OpenRaiser/Intern-Atlas huggingface dataset:
Associating Spatially-Consistent Grouping with Text-supervised Semantic Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

In this work, we investigate performing semantic segmentation solely through the training on image-sentence pairs. Due to the lack of dense annotations, existing text-supervised methods can only learn to group an image into semantic regions via pixel-insensitive feedback. As a result, their grouped results are coarse and often contain small spurious regions, limiting the upper-bound performance of segmentation. On the other hand, we observe that grouped results from self-supervised models are more semantically consistent and break the bottleneck of existing methods. Motivated by this, we introduce associate self-supervised spatially-consistent grouping with text-supervised semantic segmentation. Considering the part-like grouped results, we further adapt a text-supervised model from image-level to region-level recognition with two core designs. First, we encourage fine-grained alignment with a one-way noun-to-region contrastive loss, which reduces the mismatched noun-region pairs. Second, we adopt a contextually aware masking strategy to enable simultaneous recognition of all grouped regions. Coupled with spatially-consistent grouping and region-adapted recognition, our method achieves 59.2% mIoU and 32.4% mIoU on Pascal VOC and Pascal Context benchmarks, significantly surpassing the state-of-the-art methods.
Effective Feature Extraction for Intrusion Detection System using Non-negative Matrix Factorization and Univariate analysis
Venue: arXiv cs.CR 2023
Year: 2023

An Intrusion detection system (IDS) is essential for avoiding malicious activity. Mostly, IDS will be improved by machine learning approaches, but the model efficiency is degrading because of more headers (or features) present in the packet (each record). The proposed model extracts practical features using Non-negative matrix factorization and chi-square analysis. The more number of features increases the exponential time and risk of overfitting the model. Using both techniques, the proposed model makes a hierarchical approach that will reduce the features quadratic error and noise. The proposed model is implemented on three publicly available datasets, which gives significant improvement. According to recent research, the proposed model has improved performance by 4.66% and 0.39% with respective NSL-KDD and CICD 2017.
Automatic Geo-alignment of Artwork in Children's Story Books
Venue: arXiv cs.AI 2023
Year: 2023

A study was conducted to prove AI software could be used to translate and generate illustrations without any human intervention. This was done with the purpose of showing and distributing it to the external customer, Pratham Books. The project aligns with the company's vision by leveraging the generalisation and scalability of Machine Learning algorithms, offering significant cost efficiency increases to a wide range of literary audiences in varied geographical locations. A comparative study methodology was utilised to determine the best performant method out of the 3 devised, Prompt Augmentation using Keywords, CLIP Embedding Mask, and Cross Attention Control with Editorial Prompts. A thorough evaluation process was completed using both quantitative and qualitative measures. Each method had its own strengths and weaknesses, but through the evaluation, method 1 was found to have the best yielding results. Promising future advancements may be made to further increase image quality by incorporating Large Language Models and personalised stylistic models. The presented approach can also be adapted to Video and 3D sculpture generation for novel illustrations in digital webbooks.
PromptORE -- A Novel Approach Towards Fully Unsupervised Relation Extraction
Venue: arXiv cs.CL 2023
Year: 2023

Unsupervised Relation Extraction (RE) aims to identify relations between entities in text, without having access to labeled data during training. This setting is particularly relevant for domain specific RE where no annotated dataset is available and for open-domain RE where the types of relations are a priori unknown. Although recent approaches achieve promising results, they heavily depend on hyperparameters whose tuning would most often require labeled data. To mitigate the reliance on hyperparameters, we propose PromptORE, a ''Prompt-based Open Relation Extraction'' model. We adapt the novel prompt-tuning paradigm to work in an unsupervised setting, and use it to embed sentences expressing a relation. We then cluster these embeddings to discover candidate relations, and we experiment different strategies to automatically estimate an adequate number of clusters. To the best of our knowledge, PromptORE is the first unsupervised RE model that does not need hyperparameter tuning. Results on three general and specific domain datasets show that PromptORE consistently outperforms state-of-the-art models with a relative gain of more than 40% in B 3 , V-measure and ARI. Qualitative analysis also indicates PromptORE's ability to identify semantically coherent clusters that are very close to true relations.
Unified Emulation-Simulation Training Environment for Autonomous Cyber Agents
Venue: arXiv cs.LG 2023
Year: 2023

