Reviewerly/exHarmony huggingface dataset:
Large Deviation Asymptotics and Bayesian Posterior Consistency on Stochastic Processes and Dynamical Systems

We consider generalized Bayesian inference on stochastic processes and dynamical systems with potentially long-range dependency. Given a sequence of observations, a class of parametrized model processes with a prior distribution, and a loss function, we specify the generalized posterior distribution. The problem of frequentist posterior consistency is concerned with whether as more and more samples are observed, the posterior distribution on parameters will asymptotically concentrate on the "right" parameters. We show that posterior consistency can be derived using a combination of classical large deviation techniques, such as Varadhan's lemma, conditional/quenched large deviations, annealed large deviations, and exponential approximations. We show that the posterior distribution will asymptotically concentrate on parameters that minimize the expected loss and a divergence term, and we identify the divergence term as the Donsker-Varadhan relative entropy rate from process-level large deviations. As an application, we prove new quenched and annealed large deviation asymptotics and new Bayesian posterior consistency results for a class of mixing stochastic processes. In the case of Markov processes, one can obtain explicit conditions for posterior consistency, whenever estimates for log-Sobolev constants are available, which makes our framework essentially a black box. We also recover state-of-the-art posterior consistency on classical dynamical systems with a simple proof. Our approach has the potential of proving posterior consistency for a wide range of Bayesian procedures in a unified way.
Credit Card Fraud Detection using Machine Learning

Credit card fraud detection is presently the most frequently occurring problem in the present world. We made an attempt for finding the frauds in the credit card business by using the algorithms which adopted machine learning techniques. We are using Decision Tree, Random Forest, and Extreme Gradient boosting algorithms. The efficiency of the model can be decided by using some public data as sample. Then, an actual world credit card facts group from a financial institution is examined. Along with this, some clatter is supplemented to the data samples to auxiliary check the sturdiness of the systems. The significance of the methods used in the paper is the first method constructs a tree against the activities performed by the user and using this tree scams will be suspected. In the second method a user activity-based forest will have constructed and using this forest an attempt will be made in identifying the suspect. The investigational outcomes absolutely show that the mainstream elective technique attains decent precision degrees in sensing scam circumstances in credit cards
Application of Classification Data Mining Technique for Pattern Analysis of Student Graduation Data with Emerging Pattern Method

Data mining has been applied in various fields of life because it is very helpful in extracting information from large data sets. Student graduation data is one example of data that can be extracted for information and become a recommendation. This study used a classification data mining technique to extract information from the student graduation data. The classification technique used was the Emerging Pattern method to search for patterns in the student graduation data. The data in this study were graduation data for students of the Statistics Study Program, Faculty of Mathematics and Natural Sciences, Tanjungpura University, from 2013-2018. The sample data used amounted to 186 records. Attributes used in this study include as many as four attributes, including gender, batch, GPA, and TUTEP scores. This research began by finding the class and frequency values obtained. It was continued by calculating each item set's support, growth rate, and confidence values. This study obtained the highest confidence value among all the attributes owned, namely 91% in the 2013 batch itemized list and the 2018 batch. Female students dominated the class attribute. TUTEP dominated the TUTEP value attribute with a score of 425, and the GPA attribute of 3.51-4.00 dominated the class with a confidence value of 60%.
Penerapan Logika Fuzzy Metode Mamdani Untuk Menentukan Jumlah Produksi Ayudes (Studi Kasus: CV. Abadi Tiga Mandiri Ambon)

A company does not see a problem, namely level competition. This competition is for companies to be able to provide a marketing or productivity strategy in order to survive and even have to increase their production volume. Because the estimated number of products produced is less than the number of requests, the company will lose the opportunity to get maximum and somewhat profit. Therefore, what needs to be considered in deciding the total of production is the total of demand and supply date. Writing and discussion in this study is about the application of the Logic Fuzzy Method Mamdani (Min-Max) to determine the total of production based on the total of demand and supply where the data is taken from CV. Abadi Tiga Mandiri Ambon and by applying the fuzzy logic method mamdani and Matlab assistance obtained results with a truth level of 93,238%. So that the application of Mamdani's Fuzzy Logic Method can help companies determine the number of items that must be made.
Effect of Workshop Facilities on Practice Learning Outcomes of Students of SMKN 1 Tomohon

