Reviewerly/exHarmony huggingface dataset:
SHRIMPS: A framework for evaluating multi-user, multi-modal implicit authentication systems

Authors:
raw_author_name: Urs Hengartner
raw_affiliation_string: University of Waterloo, 200 University Ave W., Waterloo, N2L 3G1, ON, Canada
raw_author_name: Hassan Khan
raw_affiliation_string: University of Guelph, 50 Stone Road East, Guelph, N1G 2W1, ON, Canada

Abstract: Smart devices are commonly used in multi-user scenarios, such as shared household devices and shared corporate devices for front-line workers. A multi-user device requires both identification and authentication to defend against unauthorized access and distinguish between legitimate users in real-time, especially when multiple users participate in the same session. Although implicit authentication (IA) has been proposed to provide continuous and transparent authentication throughout a session, most existing IA solutions are optimized for single-user scenarios. The challenges of designing multi-user IA systems include fusing multiple modalities for good accuracy, segmenting and labeling behavioral data while authenticating, and adapting IA models to new users and new incoming data. We propose SHRIMPS, an evaluation framework to support IA researchers in the design of multi-user, multi-modal IA systems. SHRIMPS allows the evaluation of multi-user IA solutions that incorporate multiple modalities and supports adding new users and automatically labeling new incoming data for model updating. SHRIMPS supports different score fusion strategies, including a novel score fusion strategy based on Dempster-Shafer (D-S) theory to improve accuracy with considering uncertainties among different IA mechanisms. SHRIMPS enables composing tasks with public datasets to evaluate and compare different IA schemes. We present and evaluate two sample use cases to showcase how SHRIMPS helps address practical design questions of multi-user, multi-modal IA systems. The evaluation results show that D-S theory based score fusion methods can effectively reject attackers and detect user switches for the multi-user scenario in real-time.
How memory anxiety can influence password security behavior

Authors:
raw_author_name: Naomi Woods
raw_affiliation_string: University of Jyväskylä, Faculty of Information Technology, Agora, Mattilanniemi 2, 40100 Jyväskylä, Finland
raw_author_name: Mikko Siponen
raw_affiliation_string: University of Jyväskylä, Faculty of Information Technology, Agora, Mattilanniemi 2, 40100 Jyväskylä, Finland

Abstract: Password reuse and modification are insecure password behaviors that are becoming increasingly prevalent as users are obliged to remember more passwords to access various digital services. Many users adopt these risky behaviors as a memory strategy in the belief that they have too many passwords for their memories to cope with. One important avenue in password research is metamemory, which encompasses the knowledge and understanding of memory capabilities and strategies. Previous research on password metamemory has examined the role that metamemory plays in memory performance (i.e., how well memory performs) and password recall. However, no previous research to date has investigated whether password reuse and modification are adopted as memory strategies due to an increase in knowledge and understanding of metamemory. To address this gap, two survey studies (Study1: N=50, Study 2: N=303) were implemented to examine the role that password metamemory plays in reusing and modifying passwords. Our findings suggest that of all metamemory constructs, users’ anxiety regarding their perceived ability to remember passwords can influence them to reuse and modify their passwords. These findings have potentially important implications because with an enhanced understanding of how users’ anxiety towards remembering passwords influences their security behavior, this could identify means of reducing password reuse and modification, thereby increasing password security and ultimately reduce some of the consequences of insecure password behaviors.
Learning transferable targeted universal adversarial perturbations by sequential meta-learning

Authors:
raw_author_name: Juanjuan Weng
raw_affiliation_string: Department of Artificial Intelligence, Xiamen University, Xiamen 361005, China
raw_author_name: Zhiming Luo
raw_affiliation_string: Department of Artificial Intelligence, Xiamen University, Xiamen 361005, China
raw_author_name: Dazhen Lin
raw_affiliation_string: Department of Artificial Intelligence, Xiamen University, Xiamen 361005, China
raw_author_name: Shaozi Li
raw_affiliation_string: Department of Artificial Intelligence, Xiamen University, Xiamen 361005, China; Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry, Wuyi University, WuyiShan 354300, China

