Reviewerly/exHarmony huggingface dataset:
Transfer learning for just noticeable difference estimation

The just noticeable difference (JND) measures the visual redundancy of digital images and is widely used in signal processing. Conventional JND models attempt to simulate functional properties of the human visual system (HVS), which are limited by the development of cognitive psychology. In this paper, we propose a novel pixel-wise JND prediction model based on deep transfer learning. Since it is almost impossible to manually label each pixel's visibility threshold, lacking labeled training data is the crucial issue. Transfer learning addresses the problem of insufficient training data. We found an underlying correspondence between full reference image quality assessment (FR-IQA) and JND estimation, which implies that knowledge related to FR-IQA can be applied to JND estimation. To quantify the intrinsic association between JND estimation and FR-IQA, a local perceived discrepancy (LPD) index is deduced. With the guidance of the LPD index, a JND predictor based on residual dense network (RDN) is designed to discover good representations of visibility limitation from annotated image quality databases. Subjective viewing test experiments show that our model outperforms the state-of-the-art JND models. Furthermore, we apply our model to image compression, and around 14.42% of the bit rate can be reduced by removing visual redundancy.
Leveraging on few-shot learning for tire pattern classification in forensics

This paper presents a novel approach for tire-pattern classification, aimed at conducting forensic analysis on tire marks discovered at crime scenes. The classification model proposed in this study accounts for the intricate and dynamic nature of tire prints found in real-world scenarios, including accident sites. To address this complexity, the classifier model was developed to harness the meta-learning capabilities of few-shot learning algorithms (learning-to-learn). The model is meticulously designed and optimized to effectively classify both tire patterns exhibited on wheels and tire-indentation marks visible on surfaces due to friction. This is achieved by employing a semantic segmentation model to extract the tire pattern marks within the image. These marks are subsequently used as a mask channel, combined with the original image, and fed into the classifier to perform classification. Overall, The proposed model follows a three-step process: (i) the Bilateral Segmentation Network is employed to derive the semantic segmentation of the tire pattern within a given image. (ii) utilizing the semantic image in conjunction with the original image, the model learns and clusters groups to generate vectors that define the relative position of the image in the test set. (iii) the model performs predictions based on these learned features. Empirical verification demonstrates usage of semantic model to extract the tire patterns before performing classification increases the overall accuracy of classification by ∼4%.
An end-to-end Tacotron model versus pre trained Tacotron model for Arabic text-to-speech synthesis

Text-to-Speech (TTS) systems turn normal text into spoken language, which is important for accessibility and user interaction. Many of these systems make speech from phonetic or phonemic transcriptions, but another way is to make speech by connecting together pre-recorded units from a database. The size of the units varies, from diphones to whole phrases. Even though this method covers a lot of ground, it sometimes needs more clarity, especially when high-quality output requires storing whole words or phrases in certain situations. Synthesizers can also use the way humans talk and the way their vocal tracts work to make voices. The Arabic language is hard to develop TTS methods for, as a result of its complicated morphology, semantic nuances, and many different dialects. These dialects often have a lot of differences from standard Arabic and don't follow formal rules for spelling. This means that traditional Arabic that hasn't been edited often has spelling and grammar mistakes. In this study, we show and test a Tacotron model that was made just for Arabic TTS synthesis from beginning to end. This model uses the richness of acoustic information in audio files, such as frequency and pitch, to make naturalistic speech that sounds a lot like what humans say. We also compare the performance of this model with that of a pre-trained Tacotron model applied to Arabic text. This gives us important information about how well Arabic TTS systems work and where they could be improved.
Marketing insight from consumer reviews: Creating brand position through opinion mining approach

Brand managers can determine the ideas, perceptions, or experiences of consumers about their brands and their competitors through traditional marketing research techniques such as focus group discussions, surveys, and in-depth interviews. Through these methods, brands can observe the visual expression of consumer perceptions concretely by using perception maps or radar chart techniques. However, traditional methods have certain limitations. The validity of brand positioning in perception maps may be questioned since consumers' answers to survey questions can be misleading, consumers may express opinions about a product that they have not purchased yet, and other methods may divert the opinions of consumers at a specific stage. In this study, global brand positions in the minds of consumers were concretized through perception maps by analyzing product comments on e-commerce sites through the use of opinion mining/sentiment analysis, one of the text mining methods. Moreover, perception maps and radar charts were created for the selected brands by forming the dataset from consumers’ reviews about brands and products in online environments. perception map errors caused by traditional methods have been prevented by using opinion mining/sentiment analysis, which constitutes the originality of the research. In this study, the text mining method, which includes opinion mining and sentiment analysis techniques, has been used to create perceptual maps from consumer reviews about products on e-commerce platforms. The TF*IDF method is used to identify the features to be compared in the perceptual map, which selects the features with weighted importance among the features mentioned by consumers in their comments. In this study, a lexicon-based opinion mining technique has been used to assign sentiment scores to consumers' comments on e-commerce websites for each product and its specified features. The findings of the study have illustrated the areas in which the brands are evaluated as "insufficient" or "successful" by consumers within the framework of the characteristics selected in the study. It has been revealed that the method used in the research can provide accurate results for brand managers to make positioning decisions and develop effective marketing strategies. The research has been conducted by analyzing product reviews about selected brands on e-commerce platforms. The comments selected within the scope of the research include only those written in the Turkish language, and the evaluations have been carried out on the comments made until a specific date. In this study, the analyses of the comments on the selected brands have ensured that the businesses are informed of the level of perception about their products in the market and that the research is based on a holistic approach that helps other consumers to develop a perspective on a specific product based on the comments of a consumer who shares his/her experience about the aforesaid product.
Quadratic Functional Encryption for Secure Training in Vertical Federated Learning

