OpenRaiser/Intern-Atlas huggingface dataset:
LSTM-based QoE Evaluation for Web Microservices' Reputation Scoring
Venue: arXiv cs.IR 2023
Year: 2023

Sentiment analysis is the task of mining the authors' opinions about specific entities. It allows organizations to monitor different services in real time and act accordingly. Reputation is what is generally said or believed about people or things. Informally, reputation combines the measure of reliability derived from feedback, reviews, and ratings gathered from users, which reflect their quality of experience (QoE) and can either increase or harm the reputation of the provided services. In this study, we propose to perform sentiment analysis on web microservices reviews to exploit the provided information to assess and score the microservices' reputation. Our proposed approach uses the Long Short-Term Memory (LSTM) model to perform sentiment analysis and the Net Brand Reputation (NBR) algorithm to assess reputation scores for microservices. This approach is tested on a set of more than 10,000 reviews related to 15 Amazon Web microservices, and the experimental results have shown that our approach is more accurate than existing approaches, with an accuracy and precision of 93% obtained after applying an oversampling strategy and a resulting reputation score of the considered microservices community of 89%.
Block Chain in the IoT industry: A Systematic Literature Review
Venue: arXiv cs.DB 2023
Year: 2023

The possibility of block chain innovation revolutionizing business operations and interpersonal interactions in Industry 4.0 is becoming more widely acknowledged. Industry 4.0 and the Industrial Internet of Things (IoT) are among the new application fields. As a result, the purpose of this article is to investigate the block chain applications that are already being used in IoT and Industry 4.0. In particular, it looks at current research trends in various IoT applications, addressing problems, concerns, and potential future uses of integrating block chain technology. This article also includes a thorough discussion of the key elements of block chain databases, including Merkle trees, transaction management, sharding, long-term memory, and short-term memory. In order to do this, more than 46 pertinent primary research that have been published in reputable journals have been chosen for additional examination. The workflow of a block chain network utilizing IoT is also demonstrated, demonstrating how IoT devices communicate with one another and how they contribute to the network's overall operation. The taxonomy diagram below serves to illustrate the contribution.
Transcendence of Sturmian Numbers over an Algebraic Base
Venue: arXiv cs.FL 2023
Year: 2023

We consider numbers of the form $S_β(\boldsymbol{u}):=\sum_{n=0}^\infty \frac{u_n}{β^n}$ for $\boldsymbol{u}=\langle u_n \rangle_{n=0}^\infty$ a Sturmian sequence over a binary alphabet and $β$ an algebraic number with $|β|>1$. We show that every such number is transcendental. More generally, for a given base~$β$ and given irrational number~$θ$ we characterise the $\overline{\mathbb{Q}}$-linear independence of sets of the form $\left\{ 1, S_β(\boldsymbol{u}^{(1)}),\ldots,S_β(\boldsymbol{u}^{(k)}) \right\}$, where $\boldsymbol{u}^{(1)},\ldots,\boldsymbol{u}^{(k)}$ are Sturmian sequences having slope $θ$. We give an application of our main result to the theory of dynamical systems, showing that for a contracted rotation on the unit circle with algebraic slope, its limit set is either finite or consists exclusively of transcendental elements other than its endpoints $0$ and $1$. This confirms a conjecture of Bugeaud, Kim, Laurent, and Nogueira.
Unit Cell Phase-Frequency Profile Optimization in RIS-Assisted Wide-Band OFDM Systems
Venue: arXiv cs.IT 2023
Year: 2023

