Reviewerly/exHarmony huggingface dataset:
Double laser-faults based PFA on cryptographic circuits with algebraic analysis

Authors:
raw_author_name: Xue Gong
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China
raw_author_name: Ao Shen
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China
raw_author_name: Tianxiang Feng
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China
raw_author_name: Guorui Xu
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China
raw_author_name: Shize Guo
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China; Zhengzhou Xinda Institute of Advanced Technology, Zhengzhou, 450000, China
raw_author_name: Fan Zhang
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China; Zhengzhou Xinda Institute of Advanced Technology, Zhengzhou, 450000, China

Abstract: Cryptographic algorithms have been employed in a variety of fields as the primary method to protect information security. The security of a cryptographic algorithm is closely related to its operating environment and physical devices. Algebraic Persistent Fault Analysis (APFA) is a new fault analysis method for block ciphers proposed in CHES 2022, which utilizes the fault that persists in encryptions and introduces algebraic analysis in the fault analysis step. In the fault injection step, as the transistors of the integrated circuit are getting smaller and tighter, even high-precision devices may cause more than one fault per injection. However, more faults may lead to a more efficient attack in the fault analysis step. In this paper, APFA for double faults is proposed, which can deal with the double faults model and reduce the number of required ciphertexts. The practicality of our fault injection is validated by laser fault injection experiments on the SRAM embedded in an ATmega163L microcontroller. The effectiveness of our fault analysis is proven by successfully recovering the key of PRESENT-128 and AES-128. The number of ciphertexts needed for key recovery is reduced by 46% compared to PFA with a single fault.
Robust CoDA balances and the role of the variance in complex riverine geochemical systems

Authors:
raw_author_name: Caterina Gozzi
raw_affiliation_string: University of Florence, Department of Earth Sciences, Via G. La Pira 4, 50121 Firenze, Italy
raw_author_name: Matthias Templ
raw_affiliation_string: University of Applied Sciences and Arts Northwestern Switzerland, Riggenbachstrasse 16, 4600 Olten, Switzerland
raw_author_name: Antonella Buccianti
raw_affiliation_string: University of Florence, Department of Earth Sciences, Via G. La Pira 4, 50121 Firenze, Italy

Abstract: This study introduces a robust method for analyzing the geochemical behavior of chemical species in river catchment water. It focuses on isometric log-ratio coordinates obtained from a sequential partition method that successively maximizes the explained variance in the data set. Robust orthonormal coordinates are created based on hierarchical clustering and robust estimation of the variation matrix. Applying this to the water chemistry of Italy's Arno and Tiber basins, the research reveals the associations of variables in data structure and processes across varying geological and climatic conditions. The method uncovers key contrasting geochemical processes and suggests that the behavior of simple balances characterized by lower variances (i.e., Ca2+/HCO3− and Na+/Cl−) are mainly influenced by random fluctuations with no differences between classical or robust methods. However, when balances describe more complex geochemical processes resulting in frequency distributions affected by the presence of bimodality or outliers, significant differences among the two approaches emerge, compromising the data interpretation. The proposed metodology offers more insights into the investigation of catchment geochemistry's resilience to hydroclimatic changes, marking a significant step in understanding large-scale environmental dynamics.
Anonymizing eye-tracking stimuli with stable diffusion

Authors:
raw_author_name: Kuno Kurzhals
raw_affiliation_string: Allmandring 19, Stuttgart, 70569, Germany

Abstract: Casual users nowadays can create almost arbitrary image content by providing textual prompts to generative machine-learning models. These models rapidly improve image quality with each new generation, providing means to create photos, paintings in different styles, and even videos. One feature of such models is the ability to take an image as input and adjust content according to a prompt. A visual obfuscation of content can be achieved for static images and videos by slightly changing persons, text, and other objects. The potential of this technique can be applied in eye-tracking experiments for post-hoc dissemination of analysis results and visualization. In this work, we discuss how the technique could serve to anonymize stimuli (e.g., for double-blind reviews, remove product placements, etc.) and protect the privacy of people visible in the stimuli. We further investigate how the application of this anonymization process influences visual saliency and the depiction of stimuli in visualization techniques. Our results show that slight image transformations do not drastically change the saliency of a scene but obfuscate objects and faces while keeping important image structures for context.
Maximum Output Discrepancy Computation for Convolutional Neural Network Compression

Authors:
raw_author_name: Weiming Xiang
raw_affiliation_string: School of Computer and Cyber Sciences, Augusta University, 1120 15th St, Augusta, 30912, GA, United States

