Reviewerly/exHarmony huggingface dataset:
An adaptive density clustering approach with multi-granularity fusion

Authors:
raw_author_name: Jiang Xie
raw_affiliation_string: Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China
raw_author_name: Lian Jiang
raw_affiliation_string: Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China
raw_author_name: Shuyin Xia
raw_affiliation_string: Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China
raw_author_name: Xuexin Xiang
raw_affiliation_string: Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China
raw_author_name: Guoyin Wang
raw_affiliation_string: Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China

Abstract: The real-world dataset exhibits diversity, incorporating instances with complex shapes and significant differences in density hierarchy, potentially disrupted by noise. However, most clustering algorithms typically rely on single-granularity fusion, requiring the pre-setting of global parameters for the entire dataset. Nevertheless, these global parameters may not adequately adapt to clusters with varying hierarchies or shapes, consequently reducing the clustering effectiveness. Therefore, we propose an adaptive density clustering approach with multi-granularity fusion. This approach characterizes the dataset with multi-granularity, forming natural granular-ball. After processing these natural granular-ball, overlapping ones are fused to yield the final clustering result. The entire approach adeptly identifies datasets with significant differences in shape or density hierarchy and exhibits a certain level of robustness. All codes have been released at https://github.com/xjnine/NGBC.
STEFF: Spatio-temporal EfficientNet for dynamic texture classification in outdoor scenes

Authors:
raw_author_name: Kaoutar Mouhcine
raw_affiliation_string: MECAtronique Team, CPS2E Laboratory, National Superior School of Mines Rabat, 10080, Morocco
raw_author_name: Nabila Zrira
raw_affiliation_string: ADOS Team, LISTD Laboratory, National Superior School of Mines Rabat, 10080, Morocco
raw_author_name: Issam Elafi
raw_affiliation_string: Laboratory of Conception and Systems (Electronics, Signals, and Informatics), Faculty of Science, Mohammed V University, Rabat, 10102, Morocco
raw_author_name: Ibtissam Benmiloud
raw_affiliation_string: MECAtronique Team, CPS2E Laboratory, National Superior School of Mines Rabat, 10080, Morocco
raw_author_name: Haris Ahmad Khan
raw_affiliation_string: Agricultural Biosystems Engineering Group, Wageningen University & Research, Wageningen, the Netherlands; Data Science, Crop Protection Development, Syngenta, the Netherlands

Abstract: <h2>Abstract</h2> In recent years, dynamic texture classification has become an important task for computer vision. This is a challenging task due to the unknown spatial and temporal nature of dynamic texture. To overcome this challenge, we investigate the potential of deep learning approaches and propose a novel spatio-temporal approach (STEFF) for dynamic texture classification that combines the representation power of motion and appearance using the difference and average operators between video sequences. In this work, we extract deep texture features from outdoor scenes and integrate both spatial and temporal features into a pre-trained Convolutional Neural Network model, namely EfficientNet, with a fine-tuning and regularization process. The robustness of the proposed approach is reflected in the promising result when comparing our method to the proposed architectures and other existing models. The experimental results on three datasets demonstrate the effectiveness and efficiency of the proposed approach. The accuracy percentages are 95.95%, 94.09%, and 98.01% on the outdoor scenes of Yupenn, DynTex++, and Yupenn++ datasets, respectively.
Ensemble Prediction Method Based on Decomposition–Reconstitution–Integration for COVID-19 Outbreak Prediction

Authors:
raw_author_name: Wenhui Ke
raw_affiliation_string: Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, National Engineering Research Centre of Geospatial Information Technology, Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350116, China
raw_author_name: Yimin Lu
raw_affiliation_string: Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, National Engineering Research Centre of Geospatial Information Technology, Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350116, China

Abstract: Due to the non-linear and non-stationary nature of daily new 2019 coronavirus disease (COVID-19) case time series, existing prediction methods struggle to accurately forecast the number of daily new cases. To address this problem, a hybrid prediction framework is proposed in this study, which combines ensemble empirical mode decomposition (EEMD), fuzzy entropy (FE) reconstruction, and a CNN-LSTM-ATT hybrid network model. This new framework, named EEMD-FE-CNN-LSTM-ATT, is applied to predict the number of daily new COVID-19 cases. This study focuses on the daily new case dataset from the United States as the research subject to validate the feasibility of the proposed prediction framework. The results show that EEMD-FE-CNN-LSTM-ATT outperforms other baseline models in all evaluation metrics, demonstrating its efficacy in handling the non-linear and non-stationary epidemic time series. Furthermore, the generalizability of the proposed hybrid framework is validated on datasets from France and Russia. The proposed hybrid framework offers a new approach for predicting the COVID-19 pandemic, providing important technical support for future infectious disease forecasting.
Privacy-Preserving Multi-Party Cross-Chain Transaction Protocols

