OpenRaiser/Intern-Atlas huggingface dataset:
GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactions
Venue: arXiv cs.LG 2022
Year: 2022

Molecular property prediction is a fundamental task in the drug and material industries. Physically, the properties of a molecule are determined by its own electronic structure, which is a quantum many-body system and can be exactly described by the Schr"odinger equation. Full-range many-body interactions between electrons have been proven effective in obtaining an accurate solution of the Schr"odinger equation by classical computational chemistry methods, although modeling such interactions consumes an expensive computational cost. Meanwhile, deep learning methods have also demonstrated their competence in molecular property prediction tasks. Inspired by the classical computational chemistry methods, we design a novel method, namely GEM-2, which comprehensively considers full-range many-body interactions in molecules. Multiple tracks are utilized to model the full-range interactions between the many-bodies with different orders, and a novel axial attention mechanism is designed to approximate the full-range interaction modeling with much lower computational cost. Extensive experiments demonstrate the overwhelming superiority of GEM-2 over multiple baseline methods in quantum chemistry and drug discovery tasks. The ablation studies also verify the effectiveness of the full-range many-body interactions.
Speech Synthesis with Mixed Emotions
Venue: arXiv cs.CL 2022
Year: 2022

Emotional speech synthesis aims to synthesize human voices with various emotional effects. The current studies are mostly focused on imitating an averaged style belonging to a specific emotion type. In this paper, we seek to generate speech with a mixture of emotions at run-time. We propose a novel formulation that measures the relative difference between the speech samples of different emotions. We then incorporate our formulation into a sequence-to-sequence emotional text-to-speech framework. During the training, the framework does not only explicitly characterize emotion styles, but also explores the ordinal nature of emotions by quantifying the differences with other emotions. At run-time, we control the model to produce the desired emotion mixture by manually defining an emotion attribute vector. The objective and subjective evaluations have validated the effectiveness of the proposed framework. To our best knowledge, this research is the first study on modelling, synthesizing, and evaluating mixed emotions in speech.
Smoothed Analysis of Information Spreading in Dynamic Networks
Venue: arXiv cs.DC 2022
Year: 2022

The best known solutions for $k$-message broadcast in dynamic networks of size $n$ require $Ω(nk)$ rounds. In this paper, we see if these bounds can be improved by smoothed analysis. We study perhaps the most natural randomized algorithm for disseminating tokens in this setting: at every time step, choose a token to broadcast randomly from the set of tokens you know. We show that with even a small amount of smoothing (one random edge added per round), this natural strategy solves $k$-message broadcast in $\tilde{O}(n+k^3)$ rounds, with high probability, beating the best known bounds for $k=o(\sqrt{n})$ and matching the $Ω(n+k)$ lower bound for static networks for $k=O(n^{1/3})$ (ignoring logarithmic factors). In fact, the main result we show is even stronger and more general: given $\ell$-smoothing (i.e., $\ell$ random edges added per round), this simple strategy terminates in $O(kn^{2/3}\log^{1/3}(n)\ell^{-1/3})$ rounds. We then prove this analysis close to tight with an almost-matching lower bound. To better understand the impact of smoothing on information spreading, we next turn our attention to static networks, proving a tight bound of $\tilde{O}(k\sqrt{n})$ rounds to solve $k$-message broadcast, which is better than what our strategy can achieve in the dynamic setting. This confirms that although smoothed analysis reduces the difficulties induced by changing graph structures, it does not eliminate them altogether. Finally, we apply our tools to prove an optimal result for $k$-message broadcast in so-called well-mixed networks in the absence of smoothing. By comparing this result to an existing lower bound for well-mixed networks, we establish a formal separation between oblivious and strongly adaptive adversaries with respect to well-mixed token spreading, partially resolving an open question on the impact of adversary strength on the $k$-message broadcast problem.
A review of cryptosystems based on multi layer chaotic mappings
Venue: arXiv cs.CR 2022
Year: 2022