Abstract: Network compression methods minimize the number of network parameters and computation costs while maintaining desired network performance. However, the safety assurance of many compression methods is based on a large amount of experimental data, whereas unforeseen incidents beyond the experiment data may result in unsafe consequences. In this work, we developed a discrepancy computation method for two convolutional neural networks by giving a concrete value to characterize the maximum output difference between the two networks after compression. Using Imagestar-based reachability analysis, we propose a novel method to merge the two networks to compute the difference. We illustrate reachability computation for each layer in the merged network, such as the convolution, max pooling, fully connected, and ReLU layers. We apply our method to a numerical example to prove its correctness. Furthermore, we implement our developed methods on the VGG16 model with the Quantization Aware Training (QAT) compression method; the results show that our approach can efficiently compute the accurate maximum output discrepancy between the original neural network and the compressed neural network.
Path-BigBird: An AI-Driven Transformer Approach to Classification of Cancer Pathology Reports

Authors:
raw_author_name: Mayanka Chandrashekar
raw_affiliation_string: Advanced Computing for Health Sciences Section, Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN
raw_author_name: Heidi A. Hanson
raw_affiliation_string: Advanced Computing for Health Sciences Section, Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN
raw_author_name: Shang Gao
raw_affiliation_string: Advanced Computing for Health Sciences Section, Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN
raw_author_name: Xiao-Cheng Wu
raw_affiliation_string: Louisiana Tumor Registry, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA
raw_author_name: John Gounley
raw_affiliation_string: Advanced Computing for Health Sciences Section, Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN

Abstract: PURPOSE Surgical pathology reports are critical for cancer diagnosis and management. To accurately extract information about tumor characteristics from pathology reports in near real time, we explore the impact of using domain-specific transformer models that understand cancer pathology reports. METHODS We built a pathology transformer model, Path-BigBird, by using 2.7 million pathology reports from six SEER cancer registries. We then compare different variations of Path-BigBird with two less computationally intensive methods: Hierarchical Self-Attention Network (HiSAN) classification model and an off-the-shelf clinical transformer model (Clinical BigBird). We use five pathology information extraction tasks for evaluation: site, subsite, laterality, histology, and behavior. Model performance is evaluated by using macro and micro F 1 scores. RESULTS We found that Path-BigBird and Clinical BigBird outperformed the HiSAN in all tasks. Clinical BigBird performed better on the site and laterality tasks. Versions of the Path-BigBird model performed best on the two most difficult tasks: subsite (micro F 1 score of 72.53, macro F 1 score of 35.76) and histology (micro F 1 score of 80.96, macro F 1 score of 37.94). The largest performance gains over the HiSAN model were for histology, for which a Path-BigBird model increased the micro F 1 score by 1.44 points and the macro F 1 score by 3.55 points. Overall, the results suggest that a Path-BigBird model with a vocabulary derived from well-curated and deidentified data is the best-performing model. CONCLUSION The Path-BigBird pathology transformer model improves automated information extraction from pathology reports. Although Path-BigBird outperforms Clinical BigBird and HiSAN, these less computationally expensive models still have utility when resources are constrained.
Anomaly Detection of Sensor Measurements During a Turbo-Machine Prototype Testing - An Integrated ML Ops, Continual Learning Architecture

Authors:
raw_author_name: Somasundaram Palaniappan
raw_affiliation_string: Baker Hughes, Bangalore, India
raw_author_name: Giacomo Veneri
raw_affiliation_string: Baker Hughes, Florence, Italy
raw_author_name: Valentina Gori
raw_affiliation_string: Baker Hughes, Florence, Italy
raw_author_name: Valeria Ballarini
raw_affiliation_string: Baker Hughes, Florence, Italy

Abstract: Abstract Building a reliable Machine Learning infrastructure in the Energy domain is a complex task; indeed, it requires a full data integration, continual learning, continual prediction, and the integration of human feedback. Continual learning is a challenging task due to the risk of machine learning to forget former data while learning from new ones (catastrophic forgetting). We present a Machine Learning Operations (MLOPS) architecture able to perform continual learning every day on more than 500 models, to perform inference on new data using such models and to take human feedback and data shift into account. More in detail, we continuously train a recurrent Deep Neural Network to build a virtual sensor from other signals and we compare the prediction versus the real signal to raise (in case) an anomaly. Furthermore, Kullback-Leibler (KL) divergence is used to estimate the overlap between the input distributions available at training time and the distributions seen at test time to estimate the confidence level of the prediction. Finally, we integrate human feedback to tune model retraining. The tool has been applied on a set of about 500 sensors on a three-months long test campaign. Results report 100% recall and 94% accuracy. Moreover, using a recurrent neural network, the system is self-explainable (XAI) by design: indeed, the user can compare the predicted vs real signal to understand the performance of the model. We propose a data intensive MLOPS architecture integrating continual learning, anomaly detection and user feedback. The architecture is based on a standard cloud, it is event-driven and integrates retraining based on GPU capabilities.
Augmented LT codes over binary extension fields with increased ratio of full‐degree columns

