Reviewerly/exHarmony huggingface dataset:
Neural stochastic differential equations for conditional time series generation using the Signature-Wasserstein-1 metric

(Conditional) generative adversarial networks (GANs) have had great success in recent years, due to their ability to approximate (conditional) distributions over extremely high-dimensional spaces. However, they are highly unstable and computationally expensive to train, especially in the time series setting. Recently, the use of a key object in rough path theory, called the signature of a path, has been proposed. This is able to convert the min–max formulation given by the (conditional) GAN framework into a classical minimization problem. However, this method is extremely costly in terms of memory, which can sometimes become prohibitive. To overcome this, we propose the use of conditional neural stochastic differential equations, designed to have a constant memory cost as a function of depth, being more memory efficient than traditional deep learning architectures. We empirically test the efficiency of our proposed model against other classical approaches, in terms of both memory cost and computational time, and show that it usually outperforms them according to several metrics.
Towards Detecting Harmful Agendas in News Articles

Manipulated news online is a growing problem which necessitates the use of automated systems to curtail its spread.We argue that while misinformation and disinformation detection have been studied, there has been a lack of investment in the important open challenge of detecting harmful agendas in news articles; identifying harmful agendas is critical to flag news campaigns with the greatest potential for real world harm.Moreover, due to real concerns around censorship, harmful agenda detectors must be interpretable to be effective.In this work, we propose this new task and release a dataset, NEWSAGENDAS, of annotated news articles for agenda identification.We show how interpretable systems can be effective on this task and demonstrate that they can perform comparably to black-box models.
TTVFI: Learning Trajectory-Aware Transformer for Video Frame Interpolation

Video frame interpolation (VFI) aims to synthesize an intermediate frame between two consecutive frames. State-of-the-art approaches usually adopt a two-step solution, which includes 1) generating locally-warped pixels by calculating the optical flow based on pre-defined motion patterns (e.g., uniform motion, symmetric motion), 2) blending the warped pixels to form a full frame through deep neural synthesis networks. However, for various complicated motions (e.g., non-uniform motion, turn around), such improper assumptions about pre-defined motion patterns introduce the inconsistent warping from the two consecutive frames. This leads to the warped features for new frames are usually not aligned, yielding distortion and blur, especially when large and complex motions occur. To solve this issue, in this paper we propose a novel Trajectory-aware Transformer for Video Frame Interpolation (TTVFI). In particular, we formulate the warped features with inconsistent motions as query tokens, and formulate relevant regions in a motion trajectory from two original consecutive frames into keys and values. Self-attention is learned on relevant tokens along the trajectory to blend the pristine features into intermediate frames through end-to-end training. Experimental results demonstrate that our method outperforms other state-of-the-art methods in four widely-used VFI benchmarks. Both code and pre-trained models will be released at https://github.com/ChengxuLiu/TTVFI.git.
Relational Network via Cascade CRF for Video Language Grounding

Video Language Grounding is one of the most challenging cross-modal video understanding tasks. This task aims to localize a target moment semantically corresponding to a given language query in an untrimmed video. Many existing VLG methods rely on the proposal-based framework, despite the dominant performance achieved, they usually focus on interacting a few internal frames with the query to score segment proposals, trapping in the long-range dependencies when the proposal feature is limited. Meanwhile, adjacent proposals share similar visual semantics, making VLG models hard to align the accurate semantics of video-query contents and degenerating the ranking performance. To remedy the above limitations, we propose VLG-CRF by introducing the conditional random fields (CRFs) to handle the discrete yet indistinguishable proposals. Specifically, VLG-CRF consists of two cascade CRF-based modules. The AttentiveCRFs is developed for multi-modal feature fusion to better integrate temporal and semantic relation between modalities. We also devise a new variant of ConvCRFs to capture the relation of discrete segments and rectify the predicting scores to make relatively high prediction scores clustered in a range. Experiments on three benchmark datasets, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , Charades-STA, ActivityNet-Caption, and TACoS, show the superiority of our method and the state-of-the-art performance is achieved.
Memories of David S. Chapman (1929 –2023)