Autonomous cyber agents may be developed by applying reinforcement and deep reinforcement learning (RL/DRL), where agents are trained in a representative environment. The training environment must simulate with high-fidelity the network Cyber Operations (CyOp) that the agent aims to explore. Given the complexity of net-work CyOps, a good simulator is difficult to achieve. This work presents a systematic solution to automatically generate a high-fidelity simulator in the Cyber Gym for Intelligent Learning (CyGIL). Through representation learning and continuous learning, CyGIL provides a unified CyOp training environment where an emulated CyGIL-E automatically generates a simulated CyGIL-S. The simulator generation is integrated with the agent training process to further reduce the required agent training time. The agent trained in CyGIL-S is transferrable directly to CyGIL-E showing full transferability to the emulated "real" network. Experimental results are presented to demonstrate the CyGIL training performance. Enabling offline RL, the CyGIL solution presents a promising direction towards sim-to-real for leveraging RL agents in real-world cyber networks.
Automated Expected Value Analysis of Recursive Programs
Venue: arXiv cs.PL 2023
Year: 2023

In this work, we study the fully automated inference of expected result values of probabilistic programs in the presence of natural programming constructs such as procedures, local variables and recursion. While crucial, capturing these constructs becomes highly non-trivial. The key contribution is the definition of a term representation, denoted as infer[.], translating a pre-expectation semantics into first-order constraints, susceptible to automation via standard methods. A crucial step is the use of logical variables, inspired by previous work on Hoare logics for recursive programs. Noteworthy, our methodology is not restricted to tail-recursion, which could unarguably be replaced by iteration and wouldn't need additional insights. We have implemented this analysis in our prototype ev-imp. We provide ample experimental evidence of the prototype's algorithmic expressibility.
A Simple Approach for General Task-Oriented Picking using Placing constraints
Venue: arXiv cs.RO 2023
Year: 2023

Pick-and-place is an important manipulation task in domestic or manufacturing applications. There exist many works focusing on grasp detection with high picking success rate but lacking consideration of downstream manipulation tasks (e.g., placing). Although some research works proposed methods to incorporate task conditions into grasp selection, most of them are data-driven and are therefore hard to adapt to arbitrary operating environments. Observing this challenge, we propose a general task-oriented pick-place framework that treats the target task and operating environment as placing constraints into grasping optimization. Combined with existing grasp detectors, our framework is able to generate feasible grasps for different downstream tasks and adapt to environmental changes without time-consuming re-training processes. Moreover, the framework can accept different definitions of placing constraints, so it is easy to integrate with other modules. Experiments in the simulator and real-world on multiple pick-place tasks are conducted to evaluate the performance of our framework. The result shows that our framework achieves a high and robust task success rate on a wide variety of the pick-place tasks.
Clustering Social Touch Gestures for Human-Robot Interaction
Venue: arXiv cs.HC 2023
Year: 2023

Social touch provides a rich non-verbal communication channel between humans and robots. Prior work has identified a set of touch gestures for human-robot interaction and described them with natural language labels (e.g., stroking, patting). Yet, no data exists on the semantic relationships between the touch gestures in users' minds. To endow robots with touch intelligence, we investigated how people perceive the similarities of social touch labels from the literature. In an online study, 45 participants grouped 36 social touch labels based on their perceived similarities and annotated their groupings with descriptive names. We derived quantitative similarities of the gestures from these groupings and analyzed the similarities using hierarchical clustering. The analysis resulted in 9 clusters of touch gestures formed around the social, emotional, and contact characteristics of the gestures. We discuss the implications of our results for designing and evaluating touch sensing and interactions with social robots.
Faulty Branch Identification in Passive Optical Networks using Machine Learning
Venue: arXiv cs.LG 2023
Year: 2023

Passive optical networks (PONs) have become a promising broadband access network solution. To ensure a reliable transmission, and to meet service level agreements, PON systems have to be monitored constantly in order to quickly identify and localize networks faults. Typically, a service disruption in a PON system is mainly due to fiber cuts and optical network unit (ONU) transmitter/receiver failures. When the ONUs are located at different distances from the optical line terminal (OLT), the faulty ONU or branch can be identified by analyzing the recorded optical time domain reflectometry (OTDR) traces. However, faulty branch isolation becomes very challenging when the reflections originating from two or more branches with similar length overlap, which makes it very hard to discriminate the faulty branches given the global backscattered signal. Recently, machine learning (ML) based approaches have shown great potential for managing optical faults in PON systems. Such techniques perform well when trained and tested with data derived from the same PON system. But their performance may severely degrade, if the PON system (adopted for the generation of the training data) has changed, e.g. by adding more branches or varying the length difference between two neighboring branches. etc. A re-training of the ML models has to be conducted for each network change, which can be time consuming. In this paper, to overcome the aforementioned issues, we propose a generic ML approach trained independently of the network architecture for identifying the faulty branch in PON systems given OTDR signals for the cases of branches with close lengths. Such an approach can be applied to an arbitrary PON system without requiring to be re-trained for each change of the network. The proposed approach is validated using experimental data derived from PON system.
Thermal Crosstalk Analysis in RRAM Passive Crossbar Arrays
Venue: arXiv cs.ET 2023
Year: 2023

