Reviewerly/exHarmony huggingface dataset:
A New Approach Based on Quadratic Forms to Attack the McEliece Cryptosystem

We introduce a novel algebraic approach for attacking the McEliece cryptosystem which is currently at the 4-th round of the NIST competition. The contributions of the article are twofold. (1) We present a new distinguisher on alternant and Goppa codes working in a much broader range of parameters than [FGO+11]. (2) With this approach we also provide a polynomial–time key recovery attack on alternant codes which are distinguishable with the distinguisher [FGO+11]. These results are obtained by introducing a subspace of matrices representing quadratic forms. Those are associated with quadratic relations for the component-wise product in the dual of the Goppa (or alternant) code of the cryptosystem. It turns out that this subspace of matrices contains matrices of unusually small rank in the case of alternant or Goppa codes (2 or 3 depending on the field characteristic) revealing the secret polynomial structure of the code. MinRank solvers can then be used to recover the secret key of the scheme. We devise a dedicated algebraic modeling in characteristic 2 where the Gröbner basis techniques to solve it can be analyzed. This computation behaves differently when applied to the matrix space associated with a random code rather than with a Goppa or an alternant code. This gives a distinguisher of the latter code families, which contrarily to the one proposed in [FGO+11] working only in a tiny parameter regime is now able to work for code rates above $$\frac{2}{3}$$ . It applies to most of the instantiations of the McEliece cryptosystem in the literature. It coincides with the one of [FGO+11] when the latter can be applied (and is therefore of polynomial complexity in this case). However, its complexity increases significantly when [FGO+11] does not apply anymore, but stays subexponential as long as the co-dimension of the code is sublinear in the length (with an asymptotic exponent which is below those of all known key recovery or message attacks). For the concrete parameters of the McEliece NIST submission [ABC+22], its complexity is way too complex to threaten the cryptosystem, but is smaller than known key recovery attacks for most of the parameters of the submission. This subspace of quadratic forms can also be used in a different manner to give a polynomial time attack of the McEliece cryptosystem based on generic alternant codes or Goppa codes provided that these codes are distinguishable by the method of [FGO+11], and in the Goppa case we need the additional assumption that its degree is less than $$q-1$$ , where q is the alphabet size of the code.
Thermodynamics of Quantum Trajectories on a Quantum Computer

Quantum computers have recently become available as noisy intermediate-scale quantum devices. Already these machines yield a useful environment for research on quantum systems and dynamics. Building on this opportunity, we investigate open-system dynamics that are simulated on a quantum computer by coupling a system of interest to an ancilla. After each interaction the ancilla is measured, and the sequence of measurements defines a quantum trajectory. Using a thermodynamic analogy, which identifies trajectories as microstates, we show how to bias the dynamics of the open system in order to enhance the probability of quantum trajectories with desired properties, e.g., particular measurement patterns or temporal correlations. We discuss how such a biased---generally non-Markovian---dynamics can be implemented on a unitary, gate-based quantum computer and show proof-of-principle results on the publicly accessible ibmq_jakarta machine. While our analysis is solely conducted on small systems, it highlights the challenges in controlling complex aspects of open-system dynamics on digital quantum computers.
Hybrid chaos-based image encryption algorithm using Chebyshev chaotic map with deoxyribonucleic acid sequence and its performance evaluation

&lt;span lang="EN-US"&gt;The media content shared on the internet has increased tremendously nowadays. The streaming service has major role in contributing to internet traffic all over the world. As the major content shared are in the form of images and rapid increase in computing power a better and complex encryption standard is needed to protect this data from being leaked to unauthorized person. Our proposed system makes use of chaotic maps, deoxyribonucleic acid (DNA) coding and ribonucleic acid (RNA) coding technique to encrypt the image. As videos are nothing but collection of images played at the rate of minimum 30 frames/images per second, this methodology can also be used to encrypt videos. The complexity and dynamic nature of chaotic systems makes decryption of &lt;span&gt;content by unauthorized personal difficult. The hybrid usage of chaotic systems along with DNA and RNA sequencing improves the encryption efficiency of the algorithm and also makes it possible to decrypt the images at the same time without consuming too much&lt;/span&gt; of computation power.&lt;/span&gt;
Unveiling the Hidden Influence of Defects via Experiment and Data Science

