Reviewerly/exHarmony huggingface dataset:
Tackling Multimodal Device Distributions in Inverse Photonic Design using Invertible Neural Networks

Inverse design, the process of matching a device or process parameters to exhibit a desired performance, is applied in many disciplines ranging from material design over chemical processes and to engineering. Machine learning has emerged as a promising approach to overcome current limitations imposed by the dimensionality of the parameter space and multimodal parameter distributions. Most traditional optimization routines assume an invertible one-to-one mapping between the design parameters and the target performance. However, comparable or even identical performance may be realized by different designs, yielding a multimodal distribution of possible solutions to the inverse design problem which confuses the optimization algorithm. Here, we show how a generative modeling approach based on invertible neural networks can provide the full distribution of possible solutions to the inverse design problem and resolve the ambiguity of nanodevice inverse design problems featuring multimodal distributions. We implement a Conditional Invertible Neural Network (cINN) and apply it to a proof-of-principle nanophotonic problem, consisting in tailoring the transmission spectrum of a metallic film milled by subwavelength indentations. We compare our approach with the commonly used conditional Variational Autoencoder (cVAE) framework and show the superior flexibility and accuracy of the proposed cINNs when dealing with multimodal device distributions. Our work shows that invertible neural networks provide a valuable and versatile toolkit for advancing inverse design in nanoscience and nanotechnology.
Cloud-Centric Blockchain Public Key Infrastructure for Big Data Applications

A cloud-based public key infrastructure (PKI) utilizing blockchain technology is proposed. Big data ecosystems have scalable and resilient needs that current PKI cannot satisfy. Enhancements include using blockchains to establish persistent access to certificate data and certificate revocation lists, decoupling of data from certificate authority, and hosting it on a cloud provider to tap into its traffic security measures. Instead of holding data within the transaction data fields, certificate data and status were embedded into smart contracts. The tests revealed a significant performance increase over that of both traditional and the version that stored data within blocks. The proposed method reduced the mining data size, and lowered the mining time to 6.6% of the time used for the block data storage method. Also, the mining gas cost per certificate was consequently cut by 87%. In summary, completely decoupling the certificate authority portion of a PKI and storing certificate data inside smart contracts yields a sizable performance boost while decreasing the attack surface.
MODNet: Multi-offset Point Cloud Denoising Network Customized for Multi-scale Patches

The intricacy of 3D surfaces often results cutting-edge point cloud denoising (PCD) models in surface degradation including remnant noise, wrongly-removed geometric details. Although using multi-scale patches to encode the geometry of a point has become the common wisdom in PCD, we find that simple aggregation of extracted multi-scale features can not adaptively utilize the appropriate scale information according to the geometric information around noisy points. It leads to surface degradation, especially for points close to edges and points on complex curved surfaces. We raise an intriguing question -- if employing multi-scale geometric perception information to guide the network to utilize multi-scale information, can eliminate the severe surface degradation problem? To answer it, we propose a Multi-offset Denoising Network (MODNet) customized for multi-scale patches. First, we extract the low-level feature of three scales patches by patch feature encoders. Second, a multi-scale perception module is designed to embed multi-scale geometric information for each scale feature and regress multi-scale weights to guide a multi-offset denoising displacement. Third, a multi-offset decoder regresses three scale offsets, which are guided by the multi-scale weights to predict the final displacement by weighting them adaptively. Experiments demonstrate that our method achieves new state-of-the-art performance on both synthetic and real-scanned datasets.
Copy Move Forgery Detection: The Current Implications and Contemporary Practices

Abstract Copy move forgeries are a type of digital retouching that takes place in the field of image modification. This type of retouching occurs when one element of a picture is replicated over to a different segment of the photograph. This is typically done in an effort to mask or cover up undesirable qualities of the photograph thus copy move forgeries are a form of image modification. When it comes to digital picture modalities, copy move forging is by far the most prevalent form of picture manipulation that may take place. This is mainly observed when certain elements or segments of the recorded file are duplicated in the recorded file. The identification and localization of forgeries is a prominent problem that has piqued the attention of scholars working in the field of digital forensics and continues to do so. The identification of photo forgery has previously been the subject of several methodologies and uncountable publications, all of which were created and published. An abridged description of the present methodology and approaches is provided in this document, along with a survey and condensed summaries of current research and successes, among other things.
On the Trade-Off between Actionable Explanations and the Right to be Forgotten