As the packing density of resistive random access memory (RRAM) devices increases, the effect of thermal cross-talk across the devices in a crossbar array arrangement influences their overall operation significantly. The electro-thermal effects in a densely packed RRAM crossbar can accelerate the retention and endurance degradation; hence poses a serious reliability threat. This paper systematically investigates the electro-thermal effects in passive RRAM crossbar arrays using COMSOL multi-physics simulations. Furthermore, we propose a methodology to model the thermal cross-talk effect and incorporate it in a SPICE-compatible physics-based RRAM compact model. Finally, we demonstrate the impact of thermal coupling on RRAM crossbar array operation in terms of vector-matrix multiplication using calibrated SPICE simulations.
Clustering Validation with The Area Under Precision-Recall Curves
Venue: arXiv cs.LG 2023
Year: 2023

Confusion matrices and derived metrics provide a comprehensive framework for the evaluation of model performance in machine learning. These are well-known and extensively employed in the supervised learning domain, particularly classification. Surprisingly, such a framework has not been fully explored in the context of clustering validation. Indeed, just recently such a gap has been bridged with the introduction of the Area Under the ROC Curve for Clustering (AUCC), an internal/relative Clustering Validation Index (CVI) that allows for clustering validation in real application scenarios. In this work we explore the Area Under Precision-Recall Curve (and related metrics) in the context of clustering validation. We show that these are not only appropriate as CVIs, but should also be preferred in the presence of cluster imbalance. We perform a comprehensive evaluation of proposed and state-of-art CVIs on real and simulated data sets. Our observations corroborate towards an unified validation framework for supervised and unsupervised learning, given that they are consistent with existing guidelines established for the evaluation of supervised learning models.
Code-Division OFDM Joint Communication and Sensing System for 6G Machine-type Communication
Venue: arXiv cs.IT 2023
Year: 2023

The joint communication and sensing (JCS) system can provide higher spectrum efficiency and load-saving for 6G machine-type communication (MTC) applications by merging necessary communication and sensing abilities with unified spectrum and transceivers. In order to suppress the mutual interference between the communication and radar sensing signals to improve the communication reliability and radar sensing accuracy, we propose a novel code-division orthogonal frequency division multiplex (CD-OFDM) JCS MTC system, where MTC users can simultaneously and continuously conduct communication and sensing with each other. {\color{black} We propose a novel CD-OFDM JCS signal and corresponding successive-interference-cancellation (SIC) based signal processing technique that obtains code-division multiplex (CDM) gain, which is compatible with the prevalent orthogonal frequency division multiplex (OFDM) communication system.} To model the unified JCS signal transmission and reception process, we propose a novel unified JCS channel model. Finally, the simulation and numerical results are shown to verify the feasibility of the CD-OFDM JCS MTC system {\color{black} and the error propagation performance}. We show that the CD-OFDM JCS MTC system can achieve not only more reliable communication but also comparably robust radar sensing compared with the precedent OFDM JCS system, especially in low signal-to-interference-and-noise ratio (SINR) regime.
Analysis of Software Engineering Practices in General Software and Machine Learning Startups
Venue: arXiv cs.SE 2023
Year: 2023

Context: On top of the inherent challenges startup software companies face applying proper software engineering practices, the non-deterministic nature of machine learning techniques makes it even more difficult for machine learning (ML) startups. Objective: Therefore, the objective of our study is to understand the whole picture of software engineering practices followed by ML startups and identify additional needs. Method: To achieve our goal, we conducted a systematic literature review study on 37 papers published in the last 21 years. We selected papers on both general software startups and ML startups. We collected data to understand software engineering (SE) practices in five phases of the software development life-cycle: requirement engineering, design, development, quality assurance, and deployment. Results: We find some interesting differences in software engineering practices in ML startups and general software startups. The data management and model learning phases are the most prominent among them. Conclusion: While ML startups face many similar challenges to general software startups, the additional difficulties of using stochastic ML models require different strategies in using software engineering practices to produce high-quality products.
Implementing Dynamic Programming in Computability Logic Web
Venue: arXiv cs.AI 2023
Year: 2023

