Reviewerly/exHarmony huggingface dataset:
Dissolved oxygen prediction using regularized extreme learning machine with clustering mechanism in a black bass aquaculture pond

Authors:
raw_author_name: Pei Shi
raw_affiliation_string: School of IoT Engineering, Wuxi University, Wuxi 214105, China
raw_author_name: Liang Kuang
raw_affiliation_string: School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China; School of IoT Engineering, Jiangsu Vocational College of Information Technology, Wuxi 214153, China
raw_author_name: Quan Wang
raw_affiliation_string: School of IoT Engineering, Wuxi University, Wuxi 214105, China
raw_author_name: Guanghui Li
raw_affiliation_string: School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
raw_author_name: Yongming Yuan
raw_affiliation_string: Freshwater Fisheries Research Center of Chinese Academy of Fishery Sciences, Wuxi 214081,China
raw_author_name: Yonghong Zhang
raw_affiliation_string: School of IoT Engineering, Wuxi University, Wuxi 214105, China
raw_author_name: Guangyan Huang
raw_affiliation_string: School of Information Technology, Deakin University, Melbourne, Australia

Abstract: Dissolved oxygen (DO) is an important indicator of aquaculture water quality. The prediction accuracy of DO content is the key role in managing and controlling aquaculture water quality. However, potential trends of DO under various conditions (such as weather) are always overlooked. This study aims to develop a novel DO forecasting model using the optimized regularized extreme learning machine (RELM) with factor extraction operation and K-medoids clustering strategy in a black bass aquaculture pond. We adopt the leave-one-out cross (LOO) error validation to obtain the optimal regularization parameter of RELM and enhance the forecasting accuracy. We further adjust the activation function to accelerate the RELM. Next, we divide the time series into day and night segments, and construct the clustering mechanism with the K-medoids method to extract the different patterns of data streams under various weather conditions. The experiments on 14 days’ data from a real-world aquaculture pond demonstrate the efficiency and accuracy of our proposed DO prediction model. We believe that our research will facilitate the development of a forecasting tool for warning hypoxia in the near future, which combines intelligent prediction models and real-time data.
Scattering expansion for localization in one dimension

Authors:
raw_author_name: Pratik Sathe
raw_affiliation_string: Mani L. Bhaumik Institute for Theoretical Physics, Department of Physics and Astronomy, University of California, Los Angeles, Los Angeles, California 90095, USA
raw_author_name: Rahul Roy
raw_affiliation_string: Mani L. Bhaumik Institute for Theoretical Physics, Department of Physics and Astronomy, University of California, Los Angeles, Los Angeles, California 90095, USA

Abstract: We present a perturbative approach to disordered systems in one spatial dimension that accesses the full range of phase disorder and clarifies the connection between localization and phase information. We consider a long chain of identically disordered scatterers and expand in the reflection strength of any individual scatterer. As an example application, we show analytically that in a discrete-time quantum walk, the localization length can depend nonmonotonically on the strength of phase disorder (whereas expanding in weak disorder yields monotonic decrease). More generally, we obtain to all orders in the expansion a particular nonseparable form for the joint probability distribution of the transmission coefficient logarithm and reflection phase. Furthermore, we show that for weak local reflection strength, a version of the scaling theory of localization holds: the joint distribution is determined by just three parameters.
Integrability Breaking and Bound States in Google’s Decorated XXZ Circuits

Authors:
raw_author_name: Zlatko Papić
raw_affiliation_string: School of Physics and Astronomy, University of Leeds, Leeds LS2 9JT, United Kingdom

