OpenRaiser/Intern-Atlas huggingface dataset:
A Comprehensive Review of Modern Object Segmentation Approaches
Venue: arXiv cs.CV 2023
Year: 2023

Image segmentation is the task of associating pixels in an image with their respective object class labels. It has a wide range of applications in many industries including healthcare, transportation, robotics, fashion, home improvement, and tourism. Many deep learning-based approaches have been developed for image-level object recognition and pixel-level scene understanding-with the latter requiring a much denser annotation of scenes with a large set of objects. Extensions of image segmentation tasks include 3D and video segmentation, where units of voxels, point clouds, and video frames are classified into different objects. We use "Object Segmentation" to refer to the union of these segmentation tasks. In this monograph, we investigate both traditional and modern object segmentation approaches, comparing their strengths, weaknesses, and utilities. We examine in detail the wide range of deep learning-based segmentation techniques developed in recent years, provide a review of the widely used datasets and evaluation metrics, and discuss potential future research directions.
Model-agnostic machine learning of conservation laws from data
Venue: arXiv cs.LG 2023
Year: 2023

We present a machine learning based method for learning first integrals of systems of ordinary differential equations from given trajectory data. The method is model-agnostic in that it does not require explicit knowledge of the underlying system of differential equations that generated the trajectories. As a by-product, once the first integrals have been learned, also the system of differential equations will be known. We illustrate our method by considering several classical problems from the mathematical sciences.
Coverage of Course Topics in Learnersourced SQL Exercises
Venue: arXiv cs.HC 2023
Year: 2023

Learnersourcing is a common task in modern computing classrooms, where it is used, for example, for the creation of educational resources such as multiple-choice questions and programming exercises. One less studied type of learnersourced artefact is SQL exercises. In this work, we explore how well different SQL topics are covered by learnersourced SQL exercises. Covering most course topics would allow students to practice the full content of the course by completing learnersourced exercises. Our results suggest that learnersourcing can be used to create a large pool of SQL exercises that cover most of the topics of the course.
Optimization Algorithms in Smart Grids: A Systematic Literature Review
Venue: arXiv cs.NE 2023
Year: 2023

Electrical smart grids are units that supply electricity from power plants to the users to yield reduced costs, power failures/loss, and maximized energy management. Smart grids (SGs) are well-known devices due to their exceptional benefits such as bi-directional communication, stability, detection of power failures, and inter-connectivity with appliances for monitoring purposes. SGs are the outcome of different modern applications that are used for managing data and security, i.e., modeling, monitoring, optimization, and/or Artificial Intelligence. Hence, the importance of SGs as a research field is increasing with every passing year. This paper focuses on novel features and applications of smart grids in domestic and industrial sectors. Specifically, we focused on Genetic algorithm, Particle Swarm Optimization, and Grey Wolf Optimization to study the efforts made up till date for maximized energy management and cost minimization in SGs. Therefore, we collected 145 research works (2011 to 2022) in this systematic literature review. This research work aims to figure out different features and applications of SGs proposed in the last decade and investigate the trends in popularity of SGs for different regions of world. Our finding is that the most popular optimization algorithm being used by researchers to bring forward new solutions for energy management and cost effectiveness in SGs is Particle Swarm Optimization. We also provide a brief overview of objective functions and parameters used in the solutions for energy and cost effectiveness as well as discuss different open research challenges for future research works.
Quality Attributes Optimization of Software Architecture: Research Challenges and Directions
Venue: arXiv cs.SE 2023
Year: 2023

The estimation and improvement of quality attributes in software architectures is a challenging and time-consuming activity. On modern software applications, a model-based representation is crucial to face the complexity of such activity. One main challenge is that the improvement of distinctive quality attributes may require contrasting refactoring actions on the architecture, for instance when looking for trade-off between performance and reliability (or other non-functional quality attributes). In such cases, multi-objective optimization can provide the designer with a more complete view on these trade-offs and, consequently, can lead to identify suitable refactoring actions that take into account independent or even competing objectives. In this paper, we present open challenges and research directions to fill current gaps in the context of multi-objective software architecture optimization.
An Improved Approximation for Maximum Weighted $k$-Set Packing
Venue: arXiv cs.DS 2023
Year: 2023