Abstract: Recently, the transferability of adversarial perturbations in non-targeted scenarios has been extensively studied. However, changing the predictions of an unknown model to a pre-defined ‘targeted’ class still remains challenging. In this study, we aim to learn the targeted universal adversarial perturbations (UAPs) with higher transferability by the ensemble of multiple models. First, we observe the phenomenon that the logit of the target class will bias to a specific white-box model in existing ensemble-based attacks. To deal with the issue, we propose a normalized logit loss to narrow the margin of the targeted class's logits among different models. Besides, we introduce a novel sequential meta-learning optimization strategy to further increase transferability, consisting of the inner loop and the outer loop. In the inner loop, we sequentially learn task-specific targeted UAPs for each source model by jointly considering the perturbation from the previous model. In the outer loop, we optimize the task-agnostic targeted UAP by combining the targeted UAPs from the inner loop. Experimental results demonstrate the mutual benefits of the normalized logit loss and the sequential meta-learning optimization strategy for learning targeted adversarial perturbations, outperforming existing ensemble attacks in both white-box and black-box settings. The source code of this study is available at: Link.
Computational complexity of jumping block puzzles

Authors:
raw_author_name: Masaaki Kanzaki
raw_affiliation_string: Third System Department, SI Division, JMAC SOFT Co., Ltd. Johnai, Nagaoka, Niigata 940-0061, Japan
raw_author_name: Yota Otachi
raw_affiliation_string: Department of Mathematical Informatics, Graduate School of Informatics, Nagoya University, Furocho, Chikusa-ku, Nagoya, Aichi 464-8601, Japan
raw_author_name: Giovanni Viglietta
raw_affiliation_string: Department of Computer Science and Engineering, University of Aizu, Aizu Wakamatsu, Fukushima 965-8580, Japan
raw_author_name: Ryuhei Uehara
raw_affiliation_string: School of Information Science, Japan Advanced Institute of Science and Technology, Asahidai, Nomi, Ishikawa 923-1292, Japan

Abstract: In the context of computational complexity of puzzles, the sliding block puzzles play an important role. Depending on the rules and set of pieces, the sliding block puzzles can be polynomial-time solvable, NP-complete, or PSPACE-complete. On the other hand, a relatively new notion of jumping block puzzles has been proposed in the puzzle community. This is a counterpart to the token jumping model of the combinatorial reconfiguration problems in the context of block puzzles. We investigate some variants of jumping block puzzles, which are based on actual puzzles, and a natural model from the viewpoint of combinatorial reconfiguration, and determine their computational complexities. More precisely, we investigate two generalizations of two actual puzzles which are called Flip Over puzzles and Flying Block puzzles and one natural model of jumping block puzzles from the viewpoint of combinatorial reconfiguration. We prove that they are PSPACE-complete in general. We also prove the NP-completeness of these puzzles in some restricted cases, and we give polynomial-time algorithms for some restricted cases.
Robust Graph Meta-Learning for Weakly Supervised Few-Shot Node Classification

Authors:
raw_author_name: Kaize Ding
raw_affiliation_string: Northwestern University, USA
raw_author_name: Jianling Wang
raw_affiliation_string: Texas A&amp;M University, USA
raw_author_name: Jundong Li
raw_affiliation_string: University of Virginia, USA
raw_author_name: James Caverlee
raw_affiliation_string: Texas A&amp;M University, USA
raw_author_name: Huan Liu
raw_affiliation_string: Arizona State University, USA

