Reviewerly/exHarmony huggingface dataset:
Taming singularities of the quantum Fisher information

Quantum Fisher information matrices (QFIMs) are fundamental to estimation theory: they encode the ultimate limit for the sensitivity with which a set of parameters can be estimated using a given probe. Since the limit invokes the inverse of a QFIM, an immediate question is what to do with singular QFIMs. Moreover, the QFIM may be discontinuous, forcing one away from the paradigm of regular statistical models. These questions of nonregular quantum statistical models are present in both single- and multiparameter estimation. Geometrically, singular QFIMs occur when the curvature of the metric vanishes in one or more directions in the space of probability distributions, while QFIMs have discontinuities when the density matrix has parameter-dependent rank. We present a nuanced discussion of how to deal with each of these scenarios, stressing the physical implications of singular QFIMs and the ensuing ramifications for quantum metrology.
Gap-Dependent Unsupervised Exploration for Reinforcement Learning

For the problem of task-agnostic reinforcement learning (RL), an agent first collects samples from an unknown environment without the supervision of reward signals, then is revealed with a reward and is asked to compute a corresponding near-optimal policy. Existing approaches mainly concern the worst-case scenarios, in which no structural information of the reward/transition-dynamics is utilized. Therefore the best sample upper bound is $\propto\widetilde{\mathcal{O}}(1/\epsilon^2)$, where $\epsilon>0$ is the target accuracy of the obtained policy, and can be overly pessimistic. To tackle this issue, we provide an efficient algorithm that utilizes a gap parameter, $\rho>0$, to reduce the amount of exploration. In particular, for an unknown finite-horizon Markov decision process, the algorithm takes only $\widetilde{\mathcal{O}} (1/\epsilon \cdot (H^3SA / \rho + H^4 S^2 A) )$ episodes of exploration, and is able to obtain an $\epsilon$-optimal policy for a post-revealed reward with sub-optimality gap at least $\rho$, where $S$ is the number of states, $A$ is the number of actions, and $H$ is the length of the horizon, obtaining a nearly \emph{quadratic saving} in terms of $\epsilon$. We show that, information-theoretically, this bound is nearly tight for $\rho < \Theta(1/(HS))$ and $H>1$. We further show that $\propto\widetilde{\mathcal{O}}(1)$ sample bound is possible for $H=1$ (i.e., multi-armed bandit) or with a sampling simulator, establishing a stark separation between those settings and the RL setting.
ASMR: Learning Attribute-Based Person Search with Adaptive Semantic  Margin Regularizer

Attribute-based person search is the task of finding person images that are best matched with a set of text attributes given as query. The main challenge of this task is the large modality gap between attributes and images. To reduce the gap, we present a new loss for learning cross-modal embeddings in the context of attribute-based person search. We regard a set of attributes as a category of people sharing the same traits. In a joint embedding space of the two modalities, our loss pulls images close to their person categories for modality alignment. More importantly, it pushes apart a pair of person categories by a margin determined adaptively by their semantic distance, where the distance metric is learned end-to-end so that the loss considers importance of each attribute when relating person categories. Our loss guided by the adaptive semantic margin leads to more discriminative and semantically well-arranged distributions of person images. As a consequence, it enables a simple embedding model to achieve state-of-the-art records on public benchmarks without bells and whistles.
Knowledge Distillation from BERT Transformer to Speech Transformer for  Intent Classification

End-to-end intent classification using speech has numerous advantages compared to the conventional pipeline approach using automatic speech recognition (ASR), followed by natural language processing modules. It attempts to predict intent from speech without using an intermediate ASR module. However, such end-to-end framework suffers from the unavailability of large speech resources with higher acoustic variation in spoken language understanding. In this work, we exploit the scope of the transformer distillation method that is specifically designed for knowledge distillation from a transformer based language model to a transformer based speech model. In this regard, we leverage the reliable and widely used bidirectional encoder representations from transformers (BERT) model as a language model and transfer the knowledge to build an acoustic model for intent classification using the speech. In particular, a multilevel transformer based teacher-student model is designed, and knowledge distillation is performed across attention and hidden sub-layers of different transformer layers of the student and teacher models. We achieve an intent classification accuracy of 99.10% and 88.79% for Fluent speech corpus and ATIS database, respectively. Further, the proposed method demonstrates better performance and robustness in acoustically degraded condition compared to the baseline method.
Dialogue Summarization with Supporting Utterance Flow Modeling and Fact  Regularization

Dialogue summarization aims to generate a summary that indicates the key points of a given dialogue. In this work, we propose an end-to-end neural model for dialogue summarization with two novel modules, namely, the \emph{supporting utterance flow modeling module} and the \emph{fact regularization module}. The supporting utterance flow modeling helps to generate a coherent summary by smoothly shifting the focus from the former utterances to the later ones. The fact regularization encourages the generated summary to be factually consistent with the ground-truth summary during model training, which helps to improve the factual correctness of the generated summary in inference time. Furthermore, we also introduce a new benchmark dataset for dialogue summarization. Extensive experiments on both existing and newly-introduced datasets demonstrate the effectiveness of our model.
A monogamy-of-entanglement game for subspace coset states

