Reviewerly/exHarmony huggingface dataset:
Multiple Modes for Continual Learning

Adapting model parameters to incoming streams of data is a crucial factor to deep learning scalability. Interestingly, prior continual learning strategies in online settings inadvertently anchor their updated parameters to a local parameter subspace to remember old tasks, else drift away from the subspace and forget. From this observation, we formulate a trade-off between constructing multiple parameter modes and allocating tasks per mode. Mode-Optimized Task Allocation (MOTA), our contributed adaptation strategy, trains multiple modes in parallel, then optimizes task allocation per mode. We empirically demonstrate improvements over baseline continual learning strategies and across varying distribution shifts, namely sub-population, domain, and task shift.
CacheQL: Quantifying and Localizing Cache Side-Channel Vulnerabilities in Production Software

Cache side-channel attacks extract secrets by examining how victim software accesses cache. To date, practical attacks on cryptosystems and media libraries are demonstrated under different scenarios, inferring secret keys and reconstructing private media data such as images. This work first presents eight criteria for designing a full-fledged detector for cache side-channel vulnerabilities. Then, we propose CacheQL, a novel detector that meets all of these criteria. CacheQL precisely quantifies information leaks of binary code, by characterizing the distinguishability of logged side channel traces. Moreover, CacheQL models leakage as a cooperative game, allowing information leakage to be precisely distributed to program points vulnerable to cache side channels. CacheQL is meticulously optimized to analyze whole side channel traces logged from production software (where each trace can have millions of records), and it alleviates randomness introduced by cryptographic blinding, ORAM, or real-world noises. Our evaluation quantifies side-channel leaks of production cryptographic and media software. We further localize vulnerabilities reported by previous detectors and also identify a few hundred new leakage sites in recent OpenSSL (ver. 3.0.0), MbedTLS (ver. 3.0.0), Libgcrypt (ver. 1.9.4). Many of our localized program points are within the pre-processing modules of cryptosystems, which are not analyzed by existing works due to scalability. We also localize vulnerabilities in Libjpeg (ver. 2.1.2) that leak privacy about input images.
Pyramidal Denoising Diffusion Probabilistic Models

Recently, diffusion model have demonstrated impressive image generation performances, and have been extensively studied in various computer vision tasks. Unfortunately, training and evaluating diffusion models consume a lot of time and computational resources. To address this problem, here we present a novel pyramidal diffusion model that can generate high resolution images starting from much coarser resolution images using a {\em single} score function trained with a positional embedding. This enables a neural network to be much lighter and also enables time-efficient image generation without compromising its performances. Furthermore, we show that the proposed approach can be also efficiently used for multi-scale super-resolution problem using a single score function.
DOCUMENT IMAGE FORGERY AND DETECTION METHODS USING IMAGE PROCESSING TECHNIQUES – A REVIEW

Nowadays, papers are taken, stored, and shared more frequently in digital format.At the same time, document image altering software also gets more and more powerful as there is an increasing concern about the authenticity of documents.Texts on real estate agreements, for instance, can be changed to make an illegal deal, and the date on a plane ticket can be changed to get past security and into airport terminals.This study provides an overview of various image processing methods to spot document forging in order to stop such illegal actions.As digital image processing gains popularity in scientific and technical applications and forgery techniques develop quickly, the aim of forgery detection is to maximize the extraction of information from altered photos, particularly noisy and post-processed images.In order to create a new strategy for a future forensic science investigation, the major focus is on various sorts of forgery detection in digital image processing with the aid of all transform approaches and comparing their best results for further improvement.
The Research on Adjustment of University Students in Financial Hardship

Funding university students in financial hardship is one of the most important works in education field of China.This paper researches how to adjust the level of students in financial hardship with considering the effect of dormitory culture construction (DCC) and consumption.An evaluation system with considering self-satisfaction, moral performance, academic performance, academic potential, volunteering outside the dormitory, and dormitory hygiene performance is established to measure the effect of DCC and, applying the binary Logistic regression model, the validity of the system is examined.On the basis of taking subjective evaluation and objective quantitative factors into full consideration, the Analytic hierarchy process model is then utilized for the estimation of the effect of DCC, combined with the analysis of campus consumption behavior questionnaire, students' consumption behavior is divided into 4 categories by K-means clustering algorithm, meanwhile, the average value for each category can be calculated.This paper takes the necessary consumption as the base, determine the adjustment strategy according to the ranking of the effect of DCC to make our funding work more reasonable and efficient.The data from South-Central Minzu University is used to check the model, The comparison of the results before and after adjustment is shown in this paper, and the comparison results reveal that our model has a good stability and can recognize the growth of students.
The Impact of Data Corruption on Named Entity Recognition for Low-resourced Languages