Authors:
raw_author_name: Chang Chen
raw_affiliation_string: Institute of Artificial Intelligence, Guangzhou University, Guangzhou 511370, China
raw_author_name: Zhihao Li
raw_affiliation_string: Institute of Artificial Intelligence, Guangzhou University, Guangzhou 511370, China
raw_author_name: Fuan Xiao
raw_affiliation_string: Institute of Artificial Intelligence, Guangzhou University, Guangzhou 511370, China
raw_author_name: Qi Chen
raw_affiliation_string: Institute of Artificial Intelligence, Guangzhou University, Guangzhou 511370, China
raw_author_name: Jin Li
raw_affiliation_string: Institute of Artificial Intelligence, Guangzhou University, Guangzhou 511370, China

Abstract: Cross-chain transaction technologies have greatly promoted the scalability of cryptocurrencies, which then facilitates the development of Metaverse applications. However, existing solutions rely heavily on centralized middleware (notary) or smart contracts. These schemes lack privacy considerations, and users’ cross-chain transactions are easy to master by other parties. Some signature-based payment schemes have good privacy but do not support multi-party cross-chain protocols or rely heavily on some time assumptions. The uncertainty of user behavior makes it difficult to design a secure multi-party cross-chain protocol. To solve these problems, we investigate how to design a secure multi-party cross-chain transaction protocol with offline tolerance. We propose a new signature algorithm called the pre-adaptor signature scheme, an extension of the adaptor signature scheme. The pre-adaptor signature scheme combines the multi-signature and adaptor signature schemes, which can realize the secret transmission channel between multiple parties. To provide offline tolerance, we encode our protocol into the P2SH script. Our protocol provides better privacy due to no dependence on smart contracts. The performance evaluation was conducted with ten participants. For each participant of our cross-chain protocol, the initialization and execution process can be performed in 3 milliseconds and with 6 k bytes of communication overhead at most. The cost increases linearly with the increase in the number of participants.
Scattering expansion for localization in one dimension: From disordered wires to quantum walks

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. We apply this expansion to several examples, including the Anderson model, a general class of periodic-on-average-random potentials, and a two-component discrete-time quantum walk, showing analytically in the latter case that 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.
“What action should l take?”: Help-seeking behaviours of those targeted by romance fraud

Authors:
raw_author_name: Cassandra Cross
raw_affiliation_string: School of Justice, Queensland University of Technology

Abstract: Fraud uses deception to obtain a financial reward from its victim. There are strong negative stereotypes and victim blaming discourses towards fraud victims, with research indicating that fraud victims are unlikely to report incidents for many reasons, including a high degree of shame and stigmatisation. In this way, it can be assumed that fraud victims are averse to seeking help in the aftermath of an incident. Drawing from romance fraud reports lodged with Scamwatch (an Australian online reporting portal for fraud) between 1 July 2018 and 31 July 2019, this article examines the help-seeking behaviours of those targeted by romance fraud. The analysis found a variety of explicit and implicit help-seeking behaviours, encompassing both personal justice and altruistic reasons. The analysis demonstrates that despite the victim blaming that exists, this did not always act as a barrier to reporting or requesting help. Overall, this article challenges some of the perceived aversity to help-seeking behaviour of fraud victims. It also provides a foundation for improving responses and support to those who actively engage in help-seeking behaviour.
Ultra-early medical treatment-oriented system identification using High-Dimension Low-Sample-Size data

Authors:
raw_author_name: Xun Shen
raw_affiliation_string: Department of Systems and Control Engineering, Tokyo Institute of Technology, Tokyo, Japan; Graduate School of Engineering, Osaka University, Osaka, Japan
raw_author_name: Hampei Sasahara
raw_affiliation_string: Department of Systems and Control Engineering, Tokyo Institute of Technology, Tokyo, Japan

Abstract: Ultra-early detection of diseases with High-Dimension Low-Sample-Size (HDLSS) data has been effectively addressed by the Dynamical Network Biomarkers (DNBs) theory. After ultra-early detection, it is crucial to consider ultra-early medical treatment for the detected disease. From the viewpoint of control engineering, ultra-early medical treatment is achieved by increasing the system’s stability and preventing the bifurcation, called re-stabilization. To implement effective re-stabilization, the system matrix is necessary. However, the available data in biological systems are often HDLSS, which is insufficient to identify the system matrix. In this paper, to realize HDLSS-based ultra-early medical treatment, we investigate an HDLSS data-based system matrix estimation method. First, HDLSS data is applied to compute the sample covariance matrix of the steady state. By assuming that the system matrix is sparse and the structure of the system matrix is known, it can utilize the Lyapunov equation to estimate the system matrix from the covariance matrix. The Lyapunov equation-based method gives a unique optimal estimation if the covariance matrix is full-rank. Otherwise, the optimal estimation is not unique. The sample covariance matrix computed from the HDLSS data is not full-rank. Thus, we apply shrinkage estimation to overcome the under-determined issue to obtain a well-conditioned covariance matrix with full rank. In addition, we confirm the effectiveness of the proposed method through numerical simulations.
Goal-conditioned offline reinforcement learning through state space partitioning