Vertical federated learning (VFL) enables the collaborative training of machine learning (ML) models in settings where the data is distributed amongst multiple parties who wish to protect the privacy of their individual data. Notably, in VFL, the labels are available to a single party and the complete feature set is formed only when data from all parties is combined. Recently, Xu et al. [1] proposed a new framework called FedV for secure gradient computation for VFL using multi-input functional encryption. In this work, we explain how some of the information leakage in Xu et al. can be avoided by using Quadratic functional encryption when training generalized linear models for vertical federated learning.
Improved Singleton-type bounds for list-decoding and average-radius list-decoding

List-decoding and average-radius list-decoding are important generalizations of unique decoding that received considerable attention over the years. However, the optimal trade-off among list-decoding radius, list size, and the code rate are not fully understood in both problems. In this paper, firstly, we prove a new Singleton-type bound for list-decodability, which improves the results of [3]. Next, we prove a Singleton-type bound for average-radius list-decodable codes, which is to the best of our knowledge, the first such bound for average-radius list-decodable codes.
Personalized Privacy-Preserving Distributed Learning on Heterogeneous Data

One major challenge in distributed learning is to efficiently learn for each client when the data across clients is heterogeneous or non iid (not independent or identically distributed). This provides a significant challenge as the data of the other clients may not be helpful to each individual client. Thus the following question arises - can each individual client’s performance be improved with access to the data of other clients in this heterogeneous data setting? A further challenge is to have a good personalized model while still maintaining the privacy of local data samples.We consider a model where the client data distributions are not identical and can be dependent. In this heterogeneous data setting we study the problem of distributed learning of data distributions. We propose a personalized linear estimator for each client and show that this estimator is never worse and can be substantially better (up to a factor equal to the number of clients) than the sample mean estimator while still concentrating around the true probability. This estimator can be implemented by privacy-preserving schemes in both the cryptographic and differentially private settings.
Differentially Private Secure Multiplication: Hiding Information in the Rubble of Noise

We consider the problem of private distributed multiparty computation. It is well-established that coding strategies can enable perfect information-theoretic privacy in distributed computation (e.g., the BGW protocol). However, perfect privacy comes at a high computational overhead cost, requiring 2t + 1 compute nodes to ensure privacy against any t colluding nodes. By allowing for approximate computation and operations over the real numbers, we demonstrate that noise can be added to data shared with computing nodes in order to ensure differential privacy instead of perfect privacy. Specifically, the signal-to-noise ratio of the data received by colluding nodes can be mapped to differential privacy guarantees. We precisely characterize the trade-off between differential privacy and accuracy in this setting, and prove that a degree of differential privacy against t colluding nodes can always be ensured whenever there are more than t+1 computing node—a reduction of t nodes compared to perfect privacy. A particularly novel technical aspect is an achievable scheme that carefully encodes the data and noise at different magnitude levels. This coding scheme ensures that the adversary’s input appears to be layers of noise, whereas the legitimate decoder is able to uncover the desired computation by "peeling" off the noise layers.
Breaking the Quadratic Communication Overhead of Secure Multi-Party Neural Network Training

Privacy-preserving machine learning has achieved exciting breakthroughs for collaboratively training machine learning models under strong information-theoretic privacy guarantees. Despite the recent advances, communication bottleneck still remains as a major challenge against scalability to large neural networks. To address this challenge, in this work we introduce CLOVER, the first multi-party neural network training framework with linear communication complexity, significantly improving over the quadratic state-of-the-art, under strong end-to-end information-theoretic privacy guarantees. CLOVER builds on a novel degree reduction mechanism with linear communication complexity, termed Double Lagrange Coding, for coded computing. While providing strong multi-round information-theoretic privacy guarantees, CLOVER achieves equal adversary tolerance, resilience to user dropouts, and model accuracy as the state-of-the-art, while significantly cutting down the communication overhead. In doing so, CLOVER addresses a key technical challenge in collaborative neural network training, paving the way for large-scale privacy-aware deep learning applications.
Detecting a Planted Bipartite Graph