Data availability and quality are major challenges in natural language processing for low-resourced languages. In particular, there is significantly less data available than for higher-resourced languages. This data is also often of low quality, rife with errors, invalid text or incorrect annotations. Many prior works focus on dealing with these problems, either by generating synthetic data, or filtering out low-quality parts of datasets. We instead investigate these factors more deeply, by systematically measuring the effect of data quantity and quality on the performance of pre-trained language models in a low-resourced setting. Our results show that having fewer completely-labelled sentences is significantly better than having more sentences with missing labels; and that models can perform remarkably well with only 10% of the training data. Importantly, these results are consistent across ten low-resource languages, English, and four pre-trained models.
EVOLUTION OF WEB 1.0 TO WEB 3.0

The computer network is additional and additionally used for application-to-application communication.The programmatic interfaces created accessible square measures said as internet services.the general public nowadays will hardly envisage life while not the web. the net of documents has morphed into online knowledge.The linguistics wave embraces 3 stages of web growth.the primary stage, web 1.0, was concerning connecting data and the old-time information superhighway.Web 2.0 concerning connecting individuals whom golf shot the "I" in the computer program, and therefore the "we" into online social participation.The consequent stage, web 3.0, is beginning currently.It is concerning representing meanings, connecting information, and golf shot them to figure in ways in which build our expertise of web additional relevant, useful, and pleasant.
Truncated phase-based quantum arithmetic: error propagation and resource reduction

There are two important, and potentially interconnecting, avenues to the realisation of large-scale quantum algorithms: improvement of the hardware, and reduction of resource requirements demanded by algorithm components. In focusing on the latter, one crucial subroutine to many sought-after applications is the quantum adder. A variety of different implementations exist with idiosyncratic pros and cons. One of these, the Draper quantum Fourier adder, offers the lowest qubit count of any adder, but requires a substantial number of gates as well as extremely fine rotations. In this work, we present a modification of the Draper adder which eliminates small-angle rotations to highly coarse levels, matched with some strategic corrections. This reduces hardware requirements without sacrificing the qubit saving. We show that the inherited loss of fidelity is directly given by the rate of carry and borrow bits in the computation. We derive formulae to predict this, complemented by complete gate-level matrix product state simulations of the circuit. Moreover, we analytically describe the effects of possible stochastic control error. We present an in-depth analysis of this approach in the context of Shor's algorithm, focusing on the factoring of RSA-2048. Surprisingly, we find that each of the $7\times 10^7$ quantum Fourier transforms may be truncated down to $\pi/64$, with additive rotations left only slightly finer. This result is much more efficient than previously realised. We quantify savings both in terms of logical resources and raw magic states, demonstrating that phase adders can be competitive with Toffoli-based constructions.
AMRA*: Anytime Multi-Resolution Multi-Heuristic A*

Heuristic search-based motion planning algorithms typically discretise the search space in order to solve the shortest path problem. Their performance is closely related to this discretisation. A fine discretisation allows for better approximations of the continuous search space, but makes the search for a solution more computationally costly. A coarser resolution might allow the algorithms to find solutions quickly at the expense of quality. For large state spaces, it can be beneficial to search for solutions across multiple resolutions even though defining the discretisations is challenging. The recently proposed algorithm Multi-Resolution A* (MRA*) searches over multiple resolutions. It traverses large areas of obstacle-free space and escapes local minima at a coarse resolution. It can also navigate so-called narrow passageways at a finer resolution. In this work, we develop AMRA*, an anytime version of MRA*. AMRA* tries to find a solution quickly using the coarse resolution as much as possible. It then refines the solution by relying on the fine resolution to discover better paths that may not have been available at the coarse resolution. In addition to being anytime, AMRA* can also leverage information sharing between multiple heuristics. We prove that AMRA* is complete and optimal (in-the-limit of time) with respect to the finest resolution. We show its performance on 2D grid navigation and 4D kinodynamic planning problems.
New Insights into Graph Convolutional Networks using Neural Tangent Kernels