CHAPMAN, D.S. was born on August 31, 1942 on Vancouver, Canada. Son of Margaret and Harry Chapman and part of a large family with 4 more siblings. Married with Inga Hahn, who know at University British Columbia. David Chapman passed away unexpectedly on 10 March in Vancouver, British Columbia. Dave was an extraordinary researcher, teacher, mentor, and administrator. He loved to demonstrate what could be accomplished using the back of an envelope, a pencil, and a little brainpower. His research in geophysics spanned the globe, using measurements of temperature to understand topics from plate tectonics to climate change. His teaching, from equations on napkins discussed during coffee breaks to his classroom demonstrations to his exemplary example of ethical behavior, left an indelible mark on thousands of students and colleagues around the world. As Dean of the Graduate School he engineered changes to programs to the benefit of all students. We all will miss Dave and his ever upbeat attitude towards problems large and small. In honor him, the UTAH University create the Chapman Found supports unique educational and research opportunities for both graduate and undergraduate students. He receive too the Rosenblatt Prize at UTAH University.
Geothermal Resources of European Continent: A Regional Assessment

A regional assessment of geothermal resources of the continental region of Europe is attempted in the present work. It is based on results of observational data on heat transfer by conduction as well as hydrothermal and magmatic processes in the upper crust and makes use of geothermal data reported at the website of IHFC and also estimates based on available information on occurrences thermal springs and volcanic events. The resource assessment has been carried out for 8109 sites distributed in 39 countries. These datasets were reevaluated and spatially gridded using kigrid interpolation to construction of regional distribution maps of geothermal resources and interpreted on the basis of available information on tectonic setting and geological characteristics. The estimates for volcanic areas are based on a modified method of magmatic heat budget (MHB). has been employed in deriving estimates of geothermal resources in areas of recent volcanic activity. According to results obtained the total resource base (RB) at 6km on depth is estimated to be 8.653.306 ± 7 GJ. The mean resource base per unit area (RBUA) is ~ 985 GJ. The prominent features in geothermal maps are the presence of localities with significantly high values of resource base greater than 2000 GJ in countries such as Russia, Italy, Iceland, and west Norway. In addition, vertical distributions of temperatures were calculated in for depths reaching down to 6 km. The results obtained indicate potential availability of high temperature resources in vast regions of the European continent.
Cognitive Hybrid Deep Learning-based Multi-modal Sentiment Analysis for Online Product Reviews

Recently the field of sentiment analysis has gained a lot of attraction in literature. The idea that a machine can dynamically spot the text’s sentiments is fascinating. In this paper, we propose a method to classify the textual sentiments in Twitter feeds. In particular, we focus on analyzing the tweets of products as either positive or negative. The proposed technique utilizes a deep learning schema to learn and predict the sentiment by extracting features directly from the text. Specifically, we use Convolutional Neural Networks with different convolutional layers, further, we experiment with LSTMs and try an ensemble of multiple models to get the best results. We employ an n-gram-based word embeddings approach to get the machine-level word representations. Testing of the method is conducted on real-world datasets. We have discovered that the ensemble technique yields the best results after conducting experiments on a huge corpus of more than One Million tweets. To be specific, we get an accuracy of 84.95%. The proposed method is also compared with several existing methods. An extensive numerical investigation has revealed the superiority of the proposed work in actual deployment scenarios.
Design and Implementation of Medical Ultrasound Image Processing System based on MATLAB GUI

Due to the physical characteristics of ultrasonic imaging, there are many factors in the process of imaging, which lead to the low quality of imaging images. There may be artifacts, noise interference, unclear edge contour of diseased tissue, and other problems. This paper designs and implements an image processing system for medical ultrasound images based on MATLAB GUI. The system realizes the functions of image enhancement, image segmentation, image filtering, edge detection, and morphological processing of medical ultrasound images. Through the detection of breast duct ultrasound images, the noise interference is greatly reduced in the processed ultrasound images compared with the original images. In addition, there is an obvious highlighting effect on the ultrasound images of some typical lesions, which makes the detailed information of the images more obvious and the boundaries of the lesions clearer. The processed images were compared with the original images by subjective evaluation. The evaluation results of professional doctors all show that the treatment method in this paper can greatly improve the readability of medical ultrasound images.
Approximate Inference in Logical Credal Networks

The Logical Credal Network or LCN is a recent probabilistic logic designed for effective aggregation and reasoning over multiple sources of imprecise knowledge. An LCN specifies a set of probability distributions over all interpretations of a set of logical formulas for which marginal and conditional probability bounds on their truth values are known. Inference in LCNs involves the exact solution of a non-convex non-linear program defined over an exponentially large number of non-negative real valued variables and, therefore, is limited to relatively small problems. In this paper, we present ARIEL -- a novel iterative message-passing scheme for approximate inference in LCNs. Inspired by classical belief propagation for graphical models, our method propagates messages that involve solving considerably smaller local non-linear programs. Experiments on several classes of LCNs demonstrate clearly that ARIEL yields high quality solutions compared with exact inference and scales to much larger problems than previously considered.
LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity

