OpenRaiser/Intern-Atlas huggingface dataset:
DeepEpiSolver: Unravelling Inverse problems in Covid, HIV, Ebola and Disease Transmission
Venue: arXiv cs.LG 2023
Year: 2023

The spread of many infectious diseases is modeled using variants of the SIR compartmental model, which is a coupled differential equation. The coefficients of the SIR model determine the spread trajectories of disease, on whose basis proactive measures can be taken. Hence, the coefficient estimates must be both fast and accurate. Shaier et al. in the paper "Disease Informed Neural Networks" used Physics Informed Neural Networks (PINNs) to estimate the parameters of the SIR model. There are two drawbacks to this approach. First, the training time for PINNs is high, with certain diseases taking close to 90 hrs to train. Second, PINNs don't generalize for a new SIDR trajectory, and learning its corresponding SIR parameters requires retraining the PINN from scratch. In this work, we aim to eliminate both of these drawbacks. We generate a dataset between the parameters of ODE and the spread trajectories by solving the forward problem for a large distribution of parameters using the LSODA algorithm. We then use a neural network to learn the mapping between spread trajectories and coefficients of SIDR in an offline manner. This allows us to learn the parameters of a new spread trajectory without having to retrain, enabling generalization at test time. We observe a speed-up of 3-4 orders of magnitude with accuracy comparable to that of PINNs for 11 highly infectious diseases. Further finetuning of neural network inferred ODE coefficients using PINN further leads to 2-3 orders improvement of estimated coefficients.
Efficient Lipschitzian Global Optimization of Hölder Continuous Multivariate Functions
Venue: arXiv cs.LG 2023
Year: 2023

This study presents an effective global optimization technique designed for multivariate functions that are Hölder continuous. Unlike traditional methods that construct lower bounding proxy functions, this algorithm employs a predetermined query creation rule that makes it computationally superior. The algorithm's performance is assessed using the average or cumulative regret, which also implies a bound for the simple regret and reflects the overall effectiveness of the approach. The results show that with appropriate parameters the algorithm attains an average regret bound of $O(T^{-\fracα{n}})$ for optimizing a Hölder continuous target function with Hölder exponent $α$ in an $n$-dimensional space within a given time horizon $T$. We demonstrate that this bound is minimax optimal.
Adaptive Bi-Recommendation and Self-Improving Network for Heterogeneous Domain Adaptation-Assisted IoT Intrusion Detection
Venue: arXiv cs.CR 2023
Year: 2023

As Internet of Things devices become prevalent, using intrusion detection to protect IoT from malicious intrusions is of vital importance. However, the data scarcity of IoT hinders the effectiveness of traditional intrusion detection methods. To tackle this issue, in this paper, we propose the Adaptive Bi-Recommendation and Self-Improving Network (ABRSI) based on unsupervised heterogeneous domain adaptation (HDA). The ABRSI transfers enrich intrusion knowledge from a data-rich network intrusion source domain to facilitate effective intrusion detection for data-scarce IoT target domains. The ABRSI achieves fine-grained intrusion knowledge transfer via adaptive bi-recommendation matching. Matching the bi-recommendation interests of two recommender systems and the alignment of intrusion categories in the shared feature space form a mutual-benefit loop. Besides, the ABRSI uses a self-improving mechanism, autonomously improving the intrusion knowledge transfer from four ways. A hard pseudo label voting mechanism jointly considers recommender system decision and label relationship information to promote more accurate hard pseudo label assignment. To promote diversity and target data participation during intrusion knowledge transfer, target instances failing to be assigned with a hard pseudo label will be assigned with a probabilistic soft pseudo label, forming a hybrid pseudo-labelling strategy. Meanwhile, the ABRSI also makes soft pseudo-labels globally diverse and individually certain. Finally, an error knowledge learning mechanism is utilised to adversarially exploit factors that causes detection ambiguity and learns through both current and previous error knowledge, preventing error knowledge forgetfulness. Holistically, these mechanisms form the ABRSI model that boosts IoT intrusion detection accuracy via HDA-assisted intrusion knowledge transfer.
From Gödel's Incompleteness Theorem to the completeness of bot beliefs (Extended abstract)
Venue: arXiv cs.AI 2023
Year: 2023