The reflection characteristics of a reconfigurable intelligent surface (RIS) depend on the phase response of the constituent unit cells, which is necessarily frequency dependent. This paper investigates the role of an RIS constituting unit cells with different phase-frequency profiles in a wide-band orthogonal frequency division multiplexing (OFDM) system to improve the achievable rate. We first propose a mathematical model for the phase-frequency relationship parametrized by the phase-frequency profile's slope and phase-shift corresponding to a realizable resonant RIS unit cell. Then, modelling each RIS element with $b$ control bits, we propose a method for selecting the parameter pairs to obtain a set of $2^b$ phase-frequency profiles. The proposed method yields an RIS design that outperforms existing designs over a wide range of user locations in a single-input, single-output (SISO) OFDM system. We then propose a low-complexity optimization algorithm to maximize the data rate through the joint optimization of (a) power allocations across the sub-carriers and (b) phase-frequency profile for each RIS unit cell from the available set. The analysis is then extended to a multi-user multiple-input single-output (MISO) OFDM scenario. Numerical results show an improvement in the coverage and achievable rates under the proposed framework as compared to single-slope phase-frequency profiles.
On the Parameterized Complexity of Bend-Minimum Orthogonal Planarity
Venue: arXiv cs.CG 2023
Year: 2023

Computing planar orthogonal drawings with the minimum number of bends is one of the most relevant topics in Graph Drawing. The problem is known to be NP-hard, even when we want to test the existence of a rectilinear planar drawing, i.e., an orthogonal drawing without bends (Garg and Tamassia, 2001). From the parameterized complexity perspective, the problem is fixed-parameter tractable when parameterized by the sum of three parameters: the number of bends, the number of vertices of degree at most two, and the treewidth of the input graph (Di Giacomo et al., 2022). We improve this last result by showing that the problem remains fixed-parameter tractable when parameterized only by the number of vertices of degree at most two plus the number of bends. As a consequence, rectilinear planarity testing lies in \FPT~parameterized by the number of vertices of degree at most two.
WellXplain: Wellness Concept Extraction and Classification in Reddit Posts for Mental Health Analysis
Venue: arXiv cs.CL 2023
Year: 2023

During the current mental health crisis, the importance of identifying potential indicators of mental issues from social media content has surged. Overlooking the multifaceted nature of mental and social well-being can have detrimental effects on one's mental state. In traditional therapy sessions, professionals manually pinpoint the origins and outcomes of underlying mental challenges, a process both detailed and time-intensive. We introduce an approach to this intricate mental health analysis by framing the identification of wellness dimensions in Reddit content as a wellness concept extraction and categorization challenge. We've curated a unique dataset named WELLXPLAIN, comprising 3,092 entries and totaling 72,813 words. Drawing from Halbert L. Dunn's well-regarded wellness theory, our team formulated an annotation framework along with guidelines. This dataset also includes human-marked textual segments, offering clear reasoning for decisions made in the wellness concept categorization process. Our aim in publishing this dataset and analyzing initial benchmarks is to spearhead the creation of advanced language models tailored for healthcare-focused concept extraction and categorization.
ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning
Venue: arXiv cs.RO 2023
Year: 2023

Motivated by the substantial achievements observed in Large Language Models (LLMs) in the field of natural language processing, recent research has commenced investigations into the application of LLMs for complex, long-horizon sequential task planning challenges in robotics. LLMs are advantageous in offering the potential to enhance the generalizability as task-agnostic planners and facilitate flexible interaction between human instructors and planning systems. However, task plans generated by LLMs often lack feasibility and correctness. To address this challenge, we introduce ISR-LLM, a novel framework that improves LLM-based planning through an iterative self-refinement process. The framework operates through three sequential steps: preprocessing, planning, and iterative self-refinement. During preprocessing, an LLM translator is employed to convert natural language input into a Planning Domain Definition Language (PDDL) formulation. In the planning phase, an LLM planner formulates an initial plan, which is then assessed and refined in the iterative self-refinement step by using a validator. We examine the performance of ISR-LLM across three distinct planning domains. The results show that ISR-LLM is able to achieve markedly higher success rates in task accomplishments compared to state-of-the-art LLM-based planners. Moreover, it also preserves the broad applicability and generalizability of working with natural language instructions.
ZC3: Zero-Shot Cross-Language Code Clone Detection
Venue: arXiv cs.SE 2023
Year: 2023