Abstract: Recent quantum simulation by Google [Nature 612, 240 (2022)] has demonstrated the formation of bound states of interacting photons in a quantum-circuit version of the XXZ spin chain. While such bound states are protected by integrability in a one-dimensional chain, the experiment found the bound states to be unexpectedly robust when integrability was broken by decorating the circuit with additional qubits, at least for small numbers of qubits (≤24) within the experimental capability. Here we scrutinize this result by state-of-the-art classical simulations, which greatly exceed the experimental system sizes and provide a benchmark for future studies in larger circuits. We find that the bound states consisting of a finite number of photons are indeed robust in the nonintegrable regime, even after scaling to the infinite-time and infinite-system size limit. Moreover, we show that such systems possess unusual spectral properties, with level statistics that deviates from the random matrix theory expectation. On the other hand, for low but finite density of photons, we find a much faster onset of thermalization and significantly weaker signatures of bound states, suggesting that anomalous dynamics may only be a property of dilute systems with zero density of photons in the thermodynamic limit. The robustness of the bound states is also influenced by the number of decoration qubits and, to a lesser degree, by the regularity of their spatial arrangement.9 MoreReceived 6 July 2023Accepted 20 December 2023DOI:https://doi.org/10.1103/PRXQuantum.5.010316Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEigenstate thermalizationQuantum circuitsQuantum simulationPhysical SystemsFloquet systemsQuantum spin modelsTechniquesExact diagonalizationMatrix product statesRandom matrix theoryQuantum Information, Science & TechnologyStatistical Physics & Thermodynamics
It's Not a Feature, It's a Bug: Fault-Tolerant Model Mining from Noisy Data

Authors:
raw_author_name: Felix Wallner
raw_affiliation_string: Institute of Software Technology, Graz University of Technology, Graz, Austria
raw_author_name: Bernhard K. Aichernig
raw_affiliation_string: Institute of Software Technology, Graz University of Technology, Graz, Austria

Abstract: The mining of models from data finds widespread use in industry. There exists a variety of model inference methods for perfectly deterministic behaviour, however, in practice, the provided data often contains noise due to faults such as message loss or environmental factors that many of the inference algorithms have problems dealing with. We present a novel model mining approach using Partial Max-SAT solving to infer the best possible automaton from a set of noisy execution traces. This approach enables us to ignore the minimal number of presumably faulty observations to allow the construction of a deterministic automaton. No pre-processing of the data is required. The method's performance as well as a number of considerations for practical use are evaluated, including three industrial use cases, for which we inferred the correct models.
Smart Contract and DeFi Security Tools: Do They Meet the Needs of Practitioners?

Authors:
raw_author_name: Stefanos Chaliasos
raw_affiliation_string: Imperial College London, London, United Kingdom
raw_author_name: Marcos Antonios Charalambous
raw_affiliation_string: Imperial College London, London, United Kingdom
raw_author_name: Liyi Zhou
raw_affiliation_string: Imperial College London, London, United Kingdom
raw_author_name: Arthur Gervais
raw_affiliation_string: University College London &amp; UC Berkeley RDI, London, United Kingdom
raw_author_name: Benjamin Livshits
raw_affiliation_string: Imperial College London, London, United Kingdom

Abstract: The growth of the decentralized finance (DeFi) ecosystem built on blockchain technology and smart contracts has led to an increased demand for secure and reliable smart contract development. However, attacks targeting smart contracts are increasing, causing an estimated $6.45 billion in financial losses. Researchers have proposed various automated security tools to detect vulnerabilities, but their real-world impact remains uncertain.
Research on Privacy Protection in Federated Learning Combining Distillation Defense and Blockchain

Authors:
raw_author_name: Changxu Wan
raw_affiliation_string: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
raw_author_name: Jianbo Xu
raw_affiliation_string: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
raw_author_name: Junjie Wu
raw_affiliation_string: Quanzhou Institute of Information Engineering, Quanzhou 362000, China
raw_author_name: Tiantian Zhang
raw_affiliation_string: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
raw_author_name: Yulong Wang
raw_affiliation_string: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China

Abstract: Traditional federated learning addresses the data security issues arising from the need to centralize client datasets on a central server for model training. However, this approach still poses privacy protection risks. For instance, central servers cannot verify privacy leaks resulting from poisoning attacks by malicious clients. Additionally, adversarial sample attacks can infer specific samples from the original data by testing the local models on client devices. This paper proposes a federated learning privacy protection method combining distillation defense technology with blockchain architecture. The method utilizes distillation defense technology to reduce the sensitivity of client devices participating in federated learning to perturbations and enhance their ability to resist adversarial sample attacks locally. This not only reduces communication overhead and improves learning efficiency but also enhances the model’s generalization ability. Furthermore, the method leverages the “decentralized” nature of blockchain architecture as a trusted record-keeping mechanism to audit information interactions among clients and shared model parameters. This addresses privacy leakage issues resulting from poisoning attacks by some clients during the model construction process. Simulation experiment results demonstrate that the proposed method, compared with traditional federated learning, ensures model convergence, detects malicious clients, and improves the participation level of highly reputable clients. Moreover, by reducing the sensitivity of local clients to perturbations, it enhances their ability to effectively resist adversarial sample attacks.
Whole Slide Image Understanding in Pathology: What Is the Salient Scale of Analysis?