Authors:
raw_author_name: Yue Jin
raw_affiliation_string: University of Warwick, Coventry, UK
raw_author_name: Giovanni Montana
raw_affiliation_string: University of Warwick, Coventry, UK

Abstract: Abstract Offline reinforcement learning (RL) aims to create policies for sequential decision-making using exclusively offline datasets. This presents a significant challenge, especially when attempting to accomplish multiple distinct goals or outcomes within a given scenario while receiving sparse rewards. Prior methods using advantage weighting for offline goal-conditioned learning improve policies monotonically. However, they still face challenges from distribution shift and multi-modality that arise due to conflicting ways to reach a goal. This issue is especially challenging in long-horizon tasks, where the presence of multiple, often conflicting, solutions makes it hard to identify a single optimal policy for transitioning from a state to a desired goal. To address these challenges, we introduce a complementary advantage-based weighting scheme that incorporates an additional source of inductive bias. Given a value-based partitioning of the state space, the contribution of actions expected to lead to target regions that are easier to reach, compared to the final goal, is further increased. Our proposed approach, Dual-Advantage Weighted Offline Goal-conditioned RL, outperforms several competing offline algorithms in widely used benchmarks. Furthermore, we provide a theoretical guarantee that the learned policy will not be inferior to the underlying behavior policy.
Face Forgery Detection with Long-Range Noise Features and Multilevel Frequency-Aware Clues

Authors:
raw_author_name: Xin Jin
raw_affiliation_string: The Engineering Research Center of Cyberspace and the School of Software, Yunnan University, Kunming, Yunnan 650091, China
raw_author_name: Song Gao
raw_affiliation_string: The Engineering Research Center of Cyberspace and the School of Software, Yunnan University, Kunming, Yunnan 650091, China
raw_author_name: Liwen Wu
raw_affiliation_string: The Engineering Research Center of Cyberspace and the School of Software, Yunnan University, Kunming, Yunnan 650091, China
raw_author_name: Shaowen Yao
raw_affiliation_string: The Engineering Research Center of Cyberspace and the School of Software, Yunnan University, Kunming, Yunnan 650091, China
raw_author_name: Qian Jiang
raw_affiliation_string: The Engineering Research Center of Cyberspace and the School of Software, Yunnan University, Kunming, Yunnan 650091, China

Abstract: The widespread dissemination of high-fidelity fake faces created by face forgery techniques has caused serious trust concerns and ethical issues in modern society. Consequently, face forgery detection has emerged as a prominent topic of research to prevent technology abuse. Although, most existing face forgery detectors demonstrate success when evaluating high-quality faces under intra-dataset scenarios, they often overfit manipulation-specific artifacts and lack robustness to postprocessing operations. In this work, we design an innovative dual-branch collaboration framework that leverages the strengths of the transformer and CNN to thoroughly dig into the multimodal forgery artifacts from both a global and local perspective. Specifically, a novel adaptive noise trace enhancement module (ANTEM) is proposed to remove high-level face content while amplifying more generalized forgery artifacts in the noise domain. Then, the transformer-based branch can track long-range noise features. Meanwhile, considering that subtle forgery artifacts could be described in the frequency domain even in a compression scenario, a multilevel frequency-aware module (MFAM) is developed and further applied to the CNN-based branch to extract complementary frequency-aware clues. Besides, we incorporate a collaboration strategy involving cross-entropy loss and single center loss to enhance the learning of more generalized representations by optimizing the fusion features of the dual branch. Extensive experiments on various benchmark datasets substantiate the superior generalization and robustness of our framework when compared to the competing approaches.
An oscillatory particle swarm optimization feature selection algorithm for hybrid data based on mutual information entropy

Authors:
raw_author_name: Liangdong Qu
raw_affiliation_string: School of Artificial Intelligence, Guangxi Minzu University, Nanning, Guangxi 530006, PR China
raw_author_name: Pei Wang
raw_affiliation_string: Center for Applied Mathematics of Guangxi, Key Laboratory of Complex System Optimization and Big Data Processing in Department of Guangxi Education, Yulin Normal University, Yulin, Guangxi 537000, PR China
raw_author_name: Zhaowen Li
raw_affiliation_string: Fujian Key Laboratory of Financial Information Processing, Key Laboratory of Applied Mathematics in Fujian Province University, Putian University, Putian, Fujian 351100, PR China

