Reviewerly/exHarmony huggingface dataset:
Content-based Unrestricted Adversarial Attack

Unrestricted adversarial attacks typically manipulate the semantic content of an image (e.g., color or texture) to create adversarial examples that are both effective and photorealistic, demonstrating their ability to deceive human perception and deep neural networks with stealth and success. However, current works usually sacrifice unrestricted degrees and subjectively select some image content to guarantee the photorealism of unrestricted adversarial examples, which limits its attack performance. To ensure the photorealism of adversarial examples and boost attack performance, we propose a novel unrestricted attack framework called Content-based Unrestricted Adversarial Attack. By leveraging a low-dimensional manifold that represents natural images, we map the images onto the manifold and optimize them along its adversarial direction. Therefore, within this framework, we implement Adversarial Content Attack based on Stable Diffusion and can generate high transferable unrestricted adversarial examples with various adversarial contents. Extensive experimentation and visualization demonstrate the efficacy of ACA, particularly in surpassing state-of-the-art attacks by an average of 13.3-50.4% and 16.8-48.0% in normally trained models and defense methods, respectively.
a unified front-end framework for english text-to-speech synthesis

The front-end is a critical component of English text-to-speech (TTS) systems, responsible for extracting linguistic features that are essential for a text-to-speech model to synthesize speech, such as prosodies and phonemes. The English TTS front-end typically consists of a text normalization (TN) module, a prosody word prosody phrase (PWPP) module, and a grapheme-to-phoneme (G2P) module. However, current research on the English TTS front-end focuses solely on individual modules, neglecting the interdependence between them and resulting in sub-optimal performance for each module. Therefore, this paper proposes a unified front-end framework that captures the dependencies among the English TTS front-end modules. Extensive experiments have demonstrated that the proposed method achieves state-of-the-art (SOTA) performance in all modules.
Convex Equipartitions inspired by the little cubes operad

A decade ago two groups of authors, Karasev, Hubard and Aronov, and Blagojevi\'c and Ziegler, have shown that the regular convex partitions of a Euclidean space into $n$ parts yield a solution to the generalised Nandakumar and Ramana-Rao conjecture when $n$ is a prime power. This was obtained by parametrising the space of regular equipartitions of a given convex body with the classical configuration space. Now, we repeat the process of regular convex equipartitions many times, first partitioning the Euclidean space into $n_1$ parts, then each part into $n_2$ parts, and so on. In this way we obtain iterated convex equipartions of a given convex body into $n=n_1...n_k$ parts. Such iterated partitions are parametrised by the (wreath) product of classical configuration spaces. We develop a new configuration space -- test map scheme for solving the generalised Nandakumar \& Ramana-Rao conjecture using the Hausdorff metric on the space of iterated convex equipartions. The new scheme yields a solution to the conjecture if and only if all the $n_i$'s are powers of the same prime. In particular, for the failure of the scheme outside prime power case we give three different proofs.
Attention-based Encoder-Decoder Network for End-to-End Neural Speaker Diarization with Target Speaker Attractor

This paper proposes a novel Attention-based Encoder-Decoder network for End-to-End Neural speaker Diarization (AED-EEND). In AED-EEND system, we incorporate the target speaker enrollment information used in target speaker voice activity detection (TS-VAD) to calculate the attractor, which can mitigate the speaker permutation problem and facilitate easier model convergence. In the training process, we propose a teacher-forcing strategy to obtain the enrollment information using the ground-truth label. Furthermore, we propose three heuristic decoding methods to identify the enrollment area for each speaker during the evaluation process. Additionally, we enhance the attractor calculation network LSTM used in the end-to-end encoder-decoder based attractor calculation (EEND-EDA) system by incorporating an attention-based model. By utilizing such an attention-based attractor decoder, our proposed AED-EEND system outperforms both the EEND-EDA and TS-VAD systems with only 0.5s of enrollment data.
Classical shadows based on locally-entangled measurements

We study classical shadows protocols based on randomized measurements in $n$-qubit entangled bases, generalizing the random Pauli measurement protocol ($n = 1$). We show that entangled measurements ($n\geq 2$) enable nontrivial and potentially advantageous trade-offs in the sample complexity of learning Pauli expectation values. This is sharply illustrated by shadows based on two-qubit Bell measurements: the scaling of sample complexity with Pauli weight $k$ improves quadratically (from $\sim 3^k$ down to $\sim 3^{k/2}$) for many operators, while others become impossible to learn. Tuning the amount of entanglement in the measurement bases defines a family of protocols that interpolate between Pauli and Bell shadows, retaining some of the benefits of both. For large $n$, we show that randomized measurements in $n$-qubit GHZ bases further improve the best scaling to $\sim (3/2)^k$, albeit on an increasingly restricted set of operators. Despite their simplicity and lower hardware requirements, these protocols can match or outperform recently-introduced "shallow shadows" in some practically-relevant Pauli estimation tasks.
Catch-Up Distillation: You Only Need to Train Once for Accelerating Sampling