Authors:
raw_author_name: Jingsong Cui
raw_affiliation_string: Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering Wuhan University Wuhan China
raw_author_name: Jiawei Li
raw_affiliation_string: Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering Wuhan University Wuhan China

Abstract: Abstract This letter proposes an augmented scheme of LT codes to improve the decoding success rate. The method involves substituting the 1‐s in full‐degree columns of the generator matrix of conventional LT codes with binary extension field elements and simultaneously increasing the ratio of full‐degree columns in the ideal soliton distribution. For non‐full‐degree columns, we retain the nonzero elements as 1‐s in the conventional LT codes generator matrix to preserve computational efficiency. Compared to conventional LT codes, the proposed method enhances the linear independence of the generator matrix, leading to a higher decoding success rate with minimal data packets. Experimental results demonstrate the effectiveness of the method at improving the performance of LT codes, with close to 100% decoding success rate achieved with around 5% data redundancy.
Implementation and Analysis of Shallow Parsing Techniques in Khasi Language

Authors:
raw_author_name: Goutam Saha
raw_affiliation_string: North-Eastern Hill University, India
raw_author_name: Arnab Kumar Maji
raw_affiliation_string: North-Eastern Hill University, India

Abstract: In the realm of natural language processing (NLP), after part-of-speech (POS) tagging, the subsequent crucial step is shallow parsing. In this endeavour, the authors have undertaken the development of a shallow parser for the Khasi language. The work explores an array of techniques from both traditional machine learning (ML) and modern deep learning (DL) methodologies. They have employed a variety of ML algorithms, including decision trees, logistic regression, support vector machines, random forest, and multinomial naive bayes. Additionally, they have harnessed the power of DL with models such as the vanilla recurrent neural network, long short-term memory network, gated recurrent units, and bidirectional LSTM, all geared towards achieving the shallow parsing objective. The crux of the effort lies in the meticulous comparative analysis of these techniques. The chapter delves into a comprehensive discussion of their individual performances.
Language Resource Acquisition for Low-Resource Languages in Digital Discourses

Authors:
raw_author_name: Kathiravan Pannerselvam
raw_affiliation_string: Central University of Tamil Nadu, India
raw_author_name: Saranya Rajiakodi
raw_affiliation_string: Central University of Tamil Nadu, India
raw_author_name: Bharathi Raja Chakravarthi
raw_affiliation_string: University of Galway, Ireland

Abstract: This chapter explores the acquisition of language resources for low-resource languages in digital discourse, focusing on code-mixed content in social media posts and comments. Comprehensive language resources enhance our understanding of language usage patterns in online discussions. The methodology includes data collection, annotation, and automated tools, resulting in a practical framework. Challenges addressed in the chapter include data diversity, annotation complexity, linguistic variability, limited annotated data, and ethical considerations. Solutions proposed range from robust data preprocessing techniques to contextual analysis approaches, ensuring acquired quality and ethical use of resources. The chapter also discusses future directions, paving the way for further research and technology development in natural language processing and sociolinguistics. This chapter equips researchers and practitioners with essential knowledge and tools for advancing linguistic analysis and technological applications in the evolving digital landscape.
Testing high-dimensional multinomials with applications to text analysis

Authors:
raw_author_name: T Tony Cai
raw_affiliation_string: Department of Statistics and Data Science, University of Pennsylvania , Philadelphia, PA, USA
raw_author_name: Zheng T Ke
raw_affiliation_string: Department of Statistics, Harvard University , Cambridge, MA, USA

Abstract: Abstract Motivated by applications in text mining and discrete distribution inference, we test for equality of probability mass functions of K groups of high-dimensional multinomial distributions. Special cases of this problem include global testing for topic models, two-sample testing in authorship attribution, and closeness testing for discrete distributions. A test statistic, which is shown to have an asymptotic standard normal distribution under the null hypothesis, is proposed. This parameter-free limiting null distribution holds true without requiring identical multinomial parameters within each group or equal group sizes. The optimal detection boundary for this testing problem is established, and the proposed test is shown to achieve this optimal detection boundary across the entire parameter space of interest. The proposed method is demonstrated in simulation studies and applied to analyse two real-world datasets to examine, respectively, variation among customer reviews of Amazon movies and the diversity of statistical paper abstracts.
LFC-UNet: learned lossless medical image fast compression with U-Net

Authors:
raw_author_name: Hengrui Liao
raw_affiliation_string: School of Computer, University of South China, Hengyang, Hunan, China
raw_author_name: Yue Li
raw_affiliation_string: School of Computer, University of South China, Hengyang, Hunan, China