In recent years, a lot of research has gone into creating multi-layer chaotic mapping-based cryptosystems. Random-like behavior, a continuous broadband power spectrum, and a weak baseline condition dependency are all characteristics of chaotic systems. Chaos could be helpful in the three functional components of compression, encryption, and modulation in a digital communication system. To successfully use chaos theory in cryptography, chaotic maps must be built in such a way that the entropy they produce can provide the necessary confusion and diffusion. A chaotic map is used in the first layer of such cryptosystems to create confusion, and a second chaotic map is used in the second layer to create diffusion and create a ciphertext from a plaintext. A secret key generation mechanism and a key exchange method are frequently left out, and many researchers just assume that these essential components of any effective cryptosystem are always accessible. We review such cryptosystems by using a cryptosystem of our design, in which confusion in plaintext is created using Arnold's Cat Map, and logistic mapping is employed to create sufficient dispersion and ultimately get a matching ciphertext. We also address the development of key exchange protocols and secret key schemes for these cryptosystems, as well as the possible outcomes of using cryptanalysis techniques on such a system.
Reducing NEXP-complete problems to DQBF
Venue: arXiv cs.LO 2022
Year: 2022

We present an alternative proof of the NEXP-hardness of the satisfiability of {\em Dependency Quantified Boolean Formulas} (DQBF). Besides being simple, our proof also gives us a general method to reduce NEXP-complete problems to DQBF. We demonstrate its utility by presenting explicit reductions from a wide variety of NEXP-complete problems to DQBF such as (succinctly represented) 3-colorability, Hamiltonian cycle, set packing and subset-sum as well as NEXP-complete logics such as the Bernays-Schönfinkel-Ramsey class, the two-variable logic and the monadic class. Our results show the vast applications of DQBF solvers which recently have gathered a lot of attention among researchers.
Handling big tabular data of ICT supply chains: a multi-task, machine-interpretable approach
Venue: arXiv cs.CV 2022
Year: 2022

Due to the characteristics of Information and Communications Technology (ICT) products, the critical information of ICT devices is often summarized in big tabular data shared across supply chains. Therefore, it is critical to automatically interpret tabular structures with the surging amount of electronic assets. To transform the tabular data in electronic documents into a machine-interpretable format and provide layout and semantic information for information extraction and interpretation, we define a Table Structure Recognition (TSR) task and a Table Cell Type Classification (CTC) task. We use a graph to represent complex table structures for the TSR task. Meanwhile, table cells are categorized into three groups based on their functional roles for the CTC task, namely Header, Attribute, and Data. Subsequently, we propose a multi-task model to solve the defined two tasks simultaneously by using the text modal and image modal features. Our experimental results show that our proposed method can outperform state-of-the-art methods on ICDAR2013 and UNLV datasets.
Structural Biases for Improving Transformers on Translation into Morphologically Rich Languages
Venue: arXiv cs.CL 2022
Year: 2022

Machine translation has seen rapid progress with the advent of Transformer-based models. These models have no explicit linguistic structure built into them, yet they may still implicitly learn structured relationships by attending to relevant tokens. We hypothesize that this structural learning could be made more robust by explicitly endowing Transformers with a structural bias, and we investigate two methods for building in such a bias. One method, the TP-Transformer, augments the traditional Transformer architecture to include an additional component to represent structure. The second method imbues structure at the data level by segmenting the data with morphological tokenization. We test these methods on translating from English into morphologically rich languages, Turkish and Inuktitut, and consider both automatic metrics and human evaluations. We find that each of these two approaches allows the network to achieve better performance, but this improvement is dependent on the size of the dataset. In sum, structural encoding methods make Transformers more sample-efficient, enabling them to perform better from smaller amounts of data.
Comparing Baseline Shapley and Integrated Gradients for Local Explanation: Some Additional Insights
Venue: arXiv cs.LG 2022
Year: 2022