Graph Convolutional Networks (GCNs) have emerged as powerful tools for learning on network structured data. Although empirically successful, GCNs exhibit certain behaviour that has no rigorous explanation -- for instance, the performance of GCNs significantly degrades with increasing network depth, whereas it improves marginally with depth using skip connections. This paper focuses on semi-supervised learning on graphs, and explains the above observations through the lens of Neural Tangent Kernels (NTKs). We derive NTKs corresponding to infinitely wide GCNs (with and without skip connections). Subsequently, we use the derived NTKs to identify that, with suitable normalisation, network depth does not always drastically reduce the performance of GCNs -- a fact that we also validate through extensive simulation. Furthermore, we propose NTK as an efficient `surrogate model' for GCNs that does not suffer from performance fluctuations due to hyper-parameter tuning since it is a hyper-parameter free deterministic kernel. The efficacy of this idea is demonstrated through a comparison of different skip connections for GCNs using the surrogate NTKs.
Accelerating Multi-Objective Neural Architecture Search by Random-Weight Evaluation

For the goal of automated design of high-performance deep convolutional neural networks (CNNs), Neural Architecture Search (NAS) methodology is becoming increasingly important for both academia and industries.Due to the costly stochastic gradient descent (SGD) training of CNNs for performance evaluation, most existing NAS methods are computationally expensive for real-world deployments. To address this issue, we first introduce a new performance estimation metric, named Random-Weight Evaluation (RWE) to quantify the quality of CNNs in a cost-efficient manner. Instead of fully training the entire CNN, the RWE only trains its last layer and leaves the remainders with randomly initialized weights, which results in a single network evaluation in seconds.Second, a complexity metric is adopted for multi-objective NAS to balance the model size and performance. Overall, our proposed method obtains a set of efficient models with state-of-the-art performance in two real-world search spaces. Then the results obtained on the CIFAR-10 dataset are transferred to the ImageNet dataset to validate the practicality of the proposed algorithm. Moreover, ablation studies on NAS-Bench-301 datasets reveal the effectiveness of the proposed RWE in estimating the performance compared with existing methods.
Task Allocation for Multi-Robot Task and Motion Planning: a case for Object Picking in Cluttered Workspaces

We present an AND/OR graph-based, integrated multi-robot task and motion planning approach which (i) performs task allocation coordinating the activity of a given number of robots, and (ii) is capable of handling tasks which involve an a priori unknown number of object re-arrangements, such as those involved in retrieving objects from cluttered workspaces. Such situations may arise, for example, in search and rescue scenarios, while locating/picking a cluttered object of interest. The corresponding problem falls under the category of planning in clutter. One of the challenges while planning in clutter is that the number of object re-arrangements required to pick the target object is not known beforehand, in general. Moreover, such tasks can be decomposed in a variety of ways, since different cluttering object re-arrangements are possible to reach the target object. In our approach, task allocation and decomposition is achieved by maximizing a combined utility function. The allocated tasks are performed by an integrated task and motion planner, which is robust to the requirement of an unknown number of re-arrangement tasks. We demonstrate our results with experiments in simulation on two Franka Emika manipulators.
Simulations for novel problems in recommendation: analyzing misinformation and data characteristics

In this position paper, we discuss recent applications of simulation approaches for recommender systems tasks. In particular, we describe how they were used to analyze the problem of misinformation spreading and understand which data characteristics affect the performance of recommendation algorithms more significantly. We also present potential lines of future work where simulation methods could advance the work in the recommendation community.
Colour augmentation for improved semi-supervised semantic segmentation

Consistency regularization describes a class of approaches that have yielded state-of-the-art results for semi-supervised classification. While semi-supervised semantic segmentation proved to be more challenging, a number of successful approaches have been recently proposed. Recent work explored the challenges involved in using consistency regularization for segmentation problems. In their self-supervised work Chen et al. found that colour augmentation prevents a classification network from using image colour statistics as a short-cut for self-supervised learning via instance discrimination. Drawing inspiration from this we find that a similar problem impedes semi-supervised semantic segmentation and offer colour augmentation as a solution, improving semi-supervised semantic segmentation performance on challenging photographic imagery.
Complete b-symbol weight distribution of some irreducible cyclic codes

