Reviewerly/exHarmony huggingface dataset:
Missing Link Identification from Node Embeddings using Graph Auto Encoders and its Variants

Graph representation learning recently has proven their excellent competency in understanding large graphs and their inner engineering for various downstream tasks. Link completion is an important computational task to guess missing edges in a network. The traditional methods extract local, pairwise information based on specific proximity statistics that are always ineffective in inferring missing links from a global topological perspective. Graph Convolutional Network (GCN) based em-bedding methods may be an effective alternative. In this work, we try to experimentally assess the power of GCN-based graph embedding techniques, namely Graph Auto Encoder (GAE) and its variants GraphSAGE, and Graph Attention Network (GAT) for link prediction tasks. Experimental results show that the GAE-based encoding methods are able to achieve superior results for predicting missing links in various real large-scale networks in comparison to traditional link prediction methods. Interestingly, our results reveal that the above techniques successfully recreate the original network with high true positive and negative rates. However, it has been observed that they produce many extra edges with an overall very high false positive rate.
Approximation Capabilities of Neural Networks using Morphological Perceptrons and Generalizations

Standard artificial neural networks (ANNs) use sum-product or multiply-accumulate node operations with a memoryless nonlinear activation. These neural networks are known to have universal function approximation capabilities. Previously proposed morphological perceptrons use max-sum, in place of sum-product, node processing and have promising properties for circuit implementations. In this paper we show that these max-sum ANNs do not have universal approximation capabilities. Furthermore, we consider proposed signed-max-sum and max-star-sum generalizations of morphological ANNs and show that these variants also do not have universal approximation capabilities. We contrast these variations to log-number system (LNS) implementations which also avoid multiplications, but do exhibit universal approximation capabilities.
On Multi-Round Privacy in Federated Learning

Secure Aggregation is essential in federated learning to ensure that, in any given round, the server learns nothing about the local models of the users beyond their aggregate. Secure aggregation, however, does not protect the privacy of the users over multiple training rounds due to the partial user participation at each round. To quantify such long-term privacy leakage, a new metric termed as multi-round privacy has been introduced recently that requires that the server cannot reconstruct any individual model using the aggregate models from any number of training rounds. In addition, a privacy-preserving structured user selection strategy known as Multi-RoundSecAgg has been developed to ensure multi-round privacy while taking into account the convergence rate and the fairness in the user selection. Multi-RoundSecAgg, however, provides a trade-off between the multi-round privacy guarantee and the convergence rate in the sense that stronger multi-round privacy requires a larger number of training rounds. In this paper, we consider a weaker notion of multi-round privacy termed as weak multi-round privacy that still requires that the server cannot get any individual model. We show that considering this weaker notion allows for better convergence rates compared to Multi-RoundSecAgg while still protecting the privacy of the individual users in a weaker sense.
Transformers for Robust Radar Waveform Classification

Radar pulse classification in noisy environments is an important but challenging task for cognitive radar systems. Deep learning based algorithms, such as convolutional neural networks (CNN), are remarkably effective at classifying radar pulses in low signal-to-noise ratio environments. However, these same algorithms are highly vulnerable to adversarial attacks-they can be tricked into misclassifying signals using relatively weak perturbations. We study a self-attention based alternative to CNNs-the Transformer-and its effectiveness as a radar pulse classifier. We compare the adversarial robustness of CNNs and Transformers to better understand their limitations. We find that Transformers are up to 2.3x more accurate in the presence of adversarial attacks while providing comparable accuracy when not under attack.
Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders

Recommender Systems (RSs) have become increasingly important in many application domains, such as digital marketing. Conventional RSs often need to collect users' data, centralize them on the server-side, and form a global model to generate reliable recommendations. However, they suffer from two critical limitations: the personalization problem that the RSs trained traditionally may not be customized for individual users, and the privacy problem that directly sharing user data is not encouraged. We propose Personalized Federated Recommender Systems (PersonalFR), which introduces a personalized autoencoder-based recommendation model with Federated Learning (FL) to address these challenges. PersonalFR guarantees that each user can learn a personal model from the local dataset and other participating users' data without sharing local data, data embeddings, or models. PersonalFR consists of three main components, including AutoEncoder-based RSs (ARSs) that learn the user-item interactions, Partially Federated Learning (PFL) that updates the encoder locally and aggregates the decoder on the server-side, and Partial Compression (PC) that only computes and transmits active model parameters. Extensive experiments on two real-world datasets demonstrate that PersonalFR can achieve private and personalized performance comparable to that trained by centralizing all users' data. Moreover, PersonalFR requires significantly less computation and communication overhead than standard FL baselines.
Constrained Reinforcement Learning via Dissipative Saddle Flow Dynamics