Diffusion Probability Models (DPMs) have made impressive advancements in various machine learning domains. However, achieving high-quality synthetic samples typically involves performing a large number of sampling steps, which impedes the possibility of real-time sample synthesis. Traditional accelerated sampling algorithms via knowledge distillation rely on pre-trained model weights and discrete time step scenarios, necessitating additional training sessions to achieve their goals. To address these issues, we propose the Catch-Up Distillation (CUD), which encourages the current moment output of the velocity estimation model ``catch up'' with its previous moment output. Specifically, CUD adjusts the original Ordinary Differential Equation (ODE) training objective to align the current moment output with both the ground truth label and the previous moment output, utilizing Runge-Kutta-based multi-step alignment distillation for precise ODE estimation while preventing asynchronous updates. Furthermore, we investigate the design space for CUDs under continuous time-step scenarios and analyze how to determine the suitable strategies. To demonstrate CUD's effectiveness, we conduct thorough ablation and comparison experiments on CIFAR-10, MNIST, and ImageNet-64. On CIFAR-10, we obtain a FID of 2.80 by sampling in 15 steps under one-session training and the new state-of-the-art FID of 3.37 by sampling in one step with additional training. This latter result necessitated only 620k iterations with a batch size of 128, in contrast to Consistency Distillation, which demanded 2100k iterations with a larger batch size of 256. Our code is released at https://anonymous.4open.science/r/Catch-Up-Distillation-E31F.
Uncertainty Quantification in Deep Neural Networks through Statistical Inference on Latent Space

Uncertainty-quantification methods are applied to estimate the confidence of deep-neural-networks classifiers over their predictions. However, most widely used methods are known to be overconfident. We address this problem by developing an algorithm that exploits the latent-space representation of data points fed into the network, to assess the accuracy of their prediction. Using the latent-space representation generated by the fraction of training set that the network classifies correctly, we build a statistical model that is able to capture the likelihood of a given prediction. We show on a synthetic dataset that commonly used methods are mostly overconfident. Overconfidence occurs also for predictions made on data points that are outside the distribution that generated the training data. In contrast, our method can detect such out-of-distribution data points as inaccurately predicted, thus aiding in the automatic detection of outliers.
LDM3D: Latent Diffusion Model for 3D

This research paper proposes a Latent Diffusion Model for 3D (LDM3D) that generates both image and depth map data from a given text prompt, allowing users to generate RGBD images from text prompts. The LDM3D model is fine-tuned on a dataset of tuples containing an RGB image, depth map and caption, and validated through extensive experiments. We also develop an application called DepthFusion, which uses the generated RGB images and depth maps to create immersive and interactive 360-degree-view experiences using TouchDesigner. This technology has the potential to transform a wide range of industries, from entertainment and gaming to architecture and design. Overall, this paper presents a significant contribution to the field of generative AI and computer vision, and showcases the potential of LDM3D and DepthFusion to revolutionize content creation and digital experiences. A short video summarizing the approach can be found at https://t.ly/tdi2.
TextDiffuser: Diffusion Models as Text Painters

Diffusion models have gained increasing attention for their impressive generation abilities but currently struggle with rendering accurate and coherent text. To address this issue, we introduce TextDiffuser, focusing on generating images with visually appealing text that is coherent with backgrounds. TextDiffuser consists of two stages: first, a Transformer model generates the layout of keywords extracted from text prompts, and then diffusion models generate images conditioned on the text prompt and the generated layout. Additionally, we contribute the first large-scale text images dataset with OCR annotations, MARIO-10M, containing 10 million image-text pairs with text recognition, detection, and character-level segmentation annotations. We further collect the MARIO-Eval benchmark to serve as a comprehensive tool for evaluating text rendering quality. Through experiments and user studies, we show that TextDiffuser is flexible and controllable to create high-quality text images using text prompts alone or together with text template images, and conduct text inpainting to reconstruct incomplete images with text. The code, model, and dataset will be available at \url{https://aka.ms/textdiffuser}.
Latent Space Planning for Multi-Object Manipulation with Environment-Aware Relational Classifiers