We establish a strong monogamy-of-entanglement property for subspace coset states, which are uniform superpositions of vectors in a linear subspace of $\mathbb{F}_2^n$ to which has been applied a quantum one-time pad. This property was conjectured recently by [Coladangelo, Liu, Liu, and Zhandry, Crypto'21] and shown to have applications to unclonable decryption and copy-protection of pseudorandom functions. We present two proofs, one which directly follows the method of the original paper and the other which uses an observation from [Vidick and Zhang, Eurocrypt'20] to reduce the analysis to a simpler monogamy game based on BB'84 states. Both proofs ultimately rely on the same proof technique, introduced in [Tomamichel, Fehr, Kaniewski and Wehner, New Journal of Physics '13].
Convergence in quadratic mean of averaged stochastic gradient algorithms without strong convexity nor bounded gradient

Online averaged stochastic gradient algorithms are more and more studied since (i) they can deal quickly with large sample taking values in high-dimensional spaces, (ii) they enable to treat data sequentially, (iii) they are known to be asymptotically efficient. In this paper, we focus on giving explicit bounds of the quadratic mean error of the estimates, and this, without supposing that the function we would like to minimize is strongly convex or admits a bounded gradient.
Hybrid no-signaling-quantum correlations

Fundamental investigations in non-locality have shown that while the no-signaling principle alone is not sufficient to single out the set of quantum non-local correlations, local quantum mechanics and no-signaling together exactly reproduce the set of quantum correlations in the two-party Bell scenario. Here, we introduce and study an intermediate hybrid no-signaling quantum set of non-local correlations that we term $\textbf{HNSQ}$ in the multi-party Bell scenario where some subsystems are locally quantum while the remaining subsystems are only constrained by the no-signaling principle. Specifically, the set $\textbf{HNSQ}$ is a super-quantum set of correlations derived from no-signaling assemblages by performing quantum measurements on the trusted subsystems. As a tool for optimization over the set $\textbf{HNSQ}$, we introduce an outer hierarchy of semi-definite programming approximations to the set following an approach put forward by Doherty-Parillo-Spedalieri. We then show that in contrast to the set $\textbf{NS}$ of no-signaling correlations, there exist extreme points of $\textbf{HNSQ}$ in the tripartite Bell scenario that admit quantum realization. We perform an extensive numerical analysis of the maximal violation of the facet Bell inequalities in the three-party binary input-output scenario and study the corresponding self-testing properties. In contrast to the usual no-signaling correlations, the new set allows for simple security proofs of (one-sided)-device-independent applications against super-quantum adversaries.
Students’ Perception of Speaking Practice in Online Classroom Activities

Perception is individual’s feeling and thinking about something that ever done. Students should have their own perception about speaking activity that their interest with. This research is aimed to find out the students’ perception of speaking practice in online classroom activities and to find out students’ obstacles during speaking at State Islamic Senior High School 01 INHIL. This research used quantitative research. Descriptive quantitative method used in this research. This research was held on January-March 2022 at Islamic Senior High School 01 INHIL. The populations were 50 students at eleventh grade students in XI IPA, XI IPS, and XI MAK. Thus, questionnaire and interview were used to gather the data. The findings show the students gave negative perception about their speaking practice in online classroom at the last semester. Besides, most of the students claimed their obstacles during speaking were anxiety, shyness, self-confidence, do not know how to pronounced and difficulties in grammar.
Kamus Digital Tanaman Obat Menggunakan Algoritme Knuth Morris Pratt Berbasis Mobile

Digital dictionary of medicinal plants a collection of applications a collection of names of medicinal plants. This application was developed to make it easier for users to access the use of medicinal plants based on keywords, names of common diseases such as asthma, diarrhea, fever, headache, diabetes, and so on. The obstacle in using the digital dictionary application of medicinal plants is that it is difficult to find exact information on drug searches, because the vocabulary in producing several other items is not necessarily the same as what is being sought. This study uses the Knuth Morris Pratt (KMP) algorithm search technique on a digital dictionary of medicinal plants by matching keywords as input with strings between text and patterns. This study aims to develop previous research on a digital dictionary of medicinal plants using a mobile-based Rocchio algorithm that searches medicinal plant data by querying proximity in a database that has weaknesses in words that have the same meaning with different words, using another search technique, namely KMP. is expected to get optimal results on search results based on the keyword name of the disease. The test results with 30 keywords on 640 medicinal plant data resulted in a precision value of 92.4, accuracy and recall of 100%.
Rectangle Attack Against Type‐I Generalized Feistel Structures