The purpose of the study was to determine the effect of workshop facilities on students' learning outcomes of electrical power installation engineering practice at SMK Negeri 1 Tomohon. The research method used is a quantitative descriptive method with a simple regression technique. The research population was all Class XI students, Department of Electrical Power Installation Engineering, which amounted to 30 people. The research sample was the entire population, Class XI students of the Department of Electrical Power Installation Engineering, which amounted to 30 people. Workshop facilities are one of the external factors that can improve students' learning outcomes of electrical power installation engineering practice at SMK Negeri 1 Tomohon. The formulation of the research problem is whether there is an influence of workshop facilities on the learning outcomes of students' electrical power installation engineering practice at SMK Negeri 1 Tomohon. The research instrument used to measure workshop facilities was a questionnaire, while for student learning outcomes, it was through a learning evaluation. The study concludes that workshop facilities affect the learning outcomes of electric power installation engineering practices for students of SMK Negeri 1 Tomohon. The study results evidence this obtained a significance value of 0.000 &lt;0.05, and the t-count value is more significant than t-table, namely t-count = 11.191 &gt; t-table 2.048. With the magnitude of the influence of 81.7%.
Parallel bootstrap-based on-policy deep reinforcement learning for continuous flow control applications

The coupling of deep reinforcement learning to numerical flow control problems has recently received a considerable attention, leading to groundbreaking results and opening new perspectives for the domain. Due to the usually high computational cost of fluid dynamics solvers, the use of parallel environments during the learning process represents an essential ingredient to attain efficient control in a reasonable time. Yet, most of the deep reinforcement learning literature for flow control relies on on-policy algorithms, for which the massively parallel transition collection may break theoretical assumptions and lead to suboptimal control models. To overcome this issue, we propose a parallelism pattern relying on partial-trajectory buffers terminated by a return bootstrapping step, allowing a flexible use of parallel environments while preserving the on-policiness of the updates. This approach is illustrated on a CPU-intensive continuous flow control problem from the literature.
Program Comprehension Does Not Primarily Rely On the Language Centers of the Human Brain

Our goal is to identify brain regions involved in comprehending computer programs. We use functional magnetic resonance imaging (fMRI) to investigate two candidate systems of brain regions which may support this -- the Multiple Demand (MD) system, known to respond to a range of cognitively demanding tasks, and the Language system (LS), known to primarily respond to language stimuli. We devise experiment conditions to isolate the act of code comprehension, and employ a state-of-the-art method to locate brain systems of interest. We administer these experiments in Python (24 participants) and Scratch Jr. (19 participants) - which provides a visual interface to programming, thus eliminating the effect of text in code comprehension. From this robust experiment setup, we find that the Language system is not consistently involved in code comprehension, while the MD is. Further, we find no other brain regions beyond those in the MD to be responsive to code. We also find that variable names, the control flow used in the program, and the types of operations performed do not affect brain responses. We discuss the implications of our findings on the software engineering and CS education communities.
Beyond the Pixel: a Photometrically Calibrated HDR Dataset for Luminance and Color Prediction

Light plays an important role in human well-being. However, most computer vision tasks treat pixels without considering their relationship to physical luminance. To address this shortcoming, we introduce the Laval Photometric Indoor HDR Dataset, the first large-scale photometrically calibrated dataset of high dynamic range 360{\deg} panoramas. Our key contribution is the calibration of an existing, uncalibrated HDR Dataset. We do so by accurately capturing RAW bracketed exposures simultaneously with a professional photometric measurement device (chroma meter) for multiple scenes across a variety of lighting conditions. Using the resulting measurements, we establish the calibration coefficients to be applied to the HDR images. The resulting dataset is a rich representation of indoor scenes which displays a wide range of illuminance and color, and varied types of light sources. We exploit the dataset to introduce three novel tasks, where: per-pixel luminance, per-pixel color and planar illuminance can be predicted from a single input image. Finally, we also capture another smaller photometric dataset with a commercial 360{\deg} camera, to experiment on generalization across cameras. We are optimistic that the release of our datasets and associated code will spark interest in physically accurate light estimation within the community. Dataset and code are available at https://lvsn.github.io/beyondthepixel/.
AutoFocusFormer: Image Segmentation off the Grid

Real world images often have highly imbalanced content density. Some areas are very uniform, e.g., large patches of blue sky, while other areas are scattered with many small objects. Yet, the commonly used successive grid downsampling strategy in convolutional deep networks treats all areas equally. Hence, small objects are represented in very few spatial locations, leading to worse results in tasks such as segmentation. Intuitively, retaining more pixels representing small objects during downsampling helps to preserve important information. To achieve this, we propose AutoFocusFormer (AFF), a local-attention transformer image recognition backbone, which performs adaptive downsampling by learning to retain the most important pixels for the task. Since adaptive downsampling generates a set of pixels irregularly distributed on the image plane, we abandon the classic grid structure. Instead, we develop a novel point-based local attention block, facilitated by a balanced clustering module and a learnable neighborhood merging module, which yields representations for our point-based versions of state-of-the-art segmentation heads. Experiments show that our AutoFocusFormer (AFF) improves significantly over baseline models of similar sizes.
Two Decreasing Measures for Simply Typed Lambda-Terms (Extended Version)

