Reviewerly/exHarmony huggingface dataset:
Superthermal-light emission and nontrivial photon statistics in small lasers

Photon statistical measurements on a semiconductor microlaser, obtained using single-photon counting techniques, show that a newly discovered spontaneous pulsed emission regime possesses superthermal statistical properties. The observed spike dynamics, typical of small-scale devices, is at the origin of an unexpected discordance between the probability density function and its representation in terms of the first moments, a discordance so far unnoticed in all devices. The impact of this new dynamics is potentially large, since coincidence techniques are presently the sole techniques capable of characterizing light emitted by nanolasers.
Maliciously Secure Matrix Multiplication with Applications to Private Deep Learning

Computing on data in a manner that preserve the privacy is of growing importance. Multi-Party Computation (MPC) and Homomorphic Encryption (HE) are two cryptographic techniques for privacy-preserving computations. In this work, we have developed efficient UC-secure multiparty protocols for matrix multiplications and two-dimensional convolutions. We built upon the SPDZ framework and integrated the state-of-the-art HE algorithms for matrix multiplication. Our protocol achieved communication cost linear only in the input and output dimensions and not on the number of multiplication operations. We eliminate the "triple sacrifice" step of SPDZ to improve efficiency and simplify the zero-knowledge proofs. We implemented our protocols and benchmarked them against the SPDZ LowGear variant (Keller et al. Eurocrypt'18). For multiplying two square matrices of size 128, we reduced the communication cost from 1.54 GB to 12.46 MB, an improvement of over two orders of magnitude that only improves with larger matrix sizes. For evaluating all convolution layers of the ResNet-50 neural network, the communication reduces cost from 5 TB to 41 GB.
A Hybrid Method for Mathematical Expression Detection in Scientific Document Images

Mathematical expressions have been widely used in scientific documents.In order to analyze the documents, automatic detection of mathematical expressions is a crucial step.The paper presents a unified system for the detection of mathematical expressions including both inline and isolated expressions in scientific document images that usually consist of heterogeneous components (e.g., figures, tables, text and expressions).In the system, a hybrid method of two stages is proposed for the effective detection of mathematical expressions.First, the layout analysis of entire document images is introduced to improve the accuracy of text line and word segmentation.Then, both isolated and inline expressions in document images are detected.Both hand-crafted and deep learning features are extensively investigated and combined to improve the detection accuracy.Furthermore, a generic performance metric is applied to evaluate the system comprehensively.The proposed method has been evaluated on two public benchmark datasets (Marmot and GTDB).The obtained accuracies of isolated and inline expressions in the Marmot dataset are 91.18% and 81.35% while those in the GTDB dataset are 89.51% and 80.20%, respectively.The performance comparison is carried out with the conventional methods to show the outstanding effectiveness of the proposed system.Moreover, extensive experiments have been performed in order to point out the effect of document image resolution and post processing techniques on mathematical expression detection.
Cross-domain ontology construction and alignment from online customer product reviews

Online reviews often contain detailed sentiment towards different aspects of products and these opinions help consumers to be familiar with products. The introduction of domain ontology from online reviews may help consumers to obtain relevant information about products quickly. Nonetheless, they may compare products in multiple domains for purchase decisions. On this basis, the comparison of products in different domains induces that ontology alignment becomes a fundamental task to form a cross-domain ontology. However, due to large-scale text data and complex alignment mapping relations, many alignment algorithms are far from performing effectively. In this paper, a series of natural language processing approaches are applied to construct domain ontologies from online product reviews. Next, a new ontology alignment method is proposed to make purchase decisions regarding cross-domain product comparisons, in which a semantic-based algorithm and a structure-based algorithm are integrated to form a cross-domain ontology. Categories of experiments were conducted on reviews of smartphone and digital camera. Compared with benchmarked alignment tools, it shows that the proposed method yields to more accurate results. Finally, a case study with a customer friendly website is illustrated to present how the alignment of cross-domain ontology is able to help consumers on purchase decision support.
An artificial bee colony algorithm with adaptive heterogeneous competition for global optimization problems

Artificial bee colony (ABC) algorithm is an efficient bio-inspired optimizer proposed recently. Though it has gained great popularity, ABC suffers from its slow convergence and poor generalization on various problem landscapes. To address the issues, an augmented ABC with adaptive heterogeneous competition (ABC-AHC) is proposed in this study. In ABC-AHC, two bee swarms with each conducting heterogeneous but complementary capabilities are implemented to improve the search capabilities on various problem spaces. To dynamically adjust the search behaviors, an adaptive mechanism is developed to trigger the competition and migration between the bee swarms. Comparative studies are conducted for parameter tuning and the heterogeneous searching (HST). Existing algorithms including ABC variants and non-ABC variants are adopted to validate the performance of ABC-AHC. Numerical comparisons are conducted on 30D and 100D benchmark functions, CEC 2014 test function, random function and the real-world problems. Numerical results demonstrate that the proposed strategies significantly enhance ABC’s search capability and convergence speed on the various benchmark functions.
Mixed state entanglement for holographic axion model