We consider the task of detecting a hidden bipartite subgraph in a given random graph. Specifically, under the null hypothesis, the graph is a realization of an Erdős-Rényi random graph over n vertices with edge density q. Under the alternative, there exists a planted k <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</inf> × k <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</inf> bipartite subgraph with edge density p > q. We derive asymptotically tight upper and lower bounds for this detection problem in both the dense regime, where q, p = Θ(1), and the sparse regime where q, p = Θ(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−α</sup> ), α ∈ (0, 2]. Moreover, we consider a variant of the above problem, where one can only observe a relatively small part of the graph, by using at most Q edge queries. For this problem, we derive upper and lower bounds in both the dense and sparse regimes, and observe a gap between them.
Robust Unsupervised Image to Template Registration Without Image Similarity Loss

Although a giant step forward has been made in medical images analysis thanks to deep learning, good results still require a lot of tedious and costly annotations. For image registration, unsupervised methods usually consider the training of a network using classical registration dissimilarity metrics. In this paper, we focus on the case of affine registration and show that this approach is not robust when the transform to estimate is large. We propose an unsupervised method for the training of an affine image registration network without using dissimilarity metrics and show that we are able to robustly register images even when the field of view is significantly different in the image.
Repair of Reed-Solomon Codes in the Presence of Erroneous Nodes

We consider the repair scheme of Guruswami-Wootters for the Reed-Solomon code and ask: can we correctly repair a failed node in the presence of erroneous nodes? Equivalently, we consider the collection of downloaded traces as a code and investigate its code-distance properties. We propose three lower bounds on its minimum distance and study methods to efficiently correct errors close to these bounds.
On Some Z<sub>m</sub> Linear Goppa/BCH like Error Control Codes and Elementary Symmetric Functions<sup>*</sup>

Let ${\mathbb{Z}_m}\mathop = \limits^{{\text{ def }}}$ $\left\{ {0,1, \ldots ,\left( {m - 1} \right)} \right\}$ be the m-ary alphabet, $m \in \mathbb{N}$. This paper gives some new theory and designs of ${\mathbb{Z}_m}$ linear error control codes based on the elementary symmetric functions of m-ary words. Here, a ${\mathbb{Z}_m}$ linear code is a submodule of the module $\left( {\mathbb{Z}_m^n, + {\text{mod}}m,{\mathbb{Z}_m}, \cdot {\text{mod}}m} \right),n \in \mathbb{N}$, and the errors are measured in the ${L_1}$ or Lee metric. Potentially, the alphabet size, m, can be any natural, however, the described code designs and decoding methods are solely based on fields and field operations. In particular, starting from a very general class of Goppa-like ${\mathbb{Z}_m}$ linear codes, given a field, $K$, of characteristic $p = {\text{char}}\left( K \right) \in \mathbb{N}$, we consider a generalization of the ${\mathbf{BCH}}$ codes to the m-ary alphabet for $m = {p^l},l \in \mathbb{N}$. For these BCHlike codes we are able to prove a BCH-like bound with respect to both the ${L_1}$ and Lee distances. This enabled us to design a wide family of remarkable efficient codes. For example, an efficient design is given for ${\mathbb{Z}_m}$ linear codes with $m = {2^l},l \in \mathbb{N}$, length $n = m$, minimum Lee distance ${d_{Lee}} = m = n$ and the number of information m-ary digits $k = m/2 = n/2$.
Variable-Length Insertion-Based Noisy Sorting

In this work, we study the problem of sorting n elements with pairwise comparisons under the presence of observation noise. We consider variable-length algorithms with a random number of queries M, and attempt to characterize the noisy sorting capacity defined as the maximal ratio $\frac{{n\log n}}{{{\text{E}}[M]}}$ such that the ordering can be correctly estimated with a vanishing error probability. This can be viewed as a generalization of the framework introduced in [1] to allow variable-length algorithms. We provide upper and lower bounds for the noisy sorting capacity. The proposed algorithm attaining the lower bound is based on the insertion sort algorithm for the sorting problem in the noiseless case and the variable-length version of the Burnashev–Zigangirov algorithm for coding over channels with feedback. Moreover, we also derive an upper bound on the maximal ratio that can be achieved by noisy sorting algorithms that are based on insertion sort.
DKT: Diverse Knowledge Transfer Transformer for Class Incremental Learning