Abstract: Graph machine learning (Graph ML) models typically require abundant labeled instances to provide sufficient supervision signals, which is commonly infeasible in real-world scenarios since labeled data for newly emerged concepts (e.g., new categorizations of nodes) on graphs is rather limited. To efficiently learn with a small amount of data on graphs, meta-learning has been investigated in Graph ML. By transferring the knowledge learned from previous experiences to new tasks, graph meta-learning approaches have demonstrated promising performance on few-shot graph learning problems. However, most existing efforts predominately assume that all the data from the seen classes is gold labeled, yet those methods may lose their efficacy when the seen data is weakly labeled with severe label noise. As such, we aim to investigate a novel problem of weakly supervised graph meta-learning for improving the model robustness in terms of knowledge transfer. To achieve this goal, we propose Meta-GIN (Meta Graph Interpolation Network), a new graph meta-learning framework. Based on a new robustness-enhanced episodic training paradigm, Meta-GIN is meta-learned to interpolate node representations from weakly labeled data and extracts highly transferable meta-knowledge, which enables the model to quickly adapt to unseen tasks with few labeled instances. Extensive experiments demonstrate the superiority of Meta-GIN over existing graph meta-learning studies on the task of weakly supervised few-shot node classification.
Improving domain-specific neural code generation with few-shot meta-learning

Authors:
raw_author_name: Zhen Yang
raw_affiliation_string: School of Computer Science and Technology, Shandong University, Qingdao, China
raw_author_name: Jacky Wai Keung
raw_affiliation_string: Department of Computer Science, City University of Hong Kong, Hong Kong, China
raw_author_name: Zeyu Sun
raw_affiliation_string: Zhongguancun Laboratory, Beijing, China
raw_author_name: Yunfei Zhao
raw_affiliation_string: Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of Education, Beijing, China
raw_author_name: Ge Li
raw_affiliation_string: Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of Education, Beijing, China
raw_author_name: Zhi Jin
raw_affiliation_string: Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of Education, Beijing, China
raw_author_name: Yishu Li
raw_affiliation_string: Department of Computer Science, City University of Hong Kong, Hong Kong, China

Abstract: Neural code generation aims to automatically generate code snippets guided by Natural Language Descriptions (NLDs). In recent years, various neural code generation models for mainstream Programming Languages (PLs), such as Java and Python, have been proposed and demonostrated significant success in prior studies. Nonetheless, due to the scarcity of available training examples for some domain-specific PLs, such as Solidity, Bash, and Clojure, simply adopting previous neural models may lead to overfitting and inadequate learning. To overcome this challenge, we propose MetaCoder, a novel meta-learning code generation approach that efficiently extracts general-purpose knowledge from a large-scale source language and rapidly adapts to domain-specific scenarios, even with relatively few samples. MetaCoder employs MAML, a powerful few-shot meta-learning method, to construct a transfer learning framework. This framework learns general-purpose knowledge from large-scale source languages and applies it in domain-specific target languages. To acquire more general-purpose knowledge, heterogeneous sub-tasks are constructed from the source language during the pre-training phase of MAML. As such, combining with CodeBERT and K-means, we design an unsupervised category assignment method for code generation samples, thereby exploiting the n-way k-shot rule to construct the heterogeneous sub-tasks. Consequently, MetaCoder can be applied to the code generation field. We evaluate MetaCoder with both tree-based (e.g., TreeGen) and sequence-based (e.g., CodeGPT) backbones on two domain-specific PLs, including Solidity and Bash. Extensive experiments demonstrate the superior performance of our approach compared to baselines and verified its capability of code generation visually in practice. MetaCoder effectively extracts general-purpose knowledge from large-scale source languages, thereby enhancing model performance. Therefore, we highly recommend MetaCoder as a code generation approach for domain-specific PLs.
Enhanced sparrow search algorithm based on improved game predatory mechanism and its application

Authors:
raw_author_name: Jiahui Yang
raw_affiliation_string: School of Automation, Northwestern Polytechnical University, Xi’ an 710072, China
raw_author_name: Shesheng Gao
raw_affiliation_string: School of Automation, Northwestern Polytechnical University, Xi’ an 710072, China
raw_author_name: Xuehua Zhao
raw_affiliation_string: School of Automation, Northwestern Polytechnical University, Xi’ an 710072, China
raw_author_name: Zhaohui Gao
raw_affiliation_string: School of Electronic Engineering, Xi'an Shiyou University, Xi'an 710065, China