Abstract We study the mixed state entanglement in a holographic axion model. We find that the holographic entanglement entropy (HEE), mutual information (MI) and entanglement of purification (EoP) exhibit very distinct behaviors with system parameters. The HEE exhibits universal monotonic behavior with system parameters, while the behaviors of MI and EoP relate to the specific system parameters and configurations. We find that MI and EoP can characterize mixed state entanglement better than HEE since they are less affected by thermal effects. Specifically, the MI partly cancels out the thermal entropy contribution, while the holographic EoP is not dictated by the thermal entropy in any situation. Moreover, we argue that EoP is more suitable for describing mixed state entanglement than MI. Because the MI of large configurations are still dictated by the thermal entropy, while the EoP will never be controlled only by the thermal effects.
Minimal Modifications of Deep Neural Networks using Verification

Deep neural networks (DNNs) are revolutionizing the way complex systems are de- signed, developed and maintained. As part of the life cycle of DNN-based systems, there is often a need to modify a DNN in subtle ways that affect certain aspects of its behav- ior, while leaving other aspects of its behavior unchanged (e.g., if a bug is discovered and needs to be fixed, without altering other functionality). Unfortunately, retraining a DNN is often difficult and expensive, and may produce a new DNN that is quite different from the original. We leverage recent advances in DNN verification and propose a technique for modifying a DNN according to certain requirements, in a way that is provably minimal, does not require any retraining, and is thus less likely to affect other aspects of the DNN’s behavior. Using a proof-of-concept implementation, we demonstrate the usefulness and potential of our approach in addressing two real-world needs: (i) measuring the resilience of DNN watermarking schemes; and (ii) bug repair in already-trained DNNs.
Case-Initiated COVID-19 Contact Tracing Using Anonymous Notifications

We discuss the concept of a participatory digital contact notification approach to assist tracing of contacts who are exposed to confirmed cases of coronavirus disease (COVID-19); the approach is simple and affordable for countries with limited access to health care resources and advanced technology. The proposed tool serves as a supplemental contract tracing approach to counteract the shortage of health care staff while providing privacy protection for both cases and contacts. This tool can be deployed on the internet or as a plugin for a smartphone app. Confirmed cases with COVID-19 can use this tool to provide contact information (either email addresses or mobile phone numbers) of close contacts. The system will then automatically send a message to the contacts informing them of their contact status, what this status means, the actions that should follow (eg, self-quarantine, respiratory hygiene/cough etiquette), and advice for receiving early care if they develop symptoms. The name of the sender of the notification message by email or mobile phone can be anonymous or not. The message received by the contact contains no disease information but contains a security code for the contact to log on the platform to retrieve the information.
Adaptive Edge Detection Algorithm Based on Improved Grey Prediction Model

Existing edge detection algorithms suffer from inefficient edge localization, noise sensitivity, and/or relatively poor automatic detection capability.Contemporary edge detection algorithms can be improved by targeting these problems to help bolster their performance.Grey system theory can be used to resolve the small data and poor information issues in the local information of uncertain systems.An automatic edge detection algorithm was developed in this study based on a grey prediction model to remedy these problems.Noise characteristics in grey images are used to deploy a noise-filtering algorithm based on local features.A mask with twenty-four edge direction information points (345 • ) was established based on edge line texture features.By compressing the amplitude of the sequence, the randomly oscillated grey prediction sequence can be converted into a smooth, new sequence.The discrete grey model (1,1) (DGM(1,1)) was established based on this new grey prediction sequence to obtain the grey prediction maximum value.A grey prediction image with enhanced edges was obtained by replacing the pixel value in the original image with the maximum grey prediction value.A grey prediction subtraction image with edges separated from non-edge points was also obtained by subtracting the original image from the grey prediction image.The optimal separation threshold in the grey prediction subtraction image can be determined via the global adaptive threshold selection method.The neighborhood search method was then deployed to remove stray points and burrs from the image after the target was separated from the background, creating the final edge image.Experiments were performed on a computer-simulated phantom to find that both the subjective visual effects and objective evaluation criteria are better under the proposed method than several other competitive methods.The proposed edge detection algorithm shows excellent edge detection ability and is highly robust to noise, though the grey prediction model needs further improvement to optimize the run time.
Certifiable Robustness to Discrete Adversarial Perturbations for Factorization Machines