Abstract: Hybrid data lead to overfitting in machine learning models, which may reduce the accuracy of classification. Feature selection can not only reduce the computational cost of processing hybrid data but also improve the accuracy of classification. The particle swarm optimization (PSO) algorithm has clear advantages in feature selection. This paper presents an oscillatory particle swarm optimization feature selection algorithm for hybrid data based on mutual information entropy. First, a new distance function on the object set of a hybrid information system (HIS) is built, which yields a tolerance relation on this object set. Then, mutual information entropy is presented to measure the uncertainty of the HIS. On this basis, the maximum-relevance and minimal-redundancy model (MRMR model) for the HIS is proposed. Based on the MRMR model, a feature selection algorithm (denoted as MRMR) for the HIS is naturally designed. As the integration of the MRMR model and PSO can effectively explore all possible feature subsets, an oscillatory particle swarm optimization algorithm based on the MRMR model (denoted as OPSO-MRMR) for the HIS is also designed. Moreover, the MRMR model is utilized to define a fitness function that evaluates the quality of particles. The particle position update process is modified by means of a two-order oscillatory equation. Finally, an experimental analysis is conducted to compare the two designed algorithms with five other algorithms. The statistical analysis of classification accuracy and F1 score shows that OPSO-MRMR improves precision by 5.8% and 10.7% compared to the other six algorithms, respectively.
Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment

Authors:
raw_author_name: Shibbir Ahmed
raw_affiliation_string: Department of Computer Science, Iowa State University, Ames, IA, USA
raw_author_name: Hridesh Rajan
raw_affiliation_string: Department of Computer Science, Iowa State University, Ames, IA, USA

Abstract: Deep learning models are trained with certain assumptions about the data during the development stage and then used for prediction in the deployment stage. It is important to reason about the trustworthiness of the model's predictions with unseen data during deployment. Existing methods for specifying and verifying traditional software are insufficient for this task, as they cannot handle the complexity of DNN model architecture and expected outcomes. In this work, we propose a novel technique that uses rules derived from neural network computations to infer data preconditions for a DNN model to determine the trustworthiness of its predictions. Our approach, DeepInfer involves introducing a novel abstraction for a trained DNN model that enables weakest precondition reasoning using Dijkstra's Predicate Transformer Semantics. By deriving rules over the inductive type of neural network abstract representation, we can overcome the matrix dimensionality issues that arise from the backward non-linear computation from the output layer to the input layer. We utilize the weakest precondition computation using rules of each kind of activation function to compute layer-wise precondition from the given postcondition on the final output of a deep neural network. We extensively evaluated DeepInfer on 29 real-world DNN models using four different datasets collected from five different sources and demonstrated the utility, effectiveness, and performance improvement over closely related work. DeepInfer efficiently detects correct and incorrect predictions of high-accuracy models with high recall (0.98) and high F-1 score (0.84) and has significantly improved over the prior technique, SelfChecker. The average runtime overhead of DeepInfer is low, 0.22 sec for all the unseen datasets. We also compared runtime overhead using the same hardware settings and found that DeepInfer is 3.27 times faster than SelfChecker, the state-of-the-art in this area.
On the Helpfulness of Answering Developer Questions on Discord with Similar Conversations and Posts from the Past

Authors:
raw_author_name: Alexander Lill
raw_affiliation_string: Department of Informatics, University of Zurich, Zurich, Switzerland
raw_author_name: André N. Meyer
raw_affiliation_string: Department of Informatics, University of Zurich, Zurich, Switzerland
raw_author_name: Thomas Fritz
raw_affiliation_string: Department of Informatics, University of Zurich, Zurich, Switzerland

Abstract: A big part of software developers' time is spent finding answers to their coding-task-related questions. To answer their questions, developers usually perform web searches, ask questions on Q&A websites, or, more recently, in chat communities. Yet, many of these questions have frequently already been answered in previous chat conversations or other online communities. Automatically identifying and then suggesting these previous answers to the askers could, thus, save time and effort. In an empirical analysis, we first explored the frequency of repeating questions on the Discord chat platform and assessed our approach to identify them automatically. The approach was then evaluated with real-world developers in a field experiment, through which we received 142 ratings on the helpfulness of the suggestions we provided to help answer 277 questions that developers posted in four Discord communities. We further collected qualitative feedback through 53 surveys and 10 follow-up interviews. We found that the suggestions were considered helpful in 40% of the cases, that suggesting Stack Overflow posts is more often considered helpful than past Discord conversations, and that developers have difficulties describing their problems as search queries and, thus, prefer describing them as natural language questions in online communities.