
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

Microscopy structure segmentation, such as detecting cells or nuclei, generally requires a human to draw a ground truth contour around each instance. Weakly supervised approaches (e.g. consisting of only single point labels) have the potential to reduce this workload significantly. Our approach uses individual point labels for an entropy estimation to approximate an underlying distribution of cell pixels. We infer full cell masks from this distribution, and use Mask-RCNN to produce an instance segmentation output. We compare this point--annotated approach with training on the full ground truth masks. We show that our method achieves a comparatively good level of performance, despite a 95% reduction in pixel labels.