Type-I generalized Feistel networks (GFN) are widely used frameworks in symmetric-key primitive designs such as CAST-256 and Lesamnta. Different from the extensive studies focusing on specific block cipher instances, the analysis against Type-I GFN structures gives generic security evaluation of the basic frameworks and concentrates more on the effect of linear transformation. Currently, works in this field mainly evaluate the security against impossible differential attack, zero-correlation linear attack, meet-in-the-middle attack and yoyo game attack, while its security evaluation against rectangle attack is still missing. In this paper, we filled this gap and gave the first structural analytical results of Type-I GFN against rectangle attack. By exploiting its structural properties, we proved there exists a boomerang switch for Type-I GFN for the first time, which is independent of the round functions. Then we turned the boomerang switch into chosen plaintext setting and proposed a new rectangle attack model. By appending 1 more round in the beginning of the boomerang switch, we constructed a rectangle distinguisher and a key recovery attack could be performed.
Binary Image Steganalysis Based on Symmetrical Local Residual Patterns

Residual computation is an effective method for gray-scale image steganalysis. For binary images, the residual computation calculated by the XOR operation is also employed in the local residual patterns (LRP) model for steganalysis. A binary image steganalytic scheme based on symmetrical local residual patterns (SLRP) is proposed. The symmetrical relationships among residual patterns are introduced that make the features more compact while reducing the dimensionality of the features set. Multi-scale windows are utilized to construct three SLRP submodels which are further merged to construct the final features set instead of a single model. SLRPs with higher probability to be modified after embedding are emphasized and selected to construct the feature sets for training the support vector machine classifier. The experimental results show that the proposed steganalytic scheme is effective for detecting binary image steganography.
Statistical Model on CRAFT

Many cryptanalytic techniques for symmetric-key primitives rely on specific statistical analysis to extract some secrete key information from a large number of known or chosen plaintext-ciphertext pairs. For example, there is a standard statistical model for differential cryptanalysis that determines the success probability and complexity of the attack given some predefined configurations of the attack. In this work, we investigate the differential attack proposed by Guo et al. at Fast Software Encryption Conference 2020 and find that in this attack, the statistical behavior of the counters for key candidates deviate from standard scenarios, where both the correct key and the correct key xor specific difference are expected to receive the largest number of votes. Based on this bimodal behavior, we give three different statistical models for truncated differential distinguisher on CRAFT (a cryptographic algorithm name) for bimodal phenomena. Then, we provide the formulas about the success probability and data complexity for different models under the condition of a fixed threshold value. Also, we verify the validity of our models for bimodal phenomena by experiments on round-reduced of the versions distinguishers on CRAFT. We find that the success probability of theory and experiment are close when we fix the data complexity and threshold value. Finally, we compare the three models using the mathematical tool Matlab and conclude that Model 3 has better performance.
Statistical Theory for Imbalanced Binary Classification

Within the vast body of statistical theory developed for binary classification, few meaningful results exist for imbalanced classification, in which data are dominated by samples from one of the two classes. Existing theory faces at least two main challenges. First, meaningful results must consider more complex performance measures than classification accuracy. To address this, we characterize a novel generalization of the Bayes-optimal classifier to any performance metric computed from the confusion matrix, and we use this to show how relative performance guarantees can be obtained in terms of the error of estimating the class probability function under uniform ($\mathcal{L}_\infty$) loss. Second, as we show, optimal classification performance depends on certain properties of class imbalance that have not previously been formalized. Specifically, we propose a novel sub-type of class imbalance, which we call Uniform Class Imbalance. We analyze how Uniform Class Imbalance influences optimal classifier performance and show that it necessitates different classifier behavior than other types of class imbalance. We further illustrate these two contributions in the case of $k$-nearest neighbor classification, for which we develop novel guarantees. Together, these results provide some of the first meaningful finite-sample statistical theory for imbalanced binary classification.
PERT: A Progressively Region-based Network for Scene Text Removal

Scene text removal (STR) contains two processes: text localization and background reconstruction. Through integrating both processes into a single network, previous methods provide an implicit erasure guidance by modifying all pixels in the entire image. However, there exists two problems: 1) the implicit erasure guidance causes the excessive erasure to non-text areas; 2) the one-stage erasure lacks the exhaustive removal of text region. In this paper, we propose a ProgrEssively Region-based scene Text eraser (PERT), introducing an explicit erasure guidance and performing balanced multi-stage erasure for accurate and exhaustive text removal. Firstly, we introduce a new region-based modification strategy (RegionMS) to explicitly guide the erasure process. Different from previous implicitly guided methods, RegionMS performs targeted and regional erasure on only text region, and adaptively perceives stroke-level information to improve the integrity of non-text areas with only bounding box level annotations. Secondly, PERT performs balanced multi-stage erasure with several progressive erasing stages. Each erasing stage takes an equal step toward the text-erased image to ensure the exhaustive erasure of text regions. Compared with previous methods, PERT outperforms them by a large margin without the need of adversarial loss, obtaining SOTA results with high speed (71 FPS) and at least 25% lower parameter complexity. Code is available at https://github.com/wangyuxin87/PERT.
Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal  Stochastic Linear Mixing Model