Hilbert and Ackermann asked for a method to consistently extend incomplete theories to complete theories. Gödel essentially proved that any theory capable of encoding its own statements and their proofs contains statements that are true but not provable. Hilbert did not accept that Gödel's construction answered his question, and in his late writings and lectures, Gödel agreed that it did not, since theories can be completed incrementally, by adding axioms to prove ever more true statements, as science normally does, with completeness as the vanishing point. This pragmatic view of validity is familiar not only to scientists who conjecture test hypotheses but also to real estate agents and other dealers, who conjure claims, albeit invalid, as necessary to close a deal, confident that they will be able to conjure other claims, albeit invalid, sufficient to make the first claims valid. We study the underlying logical process and describe the trajectories leading to testable but unfalsifiable theories to which bots and other automated learners are likely to converge.
Multi-view knowledge distillation transformer for human action recognition
Venue: arXiv cs.CV 2023
Year: 2023

Recently, Transformer-based methods have been utilized to improve the performance of human action recognition. However, most of these studies assume that multi-view data is complete, which may not always be the case in real-world scenarios. Therefore, this paper presents a novel Multi-view Knowledge Distillation Transformer (MKDT) framework that consists of a teacher network and a student network. This framework aims to handle incomplete human action problems in real-world applications. Specifically, the multi-view knowledge distillation transformer uses a hierarchical vision transformer with shifted windows to capture more spatial-temporal information. Experimental results demonstrate that our framework outperforms the CNN-based method on three public datasets.
Reconfigurable Intelligent Surface-Aided Full-Duplex mmWave MIMO: Channel Estimation, Passive and Hybrid Beamforming
Venue: arXiv cs.IT 2023
Year: 2023

Millimeter wave (mmWave) full-duplex (FD) is a promising technique for improving capacity by maximizing the utilization of both time and the rich mmWave frequency resources. Still, it has restrictions due to FD self-interference (SI) and mmWave's limited coverage. Therefore, this study dives into FD mmWave MIMO with the assistance of reconfigurable intelligent surfaces (RIS) for capacity improvement. First, we demonstrate the angular-domain reciprocity of FD antenna arrays under the far-field planar wavefront assumption. Accordingly, a strategy for joint downlink-uplink (DL-UL) channel estimation is presented. For estimating the SI channel, the direct channel, and the cascaded channel, the Khatri-Rao product-based compressive sensing (KR-CS), distributed CS (D-CS), and two-stage multiple measurement vector-based D-CS (M-D-CS) frameworks are proposed, respectively. Additionally, we propose a passive beamforming optimization solution based on the angular-domain cascaded channel. With hybrid beamforming architectures, a novel hybrid weighted minimum mean squared error method for SI cancellation (H-WMMSE-SIC) is proposed. Simulations have revealed that joint DL-UL processing significantly improves estimation performance in comparison to separate DL/UL channel estimation. Particularly, when the interference-to-noise ratio is less than 35 dB, our proposed H-WMMSE-SIC offers spectral efficiency performance comparable to fully-digital WMMSE-SIC. Finally, the computational complexity is analyzed for our proposed methods.
Fairness meets Cross-Domain Learning: a new perspective on Models and Metrics
Venue: arXiv cs.CV 2023
Year: 2023