In constrained reinforcement learning (C-RL), an agent seeks to learn from the environment a policy that maximizes the expected cumulative reward while satisfying minimum requirements in secondary cumulative reward constraints. Several algorithms rooted in sampled-based primal-dual methods have been recently proposed to solve this problem in policy space. However, such methods are based on stochastic gradient descent-ascent algorithms whose trajectories are connected to the optimal policy only after a mixing output stage that depends on the algorithm's history. As a result, there is a mismatch between the behavioral policy and the optimal one. In this work, we propose a novel algorithm for constrained RL that does not suffer from these limitations. Leveraging recent results on regularized saddle-flow dynamics, we develop a novel stochastic gradient descent-ascent algorithm whose trajectories converge to the optimal policy almost surely.
On the Coarse Robustness of Classifiers

Standard measures of robustness, derived from the least amount of adversarial perturbation, often fail to gauge the ability of a classifier to recognize the coarse genres. It is desirable to have a classifier with high coarse robustness with respect to a grouping that is consistent with the class semantics, so that semantically-plausible coarse categories remain invariant to imperceptible perturbations. In this work, we formalize a new notion of coarse robustness that is defined with respect to a specified grouping of the class labels. We formulate an optimization problem to obtain the optimal grouping, and develop an algorithm that is shown to perform on par with brute force search. Moreover, we propose a training mechanism that incorporates the coarse label information in addition to the finer ones. We empirically and theoretically show that this mechanism improves the proposed coarse notion of robustness while only requiring a relatively small additional parameters and training time.
Joint graph learning from Gaussian observations in the presence of hidden nodes

Graph learning problems are typically approached by focusing on learning the topology of a single graph when signals from all nodes are available. However, many contemporary setups involve multiple related networks and, moreover, it is often the case that only a subset of nodes is observed while the rest remain hidden. Motivated by this, we propose a joint graph learning method that takes into account the presence of hidden (latent) variables. Intuitively, the presence of the hidden nodes renders the inference task ill-posed and challenging to solve, so we overcome this detrimental influence by harnessing the similarity of the estimated graphs. To that end, we assume that the observed signals are drawn from a Gaussian Markov random field with latent variables and we carefully model the graph similarity among hidden (latent) nodes. Then, we exploit the structure resulting from the previous considerations to propose a convex optimization problem that solves the joint graph learning task by providing a regularized maximum likelihood estimator. Finally, we compare the proposed algorithm with different baselines and evaluate its performance over synthetic and real-world graphs.
Distributed Stochastic Gradient Descent with Cost-Sensitive and Strategic Agents

This study considers a federated learning setup where cost-sensitive and strategic agents train a learning model with a server. During each round, each agent samples a minibatch of training data and sends his gradient update. As an increasing function of his minibatch size choice, the agent incurs a cost associated with the data collection, gradient computation and communication. The agents have the freedom to choose their minibatch size and may even opt out from training. To reduce his cost, an agent may diminish his minibatch size, which may also cause an increase in the noise level of the gradient update. The server can offer rewards to compensate the agents for their costs and to incentivize their participation but she lacks the capability of validating the true minibatch sizes of the agents. To tackle this challenge, the proposed reward mechanism evaluates the quality of each agent's gradient according to the its distance to a reference which is constructed from the gradients provided by other agents. It is shown that the proposed reward mechanism has a cooperative Nash equilibrium in which the agents determine the minibatch size choices according to the requests of the server.
Memory-Efficient Convex Optimization for Self-Dictionary Nonnegative Matrix Factorization

The separable nonnegative matrix factorization (NMF) model has been adopted in many applications (e.g., topic modeling and community detection), as it ensures identifiability of the latent factors with polynomial time algorithms under reasonable assumptions. Separable NMF is often formulated as a self-dictionary (SD) sparse regression problem, and algorithms under this formulation can be categorized into two classes, namely, greedy methods and all-at-once convex optimization. The latter has been shown to be more resilient to noise, but has serious scalability issues. Particularly, existing convex optimization algorithms for the SD sparse regression problem cost a memory that grows quadratically with respect to the data size-making it hardly useful in practice. This work puts forth a Frank-Wolfe algorithm to tackle this challenge. The proposed method has a memory cost that grows linearly in the data size-with provable guarantees. To our best knowledge, this is the first linear memory algorithm for this long-existing problem. Synthetic and real data experiments are used to showcase the memory efficiency of the proposed algorithm.
Contextual Explainable Video Representation: Human Perception-based Understanding