There are many different methods in the literature for local explanation of machine learning results. However, the methods differ in their approaches and often do not provide same explanations. In this paper, we consider two recent methods: Integrated Gradients (Sundararajan, Taly, & Yan, 2017) and Baseline Shapley (Sundararajan and Najmi, 2020). The original authors have already studied the axiomatic properties of the two methods and provided some comparisons. Our work provides some additional insights on their comparative behavior for tabular data. We discuss common situations where the two provide identical explanations and where they differ. We also use simulation studies to examine the differences when neural networks with ReLU activation function is used to fit the models.
Exploring High-quality Target Domain Information for Unsupervised Domain Adaptive Semantic Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

In unsupervised domain adaptive (UDA) semantic segmentation, the distillation based methods are currently dominant in performance. However, the distillation technique requires complicate multi-stage process and many training tricks. In this paper, we propose a simple yet effective method that can achieve competitive performance to the advanced distillation methods. Our core idea is to fully explore the target-domain information from the views of boundaries and features. First, we propose a novel mix-up strategy to generate high-quality target-domain boundaries with ground-truth labels. Different from the source-domain boundaries in previous works, we select the high-confidence target-domain areas and then paste them to the source-domain images. Such a strategy can generate the object boundaries in target domain (edge of target-domain object areas) with the correct labels. Consequently, the boundary information of target domain can be effectively captured by learning on the mixed-up samples. Second, we design a multi-level contrastive loss to improve the representation of target-domain data, including pixel-level and prototype-level contrastive learning. By combining two proposed methods, more discriminative features can be extracted and hard object boundaries can be better addressed for the target domain. The experimental results on two commonly adopted benchmarks (\textit{i.e.}, GTA5 $\rightarrow$ Cityscapes and SYNTHIA $\rightarrow$ Cityscapes) show that our method achieves competitive performance to complicated distillation methods. Notably, for the SYNTHIA$\rightarrow$ Cityscapes scenario, our method achieves the state-of-the-art performance with $57.8\%$ mIoU and $64.6\%$ mIoU on 16 classes and 13 classes. Code is available at https://github.com/ljjcoder/EHTDI.
Instance Image Retrieval by Learning Purely From Within the Dataset
Venue: arXiv cs.CV 2022
Year: 2022

Quality feature representation is key to instance image retrieval. To attain it, existing methods usually resort to a deep model pre-trained on benchmark datasets or even fine-tune the model with a task-dependent labelled auxiliary dataset. Although achieving promising results, this approach is restricted by two issues: 1) the domain gap between benchmark datasets and the dataset of a given retrieval task; 2) the required auxiliary dataset cannot be readily obtained. In light of this situation, this work looks into a different approach which has not been well investigated for instance image retrieval previously: {can we learn feature representation \textit{specific to} a given retrieval task in order to achieve excellent retrieval?} Our finding is encouraging. By adding an object proposal generator to generate image regions for self-supervised learning, the investigated approach can successfully learn feature representation specific to a given dataset for retrieval. This representation can be made even more effective by boosting it with image similarity information mined from the dataset. As experimentally validated, such a simple ``self-supervised learning + self-boosting'' approach can well compete with the relevant state-of-the-art retrieval methods. Ablation study is conducted to show the appealing properties of this approach and its limitation on generalisation across datasets.
Bayesian Inference with Latent Hamiltonian Neural Networks
Venue: arXiv cs.LG 2022
Year: 2022