Deep learning-based recognition systems are deployed at scale for several real-world applications that inevitably involve our social life. Although being of great support when making complex decisions, they might capture spurious data correlations and leverage sensitive attributes (e.g. age, gender, ethnicity). How to factor out this information while keeping a high prediction performance is a task with still several open questions, many of which are shared with those of the domain adaptation and generalization literature which focuses on avoiding visual domain biases. In this work, we propose an in-depth study of the relationship between cross-domain learning (CD) and model fairness by introducing a benchmark on face and medical images spanning several demographic groups as well as classification and localization tasks. After having highlighted the limits of the current evaluation metrics, we introduce a new Harmonic Fairness (HF) score to assess jointly how fair and accurate every model is with respect to a reference baseline. Our study covers 14 CD approaches alongside three state-of-the-art fairness algorithms and shows how the former can outperform the latter. Overall, our work paves the way for a more systematic analysis of fairness problems in computer vision. Code available at: https://github.com/iurada/fairness_crossdomain
Analysis and Visualization of the Parameter Space of Matrix Factorization-based Recommender Systems
Venue: arXiv cs.IR 2023
Year: 2023

Recommender system is the most successful commercial technology in the past decade. Technical mammoth such as Temu, TikTok and Amazon utilize the technology to generate enormous revenues each year. Although there have been enough research literature on accuracy enhancement of the technology, explainable AI is still a new idea to the field. In 2022, the author of this paper provides a geometric interpretation of the matrix factorization-based methods and uses geometric approximation to solve the recommendation problem. We continue the research in this direction in this paper, and visualize the inner structure of the parameter space of matrix factorization technologies. We show that the parameters of matrix factorization methods are distributed within a hyper-ball. After further analysis, we prove that the distribution of the parameters is not multivariate normal.
Design of a Smart Waste Management System for the City of Johannesburg
Venue: arXiv cs.GT 2023
Year: 2023

Every human being in this world produces waste. South Africa is a developing country with many townships that have limited waste resources. Over-increasing population growth overpowers the volume of most municipal authorities to provide even the most essential services. Waste in townships is produced via littering, dumping of bins, cutting of trees, dumping of waste near rivers, and overrunning of waste bins. Waste increases diseases, air pollution, and environmental pollution, and lastly increases gas emissions that contribute to the release of greenhouse gases. The ungathered waste is dumped widely in the streets and drains contributing to flooding, breeding of insects, rodent vectors, and spreading of diseases. Therefore, the aim of this paper is to design a smart waste management system for the city of Johannesburg. The city of Johannesburg contains waste municipality workers and has provided some areas with waste resources such as waste bins and trucks for collecting waste. But the problem is that the resources only are not enough to solve the problem of waste in the city. The waste municipality uses traditional ways of collecting waste such as going to each street and picking up waste bins. The traditional way has worked for years but as the population is increasing more waste is produced which causes various problems for the waste municipalities and the public at large. The proposed system consists of sensors, user applications, and a real-time monitoring system. This paper adopts the experimental methodology.
A novel collision model for inextensible textiles and its experimental validation
Venue: arXiv cs.RO 2023
Year: 2023

In this work, we introduce a collision model specifically tailored for the simulation of inextensible textiles. The model considers friction, contacts, and inextensibility constraints all at the same time without any decoupling. Self-collisions are modeled in a natural way that allows considering the thickness of cloth without introducing unwanted oscillations. The discretization of the equations of motion leads naturally to a sequence of quadratic problems with inequality and equality constraints. In order to solve these problems efficiently, we develop a novel active-set algorithm that takes into account past active constraints to accelerate the resolution of unresolved contacts. We put to a test the developed collision procedure with diverse scenarios involving static and dynamic friction, sharp objects, and complex-topology folding sequences. Finally, we perform an experimental validation of the collision model by comparing simulations with recordings of real textiles as given by a $\textit{Motion Capture System}$. The results are very accurate, having errors around 1 cm for DIN A2 textiles (42 x 59.4 cm) even in difficult scenarios involving fast and strong hits with a rigid object.
Indian Language Summarization using Pretrained Sequence-to-Sequence Models
Venue: arXiv cs.CL 2023
Year: 2023