Abstract: In response to the problems of strong randomness in initialization, imperfect search mechanism and tendency to fall into local optimum when dealing with complex optimization problems by sparrow search algorithm (SSA), an enhanced sparrow search algorithm (ESSA) is proposed, which combines Hammersley low difference sequence (HLDS) and centroid opposition-based learning (COBL), improved game predatory mechanism (GPM) and elastic collision strategy (EC). The ESSA can improve the initial individual quality, and avoid the attraction of local optima. In addition, the ESSA can effectively improve the search mechanism by increasing information exchange of individuals and balancing global search and local development. The effectiveness of ESSA is verified through simulation experiments of benchmark functions and variational modal decomposition (VMD) parameter optimization.
High-quality 3D shape recovery from scattering scenario via deep polarization neural networks

Authors:
raw_author_name: Pingli Han
raw_affiliation_string: School of Optoelectronic Engineering, Xidian University, Xi'an 710071, China
raw_author_name: Rui Gong
raw_affiliation_string: School of Optoelectronic Engineering, Xidian University, Xi'an 710071, China
raw_author_name: Jinpeng Liu
raw_affiliation_string: School of Optoelectronic Engineering, Xidian University, Xi'an 710071, China
raw_author_name: Zhao Fan
raw_affiliation_string: School of Optoelectronic Engineering, Xidian University, Xi'an 710071, China
raw_author_name: Fei Liu
raw_affiliation_string: School of Optoelectronic Engineering, Xidian University, Xi'an 710071, China
raw_author_name: Bingjian Wang
raw_affiliation_string: School of Physical, Xidian University, Xi'an 710071, China
raw_author_name: Xiaopeng Shao
raw_affiliation_string: School of Optoelectronic Engineering, Xidian University, Xi'an 710071, China

Abstract: High-quality three-dimension(3D) shape recovery from scattering scenario is of great importance with various potential applications. However, in turbid water scenarios, the absorption and scattering by water and suspending particles have a great disturbance on the generation of image clarity, texture details, and pixel-wise polarized properties such as degree of polarization (DoP) and angle of polarization (AoP), which significantly affect the 3D reconstructed performance. In this study, we present a data-driven polarimetric method for 3D reconstruction of underwater objects in turbid scenes, leveraging deep learning image priors. An underwater descattering network is designed to fit the underwater transport function of target signal light and generate clear underwater images, which are utilized to enhance the texture and remove the backscattering light. Subsequently, accurate pixel-wise DoP and AoP of the object could be calculated using the clear image prior and underwater imaging model. Finally, utilizing the obtained clear image and polarimetric information, polarimetric 3D reconstruction of object in turbid underwater scenarios is achieved. Experimental results demonstrate that accurate 3D shape of objects in turbid water can be well-reconstructed. The proposed technique has great potential for advancing marine exploration research.
Few-shot learning-based human behavior recognition model

Authors:
raw_author_name: V. Mahalakshmi
raw_affiliation_string: Department of Computer Science, College of Computer Science & Information Technology, Jazan University, Jazan, Saudi Arabia
raw_author_name: Mukta Sandhu
raw_affiliation_string: SVSU, Pawal, India
raw_author_name: Mohammad Shabaz
raw_affiliation_string: Model Institute of Engineering and Technology, Jammu, J&K, India
raw_author_name: Haewon Byeon
raw_affiliation_string: Department of Digital Anti-Aging Healthcare, Inje University, Gimhae, 50834, Republic of Korea
raw_author_name: Mukesh Soni
raw_affiliation_string: Department of CSE, University Centre for Research & Development, Chandigarh University, Mohali, Punjab, 140413, India