We consider the weighted $k$-set packing problem, in which we are given a collection of weighted sets, each with at most $k$ elements and must return a collection of pairwise disjoint sets with maximum total weight. For $k = 3$, this problem generalizes the classical 3-dimensional matching problem listed as one of the Karp's original 21 NP-complete problems. We give an algorithm attaining an approximation factor of $1.786$ for weighted 3-set packing, improving on the recent best result of $2-\frac{1}{63,700,992}$ due to Neuwohner. Our algorithm is based on the local search procedure of Berman that attempts to improve the sum of squared weights rather than the problem's objective. When using exchanges of size at most $k$, this algorithm attains an approximation factor of $\frac{k+1}{2}$. Using exchanges of size $k^2(k-1) + k$, we provide a relatively simple analysis to obtain an approximation factor of 1.811 when $k = 3$. We then show that the tools we develop can be adapted to larger exchanges of size $2k^2(k-1) + k$ to give an approximation factor of 1.786. Although our primary focus is on the case $k = 3$, our approach in fact gives slightly stronger improvements on the factor $\frac{k+1}{2}$ for all $k > 3$. As in previous works, our guarantees hold also for the more general problem of finding a maximum weight independent set in a $(k+1)$-claw free graph.
A Survey of Advanced Computer Vision Techniques for Sports
Venue: arXiv cs.CV 2023
Year: 2023

Computer Vision developments are enabling significant advances in many fields, including sports. Many applications built on top of Computer Vision technologies, such as tracking data, are nowadays essential for every top-level analyst, coach, and even player. In this paper, we survey Computer Vision techniques that can help many sports-related studies gather vast amounts of data, such as Object Detection and Pose Estimation. We provide a use case for such data: building a model for shot speed estimation with pose data obtained using only Computer Vision models. Our model achieves a correlation of 67%. The possibility of estimating shot speeds enables much deeper studies about enabling the creation of new metrics and recommendation systems that will help athletes improve their performance, in any sport. The proposed methodology is easily replicable for many technical movements and is only limited by the availability of video data.
Facial Thermal and Blood Perfusion Patterns of Human Emotions: Proof-of-Concept
Venue: arXiv cs.CV 2023
Year: 2023

In this work, a preliminary study of proof-of-concept was conducted to evaluate the performance of the thermographic and blood perfusion data when emotions of positive and negative valence are applied, where the blood perfusion data are obtained from the thermographic data. The images were obtained for baseline, positive, and negative valence according to the protocol of the Geneva Affective Picture Database. Absolute and percentage differences of average values of the data between the valences and the baseline were calculated for different regions of interest (forehead, periorbital eyes, cheeks, nose and upper lips). For negative valence, a decrease in temperature and blood perfusion was observed in the regions of interest, and the effect was greater on the left side than on the right side. In positive valence, the temperature and blood perfusion increased in some cases, showing a complex pattern. The temperature and perfusion of the nose was reduced for both valences, which is indicative of the arousal dimension. The blood perfusion images were found to be greater contrast; the percentage differences in the blood perfusion images are greater than those obtained in thermographic images. Moreover, the blood perfusion images, and vasomotor answer are consistent, therefore, they can be a better biomarker than thermographic analysis in identifying emotions.
An Automata-based Framework for Verification and Bug Hunting in Quantum Circuits (Technical Report)
Venue: arXiv cs.LO 2023
Year: 2023

We introduce a new paradigm for analysing and finding bugs in quantum circuits. In our approach, the problem is given by a triple $\{P\}\,C\,\{Q\}$ and the question is whether, given a set $P$ of quantum states on the input of a circuit $C$, the set of quantum states on the output is equal to (or included in) a set $Q$. While this is not suitable to specify, e.g., functional correctness of a quantum circuit, it is sufficient to detect many bugs in quantum circuits. We propose a technique based on tree automata to compactly represent sets of quantum states and develop transformers to implement the semantics of quantum gates over this representation. Our technique computes with an algebraic representation of quantum states, avoiding the inaccuracy of working with floating-point numbers. We implemented the proposed approach in a prototype tool and evaluated its performance against various benchmarks from the literature. The evaluation shows that our approach is quite scalable, e.g., we managed to verify a large circuit with 40 qubits and 141,527 gates, or catch bugs injected into a circuit with 320 qubits and 1,758 gates, where all tools we compared with failed. In addition, our work establishes a connection between quantum program verification and automata, opening new possibilities to exploit the richness of automata theory and automata-based verification in the world of quantum computing.
Round Trip Time (RTT) Delay in the Internet: Analysis and Trends
Venue: arXiv cs.NI 2023
Year: 2023

Both capacity and latency are crucial performance metrics for the optimal operation of most networking services and applications, from online gaming to futuristic holographic-type communications. Networks worldwide have witnessed important breakthroughs in terms of capacity, including fibre introduction everywhere, new radio technologies and faster core networks. However, the impact of these capacity upgrades on end-to-end delay is not straightforward as traffic has also grown exponentially. This article overviews the current status of end-to-end latency on different regions and continents worldwide and how far these are from the theoretical minimum baseline, given by the speed of light propagation over an optical fibre. We observe that the trend in the last decade goes toward latency reduction (in spite of the ever-increasing annual traffic growth), but still there are important differences between countries.
Hierarchical Reinforcement Learning Based Traffic Steering in Multi-RAT 5G Deployments
Venue: arXiv cs.NI 2023
Year: 2023