The ILSUM shared task focuses on text summarization for two major Indian languages- Hindi and Gujarati, along with English. In this task, we experiment with various pretrained sequence-to-sequence models to find out the best model for each of the languages. We present a detailed overview of the models and our approaches in this paper. We secure the first rank across all three sub-tasks (English, Hindi and Gujarati). This paper also extensively analyzes the impact of k-fold cross-validation while experimenting with limited data size, and we also perform various experiments with a combination of the original and a filtered version of the data to determine the efficacy of the pretrained models.
Indonesian Text-to-Image Synthesis with Sentence-BERT and FastGAN
Venue: arXiv cs.CV 2023
Year: 2023

Currently, text-to-image synthesis uses text encoder and image generator architecture. Research on this topic is challenging. This is because of the domain gap between natural language and vision. Nowadays, most research on this topic only focuses on producing a photo-realistic image, but the other domain, in this case, is the language, which is less concentrated. A lot of the current research uses English as the input text. Besides, there are many languages around the world. Bahasa Indonesia, as the official language of Indonesia, is quite popular. This language has been taught in Philipines, Australia, and Japan. Translating or recreating a new dataset into another language with good quality will cost a lot. Research on this domain is necessary because we need to examine how the image generator performs in other languages besides generating photo-realistic images. To achieve this, we translate the CUB dataset into Bahasa using google translate and manually by humans. We use Sentence BERT as the text encoder and FastGAN as the image generator. FastGAN uses lots of skip excitation modules and auto-encoder to generate an image with resolution 512x512x3, which is twice as bigger as the current state-of-the-art model (Zhang, Xu, Li, Zhang, Wang, Huang and Metaxas, 2019). We also get 4.76 +- 0.43 and 46.401 on Inception Score and Fréchet inception distance, respectively, and comparable with the current English text-to-image generation models. The mean opinion score also gives as 3.22 out of 5, which means the generated image is acceptable by humans. Link to source code: https://github.com/share424/Indonesian-Text-to-Image-synthesis-with-Sentence-BERT-and-FastGAN
Combining Contexts from Multiple Sources for Documentation-Specific Code Example Generation
Venue: arXiv cs.SE 2023
Year: 2023

Code example is a crucial part of good documentation. It helps the developers to understand the documentation easily and use the corresponding code unit (e.g., method) properly. However, many official documentation still lacks (good) code example and it is one of the common documentation issues as found by several studies. Hence in this paper, we consider automatic code example generation for documentation, a direction less explored by the existing research. We employ Codex, a GPT-3 based model, pre-trained on both natural and programming languages to generate code examples from source code and documentation given as input. Our preliminary investigation on 40 scikit-learn methods reveals that this approach is able to generate good code examples where 72.5% code examples were executed without error (passability) and 82.5% properly dealt with the target method and documentation (relevance). We also find that incorporation of error logs (produced by the compiler while executing a failed code example) in the input further improves the passability from 72.5% to 87.5%. Thus, our investigation sets the base of documentation-specific code example generation and warrants in-depth future studies.
Exploring Novel Quality Diversity Methods For Generalization in Reinforcement Learning
Venue: arXiv cs.NE 2023
Year: 2023

The Reinforcement Learning field is strong on achievements and weak on reapplication; a computer playing GO at a super-human level is still terrible at Tic-Tac-Toe. This paper asks whether the method of training networks improves their generalization. Specifically we explore core quality diversity algorithms, compare against two recent algorithms, and propose a new algorithm to deal with shortcomings in existing methods. Although results of these methods are well below the performance hoped for, our work raises important points about the choice of behavior criterion in quality diversity, the interaction of differential and evolutionary training methods, and the role of offline reinforcement learning and randomized learning in evolutionary search.
PORE: Provably Robust Recommender Systems against Data Poisoning Attacks
Venue: arXiv cs.CR 2023
Year: 2023

