Reviewerly/exHarmony huggingface dataset:
Comprehensive Dataset Building and Recognition of Isolated Handwritten Kannada Characters Using Machine Learning Models

In this work, an attempt is made to build a dataset for handwritten Kannada characters and also to recognize the isolated Kannada vowels, consonants, modifiers, and ottaksharas. The dataset is collected from 500 writers of varying ages, gender, qualification, and profession. This dataset will be used to recognize the handwritten kagunita’s, ottaksharas, and other base characters, where the existing works have addressed very less on the recognition of kagunita’s and ottaksharas. There are no datasets for the same. Hence, a dataset for handwritten 85 characters is built using an unsupervised machine learning technique i.e K-means hierarchical clustering with Run Length Code (RLC) features. An accuracy of 80% was achieved with the unsupervised method. The dataset consists of 130,981 samples for 85 classes, these classes are further divided into upper, lower, and middle zones based on the position of the character in the dialect. After the dataset was built SVM model with HOG features was used for recognition and an accuracy of 99.0%, 88.6%, and 92.2% was obtained for the Upper, Middle, and Lower zones respectively to increase the recognition rate, the CNN model is fine-tuned with raw input, and an accuracy of 100%, 96.15%, and 95.38% was obtained for the Upper, Middle, and Lower zones respectively. With the ResNet18 model, an accuracy of 99.88%, 98.92, and 97.55% was obtained for each of the zones respectively. The dataset will be made available online for the researchers to carry out their research on handwritten characters, kagunitas, and word recognition with segmentation.
Synthesizing efficient circuits for Hamiltonian simulation

We provide a new approach for compiling quantum simulation circuits that appear in Trotter, qDRIFT and multi-product formulas to Clifford and non-Clifford operations that can reduce the number of non-Clifford operations by a factor of up to $4$. In fact, the total number of gates reduce in many cases. We show that it is possible to implement an exponentiated sum of commuting Paulis with at most $m$ (controlled)-rotation gates, where $m$ is the number of distinct non-zero eigenvalues (ignoring sign). Thus we can collect mutually commuting Hamiltonian terms into groups that satisfy one of several symmetries identified in this work which allow an inexpensive simulation of the entire group of terms. We further show that the cost can in some cases be reduced by partially allocating Hamiltonian terms to several groups and provide a polynomial time classical algorithm that can greedily allocate the terms to appropriate groupings. We further specifically discuss these optimizations for the case of fermionic dynamics and provide extensive numerical simulations for qDRIFT of our grouping strategy to 6 and 4-qubit Heisenberg models, $LiH$, $H_2$ and observe a factor of 1.8-3.2 reduction in the number of non-Clifford gates. This suggests Trotter-based simulation of chemistry in second quantization may be even more practical than previously believed.
Leveraging ChatGPT for Efficient Fact-Checking

We explore the potential of automated online content moderation through Large Language Models (LLMs) as a remedy to the overwhelming surge of online content that fact-checking organizations cannot verify. Although LLMs, such as ChatGPT, may exacerbate this problem by facilitating content production, they could also be leveraged to enhance the efficiency and expediency of fact-checking processes. We conducted a systematic evaluation to measure ChatGPT’s fact-checking performance by submitting 21,152 fact-checked statements to ChatGPT as a zero-shot classification. We find that ChatGPT is able to accurately categorize statements as true or false in 69% of cases. Addressing memorization, the performance of ChatGPT is similar on claims that have not been fact-checked or are post its training data cutoff date. These findings demonstrate the potential of ChatGPT to help label misinformation and deepen our comprehension of how LLMs could improve content moderation practices, complementing the crucial work of human fact-checking experts in upholding the accuracy of information dissemination.
Antisemitism is predicted by anti-hierarchical aggression, totalitarianism, and belief in malevolent global conspiracies

Abstract Two cross-sectional studies were carried out in order to identify predictors of antisemitism, measured using the Generalised Antisemitism or GeAs scale. In the first, which used a self-selecting sample of UK-resident adults ( n = 809), age, gender, ethnicity, and educational level as well as a wide range of ideological predictors were analysed as bivariate predictors of antisemitism. In the second, which used a representative sample of UK-resident adults ( n = 1853), the same demographic predictors plus the non-demographic predictors found to have the strongest bivariate relationships with Generalised Antisemitism in the previous study were used to construct a linear model with multiple predictors. Ethnicity, support for totalitarian government, belief in malevolent global conspiracies, and anti-hierarchical aggression were identified as the strongest predictors of Generalised Antisemitism. However, support for totalitarian government was only found to predict ‘old’ antisemitic attitudes (measured using the Judeophobic Antisemitism or JpAs subscale) and not ‘new’ antisemitic attitudes (measured using the Antizionist Antisemitism or AzAs subscale), whereas ethnicity, anti-hierarchical aggression, and belief in malevolent global conspiracies were found to predict both ‘old’ and ‘new’ antisemitic attitudes. This finding adds nuance to ongoing debates about whether antisemitism is more prevalent on the political right or left, by suggesting that (at least in the UK) it is instead associated with a conspiracist view of the world, a desire to overturn the social order, and a preference for authoritarian forms of government—all of which may exist on the right, the left, and elsewhere. Data from both samples are open, as is the code used in order to carry out the analyses presented here.
Mean-Field Theory in Hopfield Neural Network for Doing 2 Satisfiability Logic Programming