Video understanding is a growing field and a subject of intense research, which includes many interesting tasks to understanding both spatial and temporal information, e.g., action detection, action recognition, video captioning, video retrieval. One of the most challenging problems in video understanding is dealing with feature extraction, i.e. extract contextual visual representation from given untrimmed video due to the long and complicated temporal structure of unconstrained videos. Different from existing approaches, which apply a pre-trained backbone network as a black-box to extract visual representation, our approach aims to extract the most contextual information with an explainable mechanism. As we observed, humans typically perceive a video through the interactions between three main factors, i.e., the actors, the relevant objects, and the surrounding environment. Therefore, it is very crucial to design a contextual explainable video representation extraction that can capture each of such factors and model the relationships between them. In this paper, we discuss approaches, that incorporate the human perception process into modeling actors, objects, and the environment. We choose video paragraph captioning and temporal action detection to illustrate the effectiveness of human perception based-contextual representation in video understanding. Source code is publicly available at https://github.com/UARK-AICV/Video_Representation.
A General Convolution Theorem for Graph Data

This paper focuses on the field of graph signal processing (GSP) and studies the node-varying graph filter (NV-GF) which has been proposed as a way to broaden the applicability of the classical graph filter (C-GF). In particular, we state and prove a new convolution theorem for a NV-GF which extends both the one for a C-GF and the one for a time-varying filter. The theorem relies on the definition of a so-called dual graph which characterizes the support of the frequency domain. The dual graph concept has been studied only very recently and many versions exist, yet the proposed convolution theorem is independent of the particular version. More interestingly, using non-stationary graph data on the primal graph, we can use the proposed convolution theorem to learn the dual graph and thereby introduce an innovative data-driven dual graph estimation technique.
Matching of Markov Databases Under Random Column Repetitions

Matching entries of correlated shuffled databases have practical applications ranging from privacy to biology. In this paper, motivated by synchronization errors in the sampling of time-indexed databases, matching of random databases under random column repetitions and deletions is investigated. It is assumed that for each entry (row) in the database, the attributes (columns) are correlated, which is modeled as a Markov process. Column histograms are proposed as a permutation-invariant feature to detect the repetition pattern, whose asymptotic-uniqueness is proved using information-theoretic tools. Repetition detection is then followed by a typicality-based row matching scheme. Considering this overall scheme, sufficient conditions for successful matching of databases in terms of the database growth rate are derived. A modified version of Fano's inequality leads to a tight necessary condition for successful matching, establishing the matching capacity under column repetitions. This capacity is equal to the erasure bound, which assumes the repetition locations are known a-priori. Overall, our results provide insights on privacy-preserving publication of anonymized time-indexed data.
Image Classification Based on Convolution and Lite Transformer

Utilizing the self-attention mechanism, Vision transformer (ViT) can tap long-distance dependencies and get excellent performance in vision tasks. However, it is difficult for ViT to learn rich characteristics with limited processing resources or data. As a result, based on the ViT model, we use a lightweight module that combines the information intersection of window self-attention and the inductive bias of convolutional neural network (CNN) to build a backbone network Convolution Lite Transformer (CLT). Further, experimental results show that CLT achieves 79.21 percent accuracy on the ImageNet with fewer parameters, which is more accurate than the traditional CNN model and more suitable for lightweight deployment compared with the ViT model.
A Meta-learning System for Automatic Objects Tracking

Recent years, reinforcement learning has been developed in many area and achieve high performance in many application. However, for huge amount data and training will make hard leaning for the agent of reinforcement learning, in the project, the agent of DDQN Network was integrated with LSTM meta learner for learning the game called Cartpole and LunarLander. It get a good performance for large train.
Observations on Student Help-Seeking Behaviors in Introductory Computer Science Courses