When sampling for Bayesian inference, one popular approach is to use Hamiltonian Monte Carlo (HMC) and specifically the No-U-Turn Sampler (NUTS) which automatically decides the end time of the Hamiltonian trajectory. However, HMC and NUTS can require numerous numerical gradients of the target density, and can prove slow in practice. We propose Hamiltonian neural networks (HNNs) with HMC and NUTS for solving Bayesian inference problems. Once trained, HNNs do not require numerical gradients of the target density during sampling. Moreover, they satisfy important properties such as perfect time reversibility and Hamiltonian conservation, making them well-suited for use within HMC and NUTS because stationarity can be shown. We also propose an HNN extension called latent HNNs (L-HNNs), which are capable of predicting latent variable outputs. Compared to HNNs, L-HNNs offer improved expressivity and reduced integration errors. Finally, we employ L-HNNs in NUTS with an online error monitoring scheme to prevent sample degeneracy in regions of low probability density. We demonstrate L-HNNs in NUTS with online error monitoring on several examples involving complex, heavy-tailed, and high-local-curvature probability densities. Overall, L-HNNs in NUTS with online error monitoring satisfactorily inferred these probability densities. Compared to traditional NUTS, L-HNNs in NUTS with online error monitoring required 1--2 orders of magnitude fewer numerical gradients of the target density and improved the effective sample size (ESS) per gradient by an order of magnitude.
Gradient Estimation for Binary Latent Variables via Gradient Variance Clipping
Venue: arXiv cs.LG 2022
Year: 2022

Gradient estimation is often necessary for fitting generative models with discrete latent variables, in contexts such as reinforcement learning and variational autoencoder (VAE) training. The DisARM estimator (Yin et al. 2020; Dong, Mnih, and Tucker 2020) achieves state of the art gradient variance for Bernoulli latent variable models in many contexts. However, DisARM and other estimators have potentially exploding variance near the boundary of the parameter space, where solutions tend to lie. To ameliorate this issue, we propose a new gradient estimator \textit{bitflip}-1 that has lower variance at the boundaries of the parameter space. As bitflip-1 has complementary properties to existing estimators, we introduce an aggregated estimator, \textit{unbiased gradient variance clipping} (UGC) that uses either a bitflip-1 or a DisARM gradient update for each coordinate. We theoretically prove that UGC has uniformly lower variance than DisARM. Empirically, we observe that UGC achieves the optimal value of the optimization objectives in toy experiments, discrete VAE training, and in a best subset selection problem.
Software implementation of the SNOW 3G Generator on iOS and Android platforms
Venue: arXiv cs.CR 2022
Year: 2022

The standard for wireless communication of high-speed data in mobile phones and data terminals, called LTE (Long-Term Evolution) and marketed as 4G/LTE, is quickly being adopted worldwide. The security of this type of communication is a crucial factor mainly due to its mobile and wireless nature. This work includes a practical analysis of the SNOW 3G generator used to protect the confidentiality and integrity in LTE communications. In particular, several techniques to perform multiplications and LFSR operations have been studied and implemented on both iOS and Android platforms. The evaluation of those implementations led to some conclusions that could be used to improve the efficiency of future implementations of the standard.
How to build vehicular ad-hoc networks on smartphones
Venue: arXiv cs.CR 2022
Year: 2022

Vehicular ad-hoc networks have been defined in the literature as communications networks that allow disseminating information among vehicles to help to reduce traffic accidents and congestions. The practical deployment of such networks has been delayed mainly due to economic and technical issues. This paper describes a new software application to detect traffic incidents and exchange information about them, using only smartphones, without any central authority or additional equipment. Both road safety and communication security have been taken into account in the application design. On the one hand, the interface has been designed to avoid distractions while driving because it operates automatically and independently of the driver, through voice prompts. On the other hand, communication security, which is essential in critical wireless networks, is provided through the protection of attributes such as authenticity, privacy, integrity and non-repudiation. All this is achieved without increasing the price of vehicles and without requiring the integration of new devices neither in vehicles nor on roads. The only prerequisite is to have a smartphone equipped with Wi-Fi connectivity and GPS location in each vehicle. The proposed application has been successfully validated both in large-scale NS-2 simulations and in small-scale real tests to detect traffic congestions and empty parking spaces.
What Features Influence Impact Feel? A Study of Impact Feedback in Action Games
Venue: arXiv cs.HC 2022
Year: 2022