The artificial neural network system's dynamical behaviors are greatly dependent on the construction of the network.Artificial Neural Network's outputs suffered from a shortage of interpretability and variation lead to severely limited the practical usability of artificial neural networks for doing the logical program.The goal for implementing a logical program in Hopfield neural network rotates rounding minimizing the energy function of the network to reaching the best global solution which ordinarily fetches local minimum solution also.Nevertheless, this problem can be overcome by utilizing the hyperbolic tangent activation function and the Boltzmann Machine in the Hopfield neural network.The foremost purpose of this article is to explore the solution quality obtained from the Hopfield neural network to solve 2 Satisfiability logic (2SAT) by using the Mean-Field Theory algorithm.We want for replacing the real unstable prompt local field for the separate neurons into the network by its average local field utility.By using the solution to the deterministic Mean-Field Theory (MFT) equation, the system will derive the training algorithms in which time-consuming stochastic measures of collections are rearranged.By evaluating the outputs of global minima ratio (zM), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) with computer processing unit (CPU) time as benchmarks, we find that the MFT theory successfully captures the best global solutions by relaxation effects energy function.
DCFF-MTAD: A Multivariate Time-Series Anomaly Detection Model Based on Dual-Channel Feature Fusion

The detection of anomalies in multivariate time-series data is becoming increasingly important in the automated and continuous monitoring of complex systems and devices due to the rapid increase in data volume and dimension. To address this challenge, we present a multivariate time-series anomaly detection model based on a dual-channel feature extraction module. The module focuses on the spatial and time features of the multivariate data using spatial short-time Fourier transform (STFT) and a graph attention network, respectively. The two features are then fused to significantly improve the model’s anomaly detection performance. In addition, the model incorporates the Huber loss function to enhance its robustness. A comparative study of the proposed model with existing state-of-the-art ones was presented to prove the effectiveness of the proposed model on three public datasets. Furthermore, by using in shield tunneling applications, we verify the effectiveness and practicality of the model.
External multi-modal imaging sensor calibration for sensor fusion: A review

Multi-modal data fusion has gained popularity due to its diverse applications, leading to an increased demand for external sensor calibration. Despite several proven calibration solutions, they fail to fully satisfy all the evaluation criteria, including accuracy, automation, and robustness. Thus, this review aims to contribute to this growing field by examining recent research on multi-modal imaging sensor calibration and proposing future research directions. The literature review comprehensively explains the various characteristics and conditions of different multi-modal external calibration methods, including traditional motion-based calibration and feature-based calibration. Target-based calibration and targetless calibration are two types of feature-based calibration, which are discussed in detail. Furthermore, the paper highlights systematic calibration as an emerging research direction. Finally, this review concludes crucial factors for evaluating calibration methods and provides a comprehensive discussion on their applications, with the aim of providing valuable insights to guide future research directions. Future research should focus primarily on the capability of online targetless calibration and systematic multi-modal sensor calibration.
Software Bills of Materials Are Required. Are We There Yet?

Executive order 14028 on improving the nation’s cybersecurity highlights the software bill of materials (SBOM) as an essential security practice for software security. This article outlines the top five benefits and challenges of adopting SBOMs, identified by reviewing 200 Internet articles.
Multi-key and Multi-input Predicate Encryption from Learning with Errors

We put forward two natural generalizations of predicate encryption (PE), dubbed multi-key and multi-input PE. More in details, our contributions are threefold. In particular, plugging in our constructions of multi-key and multi-input PE, under the sub-exponential LWE assumption, we obtain the first ME supporting arbitrary policies with unbounded collusions, as well as robust (resp. non-robust) NI-MPC for so-called all-or-nothing functions satisfying a non-trivial notion of reusability and supporting a constant (resp. polynomial) number of parties. Prior to our work, both of these applications required much heavier tools such as indistinguishability obfuscation or compact functional encryption.
Optimal compression of quantum many-body time evolution operators into brickwall circuits