ADVERTISEMENT RETURN TO ISSUEPREVEditorialNEXTUnveiling the Hidden Influence of Defects via Experiment and Data ScienceJustin Sambur*Justin SamburDepartment of Chemistry, Colorado State University, Fort Collins, Colorado 80523, United States*Email: [email protected]More by Justin Samburhttps://orcid.org/0000-0002-8457-4946 and Jakoah Brgoch*Jakoah BrgochDepartment of Chemistry, University of Houston, Houston, Texas 77204, United States*Email: [email protected]More by Jakoah Brgochhttps://orcid.org/0000-0002-1406-1352Cite this: Chem. Mater. 2023, 35, 18, 7351–7354Publication Date (Web):September 26, 2023Publication History Received21 July 2023Published online26 September 2023Published inissue 26 September 2023https://doi.org/10.1021/acs.chemmater.3c01817Copyright © Published 2023 by American Chemical SocietyRequest reuse permissions This publication is free to access through this site. Learn MoreArticle Views482Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (924 KB) Get e-AlertscloseSUBJECTS:Defects,Imaging,Materials,Mathematical methods,Nanoparticles Get e-Alerts
313 Leveraging Decision Trees to Forecast Ambulance Traffic in Emergency Departments

It is crucial to understand the patterns of emergency medical services (EMS) arrivals to optimize resource allocation and throughput in emergency departments (EDs). The purpose of this study was to analyze and predict daily ambulance arrivals to the ED, using data-driven insights to inform resource planning. We conducted a cohort study that examined patients visiting an urban, academic ED that cares for a predominantly Black patient population from January 1, 2021, to February 28, 2023. The data extracted from patient electronic health records comprised daily ED patient arrival and departure time stamps, demographic information (such as age, gender, and race), mode of arrival, chief complaints, primary diagnosis, ED disposition, and the patient’s emergency severity index (ESI). Only ED patients that arrived by ambulance were included in the final analytic cohort. A decision tree (DT) machine learning model was developed to forecast daily EMS arrivals. The DT model was adjusted for seasonality and trends (e.g., holidays or day-of-the-week trends). The data were split into training (90%) and testing (10%) sets to develop and evaluate the model’s performance. Evaluation criteria included root mean squared error (RMSE), defined as the average squared difference between predicted and actual ED ambulance arrival rates per day, and the mean absolute percentage error (MAPE), defined as the average of the absolute percentage errors. Smaller MAPE and RMSE values indicate more accurate forecasting. During the study period, a total of 175,577 patients arrived at the ED, out of which 26.68% (46,840) arrived via ambulance. The average age of EMS patients was 52.99 years, with a standard deviation of 19.14 years. 47.11% (22,067) of patients were female, and 70.73% (33,128) were Blacks. On average, 59 patients arrived by ambulance daily, with approximately three ambulance arrivals per hour. Of the patients arriving by ambulance, 57.77% (27,058) had an ESI triage score of 2, and 30.45% (14,264) were admitted from the ED. The five most common reasons for ambulance transport to the ED were shortness of breath (7.60%), abdominal pain (7.51%), fall (4.28%), chest pain (3.84%), and seizures (3.19%). Figure 1 shows the observed and predicted daily ED ambulance arrivals. Our DT model achieved a MAPE of 7.69% on the test data, indicating that the predicted arrival was approximately 8% different from the actual number of ambulance arrivals. The RMSE was 6 patients, suggesting that, on average, the model’s predictions deviated by approximately 6 patients from the actual values. The DT model developed in this study shows promising potential for accurately forecasting daily ambulance arrivals to the ED. By anticipating daily EMS traffic patterns, EDs can better allocate resources and streamline patient flow. The model’s ability to predict ambulance arrivals with approximately 8% MAPE has significant implications for ED operations, where resource allocation and patient flow management are critical factors affecting overall patient outcomes.
FedAWR : An Interactive Federated Active Learning Framework for Air Writing Recognition