The help-seeking interactions faculty encounter will vary depending upon the course structure and the students enrolled. While the course structure tends to remain the same, the students enrolled change each semester, presenting a new set of students who seek help in many different ways. We share our observations in investigating student behavior when using course resources, including office hours and online discussion forums, in two introductory computer science courses. Our goal is to explore differences in help-seeking behavior to construct Student "Help-Seeking" Personas. Preliminary analysis has shown that, for these two introductory CS courses, there are no well-defined personas that emerge from the grouping of help-seeking behaviors. The demographic of students exhibiting various help-seeking behaviors tends to be a near-proportionate subset of the overall course demographic. Thus, no distinct personas emerge from the students' help-seeking behaviors in introductory CS courses that faculty can utilize to better understand their students.
Association Rules Based Feature Extraction for Deep Learning Classification

The number of extracted features from medical data, such as computer-aided diagnosis, has been known to be too large and affects the performance of the used classifiers. Moreover, the large number of input features affect the accuracy of the classifiers, such as the traditional machine learning classifier. Therefore, in this paper, we proposed the use of association rules to select features from medical data, which result in dimensionality reduction of the input feature space. The selected features become the input to a deep neural network, particularly ResNet, which is known for its high accuracy of classification results. The conducted experiments prove that the use of association rules to select the most representative features and the use of deep neural networks as a classifier outperformed other traditional machine learning models in terms of accuracy of classification.
ROSE: Robust State Estimation via Online Covariance Adaption

Robust state estimation is critical for enabling reliable autonomous robot operations in challenging environments. To estimate the state, heterogeneous sensor fusion is commonly employed to enhance the reliability against perceptual failure. However, most known methods for sensor-fusion are brittle to dynamic perceptual condition changes due to the use of hand-tuned and time-constant error models. This paper introduces ROSE, a Robust Online-adaptive State Estimator, capable of adapting uncertainty statistics for individual multi-modal estimates in real-time to perform reliable sensor-fusion for robot state estimation. The proposed method leverages theory from adaptive Kalman filtering and extends it to optimization-based methods, to improve estimation accuracy while enabling integration of delayed heterogeneous sensor inputs. ROSE has been thoroughly evaluated by simulation studies and real-world demonstrations using a high-speed off-road vehicle navigating complex unstructured terrains and performing aggressive motions.
Efficient Task Planning Using Abstract Skills and Dynamic Road Map Matching

Task planning is the problem of finding a discrete sequence of actions to achieve a goal. Unfortunately, task planning in robotic domains is computationally challenging. To address this, in our prior work, we explained how knowledge from a successful task solution can be cached for later use, as an “abstract skill.” Such a skill is represented as a trace of states (“road map”) in an abstract space and can be matched with new tasks on-demand. This paper explains how one can use a library of abstract skills, derived from past planning experience, to reduce the computational cost of solving new task planning problems. As we explain, matching a skill to a task allows us to decompose it into independent sub-tasks, which can be quickly solved in parallel. This can be done automatically and dynamically during planning. We begin by formulating this problem of “planning with skills” as a constraint satisfaction problem. We then provide a hierarchical solution algorithm, which integrates with any standard task planner. Finally, we experimentally demonstrate the computational benefits of the approach for reach-avoid tasks.
Vigenère Implemented in Two Chaotic Feistel Laps for Medical Images Encryption Followed by Genetic Mutation

In this work, we will propose a new method for medical image encryption based on Vigenère injection into two greatly improved Feistel rounds, followed by a chaotic mutation applied to the integrity of the vector coming out of the first encryption stage. Chaotic maps most commonly used in cryptography were used to create this design. After vectorizing the original image, two improved Feistel circuits will be used by implementing a new Vigenère replacement function using two S-boxes generated from the chaotic maps. A genetic mutation will be applied to the resulting vector at the end of the two rounds. Simulations performed on a large number of images of different sizes and formats guarantee that our approach is not subject to known attacks.
BDIV: Healthcare Blockchain Data Integrity Schemes Verification on Storage Cloud

The health data provided by an individual’s various health care or other organizations is very sensitive and private. Firstly, this sensitive electronic health data to be stored and shared on a private or community site, in terms of privacy and security. Sometimes, existing security mechanisms in health systems find problems in preserving data on the Internet. Cloud Computing (CC) provides a solid infrastructure for health IT (HIT) on the Internet with his speed, flexibility, rapid deployment, resource sharing, infrastructure efficiency, scalability and the energy savings. The health sector needs to meet current and future demands while keeping costs to a minimum to enable collaboration between Information and communication technologies (ICT) in health care facilities.