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

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

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

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

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

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

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

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

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

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

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

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.
On Helly numbers of exponential lattices

Given a set S⊆R2, define the Helly number of S, denoted by H(S), as the smallest positive integer N, if it exists, for which the following statement is true: for any finite family F of convex sets in R2 such that the intersection of any N or fewer members of F contains at least one point of S, there is a point of S common to all members of F. We prove that the Helly numbers of exponential lattices {αn:n∈N0}2 are finite for every α>1 and we determine their exact values in some instances. In particular, we obtain H({2n:n∈N0}2)=5, solving a problem posed by Dillon (2021). For real numbers α,β>1, we also fully characterize exponential lattices L(α,β)={αn:n∈N0}×{βn:n∈N0} with finite Helly numbers by showing that H(L(α,β)) is finite if and only if logα(β) is rational.
Updatable searchable symmetric encryption: Definitions and constructions

Searchable symmetric encryption (SSE) allows a client to search over encrypted data. To address the real threat of key compromise in practice, this work initiates the study of key rotation for SSE and introduces the notion of updatable SSE (USSE). In USSE, a client can issue a single update token that permits the server to convert existing encrypted data from the old key to the new key. In particular, we formalize the syntax of USSE and define the security model that captures the inference of key, search token, update token and encrypted data with bi-/uni-/no-directional key updates and bi-/uni-directional encrypted data updates. we present a USSE scheme that supports conjunctive queries with sub-linear complexity, and prove its security with no-directional key update and bi-directional encrypted data update. We also give extensions for concerning different key/encrypted data updates. we implement our USSE schemes and evaluate the performance with real-world dataset, which illustrates that our schemes achieve practically acceptable computational overhead and communication cost.
Energy-based structural least squares twin support vector clustering

Clustering is an unsupervised learning algorithm and it is widely used in machine learning. Twin support vector clustering (TWSVC) is a new plane-based clustering algorithm, which exploits information from both within and between clusters to generate plane for each cluster. However, TWSVC suffered from relatively poor performance because it ignores the intrinsic structural information of data and solves a series of quadratic programming problems (QPPs). In order to address these problems, in this paper, we propose a novel energy-based structural least squares twin support vector clustering, termed as ESLSTWSVC. Firstly, based on least squares twin support vector clustering (LSTWSVC), we introduce within-class covariance matrix into the objective function of LSTWSVC to obtain more intrinsic structural information. ESLSTWSVC solves a series of system of linear equations rather than to solve QPPs in TWSVC, it leads to simple algorithm and less computation time. In addition, ESLSTWSVC converts the constraints of LSTWSVC into energy-based model by introducing an energy parameter for each cluster that makes ESLSTWSVC more robust. Experiments are performed on artificial datasets as well as UCI datasets, and the experimental results illustrate the effectiveness of the proposed algorithm.
Text classification supervised algorithms with term frequency inverse document frequency and global vectors for word representation: a comparative study

Over the course of the previous two decades, there has been a rise in the quantity of text documents stored digitally. The ability to organize and categorize those documents in an automated mechanism, is known as text categorization which is used to classify them into a set of predefined categories so they may be preserved and sorted more efficiently. Identifying appropriate structures, architectures, and methods for text classification presents a challenge for researchers. This is due to the significant impact this concept has on content management, contextual search, opinion mining, product review analysis, spam filtering, and text sentiment mining. This study analyzes the generic categorization strategy and examines supervised machine learning approaches and their ability to comprehend complex models and nonlinear data interactions. Among these methods are k-nearest neighbors (KNN), support vector machine (SVM), and ensemble learning algorithms employing various evaluation techniques. Thereafter, an evaluation is conducted on the constraints of every technique and how they can be applied to real-life situations.
A descriptive behavior intention inference framework using spatio-temporal semantic features for human–robot real-time interaction

Visual behavior intention inference is crucial for enabling escort robots to interact naturally with humans, which is very challenging due to the big inner-class similarity and the small intra-class distinguishability of successive actions in the assistive scenario. To attain a reliable behavior intention inference, not only the current state of behaviors is concerned, but also the semantic information in both spatial and temporal domains plays an important role. This paper presents a segmentation–detection–recognition hierarchical system to represent the spatio-temporal semantic features for formulating descriptions of body parts, trajectories and deep relationships of sub-behaviors. Specifically, a dense trajectory matching scheme based on temporal sampling and Binarized Normed Gradients (BING) algorithm is formulated to segment the 3-Dimensional (3D) behavior cubes, based on which, local trajectories are obtained by clustering dense trajectories according to the distance similarity, and the body parts are then detected by multi-kernel learning of the encoded local features. Moreover, a global three-stream context Convolutional Neural Networks (CNN) is proposed for behavior classification by designing a texture module using expansion, connection and 1D convolution implementations. Based on transfer learning, scene information is also recognized efficiently. Finally, the semantic descriptors are modeled by two cascaded And-Or Graphs (AoGs) constraining the spatial scenarios and temporal sequences. Our unified approach is demonstrated on two public benchmarks containing long-term activities and on an escort robot for real-world applications.