Dataset Obfuscation: Its Applications to and Impacts on Edge Machine Learning

Obfuscating a dataset by adding random noises to protect the privacy of sensitive samples in the training dataset is crucial to prevent data leakage to untrusted parties for edge applications. We conduct comprehensive experiments to investigate how the dataset obfuscation can affect the resultant model weights - in terms of the model accuracy, Frobenius-norm (F-norm)-based model distance, and level of data privacy - and discuss the potential applications with the proposed Privacy, Utility, and Distinguishability (PUD)-triangle diagram to visualize the requirement preferences. Our experiments are based on the popular MNIST and CIFAR-10 datasets under both independent and identically distributed (IID) and non-IID settings. Significant results include a trade-off between the model accuracy and privacy level and a trade-off between the model difference and privacy level. The results indicate broad application prospects for training outsourcing in edge computing and guarding against attacks in Federated Learning among edge devices.Comment: 6 page

Aqua­(2,9-dimethyl-1,10-phenanthroline-κ2 N,N′)diformato-κ2 O,O′;κO-nickel(II) monohydrate

The asymmetric unit of the title compound, [Ni(HCO2)2(C14H12N2)(H2O)]·H2O, contains a mononuclear complex mol­ecule hydrogen bonded to a lattice water mol­ecule. The NiII atom exhibits a distorted octa­hedral coordination geometry formed by the N atoms from a 2,9-dimethyl-1,10-phenanthroline ligand, two O atoms of a chelating formate anion, one aqua O atom and one O atom of a coordinating formate anion. The mol­ecules are assembled into chains extending along [100] through by O—H⋯O hydrogen bonds. The supra­molecular chains are further linked into layers parallel to (011) by weak π–π packing inter­actions [centroid–centroid separation = 3.768 (2) Å]. The resulting layers are stacked to meet the requirement of close-packing patterns

Spreading dynamics of a 2SIH2R, rumor spreading model in the homogeneous network

In the era of the rapid development of the Internet, the threshold for information spreading has become lower. Most of the time, rumors, as a special kind of information, are harmful to society. And once the rumor appears, the truth will follow. Considering that the rumor and truth compete with each other like light and darkness in reality, in this paper, we study a rumor spreading model in the homogeneous network called 2SIH2R, in which there are both spreader1(people who spread the rumor) and spreader2(people who spread the truth). In this model, we introduced discernible mechanism and confrontation mechanism to quantify the level of people's cognitive abilities and the competition between the rumor and truth. By mean-field equations, steady-state analysis and numerical simulations in a generated network which is closed and homogeneous, some significant results can be given: the higher discernible rate of the rumor, the smaller influence of the rumor; the stronger confrontation degree of the rumor, the smaller influence of the rumor; the large average degree of the network, the greater influence of the rumor but the shorter duration. The model and simulation results provide a quantitative reference for revealing and controlling the spread of the rumor

Learning-based Intelligent Surface Configuration, User Selection, Channel Allocation, and Modulation Adaptation for Jamming-resisting Multiuser OFDMA Systems

Reconfigurable intelligent surfaces (RISs) can potentially combat jamming attacks by diffusing jamming signals. This paper jointly optimizes user selection, channel allocation, modulation-coding, and RIS configuration in a multiuser OFDMA system under a jamming attack. This problem is non-trivial and has never been addressed, because of its mixed-integer programming nature and difficulties in acquiring channel state information (CSI) involving the RIS and jammer. We propose a new deep reinforcement learning (DRL)-based approach, which learns only through changes in the received data rates of the users to reject the jamming signals and maximize the sum rate of the system. The key idea is that we decouple the discrete selection of users, channels, and modulation-coding from the continuous RIS configuration, hence facilitating the RIS configuration with the latest twin delayed deep deterministic policy gradient (TD3) model. Another important aspect is that we show a winner-takes-all strategy is almost surely optimal for selecting the users, channels, and modulation-coding, given a learned RIS configuration. Simulations show that the new approach converges fast to fulfill the benefit of the RIS, due to its substantially small state and action spaces. Without the need of the CSI, the approach is promising and offers practical value.Comment: accepted by IEEE TCOM in Jan. 202

7-(1,3-Dioxolan-2-ylmethyl)-1,3-di­methyl-2,6-dioxo-2,3,6,7-tetra­hydro-1H-purin-9-ium tetra­chloridoferrate(III)

The asymmetric unit of the title compound, (C11H15N4O4)[FeCl4], contains two independent protonated 7-(1,3-dioxolan-2-ylmeth­yl)-3,7-dihydro-1,3-dimethyl-1H-purine-2,6-dione (doxofyllinium) and two tetrahedral tetra­chlorido­ferrate(III) anions. In the doxofyllinium, two disordered methyl­ene C atoms are observed in each dioxolane ring with an occupancy ratio of 0.54 (4):0.46 (4). In the crystal, mol­ecules are connected by N—H⋯O hydrogen bonds and weak C—H⋯O and C—H⋯Cl inter­actions

Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement Learning

Adapting to the changes in transition dynamics is essential in robotic applications. By learning a conditional policy with a compact context, context-aware meta-reinforcement learning provides a flexible way to adjust behavior according to dynamics changes. However, in real-world applications, the agent may encounter complex dynamics changes. Multiple confounders can influence the transition dynamics, making it challenging to infer accurate context for decision-making. This paper addresses such a challenge by Decomposed Mutual INformation Optimization (DOMINO) for context learning, which explicitly learns a disentangled context to maximize the mutual information between the context and historical trajectories, while minimizing the state transition prediction error. Our theoretical analysis shows that DOMINO can overcome the underestimation of the mutual information caused by multi-confounded challenges via learning disentangled context and reduce the demand for the number of samples collected in various environments. Extensive experiments show that the context learned by DOMINO benefits both model-based and model-free reinforcement learning algorithms for dynamics generalization in terms of sample efficiency and performance in unseen environments.Comment: NeurIPS 202