Heterophily has been considered as an issue that hurts the performance of Graph Neural Networks (GNNs). To address this issue, some existing work uses a graph-level weighted fusion of the information of multi-hop neighbors to include more nodes with homophily. However, the heterophily might differ among nodes, which requires to consider the local topology. Motivated by it, we propose to use the local similarity (LocalSim) to learn node-level weighted fusion, which can also serve as a plug-and-play module. For better fusion, we propose a novel and efficient Initial Residual Difference Connection (IRDC) to extract more informative multi-hop information. Moreover, we provide theoretical analysis on the effectiveness of LocalSim representing node homophily on synthetic graphs. Extensive evaluations over real benchmark datasets show that our proposed method, namely Local Similarity Graph Neural Network (LSGNN), can offer comparable or superior state-of-the-art performance on both homophilic and heterophilic graphs. Meanwhile, the plug-and-play model can significantly boost the performance of existing GNNs.
The Hardness of Reasoning about Probabilities and Causality

We study formal languages which are capable of fully expressing quantitative probabilistic reasoning and do-calculus reasoning for causal effects, from a computational complexity perspective. We focus on satisfiability problems whose instance formulas allow expressing many tasks in probabilistic and causal inference. The main contribution of this work is establishing the exact computational complexity of these satisfiability problems. We introduce a new natural complexity class, named succ∃R, which can be viewed as a succinct variant of the well-studied class ∃R, and show that these problems are complete for succ∃R. Our results imply even stronger limitations on the use of algorithmic methods for reasoning about probabilities and causality than previous state-of-the-art results that rely only on the NP- or ∃R-completeness of the satisfiability problems for some restricted languages.
Relative Inconsistency Measures for Indefinite Databases with Denial Constraints

Handling conflicting information is an important challenge in AI. Measuring inconsistency is an approach that provides ways to quantify the severity of inconsistency and helps understanding the primary sources of conflicts. In particular, a relative inconsistency measure computes, by some criteria, the proportion of the knowledge base that is inconsistent. In this paper we investigate relative inconsistency measures for indefinite databases, which allow for indefinite or partial information which is formally expressed by means of disjunctive tuples. We introduce a postulate-based definition of relative inconsistency measure for indefinite databases with denial constraints, and investigate the compliance of some relative inconsistency measures with rationality postulates for indefinite databases as well as for the special case of definite databases. Finally, we investigate the complexity of the problem of computing the value of the proposed relative inconsistency measures as well as of the problems of deciding whether the inconsistency value is lower than, greater than, or equal to a given threshold for indefinite and definite databases.
Data-Driven Invariant Learning for Probabilistic Programs (Extended Abstract)

The weakest pre-expectation framework from Morgan and McIver for deductive verification of probabilistic programs generalizes binary state assertions to real-valued expectations to measure expected values of expressions over probabilistic program variables. While loop-free programs can be analyzed by mechanically transforming expectations, verifying programs with loops requires finding an invariant expectation. We view invariant expectation synthesis as a regression problem: given an input state, predict the average value of the post-expectation in the output distribution. With this perspective, we develop the first data-driven invariant synthesis method for probabilistic programs. Unlike prior work on probabilistic invariant inference, our approach learns piecewise continuous invariants without relying on template expectations. We also develop a data-driven approach to learn sub-invariants from data, which can be used to upper- or lower-bound expected values. We implement our approaches and demonstrate their effectiveness on a variety of benchmarks from the probabilistic programming literature.
2 Numerisches Programmieren mit Python

No AccessNumerisches PythonAug 20232 Numerisches Programmieren mit Pythonhttps://doi.org/10.3139/9783446473669.002SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2023Pages: 4-6Print ISBN: 978-3-446-47170-2eISBN: 978-3-446-47366-9 PDF downloadLoading ...
Towards Robust Scene Text Image Super-resolution via Explicit Location Enhancement