Data poisoning attacks spoof a recommender system to make arbitrary, attacker-desired recommendations via injecting fake users with carefully crafted rating scores into the recommender system. We envision a cat-and-mouse game for such data poisoning attacks and their defenses, i.e., new defenses are designed to defend against existing attacks and new attacks are designed to break them. To prevent such a cat-and-mouse game, we propose PORE, the first framework to build provably robust recommender systems in this work. PORE can transform any existing recommender system to be provably robust against any untargeted data poisoning attacks, which aim to reduce the overall performance of a recommender system. Suppose PORE recommends top-$N$ items to a user when there is no attack. We prove that PORE still recommends at least $r$ of the $N$ items to the user under any data poisoning attack, where $r$ is a function of the number of fake users in the attack. Moreover, we design an efficient algorithm to compute $r$ for each user. We empirically evaluate PORE on popular benchmark datasets.
Deepfake in the Metaverse: Security Implications for Virtual Gaming, Meetings, and Offices
Venue: arXiv cs.CR 2023
Year: 2023

The metaverse has gained significant attention from various industries due to its potential to create a fully immersive and interactive virtual world. However, the integration of deepfakes in the metaverse brings serious security implications, particularly with regard to impersonation. This paper examines the security implications of deepfakes in the metaverse, specifically in the context of gaming, online meetings, and virtual offices. The paper discusses how deepfakes can be used to impersonate in gaming scenarios, how online meetings in the metaverse open the door for impersonation, and how virtual offices in the metaverse lack physical authentication, making it easier for attackers to impersonate someone. The implications of these security concerns are discussed in relation to the confidentiality, integrity, and availability (CIA) triad. The paper further explores related issues such as the darkverse, and digital cloning, as well as regulatory and privacy concerns associated with addressing security threats in the virtual world.
GestureDiffuCLIP: Gesture Diffusion Model with CLIP Latents
Venue: arXiv cs.CV 2023
Year: 2023

The automatic generation of stylized co-speech gestures has recently received increasing attention. Previous systems typically allow style control via predefined text labels or example motion clips, which are often not flexible enough to convey user intent accurately. In this work, we present GestureDiffuCLIP, a neural network framework for synthesizing realistic, stylized co-speech gestures with flexible style control. We leverage the power of the large-scale Contrastive-Language-Image-Pre-training (CLIP) model and present a novel CLIP-guided mechanism that extracts efficient style representations from multiple input modalities, such as a piece of text, an example motion clip, or a video. Our system learns a latent diffusion model to generate high-quality gestures and infuses the CLIP representations of style into the generator via an adaptive instance normalization (AdaIN) layer. We further devise a gesture-transcript alignment mechanism that ensures a semantically correct gesture generation based on contrastive learning. Our system can also be extended to allow fine-grained style control of individual body parts. We demonstrate an extensive set of examples showing the flexibility and generalizability of our model to a variety of style descriptions. In a user study, we show that our system outperforms the state-of-the-art approaches regarding human likeness, appropriateness, and style correctness.
Temporal Egonet Subgraph Transitions
Venue: arXiv cs.SI 2023
Year: 2023

How do we summarize dynamic behavioral interactions? We introduce a possible node-embedding-based solution to this question: temporal egonet subgraph transitions.
A Survey of Machine Learning-Based Ride-Hailing Planning
Venue: arXiv cs.LG 2023
Year: 2023

Ride-hailing is a sustainable transportation paradigm where riders access door-to-door traveling services through a mobile phone application, which has attracted a colossal amount of usage. There are two major planning tasks in a ride-hailing system: (1) matching, i.e., assigning available vehicles to pick up the riders, and (2) repositioning, i.e., proactively relocating vehicles to certain locations to balance the supply and demand of ride-hailing services. Recently, many studies of ride-hailing planning that leverage machine learning techniques have emerged. In this article, we present a comprehensive overview on latest developments of machine learning-based ride-hailing planning. To offer a clear and structured review, we introduce a taxonomy into which we carefully fit the different categories of related works according to the types of their planning tasks and solution schemes, which include collective matching, distributed matching, collective repositioning, distributed repositioning, and joint matching and repositioning. We further shed light on many real-world datasets and simulators that are indispensable for empirical studies on machine learning-based ride-hailing planning strategies. At last, we propose several promising research directions for this rapidly growing research and practical field.