This paper defines two decreasing measures for terms of the simply typed lambda-calculus, called the W-measure and the Tm-measure. A decreasing measure is a function that maps each typable lambda-term to an element of a well-founded ordering, in such a way that contracting any beta-redex decreases the value of the function, entailing strong normalization. Both measures are defined constructively, relying on an auxiliary calculus, a non-erasing variant of the lambda-calculus. In this system, dubbed the m-calculus, each beta-step creates a "wrapper" containing a copy of the argument that cannot be erased and cannot interact with the context in any other way. Both measures rely crucially on the observation, known to Turing and Prawitz, that contracting a redex cannot create redexes of higher degree, where the degree of a redex is defined as the height of the type of its lambda-abstraction. The W-measure maps each lambda-term to a natural number, and it is obtained by evaluating the term in the m-calculus and counting the number of remaining wrappers. The Tm-measure maps each lambda-term to a structure of nested multisets, where the nesting depth is proportional to the maximum redex degree.
Towards Realistic Generative 3D Face Models

In recent years, there has been significant progress in 2D generative face models fueled by applications such as animation, synthetic data generation, and digital avatars. However, due to the absence of 3D information, these 2D models often struggle to accurately disentangle facial attributes like pose, expression, and illumination, limiting their editing capabilities. To address this limitation, this paper proposes a 3D controllable generative face model to produce high-quality albedo and precise 3D shape leveraging existing 2D generative models. By combining 2D face generative models with semantic face manipulation, this method enables editing of detailed 3D rendered faces. The proposed framework utilizes an alternating descent optimization approach over shape and albedo. Differentiable rendering is used to train high-quality shapes and albedo without 3D supervision. Moreover, this approach outperforms the state-of-the-art (SOTA) methods in the well-known NoW benchmark for shape reconstruction. It also outperforms the SOTA reconstruction models in recovering rendered faces' identities across novel poses by an average of 10%. Additionally, the paper demonstrates direct control of expressions in 3D faces by exploiting latent space leading to text-based editing of 3D faces.
DualSlide: Global-to-Local Sketching Interface for Slide Content and Layout Design

Online learning and academic conferences have become pervasive and essential for education and professional development, especially since the onset of pandemics. Academic presentations usually require well-designed slides that are easily understood. Sketches that visually represent design intentions and are readily accessible to the average users. To assist non-expert users in creating visually appealing academic slides, we propose DualSlide, a global and local two-stage sketching interface system that provides image retrieval and user guidance. At the global stage, DualSlide provides a heat map canvas to display the distribution of all slide layouts in a dataset, allowing users to explore the reference slides efficiently. At the local stage of the system, detailed references and guidance for designing slide content, such as diagrams and fonts, can be provided. We further propose a sketch-matching algorithm to compare the user's input sketch and similar diagrams. All user guidance can be adapted in real-time editing, and users can design slides with a high degree of freedom. We conducted a user study to verify the effectiveness and usability of the proposed DualSlide system confirming that DualSlide provides high retrieval accuracy and satisfactory design results with a good user experience. Video: https://youtu.be/lUI1zjxCdM0
GlyphDiffusion: Text Generation as Image Generation

Diffusion models have become a new generative paradigm for text generation. Considering the discrete categorical nature of text, in this paper, we propose GlyphDiffusion, a novel diffusion approach for text generation via text-guided image generation. Our key idea is to render the target text as a glyph image containing visual language content. In this way, conditional text generation can be cast as a glyph image generation task, and it is then natural to apply continuous diffusion models to discrete texts. Specially, we utilize a cascaded architecture (ie a base and a super-resolution diffusion model) to generate high-fidelity glyph images, conditioned on the input text. Furthermore, we design a text grounding module to transform and refine the visual language content from generated glyph images into the final texts. In experiments over four conditional text generation tasks and two classes of metrics (ie quality and diversity), GlyphDiffusion can achieve comparable or even better results than several baselines, including pretrained language models. Our model also makes significant improvements compared to the recent diffusion model.
Resource Specifications for Resource-Manipulating Programs