Recently, $b$-symbol codes are proposed to protect against $b$-symbol errors in $b$-symbol read channels. It is an interesting subject of study to consider the complete $b$-symbol weight distribution of cyclic codes since $b$-symbol metric is a generalization for Hamming metric. The complete $b$-symbol Hamming weight distribution of irreducible codes is known in only a few cases. In this paper, we give a complete $b$-symbol Hamming weight distribution of a class of irreducible codes with two nonzero $b$-symbol Hamming weights.
Empirical verification of the suggested hyperparameters for data augmentation using the fast.ai library

Data augmentation consists in adding slightly modified copies of the existing data to the training set, which increases the total amount of data and generally results in better results obtained by machine learning algorithms. The fast.ai library has some predefined values for data augmentation hyperparameters for visual data. It is claimed that these predefined parameters are to be the best for most data types, however, no empirical support for this statement has been provided. The aim of this research is to determine whether the suggested hyperparameter values for data augmentation in the fast.ai library are indeed optimal for the highest accuracy for image classification tasks. In order to answer this question, a detailed research was conducted, consisting of a series of experiments for subsequent data augmentation tools (rotation, magnification, contrast change, etc.). Three variables were modified for each tool: 1. maximal and minimal value of transformation (depending on the transformation type), 2. probability of the transformation, 3. padding behaviour. The results of the presented research lead to the conclusion that the suggested values of data augmentation implemented in the fast.ai library provides the good parameters of the model aimed at differentiating male and female faces, however in case of that classification slightly different parameters could be taken into consideration. The results are published in open-source repository (Open Science Framework, DOI:10.17605/OSF.IO/38UJG).
Improved differential privacy K-means clustering algorithm for privacy budget allocation

In the differential privacy clustering algorithm, the added random noise causes the clustering centroids to be shifted, which affects the usability of the clustering results. To address this problem, we design a differential privacy K-means clustering algorithm based on an adaptive allocation of privacy budget to the clustering effect: Adaptive Differential Privacy K-means (ADPK-means). The method is based on the evaluation results generated at the end of each iteration in the clustering algorithm. First, it dynamically evaluates the effect of the clustered sets at the end of each iteration by measuring the separation and tightness between the clustered sets. Then, the evaluation results are introduced into the process of privacy budget allocation by weighting the traditional privacy budget allocation. Finally, different privacy budgets are assigned to different sets of clusters in the iteration to achieve the purpose of adaptively adding perturbation noise to each set. In this paper, both theoretical and experimental results are analyzed, and the results show that the algorithm satisfies e-differential privacy and achieves better results in terms of the availability of clustering results for the three standard datasets.
A Physical Device to Help the Visually Impaired Read Money Using AI/Machine Learning in Third World Countries

There are over 285 million blind people in the world, with approximately 87% of them living in developing countries. However, in the third world countries, there is currently very little technology to help the visually impaired, especially with financial independence. In this article we present the machine learning algorithms used to develop the device to help visually impaired distinguish between different forms of currency. Using the various currency images, we form a data set that is used to train the transfer learning model. Experimental results show over 94% accuracy with transfer learning model. The device is designed to be portable and hand-held. The device can distinguish between 1, 5, 10, and 20 dollar currency bills. Additionally, the model can work offline. Overall, the device is cost effective, portable, and can be used in the absence of internet connectivity.
RealPatch: A Statistical Matching Framework for Model Patching with Real Samples

Machine learning classifiers are typically trained to minimise the average error across a dataset. Unfortunately, in practice, this process often exploits spurious correlations caused by subgroup imbalance within the training data, resulting in high average performance but highly variable performance across subgroups. Recent work to address this problem proposes model patching with CAMEL. This previous approach uses generative adversarial networks to perform intra-class inter-subgroup data augmentations, requiring (a) the training of a number of computationally expensive models and (b) sufficient quality of model's synthetic outputs for the given domain. In this work, we propose RealPatch, a framework for simpler, faster, and more data-efficient data augmentation based on statistical matching. Our framework performs model patching by augmenting a dataset with real samples, mitigating the need to train generative models for the target task. We demonstrate the effectiveness of RealPatch on three benchmark datasets, CelebA, Waterbirds and a subset of iWildCam, showing improvements in worst-case subgroup performance and in subgroup performance gap in binary classification. Furthermore, we conduct experiments with the imSitu dataset with 211 classes, a setting where generative model-based patching such as CAMEL is impractical. We show that RealPatch can successfully eliminate dataset leakage while reducing model leakage and maintaining high utility. The code for RealPatch can be found at https://github.com/wearepal/RealPatch.