In 5G non-standalone mode, an intelligent traffic steering mechanism can vastly aid in ensuring smooth user experience by selecting the best radio access technology (RAT) from a multi-RAT environment for a specific traffic flow. In this paper, we propose a novel load-aware traffic steering algorithm based on hierarchical reinforcement learning (HRL) while satisfying diverse QoS requirements of different traffic types. HRL can significantly increase system performance using a bi-level architecture having a meta-controller and a controller. In our proposed method, the meta-controller provides an appropriate threshold for load balancing, while the controller performs traffic admission to an appropriate RAT in the lower level. Simulation results show that HRL outperforms a Deep Q-Learning (DQN) and a threshold-based heuristic baseline with 8.49%, 12.52% higher average system throughput and 27.74%, 39.13% lower network delay, respectively.
Supercharging Distributed Computing Environments For High Performance Data Engineering
Venue: arXiv cs.DC 2023
Year: 2023

The data engineering and data science community has embraced the idea of using Python & R dataframes for regular applications. Driven by the big data revolution and artificial intelligence, these applications are now essential in order to process terabytes of data. They can easily exceed the capabilities of a single machine, but also demand significant developer time & effort. Therefore it is essential to design scalable dataframe solutions. There have been multiple attempts to tackle this problem, the most notable being the dataframe systems developed using distributed computing environments such as Dask and Ray. Even though Dask/Ray distributed computing features look very promising, we perceive that the Dask Dataframes/Ray Datasets still have room for optimization. In this paper, we present CylonFlow, an alternative distributed dataframe execution methodology that enables state-of-the-art performance and scalability on the same Dask/Ray infrastructure (thereby supercharging them!). To achieve this, we integrate a high performance dataframe system Cylon, which was originally based on an entirely different execution paradigm, into Dask and Ray. Our experiments show that on a pipeline of dataframe operators, CylonFlow achieves 30x more distributed performance than Dask Dataframes. Interestingly, it also enables superior sequential performance due to the native C++ execution of Cylon. We believe the success of Cylon & CylonFlow extends beyond the data engineering domain, and can be used to consolidate high performance computing and distributed computing ecosystems.
Semantic-aware Contrastive Learning for More Accurate Semantic Parsing
Venue: arXiv cs.CL 2023
Year: 2023

Since the meaning representations are detailed and accurate annotations which express fine-grained sequence-level semtantics, it is usually hard to train discriminative semantic parsers via Maximum Likelihood Estimation (MLE) in an autoregressive fashion. In this paper, we propose a semantic-aware contrastive learning algorithm, which can learn to distinguish fine-grained meaning representations and take the overall sequence-level semantic into consideration. Specifically, a multi-level online sampling algorithm is proposed to sample confusing and diverse instances. Three semantic-aware similarity functions are designed to accurately measure the distance between meaning representations as a whole. And a ranked contrastive loss is proposed to pull the representations of the semantic-identical instances together and push negative instances away. Experiments on two standard datasets show that our approach achieves significant improvements over MLE baselines and gets state-of-the-art performances by simply applying semantic-aware contrastive learning on a vanilla Seq2Seq model.
SpotHitPy: A Study For ML-Based Song Hit Prediction Using Spotify
Venue: arXiv cs.SD 2023
Year: 2023

In this study, we approached the Hit Song Prediction problem, which aims to predict which songs will become Billboard hits. We gathered a dataset of nearly 18500 hit and non-hit songs and extracted their audio features using the Spotify Web API. We test four machine-learning models on our dataset. We were able to predict the Billboard success of a song with approximately 86\% accuracy. The most succesful algorithms were Random Forest and Support Vector Machine.
A Meta-Learning Approach for Software Refactoring
Venue: arXiv cs.SE 2023
Year: 2023