Factorization machines (FMs) have been widely adopted to model the discrete feature interactions in recommender systems. Despite their great success, currently there is no study of their robustness to discrete adversarial perturbations. Whether modifying a certain number of the discrete input features has a dramatic effect on the FM's prediction? Although there exist robust training methods for FMs, they neglect the discrete property of input features and lack of an effective mechanism to verify the model robustness.
Self-paced Consensus Clustering with Bipartite Graph

Consensus clustering provides a framework to ensemble multiple clustering results to obtain a consensus and robust result. Most existing consensus clustering methods usually apply all data to ensemble learning, whereas ignoring the side effects caused by some difficult or unreliable instances. To tackle this problem, we propose a novel self-paced consensus clustering method to gradually involve instances from more reliable to less reliable ones into the ensemble learning. We first construct an initial bipartite graph from the multiple base clustering results, where the nodes represent the instances and clusters and the edges indicate that an instance belongs to a cluster. Then, we learn a structured bipartite graph from the initial one by self-paced learning, i.e., we automatically decide the reliability of each edge and involves the edges into graph learning in order of their reliability. At last, we obtain the final consensus clustering result from the learned bipartite graph. The extensive experimental results demonstrate the effectiveness and superiority of the proposed method.
Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction

Automatic sentence summarization produces a shorter version of a sentence, while preserving its most important information.A good summary is characterized by language fluency and high information overlap with the source sentence.We model these two aspects in an unsupervised objective function, consisting of language modeling and semantic similarity metrics.We search for a high-scoring summary by discrete optimization.Our proposed method achieves a new state-of-the art for unsupervised sentence summarization according to ROUGE scores.Additionally, we demonstrate that the commonly reported ROUGE F1 metric is sensitive to summary length.Since this is unwillingly exploited in recent work, we emphasize that future evaluation should explicitly group summarization systems by output length brackets. 1
Gradient Perturbation is Underrated for Differentially Private Convex Optimization

Gradient perturbation, widely used for differentially private optimization, injects noise at every iterative update to guarantee differential privacy. Previous work first determines the noise level that can satisfy the privacy requirement and then analyzes the utility of noisy gradient updates as in the non-private case. In contrast, we explore how the privacy noise affects the optimization property. We show that for differentially private convex optimization, the utility guarantee of differentially private (stochastic) gradient descent is determined by an expected curvature rather than the minimum curvature. The expected curvature, which represents the average curvature over the optimization path, is usually much larger than the minimum curvature. By using the expected curvature, we show that gradient perturbation can achieve a significantly improved utility guarantee that can theoretically justify the advantage of gradient perturbation over other perturbation methods. Finally, our extensive experiments suggest that gradient perturbation with the advanced composition method indeed outperforms other perturbation approaches by a large margin, matching our theoretical findings.
Lorentz Group Equivariant Neural Network for Particle Physics

We present a neural network architecture that is fully equivariant with respect to transformations under the Lorentz group, a fundamental symmetry of space and time in physics. The architecture is based on the theory of the finite-dimensional representations of the Lorentz group and the equivariant nonlinearity involves the tensor product. For classification tasks in particle physics, we demonstrate that such an equivariant architecture leads to drastically simpler models that have relatively few learnable parameters and are much more physically interpretable than leading approaches that use CNNs and point cloud approaches. The competitive performance of the network is demonstrated on a public classification dataset [27] for tagging top quark decays given energy-momenta of jet constituents produced in proton-proton collisions.
Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations

We introduce Span-ConveRT, a light-weight model for dialog slot-filling which frames the task as a turn-based span extraction task.This formulation allows for a simple integration of conversational knowledge coded in large pretrained conversational models such as Con-veRT (Henderson et al., 2019a).We show that leveraging such knowledge in Span-ConveRT is especially useful for few-shot learning scenarios: we report consistent gains over 1) a span extractor that trains representations from scratch in the target domain, and 2) a BERTbased span extractor.In order to inspire more work on span extraction for the slot-filling task, we also release RESTAURANTS-8K, a new challenging data set of 8,198 utterances, compiled from actual conversations in the restaurant booking domain.
The Sensitivity of Language Models and Humans to Winograd Schema Perturbations