Abstract: In the field of medicine, the rapid advancement of medical technology has significantly increased the speed of medical image generation, compelling us to seek efficient methods for image compression. Neural networks, owing to their outstanding image estimation capabilities, have provided new avenues for lossless compression. In recent years, learning-based lossless image compression methods, combining neural network predictions with residuals, have achieved performance comparable to traditional non-learning algorithms. However, existing methods have not taken into account that residuals often concentrate excessively, hindering the neural network's ability to learn accurate residual probability estimation. To address this issue, this study employs a weighted cross-entropy method to handle the imbalance in residual categories. In terms of network architecture, we introduce skip connections from U-Net to better capture image features, thereby obtaining accurate probability estimates. Furthermore, our framework boasts excellent encoding speed, as the model is able to acquire all residuals and residual probabilities in a single inference pass. The experimental results demonstrate that the proposed method achieves state-of-the-art performance on medical datasets while also offering the fastest processing speed. As illustrated by an instance using head CT data, our approach achieves a compression efficiency of 2.30 bits per pixel, with a processing time of only 0.320 seconds per image.
Logical generation of groups

Authors:
raw_author_name: Alireza Abdollahi
raw_affiliation_string: Department of Pure Mathematics, Faculty of Mathematics and Statistics, University of Isfahan, Isfahan, Iran

Abstract: A group G is called logically generated by a subset S, if every element of G can be defined by a first order formula with parameters from S. We consider the case where G is a direct product of finite nilpotent groups with mutually coprime orders and we show that logical and algebraic generations are equivalent in G. We also prove that in the case when G is a free non-abelian group, if S logically generates G then either it generates G algebraically or 〈S〉 is not a free factor of G.
Photonic Reservoir Computing System for Pattern Recognition Based on an Array of Four Distributed Feedback Lasers

Authors:
raw_author_name: Xingxing Guo
raw_affiliation_string: State Key Discipline Laboratory of Wide Bandgap Semiconductor Technology, School of Microelectronics, Xidian University, Xi’an 710071, China; State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, China
raw_author_name: Hanxu Zhou
raw_affiliation_string: State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, China
raw_author_name: Shuiying Xiang
raw_affiliation_string: State Key Discipline Laboratory of Wide Bandgap Semiconductor Technology, School of Microelectronics, Xidian University, Xi’an 710071, China; State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, China
raw_author_name: Qian Yu
raw_affiliation_string: State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, China
raw_author_name: Yahui Zhang
raw_affiliation_string: State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, China
raw_author_name: Yanan Han
raw_affiliation_string: State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, China
raw_author_name: Yue Hao
raw_affiliation_string: State Key Discipline Laboratory of Wide Bandgap Semiconductor Technology, School of Microelectronics, Xidian University, Xi’an 710071, China

Abstract: Reservoir computing (RC), especially photonic RC based on a single semiconductor laser with a feedback loop, as a method of machine learning, shows excellent performance in time series prediction and classification tasks. The faster the processing speed, the shorter the feedback loop should be employed. However, the performance of the system is not ideal enough due to the limited reservoir nodes caused by the short feedback loop. To overcome this drawback, it is necessary and challenging to develop integrated photonic RC. In this paper, we propose an integrated neuromorphic photonic time-delayed RC scheme based on an array of four distributed feedback lasers (F-DFBs) with optical feedback and injection. Here, we investigate the feasibility of using larger laser arrays and shorter external feedback cavities to provide the RC system with more virtual nodes at the same processing speed, enabling it to handle more complex tasks. Additionally, we compare the performance of the systems with a single DFB (the S-DFB RC) and the F-DFBs RC through simulations and experiments involving iris recognition tasks. Furthermore, the minimum symbol error rate values can reach 0.052 in experiments (and 0 in simulations).
Risk assessment of aircraft anti-skid braking system based on improved fuzzy FMEA

Authors:
raw_author_name: Shengli ZHANG
raw_affiliation_string: Chengdu Aeronautic Polytechnic (China)

Abstract: A risk assessment method based on improved fuzzy FMEA is proposed to solve the problem of frequent failures caused by complex working environment of aircraft anti-skid braking system. On the basis of analyzing the application of FMEA, the fuzzy language of fault evaluation is established according to fuzzy set theory, and the standardized evaluation matrix of failure mode is established. is established according to fuzzy set theory, and the standardized evaluation matrix of failure mode is constructed by quantitatively describing expert experience through triangular fuzzy numbers; Then the grey relational-TOPSIS model is nested to calculate the comprehensive closeness degree of various failure modes, and the grey relational-TOPSIS model is nested to calculate the comprehensive closeness degree of various failure modes. Then the grey relational-TOPSIS model is nested to calculate the comprehensive closeness degree of various failure modes, and the final risk assessment conclusion is obtained. The research results show that the improved fuzzy FMEA is better than the The research results show that the improved fuzzy FMEA is better than the FMEA in evaluating risk of aircraft anti-skid braking system,and has good practical application value.