Making the hit effect satisfy players is a long-standing problem faced by action game designers. However, no research systematically analyzed which game design elements affect such game feel. There is not even a term to describe it. So, we propose to use impact feel to describe the player's feeling when receiving juicy impact feedback. After collecting player's comments on action games from Steam's top seller list, we trained a natural language processing (NLP) model to rank action games with their performance on impact feel. We presented a 19-feature framework of impact feedback design and examined it in the top eight and last eight games. We listed an inventory of the usage of features and found that hit stop, sound coherence, and camera control may strongly influence players' impact feel. A lack of dedicated design on one of these three features may ruin players' impact feel. Our findings may become an evaluation metric for future studies.
Dropout is NOT All You Need to Prevent Gradient Leakage
Venue: arXiv cs.LG 2022
Year: 2022

Gradient inversion attacks on federated learning systems reconstruct client training data from exchanged gradient information. To defend against such attacks, a variety of defense mechanisms were proposed. However, they usually lead to an unacceptable trade-off between privacy and model utility. Recent observations suggest that dropout could mitigate gradient leakage and improve model utility if added to neural networks. Unfortunately, this phenomenon has not been systematically researched yet. In this work, we thoroughly analyze the effect of dropout on iterative gradient inversion attacks. We find that state of the art attacks are not able to reconstruct the client data due to the stochasticity induced by dropout during model training. Nonetheless, we argue that dropout does not offer reliable protection if the dropout induced stochasticity is adequately modeled during attack optimization. Consequently, we propose a novel Dropout Inversion Attack (DIA) that jointly optimizes for client data and dropout masks to approximate the stochastic client model. We conduct an extensive systematic evaluation of our attack on four seminal model architectures and three image classification datasets of increasing complexity. We find that our proposed attack bypasses the protection seemingly induced by dropout and reconstructs client data with high fidelity. Our work demonstrates that privacy inducing changes to model architectures alone cannot be assumed to reliably protect from gradient leakage and therefore should be combined with complementary defense mechanisms.
Customer Empowered Privacy-Preserving Secure Verification using Decentralized Identifier and Verifiable Credentials For Product Delivery Using Robots
Venue: arXiv cs.CR 2022
Year: 2022

In the age of respiratory illnesses like COVID 19, we understand the necessity for a robot based delivery system to ensure safe and contact free courier delivery. A blockchain based Dynamic IDentifier gives people total power over their identities while preserving auditability and anonymity. A human mobile phone and a robot are machines created with a chip, making it simple to deploy a physical unclonable function based verification system between the robot and the customer. This article presents a novel framework and a first customer verification scheme for verified courier delivery utilizing the blockchain enabled DID and PUF enabled robots. We employ DID for customer authentication between a robot (a service provider) and a customer and PUF for robot verification by the customer. We ve also put the proposed work into practice and demonstrated its capabilities in terms of throughput, latency, computing cost, and communication cost. We also show formal security proof for the proposed user verification scheme based on the tamarin prover.
Two-person Graph Convolutional Network for Skeleton-based Human Interaction Recognition
Venue: arXiv cs.CV 2022
Year: 2022

Graph convolutional networks (GCNs) have been the predominant methods in skeleton-based human action recognition, including human-human interaction recognition. However, when dealing with interaction sequences, current GCN-based methods simply split the two-person skeleton into two discrete graphs and perform graph convolution separately as done for single-person action classification. Such operations ignore rich interactive information and hinder effective spatial inter-body relationship modeling. To overcome the above shortcoming, we introduce a novel unified two-person graph to represent inter-body and intra-body correlations between joints. Experiments show accuracy improvements in recognizing both interactions and individual actions when utilizing the proposed two-person graph topology. In addition, We design several graph labeling strategies to supervise the model to learn discriminant spatial-temporal interactive features. Finally, we propose a two-person graph convolutional network (2P-GCN). Our model achieves state-of-the-art results on four benchmarks of three interaction datasets: SBU, interaction subsets of NTU-RGB+D and NTU-RGB+D 120.