In the context of incremental class learning, deep neural networks are prone to catastrophic forgetting, where the accuracy of old classes declines substantially as new knowledge is learned. While recent studies have sought to address this issue, most approaches suffer from either the stability-plasticity dilemma or excessive computational and parameter requirements. To tackle these challenges, we propose a novel framework, the Diverse Knowledge Transfer Transformer (DKT), which incorporates two knowledge transfer mechanisms that use attention mechanisms to transfer both task-specific and task-general knowledge to the current task, along with a duplex classifier to address the stability-plasticity dilemma. Additionally, we design a loss function that clusters similar categories and discriminates between old and new tasks in the feature space. The proposed method requires only a small number of extra parameters, which are negligible in comparison to the increasing number of tasks. We perform extensive experiments on CIFAR100, ImageNet100, and ImageNet1000 datasets, which demonstrate that our method outperforms other competitive methods and achieves state-of-the-art performance. Our source code is available at https://github.com/MIVXJTU/DKT.
Inverse Rendering of Translucent Objects using Physical and Neural Renderers

In this work, we propose an inverse rendering model that estimates 3D shape, spatially-varying reflectance, homogeneous subsurface scattering parameters, and an environment illumination jointly from only a pair of captured images of a translucent object. In order to solve the ambiguity problem of inverse rendering, we use a physically-based renderer and a neural renderer for scene reconstruction and material editing. Because two renderers are differentiable, we can compute a reconstruction loss to assist parameter estimation. To enhance the supervision of the proposed neural renderer, we also propose an augmented loss. In addition, we use a flash and no-flash image pair as the input. To supervise the training, we constructed a large-scale synthetic dataset of translucent objects, which consists of 117K scenes. Qualitative and quantitative results on both synthetic and real-world datasets demonstrated the effectiveness of the proposed model. Code and Data are available at https://github.com/ligoudaner377/homo_translucent
Implicit View-Time Interpolation of Stereo Videos Using Multi-Plane Disparities and Non-Uniform Coordinates

In this paper, we propose an approach for view-time interpolation of stereo videos. Specifically, we build upon X-Fields that approximates an interpolatable mapping between the input coordinates and 2D RGB images using a convolutional decoder. Our main contribution is to analyze and identify the sources of the problems with using X-Fields in our application and propose novel techniques to overcome these challenges. Specifically, we observe that X-Fields struggles to implicitly interpolate the disparities for large baseline cameras. Therefore, we propose multi-plane disparities to reduce the spatial distance of the objects in the stereo views. Moreover, we propose non-uniform time coordinates to handle the non-linear and sudden motion spikes in videos. We additionally introduce several simple, but important, improvements over X-Fields. We demonstrate that our approach is able to produce better results than the state of the art, while running in near real-time rates and having low memory and storage costs.
On-the-Fly Category Discovery

Although machines have surpassed humans on visual recognition problems, they are still limited to providing closed-set answers. Unlike machines, humans can cognize novel categories at the first observation. Novel category discovery (NCD) techniques, transferring knowledge from seen categories to distinguish unseen categories, aim to bridge the gap. However, current NCD methods assume a transductive learning and offline inference paradigm, which restricts them to a predefined query set and renders them unable to deliver instant feedback. In this paper, we study on-the-fly category discovery (OCD) aimed at making the model instantaneously aware of novel category samples (i.e., enabling inductive learning and streaming inference). We first design a hash coding-based expandable recognition model as a practical baseline. Afterwards, noticing the sensitivity of hash codes to intra-category variance, we further propose a novel Sign-Magnitude dIsentangLEment (SMILE) architecture to alleviate the disturbance it brings. Our experimental results demonstrate the superiority of SMILE against our baseline model and prior art. Our code is available at https://github.com/PRIS-CV/On-the-fly-Category-Discovery.
Architectural Backdoors in Neural Networks

Machine learning is vulnerable to adversarial manipulation. Previous literature demonstrated that at the training stage attackers can manipulate data [14] and data sampling procedures [29] to control model behaviour. A common attack goal is to plant backdoors i.e. force the victim model to learn to recognise a trigger known only by the adversary. In this paper, we introduce a new class of backdoor attacks that hide inside model architectures i.e. in the inductive bias of the functions used to train. These backdoors are simple to implement, for instance by publishing open-source code for a backdoored model architecture that others will reuse unknowingly. We demonstrate that model architectural backdoors represent a real threat and, unlike other approaches, can survive a complete re-training from scratch. We formalise the main construction principles behind architectural backdoors, such as a connection between the input and the output, and describe some possible protections against them. We evaluate our attacks on computer vision benchmarks of different scales and demonstrate the underlying vulnerability is pervasive in a variety of common training settings.