Developers introduce code clones to improve programming productivity. Many existing studies have achieved impressive performance in monolingual code clone detection. However, during software development, more and more developers write semantically equivalent programs with different languages to support different platforms and help developers translate projects from one language to another. Considering that collecting cross-language parallel data, especially for low-resource languages, is expensive and time-consuming, how designing an effective cross-language model that does not rely on any parallel data is a significant problem. In this paper, we propose a novel method named ZC3 for Zero-shot Cross-language Code Clone detection. ZC3 designs the contrastive snippet prediction to form an isomorphic representation space among different programming languages. Based on this, ZC3 exploits domain-aware learning and cycle consistency learning to further constrain the model to generate representations that are aligned among different languages meanwhile are diacritical for different types of clones. To evaluate our approach, we conduct extensive experiments on four representative cross-language clone detection datasets. Experimental results show that ZC3 outperforms the state-of-the-art baselines by 67.12%, 51.39%, 14.85%, and 53.01% on the MAP score, respectively. We further investigate the representational distribution of different languages and discuss the effectiveness of our method.
SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

The Segment Anything Model (SAM) exhibits a capability to segment a wide array of objects in natural images, serving as a versatile perceptual tool for various downstream image segmentation tasks. In contrast, medical image segmentation tasks often rely on domain-specific knowledge (DSK). In this paper, we propose a novel method that combines the segmentation foundation model (i.e., SAM) with domain-specific knowledge for reliable utilization of unlabeled images in building a medical image segmentation model. Our new method is iterative and consists of two main stages: (1) segmentation model training; (2) expanding the labeled set by using the trained segmentation model, an unlabeled set, SAM, and domain-specific knowledge. These two stages are repeated until no more samples are added to the labeled set. A novel optimal-matching-based method is developed for combining the SAM-generated segmentation proposals and pixel-level and image-level DSK for constructing annotations of unlabeled images in the iterative stage (2). In experiments, we demonstrate the effectiveness of our proposed method for breast cancer segmentation in ultrasound images, polyp segmentation in endoscopic images, and skin lesion segmentation in dermoscopic images. Our work initiates a new direction of semi-supervised learning for medical image segmentation: the segmentation foundation model can be harnessed as a valuable tool for label-efficient segmentation learning in medical image segmentation.
DiffI2I: Efficient Diffusion Model for Image-to-Image Translation
Venue: arXiv cs.CV 2023
Year: 2023

The Diffusion Model (DM) has emerged as the SOTA approach for image synthesis. However, the existing DM cannot perform well on some image-to-image translation (I2I) tasks. Different from image synthesis, some I2I tasks, such as super-resolution, require generating results in accordance with GT images. Traditional DMs for image synthesis require extensive iterations and large denoising models to estimate entire images, which gives their strong generative ability but also leads to artifacts and inefficiency for I2I. To tackle this challenge, we propose a simple, efficient, and powerful DM framework for I2I, called DiffI2I. Specifically, DiffI2I comprises three key components: a compact I2I prior extraction network (CPEN), a dynamic I2I transformer (DI2Iformer), and a denoising network. We train DiffI2I in two stages: pretraining and DM training. For pretraining, GT and input images are fed into CPEN$_{S1}$ to capture a compact I2I prior representation (IPR) guiding DI2Iformer. In the second stage, the DM is trained to only use the input images to estimate the same IRP as CPEN$_{S1}$. Compared to traditional DMs, the compact IPR enables DiffI2I to obtain more accurate outcomes and employ a lighter denoising network and fewer iterations. Through extensive experiments on various I2I tasks, we demonstrate that DiffI2I achieves SOTA performance while significantly reducing computational burdens.
Bengali Document Layout Analysis with Detectron2
Venue: arXiv cs.CV 2023
Year: 2023