Abstract: The challenges of human behavior recognition based on sensor data often require addressing the needs of various new users in real-world situations, which leads to difficulties in personalizing user data and resolving concerns related to resolution. This relates to the fact that persons with different characteristics who engage in the same behavior inevitably provide varying data discrepancies. Models that are faced with unfamiliar users encounter difficulties in accurately recognizing expected behavior, and it is not practicable to gather a large amount of training data every time the model needs to be reconfigured for new users. This study introduces a novel approach known as the FSLBR model, which combines the domains of few-shot learning and behavior recognition algorithms. Initially, a meta-learning technique focused on optimization is employed to categories datasets according to user categories. The paradigm of few-shot learning has shown significant effectiveness by leveraging a limited amount of data for new tasks. The FSLBR method and an attention-centered Memory module are then smoothly integrated into the FSLBR model. In the field of behavior recognition, it is observed that a small selection of data is sufficient for good classification when dealing with new users. The capacity of the model network to extract and extrapolate data features is improved. The empirical experiments conducted on the MEx dataset support the assertion that the proposed FSLBR model demonstrates improved performance under the few-shot learning framework compared to standard deep learning methods.
Multi-Stage Asynchronous Federated Learning With Adaptive Differential Privacy

Authors:
raw_author_name: Shusen Yang
raw_affiliation_string: National Engineering Laboratory for Big Data Analytics (NEL-BDA), Xi&#x2019;an Jiaotong University, Shaanxi, China
raw_author_name: Xuebin Ren
raw_affiliation_string: National Engineering Laboratory for Big Data Analytics (NEL-BDA), Xi&#x2019;an Jiaotong University, Shaanxi, China
raw_author_name: Liang Shi
raw_affiliation_string: National Engineering Laboratory for Big Data Analytics (NEL-BDA), Xi&#x2019;an Jiaotong University, Shaanxi, China
raw_author_name: Cong Zhao
raw_affiliation_string: National Engineering Laboratory for Big Data Analytics (NEL-BDA), Xi&#x2019;an Jiaotong University, Shaanxi, China

Abstract: The fusion of federated learning and differential privacy can provide more comprehensive and rigorous privacy protection, thus attracting extensive interests from both academia and industry. However, facing the system-level challenge of device heterogeneity, most current synchronous FL paradigms exhibit low efficiency due to the straggler effect, which can be significantly reduced by Asynchronous FL (AFL). However, AFL has never been comprehensively studied, which imposes a major challenge in the utility optimization of DP-enhanced AFL. Here, theoretically motivated multi-stage adaptive private algorithms are proposed to improve the trade-off between model utility and privacy for DP-enhanced AFL. In particular, we first build two DP-enhanced AFL frameworks with consideration of universal factors for different adversary models. Then, we give a solid analysis on the model convergence of AFL, based on which, DP can be adaptively achieved with high utility. Through extensive experiments on different training models and benchmark datasets, we demonstrate that the proposed algorithms achieve the overall best performances and improve up to 24% test accuracy with the same privacy loss and have faster convergence compared with the state-of-the-art algorithms. Our frameworks provide an analytical way for private AFL and adapt to more complex FL application scenarios.
Neural Network-Based Method for Orbit Uncertainty Propagation and Estimation

Authors:
raw_author_name: Xingyu Zhou
raw_affiliation_string: School of Aerospace Engineering, Beijing Institute of Technology, Beijing, China
raw_author_name: Xiangyu Li
raw_affiliation_string: School of Aerospace Engineering, Beijing Institute of Technology, Beijing, China

Abstract: This paper proposes a fast method for orbit uncertainty propagation and estimation. The proposed method is based on an orbit deviation propagation approach, which consists of an analytical two-body deviation propagation solution and a deep neural network (DNN) to compensate for the errors between the two-body and the true solutions. First, five types of sample forms for training the DNN are investigated, and the optimal one is selected through learning feature and training performance analyses. Then, an uncertainty propagation solution for propagating the mean and covariance is formulated by combining the DNN-based deviation propagation approach with an unscented transformation process. Finally, a more efficient version of the unscented Kalman filter (UKF) for orbit estimation is developed based on the formulated uncertainty propagation solution. The advantage of the proposed DNN-based method is that it avoids the integration of the state transition matrix or dozens of sigma points. The performance of the proposed method is investigated on a low-Earth-orbit example. Numerical results show that the proposed DNN-based estimation method can be one order of magnitude faster than the UKF and is comparable to the UKF in estimation accuracy. In addition, it estimates more accurately than the extended Kalman filter (EKF) and is approximately 10% faster than the EKF.