Scene text image super-resolution (STISR), aiming to improve image quality while boosting downstream scene text recognition accuracy, has recently achieved great success. However, most existing methods treat the foreground (character regions) and background (non-character regions) equally in the forward process, and neglect the disturbance from the complex background, thus limiting the performance. To address these issues, in this paper, we propose a novel method LEMMA that explicitly models character regions to produce high-level text-specific guidance for super-resolution. To model the location of characters effectively, we propose the location enhancement module to extract character region features based on the attention map sequence. Besides, we propose the multi-modal alignment module to perform bidirectional visual-semantic alignment to generate high-quality prior guidance, which is then incorporated into the super-resolution branch in an adaptive manner using the proposed adaptive fusion module. Experiments on TextZoom and four scene text recognition benchmarks demonstrate the superiority of our method over other state-of-the-art methods. Code is available at https://github.com/csguoh/LEMMA.
Negative Flux Aggregation to Estimate Feature Attributions

There are increasing demands for understanding deep neural networks' (DNNs) behavior spurred by growing security and/or transparency concerns. Due to multi-layer nonlinearity of the deep neural network architectures, explaining DNN predictions still remains as an open problem, preventing us from gaining a deeper understanding of the mechanisms. To enhance the explainability of DNNs, we estimate the input feature's attributions to the prediction task using divergence and flux. Inspired by the divergence theorem in vector analysis, we develop a novel Negative Flux Aggregation (NeFLAG) formulation and an efficient approximation algorithm to estimate attribution map. Unlike the previous techniques, ours doesn't rely on fitting a surrogate model nor need any path integration of gradients. Both qualitative and quantitative experiments demonstrate a superior performance of NeFLAG in generating more faithful attribution maps than the competing methods. Our code is available at https://github.com/xinli0928/NeFLAG.
Design and operation of online video production workshop conducted during COVID-19

In the post-coronavirus situation in which the educational environment is being gradually digitalized and educational subjects are being converted to digital natives, it is necessary to develop a new online and offline linked class method by actively utilizing the advantages of online education discovered during the last pandemic period. As a new type of class for video production practice during the pandemic period, a mixed class of prerecorded video and real-time video was administered, in which learners conducted selfdirected learning through prerecorded video and then received feedback from instructors and fellow learners in real-time. This type of class realized flipped learning, which reversed the existing teacher-centered class. The advantage of prerecorded video lessons is that learners can freely select time and space, and it is possible to repeat learning and change the video speed. In addition, the instructor can focus on the role of a facilitator or evaluator rather than lecturer, replacing lectures with videos. On the other hand, the disadvantages of online practice classes include weakened learner concentration, lack of audience effect, lack of ties between members, and difficulty in content creation. To supplement this, online education systems such as small meeting rooms, chat windows, and comments can be actively used. This study has the significance of exploring resources that can be used to develop innovative teaching methods in the future by analyzing the structure and process of online video production practice classes conducted during the COVID-19 period and deriving the meaningful effects from these classes.
PERANCANGAN SISTEM PENGGAJIAN KARYAWAN PADA PT. MUSTIKA BAHARI

In the age of information technology as it now has made humans. Entering a new era, namely the information age that demands everything running fast, CV. Mustika Bahari is a company engaged in the industrial sector that has not implemented a computerized system so that existing payroll information for new employees can not be fulfilled optimally. To get information, the data processing. Slow can result in slow presentation, a problem that exists in CV. Mustika Bhari than the finance and admiistartion departemen. The design uses the waterfall method, data flow, and entity relationship diagram. The employee payroll system, because this computerized payroll information system can change work that was previously done manually and seems to take a long time to provide information and make a decision in doing attendance employee hiring employees &#x0D;
Character As Pixels: A Controllable Prompt Adversarial Attacking Framework for Black-Box Text Guided Image Generation Models

In this paper, we study a controllable prompt adversarial attacking problem for text guided image generation (Text2Image) models in the black-box scenario, where the goal is to attack specific visual subjects (e.g., changing a brown dog to white) in a generated image by slightly, if not imperceptibly, perturbing the characters of the driven prompt (e.g., ``brown'' to ``br0wn''). Our study is motivated by the limitations of current Text2Image attacking approaches that still rely on manual trials to create adversarial prompts. To address such limitations, we develop CharGrad, a character-level gradient based attacking framework that replaces specific characters of a prompt with pixel-level similar ones by interactively learning the perturbation direction for the prompt and updating the attacking examiner for the generated image based on a novel proxy perturbation representation for characters. We evaluate CharGrad using the texts from two public image captioning datasets. Results demonstrate that CharGrad outperforms existing text adversarial attacking approaches on attacking various subjects of generated images by black-box Text2Image models in a more effective and efficient way with less perturbation on the characters of the prompts.