Document digitization is vital for preserving historical records, efficient document management, and advancing OCR (Optical Character Recognition) research. Document Layout Analysis (DLA) involves segmenting documents into meaningful units like text boxes, paragraphs, images, and tables. Challenges arise when dealing with diverse layouts, historical documents, and unique scripts like Bengali, hindered by the lack of comprehensive Bengali DLA datasets. We improved the accuracy of the DLA model for Bengali documents by utilizing advanced Mask R-CNN models available in the Detectron2 library. Our evaluation involved three variants: Mask R-CNN R-50, R-101, and X-101, both with and without pretrained weights from PubLayNet, on the BaDLAD dataset, which contains human-annotated Bengali documents in four categories: text boxes, paragraphs, images, and tables. Results show the effectiveness of these models in accurately segmenting Bengali documents. We discuss speed-accuracy tradeoffs and underscore the significance of pretrained weights. Our findings expand the applicability of Mask R-CNN in document layout analysis, efficient document management, and OCR research while suggesting future avenues for fine-tuning and data augmentation.
Large-scale gradient-based training of Mixtures of Factor Analyzers
Venue: arXiv cs.LG 2023
Year: 2023

Gaussian Mixture Models (GMMs) are a standard tool in data analysis. However, they face problems when applied to high-dimensional data (e.g., images) due to the size of the required full covariance matrices (CMs), whereas the use of diagonal or spherical CMs often imposes restrictions that are too severe. The Mixture of Factor analyzers (MFA) model is an important extension of GMMs, which allows to smoothly interpolate between diagonal and full CMs based on the number of \textit{factor loadings} $l$. MFA has successfully been applied for modeling high-dimensional image data. This article contributes both a theoretical analysis as well as a new method for efficient high-dimensional MFA training by stochastic gradient descent, starting from random centroid initializations. This greatly simplifies the training and initialization process, and avoids problems of batch-type algorithms such Expectation-Maximization (EM) when training with huge amounts of data. In addition, by exploiting the properties of the matrix determinant lemma, we prove that MFA training and inference/sampling can be performed based on precision matrices, which does not require matrix inversions after training is completed. At training time, the methods requires the inversion of $l\times l$ matrices only. Besides the theoretical analysis and proofs, we apply MFA to typical image datasets such as SVHN and MNIST, and demonstrate the ability to perform sample generation and outlier detection.
Zero-Shot Edge Detection with SCESAME: Spectral Clustering-based Ensemble for Segment Anything Model Estimation
Venue: arXiv cs.CV 2023
Year: 2023

This paper proposes a novel zero-shot edge detection with SCESAME, which stands for Spectral Clustering-based Ensemble for Segment Anything Model Estimation, based on the recently proposed Segment Anything Model (SAM). SAM is a foundation model for segmentation tasks, and one of the interesting applications of SAM is Automatic Mask Generation (AMG), which generates zero-shot segmentation masks of an entire image. AMG can be applied to edge detection, but suffers from the problem of overdetecting edges. Edge detection with SCESAME overcomes this problem by three steps: (1) eliminating small generated masks, (2) combining masks by spectral clustering, taking into account mask positions and overlaps, and (3) removing artifacts after edge detection. We performed edge detection experiments on two datasets, BSDS500 and NYUDv2. Although our zero-shot approach is simple, the experimental results on BSDS500 showed almost identical performance to human performance and CNN-based methods from seven years ago. In the NYUDv2 experiments, it performed almost as well as recent CNN-based methods. These results indicate that our method effectively enhances the utility of SAM and can be a new direction in zero-shot edge detection methods.
Implementing Performance Portability of High Performance Computing Programs in the New Golden Age of Chip Architecture
Venue: arXiv cs.AR 2023
Year: 2023

As an important goal of high-performance computing, the concept of performance portability has been around for many years. As the failure of Moore's Law, it is no longer feasible to improve computer performance by simply increasing the number of existing hardware. The innovation of high performance computer is imperative, which makes high-performance computers with multiple architectures coexist in the production environment. For example, current high-performance computing nodes often use co-accelerators such like general-purpose GPUs and Intel Xeon Phis to accelerate general-purpose processors. With the flourishing of deep learning, dedicated neural network acceleration chips are also arising. The emergence of co-accelerators with different architectures and their wide application in high-performance computers have challenged the performance portability of programs between high-performance computers with different architectures. This article summarizes the current performance portability technology from the programming model, serial code automatic parallelization, parallel code automatic conversion, etc. at the end of the article, it also summarizes how to use scientific computing function libraries to improve performance and performance portability of a program. Different application scenarios need different implementation technologies to get performance portability. Program developers choose performance portability solutions for their programs. In fact, they balance programming efficiency and optimization effects under various constraints.
Throughput Maximization of DNN Inference: Batching or Multi-Tenancy?
Venue: arXiv cs.DC 2023
Year: 2023