As machine learning (ML) models are increasingly being deployed in high-stakes applications, policymakers have suggested tighter data protection regulations (e.g., GDPR, CCPA). One key principle is the "right to be forgotten" which gives users the right to have their data deleted. Another key principle is the right to an actionable explanation, also known as algorithmic recourse, allowing users to reverse unfavorable decisions. To date, it is unknown whether these two principles can be operationalized simultaneously. Therefore, we introduce and study the problem of recourse invalidation in the context of data deletion requests. More specifically, we theoretically and empirically analyze the behavior of popular state-of-the-art algorithms and demonstrate that the recourses generated by these algorithms are likely to be invalidated if a small number of data deletion requests (e.g., 1 or 2) warrant updates of the predictive model. For the setting of differentiable models, we suggest a framework to identify a minimal subset of critical training points which, when removed, maximize the fraction of invalidated recourses. Using our framework, we empirically show that the removal of as little as 2 data instances from the training set can invalidate up to 95 percent of all recourses output by popular state-of-the-art algorithms. Thus, our work raises fundamental questions about the compatibility of "the right to an actionable explanation" in the context of the "right to be forgotten", while also providing constructive insights on the determining factors of recourse robustness.
The argument and demonstration exemplified in a mathematical dialogue

The teaching-learning process is analyzed in a course for a group of professors who were taught subjects on Calculus, to study the episodes of problem-solving in them, focused on the identification of patterns and argumentation using counterexamples. The explanation and the argument in the classroom can be used together so that the argument (issued as a counterexample) supports the explanation (conjecture). Developing the mathematics class so that the above occurs is a form of interaction and how to encourage students to move from explanation to argumentation (placing a hybrid system). Furthermore, both forms of reasoning can influence dialogue protocols and strategies. In this work, the dialogue model is described as a tool to address the problem that arises when working with students.
Learned Lossless Image Compression With Combined Autoregressive Models And Attention Modules

Lossless image compression is an essential research field in image compression. Recently, learning-based image compression methods achieved impressive performance compared with traditional lossless methods, such as WebP, JPEG2000, and FLIF. However, there are still many impressive lossy compression methods that can be applied to lossless compression. Therefore, in this paper, we explore the methods widely used in lossy compression and apply them to lossless compression. Inspired by the impressive performance of the Gaussian mixture model (GMM) shown in lossy compression, we generate a lossless network architecture with GMM. Besides noticing the successful achievements of attention modules and autoregressive models, we propose to utilize attention modules and add an extra autoregressive model for raw images in our network architecture to boost the performance. Experimental results show that our approach outperforms most classical lossless compression methods and existing learning-based methods.
MRL: Learning to Mix with Attention and Convolutions

In this paper, we present a new neural architectural block for the vision domain, named Mixing Regionally and Locally (MRL), developed with the aim of effectively and efficiently mixing the provided input features. We bifurcate the input feature mixing task as mixing at a regional and local scale. To achieve an efficient mix, we exploit the domain-wide receptive field provided by self-attention for regional-scale mixing and convolutional kernels restricted to local scale for local-scale mixing. More specifically, our proposed method mixes regional features associated with local features within a defined region, followed by a local-scale features mix augmented by regional features. Experiments show that this hybridization of self-attention and convolution brings improved capacity, generalization (right inductive bias), and efficiency. Under similar network settings, MRL outperforms or is at par with its counterparts in classification, object detection, and segmentation tasks. We also show that our MRL-based network architecture achieves state-of-the-art performance for H&E histology datasets. We achieved DICE of 0.843, 0.855, and 0.892 for Kumar, CoNSep, and CPM-17 datasets, respectively, while highlighting the versatility offered by the MRL framework by incorporating layers like group convolutions to improve dataset-specific generalization.
Combining keyphrase extraction and lexical diversity to characterize ideas in publication titles

Beyond bibliometrics, there is interest in characterizing the evolution of the number of ideas in scientific papers. A common approach for investigating this involves analyzing the titles of publications to detect vocabulary changes over time. With the notion that phrases, or more specifically keyphrases, represent concepts, lexical diversity metrics are applied to phrased versions of the titles. Thus changes in lexical diversity are treated as indicators of shifts, and possibly expansion, of research. Therefore, optimizing detection of keyphrases is an important aspect of this process. Rather than just one, we propose to use multiple phrase detection models with the goal to produce a more comprehensive set of keyphrases from the source corpora. Another potential advantage to this approach is that the union and difference of these sets may provide automated techniques for identifying and omitting non-specific phrases. We compare the performance of several phrase detection models, analyze the keyphrase sets output of each, and calculate lexical diversity of corpora variants incorporating keyphrases from each model, using four common lexical diversity metrics.
SoMoFormer: Multi-Person Pose Forecasting with Transformers