The rapid development of technology such as virtual reality and augmented reality, coupled with the reduced direct contact due to the COVID-19 pandemic, has led to the emergence of a more advanced mode of interaction: air handwriting. This new form of human-computer interaction allows users to input text by writing in the air freely. However, deploying and applying existing air handwriting recognition systems in real-world scenarios still presents challenges, particularly in real-time performance, privacy protection, and label scarcity. To address these challenges, we propose a federated active learning framework called FedAWR for air handwriting recognition tasks. FedAWR utilizes distributed learning to train a shared global model in the cloud from multiple user devices at the network's edge, while keeping the user's handwritten data local to ensure privacy. In addition, FedAWR employs an interactive active learning strategy to collect user-provided annotations for iterative training during the online federated learning process, bootstrapping personalized models for each client. To further enhance interactivity and real-time performance, we designed a lightweight recognition model, which is integrated into FedAWR. Finally, extensive experiments were conducted on real-world air handwritten datasets to validate the superiority of FedAWR.
Incomplete Bipolar Argumentation Frameworks

We introduce Incomplete Bipolar Argumentation Frameworks (iBAFs), the extension of Dung’s Abstract Argumentation Frameworks (AAFs) allowing the simultaneous presence of supports (borrowed from BAFs – Bipolar AAFs) and of uncertain elements of the argumentation graph (borrowed from iAAFs – incomplete AAFs). We investigate the computational complexity of verification problem (under the possible perspective) and the acceptance problem, by studying its sensitivity to the semantics of supports and the semantics of extensions. On the one hand, we show that adding supports on top of incompleteness does not affect the complexity of the acceptance. On the other hand, surprisingly, we show that the joint use of bipolarity and incompleteness has a deep impact on the complexity of the verification: for the semantics under which the verification over AAFs is polynomial-time solvable, although moving from AAFs to BAFs or to iAAFs does not change the complexity, the complexity of the verification over iBAFs may increase up to NP-complete.
Combination of Smote and Random Forest Methods for Lung Cancer Classification

Lung cancer is a network of cells that grow abnormally in the lungs. Lung cancer has four severity levels, namely stages 1 to 4. If lung cancer is not treated quickly, it is at risk of causing death. This research aimed to combine Synthetic Minority Over-sampling (Smote) and Random Forest methods for lung cancer classification. The method used was a combination of Smote and Random Forest. Smote was used to balance the data, while Random Forest was used to classify lung cancer data. The results showed that the combination of Smote and Random Forest methods obtained an accuracy of 94.1%, sensitivity of 94.5, and specificity of 93.7%. Meanwhile, without Smote, the accuracy is 89.1%, sensitivity is 55%, and specificity is 94.5%. The use of Smote can improve the performance of the Random Forest classification method based on accuracy and sensitivity. There was an increase of 5% in accuracy and a 39% increase in sensitivity.
Representative Answer Sets: Collecting Something of Everything

Answer set programming (ASP) is a popular problem solving paradigm with applications in planning and configuration. In practice, the number of answer sets can be overwhelmingly high, which naturally causes interest in a concise characterisation of the solution space in terms of representative answer sets. We establish a notion of representativeness that refers to the entropy of specified target atoms within a collection of answer sets. Accordingly, we propose different approaches for collecting such representative answer sets, based on answer set navigation. Finally, we conduct experiments using our prototypical implementation, which reveals promising results.
On the Robustness of Split Learning Against Adversarial Attacks

Split learning enables collaborative deep learning model training while preserving data privacy and model security by avoiding direct sharing of raw data and model details (i.e., server and clients only hold partial sub-networks and exchange intermediate computations). However, existing research has mainly focused on examining its reliability for privacy protection, with little investigation into model security. Specifically, by exploring full models, attackers can launch adversarial attacks, and split learning can mitigate this severe threat by only disclosing part of models to untrusted servers. This paper aims to evaluate the robustness of split learning against adversarial attacks, particularly in the most challenging setting where untrusted servers only have access to the intermediate layers of the model. Existing adversarial attacks mostly focus on the centralized setting instead of the collaborative setting, thus, to better evaluate the robustness of split learning, we develop a tailored attack called SLADV, which comprises two stages: 1) shadow model training that addresses the issue of lacking part of the model and 2) local adversarial attack that produces adversarial examples to evaluate. The first stage only requires a few unlabeled non-IID data, and, in the second stage, SLADV perturbs the intermediate output of natural samples to craft the adversarial ones. The overall cost of the proposed attack process is relatively low, yet the empirical attack effectiveness is significantly high, demonstrating the surprising vulnerability of split learning to adversarial attacks.
Credal Learning: Weakly Supervised Learning from Credal Sets