Objects rarely sit in isolation in everyday human environments. If we want robots to operate and perform tasks in our human environments, they must understand how the objects they manipulate will interact with structural elements of the environment for all but the simplest of tasks. As such, we'd like our robots to reason about how multiple objects and environmental elements relate to one another and how those relations may change as the robot interacts with the world. We examine the problem of predicting inter-object and object-environment relations between previously unseen objects and novel environments purely from partial-view point clouds. Our approach enables robots to plan and execute sequences to complete multi-object manipulation tasks defined from logical relations. This removes the burden of providing explicit, continuous object states as goals to the robot. We explore several different neural network architectures for this task. We find the best performing model to be a novel transformer-based neural network that both predicts object-environment relations and learns a latent-space dynamics function. We achieve reliable sim-to-real transfer without any fine-tuning. Our experiments show that our model understands how changes in observed environmental geometry relate to semantic relations between objects. We show more videos on our website: https://sites.google.com/view/erelationaldynamics.
AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction

Deep neural models for named entity recognition (NER) have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervision and auxiliary information such as explanations. However, the costs of acquiring such additional information are generally prohibitive. In this paper, we present a novel two-stage framework (AutoTriggER) to improve NER performance by automatically generating and leveraging ``entity triggers'' which are human-readable cues in the text that help guide the model to make better decisions. Our framework leverages post-hoc explanation to generate rationales and strengthens a model's prior knowledge using an embedding interpolation technique. This approach allows models to exploit triggers to infer entity boundaries and types instead of solely memorizing the entity words themselves. Through experiments on three well-studied NER datasets, AutoTriggER shows strong label-efficiency, is capable of generalizing to unseen entities, and outperforms the RoBERTa-CRF baseline by nearly 0.5 F1 points on average.
Minimum-Risk Recalibration of Classifiers

Recalibrating probabilistic classifiers is vital for enhancing the reliability and accuracy of predictive models. Despite the development of numerous recalibration algorithms, there is still a lack of a comprehensive theory that integrates calibration and sharpness (which is essential for maintaining predictive power). In this paper, we introduce the concept of minimum-risk recalibration within the framework of mean-squared-error (MSE) decomposition, offering a principled approach for evaluating and recalibrating probabilistic classifiers. Using this framework, we analyze the uniform-mass binning (UMB) recalibration method and establish a finite-sample risk upper bound of order $\tilde{O}(B/n + 1/B^2)$ where $B$ is the number of bins and $n$ is the sample size. By balancing calibration and sharpness, we further determine that the optimal number of bins for UMB scales with $n^{1/3}$, resulting in a risk bound of approximately $O(n^{-2/3})$. Additionally, we tackle the challenge of label shift by proposing a two-stage approach that adjusts the recalibration function using limited labeled data from the target domain. Our results show that transferring a calibrated classifier requires significantly fewer target samples compared to recalibrating from scratch. We validate our theoretical findings through numerical simulations, which confirm the tightness of the proposed bounds, the optimal number of bins, and the effectiveness of label shift adaptation.
RobustFair: Adversarial Evaluation through Fairness Confusion Directed Gradient Search

Deep neural networks (DNNs) often face challenges due to their vulnerability to various adversarial perturbations, including false perturbations that undermine prediction accuracy and biased perturbations that cause biased predictions for similar inputs. This paper introduces a novel approach, RobustFair, to evaluate the accurate fairness of DNNs when subjected to these false or biased perturbations. RobustFair employs the notion of the fairness confusion matrix induced in accurate fairness to identify the crucial input features for perturbations. This matrix categorizes predictions as true fair, true biased, false fair, and false biased, and the perturbations guided by it can produce a dual impact on instances and their similar counterparts to either undermine prediction accuracy (robustness) or cause biased predictions (individual fairness). RobustFair then infers the ground truth of these generated adversarial instances based on their loss function values approximated by the total derivative. To leverage the generated instances for trustworthiness improvement, RobustFair further proposes a data augmentation strategy to prioritize adversarial instances resembling the original training set, for data augmentation and model retraining. Notably, RobustFair excels at detecting intertwined issues of robustness and individual fairness, which are frequently overlooked in standard robustness and individual fairness evaluations. This capability empowers RobustFair to enhance both robustness and individual fairness evaluations by concurrently identifying defects in either domain. Empirical case studies and quantile regression analyses on benchmark datasets demonstrate the effectiveness of the fairness confusion matrix guided perturbation for false or biased adversarial instance generation.
A privacy-preserving publicly verifiable quantum random number generator

Verifying the quality of a random number generator involves performing computationally intensive statistical tests on large data sets commonly in the range of gigabytes. Limitations on computing power can restrict an end-user's ability to perform such verification. There are also applications where the user needs to publicly demonstrate that the random bits they are using pass the statistical tests without the bits being revealed. We report the implementation of an entanglement-based protocol that allows a third party to publicly perform statistical tests without compromising the privacy of the random bits.
THE ATTITUDE OF FIRST-YEAR STUDENTS OF A PEDAGOGICAL UNIVERSITY TO MOTIVATION AND DISCIPLINE IN PERSONAL SELF-DEVELOPMENT