Large-scale pretrained language models are the major driving force behind recent improvements in performance on the Winograd Schema Challenge, a widely employed test of commonsense reasoning ability.We show, however, with a new diagnostic dataset, that these models are sensitive to linguistic perturbations of the Winograd examples that minimally affect human understanding.Our results highlight interesting differences between humans and language models: language models are more sensitive to number or gender alternations and synonym replacements than humans, and humans are more stable and consistent in their predictions, maintain a much higher absolute performance, and perform better on non-associative instances than associative ones.Overall, humans are correct more often than out-of-the-box models, and the models are sometimes right for the wrong reasons.Finally, we show that fine-tuning on a large, task-specific dataset can offer a solution to these issues.
On the Robustness of Language Encoders against Grammatical Errors

We conduct a thorough study to diagnose the behaviors of pre-trained language encoders (ELMo, BERT, and RoBERTa) when confronted with natural grammatical errors.Specifically, we collect real grammatical errors from non-native speakers and conduct adversarial attacks to simulate these errors on clean text data.We use this approach to facilitate debugging models on downstream applications.Results confirm that the performance of all tested models is affected but the degree of impact varies.To interpret model behaviors, we further design a linguistic acceptability task to reveal their abilities in identifying ungrammatical sentences and the position of errors.We find that fixed contextual encoders with a simple classifier trained on the prediction of sentence correctness are able to locate error positions.We also design a cloze test for BERT and discover that BERT captures the interaction between errors and specific tokens in context.Our results shed light on understanding the robustness and behaviors of language encoders against grammatical errors.
Bidirectional Residual LSTM-based Human Activity Recognition

The Residual Long Short Term Memory (LSTM) deep learning approach is attracting attension of many researchers due to its efficiency when trained on high dimensional datasets. Nowadays, Human Activity Recognition (HAR) has come with enormous challenges that have to be addressed. In addressing such a problem, one can think of developing an application that can help the elderly people as an assistant when it works in collaboration with other timely technologies such as wearable devices with the help of IoT. Many research works are using a standard dataset in evaluating their proposed method in this regard. The dataset comes with its own challenge such as imbalanced classes. In this work, we propose to apply different machine learning techniques to address the specified problems and the method is validated on a standard dataset. To validate the proposed method, we evaluated using different standard metrics such as classification accuracy, precision, recall, f1-score, and Receiver Operating Characteristic (ROC) curve. The proposed method achieves an Area Under Curve (AUC) of 100%, 97.66% of accuracy, 91.59% precision,&amp;nbsp; 93.75% of recall and 92.66% of F1-score respectively.
A Graph-based Interactive Reasoning for Human-Object Interaction Detection

Human-Object Interaction (HOI) detection devotes to learn how humans interact with surrounding objects via inferring triplets of &lt; human, verb, object &gt;. However, recent HOI detection methods mostly rely on additional annotations (e.g., human pose) and neglect powerful interactive reasoning beyond convolutions. In this paper, we present a novel graph-based interactive reasoning model called Interactive Graph (abbr. in-Graph) to infer HOIs, in which interactive semantics implied among visual targets are efficiently exploited. The proposed model consists of a project function that maps related targets from convolution space to a graph-based semantic space, a message passing process propagating semantics among all nodes and an update function transforming the reasoned nodes back to convolution space. Furthermore, we construct a new framework to assemble in-Graph models for detecting HOIs, namely in-GraphNet. Beyond inferring HOIs using instance features respectively, the framework dynamically parses pairwise interactive semantics among visual targets by integrating two-level in-Graphs, i.e., scene-wide and instance-wide in-Graphs. Our framework is end-to-end trainable and free from costly annotations like human pose. Extensive experiments show that our proposed framework outperforms existing HOI detection methods on both V-COCO and HICO-DET benchmarks and improves the baseline about 9.4% and 15% relatively, validating its efficacy in detecting HOIs.
Learning to Have an Ear for Face Super-Resolution

We propose a novel method to use both audio and a low-resolution image to perform extreme face super-resolution (a 16x increase of the input size). When the resolution of the input image is very low (e.g., 8x8 pixels), the loss of information is so dire that important details of the original identity have been lost and audio can aid the recovery of a plausible high-resolution image. In fact, audio carries information about facial attributes, such as gender and age. To combine the aural and visual modalities, we propose a method to first build the latent representations of a face from the lone audio track and then from the lone low-resolution image. We then train a network to fuse these two representations. We show experimentally that audio can assist in recovering attributes such as the gender, the age and the identity, and thus improve the correctness of the high-resolution image reconstruction process. Our procedure does not make use of human annotation and thus can be easily trained with existing video datasets. Moreover, we show that our model builds a factorized representation of images and audio as it allows one to mix low-resolution images and audio from different videos and to generate realistic faces with semantically meaningful combinations.