Deployment of real-time ML services on warehouse-scale infrastructures is on the increase. Therefore, decreasing latency and increasing throughput of deep neural network (DNN) inference applications that empower those services have attracted attention from both academia and industry. A common solution to address this challenge is leveraging hardware accelerators such as GPUs. To improve the inference throughput of DNNs deployed on GPU accelerators, two common approaches are employed: Batching and Multi-Tenancy. Our preliminary experiments show that the effect of these approaches on the throughput depends on the DNN architecture. Taking this observation into account, we design and implement DNNScaler which aims to maximize the throughput of interactive AI-powered services while meeting their latency requirements. DNNScaler first detects the suitable approach (Batching or Multi-Tenancy) that would be most beneficial for a DNN regarding throughput improvement. Then, it adjusts the control knob of the detected approach (batch size for Batching and number of co-located instances for Multi-Tenancy) to maintain the latency while increasing the throughput. Conducting an extensive set of experiments using well-known DNNs from a variety of domains, several popular datasets, and a cutting-edge GPU, the results indicate that DNNScaler can improve the throughput by up to 14x (218% on average) compared with the previously proposed approach, while meeting the latency requirements of the services.
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions
Venue: arXiv cs.LG 2023
Year: 2023

Graphs represent interconnected structures prevalent in a myriad of real-world scenarios. Effective graph analytics, such as graph learning methods, enables users to gain profound insights from graph data, underpinning various tasks including node classification and link prediction. However, these methods often suffer from data imbalance, a common issue in graph data where certain segments possess abundant data while others are scarce, thereby leading to biased learning outcomes. This necessitates the emerging field of imbalanced learning on graphs, which aims to correct these data distribution skews for more accurate and representative learning outcomes. In this survey, we embark on a comprehensive review of the literature on imbalanced learning on graphs. We begin by providing a definitive understanding of the concept and related terminologies, establishing a strong foundational understanding for readers. Following this, we propose two comprehensive taxonomies: (1) the problem taxonomy, which describes the forms of imbalance we consider, the associated tasks, and potential solutions; (2) the technique taxonomy, which details key strategies for addressing these imbalances, and aids readers in their method selection process. Finally, we suggest prospective future directions for both problems and techniques within the sphere of imbalanced learning on graphs, fostering further innovation in this critical area.
Deep Learning for Structure-Preserving Universal Stable Koopman-Inspired Embeddings for Nonlinear Canonical Hamiltonian Dynamics
Venue: arXiv cs.LG 2023
Year: 2023

Discovering a suitable coordinate transformation for nonlinear systems enables the construction of simpler models, facilitating prediction, control, and optimization for complex nonlinear systems. To that end, Koopman operator theory offers a framework for global linearization for nonlinear systems, thereby allowing the usage of linear tools for design studies. In this work, we focus on the identification of global linearized embeddings for canonical nonlinear Hamiltonian systems through a symplectic transformation. While this task is often challenging, we leverage the power of deep learning to discover the desired embeddings. Furthermore, to overcome the shortcomings of Koopman operators for systems with continuous spectra, we apply the lifting principle and learn global cubicized embeddings. Additionally, a key emphasis is paid to enforce the bounded stability for the dynamics of the discovered embeddings. We demonstrate the capabilities of deep learning in acquiring compact symplectic coordinate transformation and the corresponding simple dynamical models, fostering data-driven learning of nonlinear canonical Hamiltonian systems, even those with continuous spectra.