We present a novel definition of an algorithm and its corresponding algorithm language called CoLweb. The merit of CoLweb [1] is that it makes algorithm design so versatile. That is, it forces us to a high-level, proof-carrying, distributed-style approach to algorithm design for both non-distributed computing and distributed one. We argue that this approach simplifies algorithm design. In addition, it unifies other approaches including recursive logical/functional algorithms, imperative algorithms, object-oriented imperative algorithms, neural-nets, interaction nets, proof-carrying code, etc. As an application, we refine Horn clause definitions into two kinds: blind-univerally-quantified (BUQ) ones and parallel-universally-quantified (PUQ) ones. BUQ definitions corresponds to the traditional ones such as those in Prolog where knowledgebase is $not$ expanding and its proof procedure is based on the backward chaining. On the other hand, in PUQ definitions, knowledgebase is $expanding$ and its proof procedure leads to forward chaining and {\it automatic memoization}.
Information and Energy Transmission with Wavelet-Reconstructed Harvesting Functions
Venue: arXiv cs.IT 2023
Year: 2023

In practical simultaneous information and energy transmission (SIET), the exact energy harvesting function is usually unavailable because an energy harvesting circuit is nonlinear and nonideal. In this work, we consider a SIET problem where the harvesting function is accessible only at experimentally-taken sample points and study how close we can design SIET to the optimal system with such sampled knowledge. Assuming that the harvesting function is of bounded variation that may have discontinuities, we separately consider two settings where samples are taken without and with additive noise. For these settings, we propose to design a SIET system as if a wavelet-reconstructed harvesting function is the true one and study its asymptotic performance loss of energy and information delivery from the true optimal one. Specifically, for noiseless samples, it is shown that designing SIET as if the wavelet-reconstructed harvesting function is the truth incurs asymptotically vanishing energy and information delivery loss with the number of samples. For noisy samples, we propose to reconstruct wavelet coefficients via soft-thresholding estimation. Then, we not only obtain similar asymptotic losses to the noiseless case but also show that the energy loss by wavelets is asymptotically optimal up to a logarithmic factor.
A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material
Venue: arXiv cs.LG 2023
Year: 2023

Diffusion models have become a new SOTA generative modeling method in various fields, for which there are multiple survey works that provide an overall survey. With the number of articles on diffusion models increasing exponentially in the past few years, there is an increasing need for surveys of diffusion models on specific fields. In this work, we are committed to conducting a survey on the graph diffusion models. Even though our focus is to cover the progress of diffusion models in graphs, we first briefly summarize how other generative modeling methods are used for graphs. After that, we introduce the mechanism of diffusion models in various forms, which facilitates the discussion on the graph diffusion models. The applications of graph diffusion models mainly fall into the category of AI-generated content (AIGC) in science, for which we mainly focus on how graph diffusion models are utilized for generating molecules and proteins but also cover other cases, including materials design. Moreover, we discuss the issue of evaluating diffusion models in the graph domain and the existing challenges.
Distributed Block Coordinate Moving Horizon Estimation for 2D Visual-Inertial-Odometry SLAM
Venue: arXiv cs.RO 2023
Year: 2023

This paper presents a Visual Inertial Odometry Landmark-based Simultaneous Localisation and Mapping algorithm based on a distributed block coordinate nonlinear Moving Horizon Estimation scheme. The main advantage of the proposed method is that the updates on the position of the landmarks are based on a Bundle Adjustment technique that can be parallelised over the landmarks. The performance of the method is demonstrated in simulations in different environments and with different types of robot trajectory. Circular and wiggling patterns in the trajectory lead to better estimation performance than straight ones, confirming what is expected from recent nonlinear observability theory.
Image Blind Denoising Using Dual Convolutional Neural Network with Skip Connection
Venue: arXiv cs.CV 2023
Year: 2023

In recent years, deep convolutional neural networks have shown fascinating performance in the field of image denoising. However, deeper network architectures are often accompanied with large numbers of model parameters, leading to high training cost and long inference time, which limits their application in practical denoising tasks. In this paper, we propose a novel dual convolutional blind denoising network with skip connection (DCBDNet), which is able to achieve a desirable balance between the denoising effect and network complexity. The proposed DCBDNet consists of a noise estimation network and a dual convolutional neural network (CNN). The noise estimation network is used to estimate the noise level map, which improves the flexibility of the proposed model. The dual CNN contains two branches: a u-shaped sub-network is designed for the upper branch, and the lower branch is composed of the dilated convolution layers. Skip connections between layers are utilized in both the upper and lower branches. The proposed DCBDNet was evaluated on several synthetic and real-world image denoising benchmark datasets. Experimental results have demonstrated that the proposed DCBDNet can effectively remove gaussian noise in a wide range of levels, spatially variant noise and real noise. With a simple model structure, our proposed DCBDNet still can obtain competitive denoising performance compared to the state-of-the-art image denoising models containing complex architectures. Namely, a favorable trade-off between denoising performance and model complexity is achieved. Codes are available at https://github.com/WenCongWu/DCBDNet.