Authors:
raw_author_name: Ognjen Arandjelović
raw_affiliation_string: School of Computer Science, University of St Andrews, St Andrews KY16 9AJ, UK

Abstract: Background: In recent years, there has been increasing research in the applications of Artificial Intelligence in the medical industry. Digital pathology has seen great success in introducing the use of technology in the digitisation and analysis of pathology slides to ease the burden of work on pathologists. Digitised pathology slides, otherwise known as whole slide images, can be analysed by pathologists with the same methods used to analyse traditional glass slides. Methods: The digitisation of pathology slides has also led to the possibility of using these whole slide images to train machine learning models to detect tumours. Patch-based methods are common in the analysis of whole slide images as these images are too large to be processed using normal machine learning methods. However, there is little work exploring the effect that the size of the patches has on the analysis. A patch-based whole slide image analysis method was implemented and then used to evaluate and compare the accuracy of the analysis using patches of different sizes. In addition, two different patch sampling methods are used to test if the optimal patch size is the same for both methods, as well as a downsampling method where whole slide images of low resolution images are used to train an analysis model. Results: It was discovered that the most successful method uses a patch size of 256 × 256 pixels with the informed sampling method, using the location of tumour regions to sample a balanced dataset. Conclusion: Future work on batch-based analysis of whole slide images in pathology should take into account our findings when designing new models.
Learning Output Relevant Features by Joint Autoencoder

Authors:
raw_author_name: Xuefeng Yan
raw_affiliation_string: Ministry of Education, Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Shanghai, China

Abstract: Classical regression is a supervised task that uses target output to guide the modeling. Generally, the original input contains both output relevant and irrelevant information, whereas the latter may decrease the predictive performance to a certain extent. Without prior information about what information contributes to the prediction, the predictive performance of regression models can be improved based on such output relevant information as input. Thus, a joint autoencoder (JAE) combining the supervised and unsupervised mechanisms is proposed to learn output relevant features from the original input. In this way, both predictive and reconstructive performance are considered to learn the essential characteristics and avoid poor generalized performance on untrained instances. Meanwhile, hierarchy representations can be learned by successive JAEs with a local parameter embedding strategy, which is presented to preserve the predictive performance in this structure. In the experiments, the predictive performance and robustness of the proposal are verified on nine datasets with different sample sizes.
EDSUCh: A robust ensemble data summarization method for effective medical diagnosis

Authors:
raw_author_name: Mohiuddin Ahmed
raw_affiliation_string: School of Science, Edith Cowan University, Perth, Australia
raw_author_name: A.N.M. Bazlur Rashid
raw_affiliation_string: School of Science, Edith Cowan University, Perth, Australia

Abstract: Identifying rare patterns for medical diagnosis is a challenging task due to heterogeneity and the volume of data. Data summarization can create a concise version of the original data that can be used for effective diagnosis. In this paper, we propose an ensemble summarization method that combines clustering and sampling to create a summary of the original data to ensure the inclusion of rare patterns. To the best of our knowledge, there has been no such technique available to augment the performance of anomaly detection techniques and simultaneously increase the efficiency of medical diagnosis. The performance of popular anomaly detection algorithms increases significantly in terms of accuracy and computational complexity when the summaries are used. Therefore, the medical diagnosis becomes more effective, and our experimental results reflect that the combination of the proposed summarization scheme and all underlying algorithms used in this paper outperforms the most popular anomaly detection techniques.
SHT-based public auditing protocol with error tolerance in FDL-empowered IoVs

Authors:
raw_author_name: Kui Zhu
raw_affiliation_string: School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China
raw_author_name: Yongjun Ren
raw_affiliation_string: School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China
raw_author_name: Jian Shen
raw_affiliation_string: Peng Cheng Laboratory, Shenzhen, 518000, China; School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China
raw_author_name: P. Vijayakumar
raw_affiliation_string: University College of Engineering Tindivanam, Anna University, Tamil Nadu, 600025, India