In this article we study the problem of credal learning, a general form of weakly supervised learning in which instances are associated with credal sets (i.e., closed, convex sets of probabilities), which are assumed to represent the partial knowledge of an annotating agent about the true conditional label distribution. A variety of algorithms have been proposed in this setting, chiefly among them the generalized risk minimization method, a class of algorithms that extend empirical risk minimization. Despite its popularity and promising empirical results, however, the theoretical properties of this algorithm (as well as of credal learning more in general) have not been previously studied. In this article we address this gap by studying the problem of credal learning from the learning-theoretic and complexity-theoretic perspectives. We provide, in particular, three main contributions: 1) we show that, under weak assumptions about the accuracy of the annotating agent, credal learning is learnable in the convex learning setting, providing effective risk bounds; 2) we study the properties of generalized risk minimization and, in particular, identify the optimal instance of this approach, that we call trade-off risk minimization; 3) we study the computational complexity of generalized risk minimization, showing effective algorithms based on gradient descent and providing sufficient and necessary conditions for them being computationally efficient.
Comparison of Error Rate Prediction Methods in Classification Modeling with the CHAID Method for Imbalanced Data

CHAID (Chi-Square Automatic Interaction Detection) is one of the classification algorithms in the decision tree method. The classification results are displayed in the form of a tree diagram model. After the model is formed, it is necessary to calculate the accuracy of the model. The aims is to see the performance of the model. The accuracy of this model can be done by calculating the predicted error rate in the model. There are three methods, such as Leave one out cross-validation (LOOCV), Hold-out, and K-fold cross-validation. These methods have different performances in dividing data into training and testing data, so each method has advantages and disadvantages. Imbalanced data is data that has a different number of class observations. In the CHAID method, imbalanced data affects the prediction results. When the data is increasingly imbalanced the prediction result will approach the number of minority classes. Therefore, a comparison was made for the three error rate prediction methods to determine the appropriate method for the CHAID method in imbalanced data. This research is included in experimental research and uses simulated data from the results of generating data in RStudio. This comparison was made by considering several factors, for the marginal opportunity matrix, different correlations, and several observation ratios. The results of the comparison will be observed using a boxplot by looking at the median error rate and the lowest variance. This research finds that K-fold cross-validation is the most suitable error rate prediction method applied to the CHAID method for imbalanced data.
The Power of Identity Cues in Text-Based Customer Service: Evidence from Twitter

Text-based customer service is emerging as an important channel through which companies can assist customers. However, the use of few identity cues may cause customers to feel limited social presence and even suspect the human identity of agents, especially in the current age of advanced algorithms. Does such a lack of social presence affect service interactions? We studied this timely question by evaluating the impact of customers’ perceived social presence on service outcomes and customers’ attitudes toward agents. Our identification strategy hinged on Southwest Airlines’ sudden requirement to include a first name in response to service requests on Twitter, which enhanced customers’ perceived level of social presence. This change led customers to become more willing to engage and more likely to reach a resolution upon engagement. We further conducted a randomized experiment to understand the underlying mechanisms. We found that the effects were mainly driven by customers who were ex ante uncertain or suspicious about the human identity of agents, and the presence of identity cues improved service outcomes by enhancing customers’ perceived levels of trust and empathy. Additionally, we found no evidence of elevated verbal aggression from customers toward agents with identity cues, although a mechanism test revealed the moderating role of customers’ emotional states. Our study highlights the importance of social presence in text-based customer service and suggests a readily available and almost costless strategy for firms: signal humanization through identity cues.
A study on the role of latent variables in the encoder-decoder model using image datasets

An encoder-decoder model consists of an encoder that encodes the input into a low-dimensional latent variable and a decoder that decodes the obtained latent variable to the same dimension as the input. The encoder-decoder model performs representation learning to automatically extract features of the data, but the model is a black box and it is not clear what features are extracted. We focused on whether including a skip connection between the encoder and decoder increased accuracy. It is generally believed that this skip connection plays a role in conveying high-resolution information. However, its actual role remains unclear. In this study, we focused on this concatenation. We experimentally clarified the role of the latent variables conveyed by this concatenation when the images given to the input and output were the same or different during training.