Near term quantum computers suffer from a degree of decoherence which is prohibitive for high fidelity simulations with deep circuits. An economical use of circuit depth is therefore paramount. For digital quantum simulation of quantum many-body systems, real time evolution is typically achieved by a Trotter decomposition of the time evolution operator into circuits consisting only of two qubit gates. To match the geometry of the physical system and the CNOT connectivity of the quantum processor, additional SWAP gates are needed. We show that optimal fidelity, beyond what is achievable by simple Trotter decompositions for a fixed gate count, can be obtained by compiling the evolution operator into optimal brickwall circuits for the S=1/2 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>S</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mi>/</mml:mi><mml:mn>2</mml:mn></mml:mrow></mml:math> quantum Heisenberg model on chains and ladders, when mapped to one dimensional quantum processors without the need of additional SWAP gates.
A systematic literature review of how cybersecurity-related behavior has been assessed

Purpose Cybersecurity attacks on critical infrastructures, businesses and nations are rising and have reached the interest of mainstream media and the public’s consciousness. Despite this increased awareness, humans are still considered the weakest link in the defense against an unknown attacker. Whatever the reason, naïve-, unintentional- or intentional behavior of a member of an organization, the result of an incident can have a considerable impact. A security policy with guidelines for best practices and rules should guide the behavior of the organization’s members. However, this is often not the case. This paper aims to provide answers to how cybersecurity-related behavior is assessed. Design/methodology/approach Research questions were formulated, and a systematic literature review (SLR) was performed by following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. The SLR initially identified 2,153 articles, and the paper reviews and reports on 26 articles. Findings The assessment of cybersecurity-related behavior can be classified into three components, namely, data collection, measurement scale and analysis. The findings show that subjective measurements from self-assessment questionnaires are the most frequently used method. Measurement scales are often composed based on existing literature and adapted by the researchers. Partial least square analysis is the most frequently used analysis technique. Even though useful insight and noteworthy findings regarding possible differences between manager and employee behavior have appeared in some publications, conclusive answers to whether such differences exist cannot be drawn. Research limitations/implications Research gaps have been identified, that indicate areas of interest for future work. These include the development and employment of methods for reducing subjectivity in the assessment of cybersecurity-related behavior. Originality/value To the best of the authors’ knowledge, this is the first SLR on how cybersecurity-related behavior can be assessed. The SLR analyzes relevant publications and identifies current practices as well as their shortcomings, and outlines gaps that future research may bridge.
A Machine Learning Model for Content-Based Image Retrieval

With advancement in the modern era, digital data is the new asset. Now making large image datasets is not that laborious task with improvement in image collection and data storage technology. Image processing is a method to perform some operations in order to enhance an image or to extract some useful information from it. It is a type of single processing in which input is an image and output may be image or characteristics/features associated with that image. Nowadays image processing is a rapidly growing technology in the computer science field. It forms a core research area within engineering and computer science disciplines too. In the last one decade, we have witnessed various theories and models related to image retrieval from databases. Content based image retrieval is one of the frameworks image retrievals, having the advantage of reducing ambiguity related to textual indexation and less time consuming. It scans the database to find the most relative images according to the query. In this paper, we have worked on a model that retrieves images based on feature sets and displays the resultant images with minimum vector distance.
Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations

Recommender systems are seen as an effective tool to address information overload, but it is widely known that the presence of various biases makes direct training on large-scale observational data result in sub-optimal prediction performance. In contrast, unbiased ratings obtained from randomized controlled trials or A/B tests are considered to be the golden standard, but are costly and small in scale in reality. To exploit both types of data, recent works proposed to use unbiased ratings to correct the parameters of the propensity or imputation models trained on the biased dataset. However, the existing methods fail to obtain accurate predictions in the presence of unobserved confounding or model misspecification. In this paper, we propose a theoretically guaranteed model-agnostic balancing approach that can be applied to any existing debiasing method with the aim of combating unobserved confounding and model misspecification. The proposed approach makes full use of unbiased data by alternatively correcting model parameters learned with biased data, and adaptively learning balance coefficients of biased samples for further debiasing. Extensive real-world experiments are conducted along with the deployment of our proposal on four representative debiasing methods to demonstrate the effectiveness.
ARound the Smartphone: Investigating the Effects of Virtually-Extended Display Size on Spatial Memory