Specifications for modular program verifiers are expressed as constraints on program states (e.g. preconditions) and relations on program states (e.g. postconditions). For programs whose domain is managing resources of any kind (e.g. cryptocurrencies), such state-based specifications must make explicit properties that a human would implicitly understand for free. For example, it's clear that depositing into your bank account will not change other balances, but classically this must be stated as a frame condition. As a result, specifications for resource-manipulating programs quickly become verbose and difficult to interpret, write and debug. In this paper, we present a novel methodology that introduces user-defined first-class resources in the specification language, allowing resource-related operations and properties to be expressed directly and eliminating the need to reify implicit knowledge in the specifications. We implement our methodology as an extension of the program verifier Prusti, and use it to verify a key part of a real-world blockchain application. As we demonstrate in our evaluation, specifications written with our methodology are more concise, syntactically simpler, and easier to understand than alternative specifications written purely in terms of program states.
Weakly-Supervised Temporal Action Localization with Bidirectional Semantic Consistency Constraint

Weakly Supervised Temporal Action Localization (WTAL) aims to classify and localize temporal boundaries of actions for the video, given only video-level category labels in the training datasets. Due to the lack of boundary information during training, existing approaches formulate WTAL as a classificationproblem, i.e., generating the temporal class activation map (T-CAM) for localization. However, with only classification loss, the model would be sub-optimized, i.e., the action-related scenes are enough to distinguish different class labels. Regarding other actions in the action-related scene ( i.e., the scene same as positive actions) as co-scene actions, this sub-optimized model would misclassify the co-scene actions as positive actions. To address this misclassification, we propose a simple yet efficient method, named bidirectional semantic consistency constraint (Bi-SCC), to discriminate the positive actions from co-scene actions. The proposed Bi-SCC firstly adopts a temporal context augmentation to generate an augmented video that breaks the correlation between positive actions and their co-scene actions in the inter-video; Then, a semantic consistency constraint (SCC) is used to enforce the predictions of the original video and augmented video to be consistent, hence suppressing the co-scene actions. However, we find that this augmented video would destroy the original temporal context. Simply applying the consistency constraint would affect the completeness of localized positive actions. Hence, we boost the SCC in a bidirectional way to suppress co-scene actions while ensuring the integrity of positive actions, by cross-supervising the original and augmented videos. Finally, our proposed Bi-SCC can be applied to current WTAL approaches, and improve their performance. Experimental results show that our approach outperforms the state-of-the-art methods on THUMOS14 and ActivityNet.
Explain like I am BM25: Interpreting a Dense Model's Ranked-List with a Sparse Approximation

Neural retrieval models (NRMs) have been shown to outperform their statistical counterparts owing to their ability to capture semantic meaning via dense document representations. These models, however, suffer from poor interpretability as they do not rely on explicit term matching. As a form of local per-query explanations, we introduce the notion of equivalent queries that are generated by maximizing the similarity between the NRM's results and the result set of a sparse retrieval system with the equivalent query. We then compare this approach with existing methods such as RM3-based query expansion and contrast differences in retrieval effectiveness and in the terms generated by each approach.
Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and Techniques

Real-time safety assessment (RTSA) of dynamic systems is a critical task that has significant implications for various fields such as industrial and transportation applications, especially in non-stationary environments. However, the absence of a comprehensive review of real-time safety assessment methods in non-stationary environments impedes the progress and refinement of related methods. In this paper, a review of methods and techniques for RTSA tasks in non-stationary environments is provided. Specifically, the background and significance of RTSA approaches in non-stationary environments are firstly highlighted. We then present a problem description that covers the definition, classification, and main challenges. We review recent developments in related technologies such as online active learning, online semi-supervised learning, online transfer learning, and online anomaly detection. Finally, we discuss future outlooks and potential directions for further research. Our review aims to provide a comprehensive and up-to-date overview of real-time safety assessment methods in non-stationary environments, which can serve as a valuable resource for researchers and practitioners in this field.
Bayesian Optimization Meets Self-Distillation

Bayesian optimization (BO) has contributed greatly to improving model performance by suggesting promising hyperparameter configurations iteratively based on observations from multiple training trials. However, only partial knowledge (i.e., the measured performances of trained models and their hyperparameter configurations) from previous trials is transferred. On the other hand, Self-Distillation (SD) only transfers partial knowledge learned by the task model itself. To fully leverage the various knowledge gained from all training trials, we propose the BOSS framework, which combines BO and SD. BOSS suggests promising hyperparameter configurations through BO and carefully selects pre-trained models from previous trials for SD, which are otherwise abandoned in the conventional BO process. BOSS achieves significantly better performance than both BO and SD in a wide range of tasks including general image classification, learning with noisy labels, semi-supervised learning, and medical image analysis tasks.