Software refactoring is the process of changing the structure of software without any alteration in its behavior and functionality. Presuming it is carried out in appropriate opportunities, refactoring enhances software quality characteristics such as maintainability and extensibility. Thus far, various studies have addressed the problem of detecting proper opportunities for refactoring. Most of them are based on human expertise and are prone to error and non-meticulous. Fortunately, in recent efforts, machine learning methods have produced outstanding results in finding appropriate opportunities for refactoring. Sad to say, Machine learning methods mostly need plenty of data and, consequently, long processing time. Furthermore, there needs to be more annotated data for many types of refactoring, and data collection is time-consuming and costly. Accordingly, in this paper, we have formulated the problem of detecting appropriate opportunities for refactoring as a few-shot classification problem. We have utilized model-agnostic meta-learning (MAML), a recognized meta-learning algorithm, to learn a neural network on tasks from high-resource data. The trained model, then, is adapted to a model with high accuracy for tasks from low-resource data. Experimental results revealed 91% accuracy, which illustrates the effectiveness and competitiveness of our proposed meta-learning model.
Interpreting CNN Predictions using Conditional Generative Adversarial Networks
Venue: arXiv cs.CV 2023
Year: 2023

We propose a novel method that trains a conditional Generative Adversarial Network (GAN) to generate visual interpretations of a Convolutional Neural Network (CNN). To comprehend a CNN, the GAN is trained with information on how the CNN processes an image when making predictions. Supplying that information has two main challenges: how to represent this information in a form that is feedable to the GANs and how to effectively feed the representation to the GAN. To address these issues, we developed a suitable representation of CNN architectures by cumulatively averaging intermediate interpretation maps. We also propose two alternative approaches to feed the representations to the GAN and to choose an effective training strategy. Our approach learned the general aspects of CNNs and was agnostic to datasets and CNN architectures. The study includes both qualitative and quantitative evaluations and compares the proposed GANs with state-of-the-art approaches. We found that the initial layers of CNNs and final layers are equally crucial for interpreting CNNs upon interpreting the proposed GAN. We believe training a GAN to interpret CNNs would open doors for improved interpretations by leveraging fast-paced deep learning advancements. The code used for experimentation is publicly available at https://github.com/Akash-guna/Explain-CNN-With-GANS
Dif-Fusion: Towards High Color Fidelity in Infrared and Visible Image Fusion with Diffusion Models
Venue: arXiv cs.CV 2023
Year: 2023

Color plays an important role in human visual perception, reflecting the spectrum of objects. However, the existing infrared and visible image fusion methods rarely explore how to handle multi-spectral/channel data directly and achieve high color fidelity. This paper addresses the above issue by proposing a novel method with diffusion models, termed as Dif-Fusion, to generate the distribution of the multi-channel input data, which increases the ability of multi-source information aggregation and the fidelity of colors. In specific, instead of converting multi-channel images into single-channel data in existing fusion methods, we create the multi-channel data distribution with a denoising network in a latent space with forward and reverse diffusion process. Then, we use the the denoising network to extract the multi-channel diffusion features with both visible and infrared information. Finally, we feed the multi-channel diffusion features to the multi-channel fusion module to directly generate the three-channel fused image. To retain the texture and intensity information, we propose multi-channel gradient loss and intensity loss. Along with the current evaluation metrics for measuring texture and intensity fidelity, we introduce a new evaluation metric to quantify color fidelity. Extensive experiments indicate that our method is more effective than other state-of-the-art image fusion methods, especially in color fidelity.
Predicting the Channel Access of Bluetooth Low Energy
Venue: arXiv cs.IT 2023
Year: 2023

Bluetooth Low Energy (BLE) is one of the key enablers for low-power and low-cost applications in consumer electronics and the Internet of Things. The latest features such as audio and direction finding will introduce more and more devices that rely on BLE for communication. However, like many other wireless standards, BLE relies on the unlicensed 2.4 GHz frequency band where the spectrum is already very crowded and a channel access without collisions with other devices is difficult to guarantee. For applications with high reliability requirements, it will be beneficial to actively consider channel access from other devices or standards. In this work, we present an approach to estimate the connection parameters of multiple BLE connections outside our control and knowledge by passively listening to the channel. With this, we are able to predict future channel access of these BLE connections that can be used by other wireless networks to avoid collisions. We show the applicability of our algorithm with measurements from which we are able to identify unknown BLE connections, reconstruct their specific connection parameters, and predict their future channel access.
Soft Thresholding for Visual Image Enhancement
Venue: arXiv cs.CV 2023
Year: 2023

Thresholding converts a greyscale image into a binary image, and is thus often a necessary segmentation step in image processing. For a human viewer however, thresholding usually has a negative impact on the legibility of document images. This report describes a simple method for "smearing out" the threshold and transforming the greyscale image into a different greyscale image. The method is similar to fuzzy thresholding, but is discussed here in the simpler context of greyscale transformations and, unlike fuzzy thresholding, it is independent from the method for finding the threshold. A simple formula is presented for automatically determining the width of the threshold spread. The method can be used, e.g., for enhancing images for the presentation in online facsimile repositories.