Smartphones conveniently place large information spaces in the palms of our hands. While research has shown that larger screens positively affect spatial memory, workload, and user experience, smartphones remain fairly compact for the sake of device ergonomics and portability. Thus, we investigate the use of hybrid user interfaces to virtually increase the available display size by complementing the smartphone with an augmented reality head-worn display. We thereby combine the benefits of familiar touch interaction with the near-infinite visual display space afforded by augmented reality. To better understand the potential of virtually-extended displays and the possible issues of splitting the user's visual attention between two screens (real and virtual), we conducted a within-subjects experiment with 24 participants completing navigation tasks using different virtually-augmented display sizes. Our findings reveal that a desktop monitor size represents a "sweet spot" for extending smartphones with augmented reality, informing the design of hybrid user interfaces.
IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds

Tracking body pose on-the-go could have powerful uses in fitness, mobile gaming, context-aware virtual assistants, and rehabilitation. However, users are unlikely to buy and wear special suits or sensor arrays to achieve this end. Instead, in this work, we explore the feasibility of estimating body pose using IMUs already in devices that many users own — namely smartphones, smartwatches, and earbuds. This approach has several challenges, including noisy data from low-cost commodity IMUs, and the fact that the number of instrumentation points on a user’s body is both sparse and in flux. Our pipeline receives whatever subset of IMU data is available, potentially from just a single device, and produces a best-guess pose. To evaluate our model, we created the IMUPoser Dataset, collected from 10 participants wearing or holding off-the-shelf consumer devices and across a variety of activity contexts. We provide a comprehensive evaluation of our system, benchmarking it on both our own and existing IMU datasets.
Efficient shallow learning as an alternative to deep learning

The realization of complex classification tasks requires training of deep learning (DL) architectures consisting of tens or even hundreds of convolutional and fully connected hidden layers, which is far from the reality of the human brain. According to the DL rationale, the first convolutional layer reveals localized patterns in the input and large-scale patterns in the following layers, until it reliably characterizes a class of inputs. Here, we demonstrate that with a fixed ratio between the depths of the first and second convolutional layers, the error rates of the generalized shallow LeNet architecture, consisting of only five layers, decay as a power law with the number of filters in the first convolutional layer. The extrapolation of this power law indicates that the generalized LeNet can achieve small error rates that were previously obtained for the CIFAR-10 database using DL architectures. A power law with a similar exponent also characterizes the generalized VGG-16 architecture. However, this results in a significantly increased number of operations required to achieve a given error rate with respect to LeNet. This power law phenomenon governs various generalized LeNet and VGG-16 architectures, hinting at its universal behavior and suggesting a quantitative hierarchical time-space complexity among machine learning architectures. Additionally, the conservation law along the convolutional layers, which is the square-root of their size times their depth, is found to asymptotically minimize error rates. The efficient shallow learning that is demonstrated in this study calls for further quantitative examination using various databases and architectures and its accelerated implementation using future dedicated hardware developments.
Graph convolutional network-based feature selection for high-dimensional and low-sample size data

Feature selection is a powerful dimension reduction technique which selects a subset of relevant features for model construction. Numerous feature selection methods have been proposed, but most of them fail under the high-dimensional and low-sample size (HDLSS) setting due to the challenge of overfitting.We present a deep learning-based method-GRAph Convolutional nEtwork feature Selector (GRACES)-to select important features for HDLSS data. GRACES exploits latent relations between samples with various overfitting-reducing techniques to iteratively find a set of optimal features which gives rise to the greatest decreases in the optimization loss. We demonstrate that GRACES significantly outperforms other feature selection methods on both synthetic and real-world datasets.The source code is publicly available at https://github.com/canc1993/graces.
OccRob: Efficient SMT-Based Occlusion Robustness Verification of Deep Neural Networks

Abstract Occlusion is a prevalent and easily realizable semantic perturbation to deep neural networks (DNNs). It can fool a DNN into misclassifying an input image by occluding some segments, possibly resulting in severe errors. Therefore, DNNs planted in safety-critical systems should be verified to be robust against occlusions prior to deployment. However, most existing robustness verification approaches for DNNs are focused on non-semantic perturbations and are not suited to the occlusion case. In this paper, we propose the first efficient, SMT-based approach for formally verifying the occlusion robustness of DNNs. We formulate the occlusion robustness verification problem and prove it is NP-complete. Then, we devise a novel approach for encoding occlusions as a part of neural networks and introduce two acceleration techniques so that the extended neural networks can be efficiently verified using off-the-shelf, SMT-based neural network verification tools. We implement our approach in a prototype called OccRob and extensively evaluate its performance on benchmark datasets with various occlusion variants. The experimental results demonstrate our approach’s effectiveness and efficiency in verifying DNNs’ robustness against various occlusions, and its ability to generate counterexamples when these DNNs are not robust.