Relevance. Self-development is a recognized existential value of the modern personality. This process requires taking into account many factors, including the influence of motivation and discipline. However, people do not fully realize the difference between them within the framework of self-development, which affects its effectiveness.&#x0D; The purpose of this work is to study the role of motivation and discipline in personal self-development, as well as a comparative assessment of their contribution to the formation of the meaningfulness of self-development of a person’s personality.&#x0D; Research methods: analysis of scientific literature from the field of psychology, pedagogy and sociology, observation, comparative analysis.&#x0D; Results. The study showed that many consider the contribution of motivation to the process of personal self-development more significant than discipline. The authors consider disciplined self-development to be more rational and effective for achieving success in any activity and self-development.
CLUSTERING ZONASI DAERAH RAWAN BENCANA ALAM DI PROVINSI SUMATERA BARAT MENGGUNAKAN ALGORITMA K-MEANS DAN LIBRARY GEOPANDAS

Indonesia is one of the countries that are often affected by natural disasters because it is located on the Asia Pacific Ring of Fire. West Sumatra province is one of the most frequently hit by natural disasters including earthquakes. According to the Central Bureau of Statistics of West Sumatra from 2018-2021, there were 1,148 earthquake cases and 1,503 flood cases. The objective of this study is to reveal areas prone to natural disasters in West Sumatra province using K-Means algorithm and GeoPandas visualization. After doing data processing for the optimal K value, 3 clusters were obtained, namely, cluster 1 is not prone to natural disasters, cluster 2 is prone to natural disasters and cluster 3 is very prone to natural disasters. From this research, it is found that cluster 1 has 7 members of the region, cluster 2 has 11 members of the region, and cluster 3 has 1 member of the region.
Architecture of Books on Indian Topics by Mykola Pshinka

The purpose of the research: analysis of the architecture of Mykola Pshinka’s Indi- an-themed books as a harmonious unity of ar- tistic-graphic and structural-compositional ele- ments, the role of stylization tools in the design of children’s publications to achieve synergy of the text and the illustrative series (collections "Feather of the Pink Chakva", "Indian Folk Tales", "Myths of Ancient India"). Research methods: systematization of scientific sources, inter- views, empirical observations and their descrip- tion, artistic analysis, synthesis and generaliza- tion of the obtained results. The results of the research: the peculiarities of the architecture of M. Pshinka’s editions of Indian themes and the artistic interpretation of the ancient Indian epic as a synthesis of the features of the indi- vidual author’s style of the Ukrainian artist and the elements of Indian national color used by him are considered. Emphasis is placed on the importance of visual communication, achiev- ing empathy with the young reader, who is the most important participant in the creative pro- cess, conveying not only the ideological, but also the emotional content of the book through illustrative and constructive means. A compar- ative characterization of the means of artistic expression in the illustration and design-layout of books for readers of different ages was car- ried out: taking into account the psychological features of the perception of visual images by children and the criteria of the quality of the publication: ideological, aesthetic, socio-psy- chological, ergonomic qualities. Scientific nov- elty: books on Indian subjects by M. Pshinka as a syncretic artistic phenomenon, characterized by integral architecture, a synthesis of texts, il- lustrations, decorative elements, design-layout are for the first time comprehensively and multi- facetedly considered. On the basis of a compar- ative analysis of the artistic design of the books "Indian Folk Tales" and "Myths of Ancient India", the features of the design of publications for children of different ages, junior and senior high school, were commented.
RANCANG BANGUN SISTEM INFORMASI EVALUASI PENYELENGGARAAN PELATIHAN BIDANG KESEHATAN BERBASIS WEB

Evaluation is carried out to determine the effectiveness of the training. Evaluations that are still carried out manually/conventionally by collecting data through filling in instruments, have constraints including; Speed of submission of evaluation results report; delay in submitting the issuance of a certificate because the results of the report are used as a requirement for filing; limited storage space for files/report archives; risk of damage, loss of data either caused by viruses, dysfunction of device operations or accidental factors in deleting files. For this reason, a system is needed that can help and facilitate the evaluation process starting from recording, storing and reporting. System development was carried out using qualitative research methods and input-process-output system development methods using the steps of the System Development Life Cycle (SDLC) method of the prototype model. The PIECES Framework is used to analyze system requirements to produce user satisfaction levels. The results of the research are the design of a system consisting of Context Diagrams, Entity Relationship Diagrams (ERD), Table Relational Diagrams (TRD), Standard Operational Procedure (SOP) Flow Diagrams and Data Flow Diagrams (DFD). Users of this system are Admin (program person in charge), Assessors, Organizers and Leaders. This information system is able to facilitate the process of implementing and reporting the results of evaluation implementation, ease of accessing and presenting reports, providing data storage space and better data management compared to conventional methods.