Many modern time-series datasets contain large numbers of output response variables sampled for prolonged periods of time. For example, in neuroscience, the activities of 100s-1000's of neurons are recorded during behaviors and in response to sensory stimuli. Multi-output Gaussian process models leverage the nonparametric nature of Gaussian processes to capture structure across multiple outputs. However, this class of models typically assumes that the correlations between the output response variables are invariant in the input space. Stochastic linear mixing models (SLMM) assume the mixture coefficients depend on input, making them more flexible and effective to capture complex output dependence. However, currently, the inference for SLMMs is intractable for large datasets, making them inapplicable to several modern time-series problems. In this paper, we propose a new regression framework, the orthogonal stochastic linear mixing model (OSLMM) that introduces an orthogonal constraint amongst the mixing coefficients. This constraint reduces the computational burden of inference while retaining the capability to handle complex output dependence. We provide Markov chain Monte Carlo inference procedures for both SLMM and OSLMM and demonstrate superior model scalability and reduced prediction error of OSLMM compared with state-of-the-art methods on several real-world applications. In neurophysiology recordings, we use the inferred latent functions for compact visualization of population responses to auditory stimuli, and demonstrate superior results compared to a competing method (GPFA). Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale time-series datasets.
Minimum sharpness: Scale-invariant parameter-robustness of neural  networks

Toward achieving robust and defensive neural networks, the robustness against the weight parameters perturbations, i.e., sharpness, attracts attention in recent years (Sun et al., 2020). However, sharpness is known to remain a critical issue, "scale-sensitivity." In this paper, we propose a novel sharpness measure, Minimum Sharpness. It is known that NNs have a specific scale transformation that constitutes equivalent classes where functional properties are completely identical, and at the same time, their sharpness could change unlimitedly. We define our sharpness through a minimization problem over the equivalent NNs being invariant to the scale transformation. We also develop an efficient and exact technique to make the sharpness tractable, which reduces the heavy computational costs involved with Hessian. In the experiment, we observed that our sharpness has a valid correlation with the generalization of NNs and runs with less computational cost than existing sharpness measures.
A compressive multi-kernel method for privacy-preserving machine  learning

As the analytic tools become more powerful, and more data are generated on a daily basis, the issue of data privacy arises. This leads to the study of the design of privacy-preserving machine learning algorithms. Given two objectives, namely, utility maximization and privacy-loss minimization, this work is based on two previously non-intersecting regimes -- Compressive Privacy and multi-kernel method. Compressive Privacy is a privacy framework that employs utility-preserving lossy-encoding scheme to protect the privacy of the data, while multi-kernel method is a kernel based machine learning regime that explores the idea of using multiple kernels for building better predictors. The compressive multi-kernel method proposed consists of two stages -- the compression stage and the multi-kernel stage. The compression stage follows the Compressive Privacy paradigm to provide the desired privacy protection. Each kernel matrix is compressed with a lossy projection matrix derived from the Discriminant Component Analysis (DCA). The multi-kernel stage uses the signal-to-noise ratio (SNR) score of each kernel to non-uniformly combine multiple compressive kernels. The proposed method is evaluated on two mobile-sensing datasets -- MHEALTH and HAR -- where activity recognition is defined as utility and person identification is defined as privacy. The results show that the compression regime is successful in privacy preservation as the privacy classification accuracies are almost at the random-guess level in all experiments. On the other hand, the novel SNR-based multi-kernel shows utility classification accuracy improvement upon the state-of-the-art in both datasets. These results indicate a promising direction for research in privacy-preserving machine learning.
Fusion of Embeddings Networks for Robust Combination of Text Dependent  and Independent Speaker Recognition

By implicitly recognizing a user based on his/her speech input, speaker identification enables many downstream applications, such as personalized system behavior and expedited shopping checkouts. Based on whether the speech content is constrained or not, both text-dependent (TD) and text-independent (TI) speaker recognition models may be used. We wish to combine the advantages of both types of models through an ensemble system to make more reliable predictions. However, any such combined approach has to be robust to incomplete inputs, i.e., when either TD or TI input is missing. As a solution we propose a fusion of embeddings network foenet architecture, combining joint learning with neural attention. We compare foenet with four competitive baseline methods on a dataset of voice assistant inputs, and show that it achieves higher accuracy than the baseline and score fusion methods, especially in the presence of incomplete inputs.