Human pose forecasting is a challenging problem involving complex human body motion and posture dynamics. In cases that there are multiple people in the environment, one's motion may also be influenced by the motion and dynamic movements of others. Although there are several previous works targeting the problem of multi-person dynamic pose forecasting, they often model the entire pose sequence as time series (ignoring the underlying relationship between joints) or only output the future pose sequence of one person at a time. In this paper, we present a new method, called Social Motion Transformer (SoMoFormer), for multi-person 3D pose forecasting. Our transformer architecture uniquely models human motion input as a joint sequence rather than a time sequence, allowing us to perform attention over joints while predicting an entire future motion sequence for each joint in parallel. We show that with this problem reformulation, SoMoFormer naturally extends to multi-person scenes by using the joints of all people in a scene as input queries. Using learned embeddings to denote the type of joint, person identity, and global position, our model learns the relationships between joints and between people, attending more strongly to joints from the same or nearby people. SoMoFormer outperforms state-of-the-art methods for long-term motion prediction on the SoMoF benchmark as well as the CMU-Mocap and MuPoTS-3D datasets. Code will be made available after publication.
Robins-Monro Augmented Lagrangian Method for Stochastic Convex Optimization

In this paper, we propose a Robbins-Monro augmented Lagrangian method (RMALM) to solve a class of constrained stochastic convex optimization, which can be regarded as a hybrid of the Robbins-Monro type stochastic approximation method and the augmented Lagrangian method of convex optimizations. Under mild conditions, we show that the proposed algorithm exhibits a linear convergence rate. Moreover, instead of verifying a computationally intractable stopping criteria, we show that the RMALM with the increasing subproblem iteration number has a global complexity $\mathcal{O}(1/\varepsilon^{1+q})$ for the $\varepsilon$-solution (i.e., $\mathbb{E}\left(\|x^k-x^*\|^2\right) < \varepsilon$), where $q$ is any positive number. Numerical results on synthetic and real data demonstrate that the proposed algorithm outperforms the existing algorithms.
Personalized Recommendations in EdTech: Evidence from a Randomized Controlled Trial

We study the impact of personalized content recommendations on the usage of an educational app for children. In a randomized controlled trial, we show that the introduction of personalized recommendations increases the consumption of content in the personalized section of the app by approximately 60%. We further show that the overall app usage increases by 14%, compared to the baseline system where human content editors select stories for all students at a given grade level. The magnitude of individual gains from personalized content increases with the amount of data available about a student and with preferences for niche content: heavy users with long histories of content interactions who prefer niche content benefit more than infrequent, newer users who like popular content. To facilitate the move to personalized recommendation systems from a simpler system, we describe how we make important design decisions, such as comparing alternative models using offline metrics and choosing the right target audience.
PercentMatch: Percentile-based Dynamic Thresholding for Multi-Label Semi-Supervised Classification

While much of recent study in semi-supervised learning (SSL) has achieved strong performance on single-label classification problems, an equally important yet underexplored problem is how to leverage the advantage of unlabeled data in multi-label classification tasks. To extend the success of SSL to multi-label classification, we first analyze with illustrative examples to get some intuition about the extra challenges exist in multi-label classification. Based on the analysis, we then propose PercentMatch, a percentile-based threshold adjusting scheme, to dynamically alter the score thresholds of positive and negative pseudo-labels for each class during the training, as well as dynamic unlabeled loss weights that further reduces noise from early-stage unlabeled predictions. Without loss of simplicity, we achieve strong performance on Pascal VOC2007 and MS-COCO datasets when compared to recent SSL methods.
Evolving Label Usage within Generation Z when Self-Describing Sexual Orientation

Evaluating change in ranked term importance in a growing corpus is a powerful tool for understanding changes in vocabulary usage. In this paper, we analyze a corpus of free-response answers where 33,993 LGBTQ Generation Z respondents from age 13 to 24 in the United States are asked to self-describe their sexual orientation. We observe that certain labels, such as bisexual, pansexual, and lesbian, remain equally important across age groups. The importance of other labels, such as homosexual, demisexual, and omnisexual, evolve across age groups. Although Generation Z is often stereotyped as homogenous, we observe noticeably different label usage when self-describing sexual orientation within it. We urge that interested parties must routinely survey the most important sexual orientation labels to their target audience and refresh their materials (such as demographic surveys) to reflect the constantly evolving LGBTQ community and create an inclusive environment.
StableFace: Analyzing and Improving Motion Stability for Talking Face Generation