Abstract: With the intelligentization of the Internet of Vehicles (IoVs), Artificial Intelligence (AI) technology is becoming more and more essential, especially deep learning. Federated Deep Learning (FDL) is a novel distributed machine learning technology and is able to address the challenges like data security, privacy risks, and huge communication overheads from big raw data sets. However, FDL can only guarantee data security and privacy among multiple clients during data training. If the data sets stored locally in clients are corrupted, including being tampered with and lost, the training results of the FDL in intelligent IoVs must be negatively affected. In this paper, we are the first to design a secure data auditing protocol to guarantee the integrity and availability of data sets in FDL-empowered IoVs. Specifically, the cuckoo filter and Reed-Solomon codes are utilized to guarantee error tolerance, including efficient corrupted data locating and recovery. In addition, a novel data structure, Skip Hash Table (SHT) is designed to optimize data dynamics. Finally, we illustrate the security of the scheme with the Computational Diffie-Hellman (CDH) assumption on bilinear groups. Sufficient theoretical analyses and performance evaluations demonstrate the security and efficiency of our scheme for data sets in FDL-empowered IoVs.
A Recognizable Expression Line Portrait Synthesis Method in Portrait Rendering Robot

Authors:
raw_author_name: Xin Ning
raw_affiliation_string: Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
raw_author_name: Lina Yu
raw_affiliation_string: Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
raw_author_name: Weijun Li
raw_affiliation_string: Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
raw_author_name: Liping Zhang
raw_affiliation_string: Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China

Abstract: An artistic line portrait robot can generate, process, and draw line portraits. Compared to real face images, line portraits lose some recognizable information. Maintaining recognizability during the process of expression edition of line portraits is an important challenge for artistic portrait robots. A recognizable expression line portrait synthesis method based on a triangle coordinate system (TCS) is proposed. First, based on public facial expression databases JAFFE, Oulu CASIA, RaFD, and Cohn-Kanade (CK), by studying the feature deviations between different expressions of the same person, an expression deformation constraint criterion (EDCC) that is conducive to maintaining recognizable features is proposed. Then, by comparing features between the source line portrait and reference expression portrait, the expression features are calculated. Finally, under the EDCC, based on expression features, a recognizable expression line portrait is generated through image topological deformation based on TCS. In addition, we can synthesize different degrees of expression line portraits. On the public face datasets (FHHQ, CelebA-HQ, and CK), we implemented qualitative and quantitative contrast experiments. Experimental results demonstrate that this method can automatically synthesize an expression line portrait with reference expression, where the expression degree of the reference expression is controllable, and the generated expression portrait still has high recognizability. The expression samples generated by the proposed method are used for face authentication on the CK dataset, and only 0.22% of the samples fail to pass the authentication.
Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection

Authors:
raw_author_name: Md Rabiul Awal
raw_affiliation_string: Department of Computer Science, University of Saskatchewan, Saskatoon, Canada
raw_author_name: Roy Ka-Wei Lee
raw_affiliation_string: Design and Artificial Intelligence, Singapore University of Technology and Design, Singapore, Singapore
raw_author_name: Eshaan Tanwar
raw_affiliation_string: College of Engineering, DTU-Delhi, Delhi, India
raw_author_name: Tanmay Garg
raw_affiliation_string: Department of Computer Science and Engineering, IIIT-Delhi, Delhi, India
raw_author_name: Tanmoy Chakraborty
raw_affiliation_string: Department of Electrical Engineering, IIT Delhi, Delhi, India

Abstract: Hate speech in social media is a growing phenomenon, and detecting such toxic content has recently gained significant traction in the research community. Existing studies have explored fine-tuning language models (LMs) to perform hate speech detection, and these solutions have yielded significant performance. However, most of these studies are limited to detecting hate speech only in English, neglecting the bulk of hateful content that is generated in other languages, particularly in low-resource languages. Developing a classifier that captures hate speech and nuances in a low-resource language with limited data is extremely challenging. To fill the research gap, we propose HateMAML, a model-agnostic meta-learning (MAML)-based framework that effectively performs hate speech detection in low-resource languages. HateMAML utilizes a self-supervision strategy to overcome the limitation of data scarcity and produces better LM initialization for fast adaptation to an unseen target language (i.e., cross-lingual transfer) or other hate speech datasets (i.e., domain generalization). Extensive experiments are conducted on five datasets across eight different low-resource languages. The results show that HateMAML outperforms the state-of-the-art baselines by more than 3% in the cross-domain multilingual transfer setting. We also conduct ablation studies to analyze the characteristics of HateMAML.