While previous speech-driven talking face generation methods have made significant progress in improving the visual quality and lip-sync quality of the synthesized videos, they pay less attention to lip motion jitters which greatly undermine the realness of talking face videos. What causes motion jitters, and how to mitigate the problem? In this paper, we conduct systematic analyses on the motion jittering problem based on a state-of-the-art pipeline that uses 3D face representations to bridge the input audio and output video, and improve the motion stability with a series of effective designs. We find that several issues can lead to jitters in synthesized talking face video: 1) jitters from the input 3D face representations; 2) training-inference mismatch; 3) lack of dependency modeling among video frames. Accordingly, we propose three effective solutions to address this issue: 1) we propose a gaussian-based adaptive smoothing module to smooth the 3D face representations to eliminate jitters in the input; 2) we add augmented erosions on the input data of the neural renderer in training to simulate the distortion in inference to reduce mismatch; 3) we develop an audio-fused transformer generator to model dependency among video frames. Besides, considering there is no off-the-shelf metric for measuring motion jitters in talking face video, we devise an objective metric (Motion Stability Index, MSI), to quantitatively measure the motion jitters by calculating the reciprocal of variance acceleration. Extensive experimental results show the superiority of our method on motion-stable face video generation, with better quality than previous systems.
Reweighting Strategy based on Synthetic Data Identification for Sentence Similarity

Semantically meaningful sentence embeddings are important for numerous tasks in natural language processing. To obtain such embeddings, recent studies explored the idea of utilizing synthetically generated data from pretrained language models (PLMs) as a training corpus. However, PLMs often generate sentences much different from the ones written by human. We hypothesize that treating all these synthetic examples equally for training deep neural networks can have an adverse effect on learning semantically meaningful embeddings. To analyze this, we first train a classifier that identifies machine-written sentences, and observe that the linguistic features of the sentences identified as written by a machine are significantly different from those of human-written sentences. Based on this, we propose a novel approach that first trains the classifier to measure the importance of each sentence. The distilled information from the classifier is then used to train a reliable sentence embedding model. Through extensive evaluation on four real-world datasets, we demonstrate that our model trained on synthetic data generalizes well and outperforms the existing baselines. Our implementation is publicly available at https://github.com/ddehun/coling2022_reweighting_sts.
Rotation-invariant image classification using a novel 1D CNN and Multichannel Accurate Bessel-Fourier moments

This work presents a proposal to use Bessel-Fourier moments as inputs to 1D convolutional neural networks in such a way that they take advantage of the inherent characteristics of moment type descriptors such as rotational invariance and minimal information redundancy. The results presented show that the proposal has a better performance than the deep neural network with rotation invariance.
Gaussian Regularization Based Active Contour Model for Extraction of Shape Boundaries in Vector Valued Images

—Extraction of shape boundaries also known as image segmentation that tries to divide a digital image into several segments for further processing is an important step in digital image analysis. The segmented images are useful in many applications such as object detection, object classification, biometric authentication, and disease diagnosis. This task can be accomplished effectively using active contour model. The two types of active contour models are global segmentation and selective segmentation. A global segmentation model is a way for segmenting an image's entire objects. Global models, unfortunately, are unable to segment a single object’s shape boundary that must be extracted. A selective segmentation model, which seeks to segment selected object’s shape boundary in an image, can overcome this restriction. The recent selective segmentation model for vector valued images has a significant computational cost due to the requirement of solving the curvature term, that result from the usage of traditional regularization term in the formulation. Hence, we proposed a new selective segmentation model for vector valued image by replacing the traditional regularization term with Gaussian function which is easier and faster to solve. The proposed model was tested using MATLAB software in segmenting synthetic, natural and medical images. Numerical experiments shows that the proposed model was about 247 times faster than the existing model with a comparable accuracy.
Proposition of Web Services Discovery and Composition Approach: Application in E-learning Platform

The rapid penetration of Web technologies and strong economic pressure have led to an irresistible proliferation of Web services. In this paper, we are interested in the discovery and composition of Web services. In this sense, we propose a conceptual architecture and technical tools to carry out our approach. The originality of the proposed solution lies in the use of mixed tools ranging from multi-agents systems to Business Process Management (BPM) techniques, including Matchmaking algorithms and semantic models.