OpenRaiser/Intern-Atlas huggingface dataset:
Experimental Investigation of 5G Base Station functionalities in Reverberation Chamber at Millimeter-Wave
Venue: arXiv cs.NI 2023
Year: 2023

The performance and functionalities of a commercial fifth generation base station are evaluated inside the reverberation chamber at the mmWave frequency range. The base station capability to operates in different propagation environment conditions reproduced by the reverberation chamber is investigated. Throughput, modulation code scheme and beamforming are analyzed for different real life scenarios both in uplink and downlink. Experimental results inform network operators in their evaluation of the base station operation: i) in many scenarios within a laboratory; ii) in the assessment of whether expected benefit justifies the additional costs in an operating actual network.
Zero Bubble Pipeline Parallelism
Venue: arXiv cs.DC 2023
Year: 2023

Pipeline parallelism is one of the key components for large-scale distributed training, yet its efficiency suffers from pipeline bubbles which were deemed inevitable. In this work, we introduce a scheduling strategy that, to our knowledge, is the first to successfully achieve zero pipeline bubbles under synchronous training semantics. The key idea behind this improvement is to split the backward computation into two parts, one that computes gradient for the input and another that computes for the parameters. Based on this idea, we handcraft novel pipeline schedules that significantly outperform the baseline methods. We further develop an algorithm that automatically finds an optimal schedule based on specific model configuration and memory limit. Additionally, to truly achieve zero bubble, we introduce a novel technique to bypass synchronizations during the optimizer step. Experimental evaluations show that our method outperforms the 1F1B schedule up to 23% in throughput under a similar memory limit. This number can be further pushed to 31% when the memory constraint is relaxed. We believe our results mark a major step forward in harnessing the true potential of pipeline parallelism. We open sourced our implementation based on the popular Megatron-LM repository on https://github.com/sail-sg/zero-bubble-pipeline-parallelism.
ReliCD: A Reliable Cognitive Diagnosis Framework with Confidence Awareness
Venue: arXiv cs.CY 2023
Year: 2023

During the past few decades, cognitive diagnostics modeling has attracted increasing attention in computational education communities, which is capable of quantifying the learning status and knowledge mastery levels of students. Indeed, the recent advances in neural networks have greatly enhanced the performance of traditional cognitive diagnosis models through learning the deep representations of students and exercises. Nevertheless, existing approaches often suffer from the issue of overconfidence in predicting students' mastery levels, which is primarily caused by the unavoidable noise and sparsity in realistic student-exercise interaction data, severely hindering the educational application of diagnostic feedback. To address this, in this paper, we propose a novel Reliable Cognitive Diagnosis(ReliCD) framework, which can quantify the confidence of the diagnosis feedback and is flexible for different cognitive diagnostic functions. Specifically, we first propose a Bayesian method to explicitly estimate the state uncertainty of different knowledge concepts for students, which enables the confidence quantification of diagnostic feedback. In particular, to account for potential differences, we suggest modeling individual prior distributions for the latent variables of different ability concepts using a pre-trained model. Additionally, we introduce a logical hypothesis for ranking confidence levels. Along this line, we design a novel calibration loss to optimize the confidence parameters by modeling the process of student performance prediction. Finally, extensive experiments on four real-world datasets clearly demonstrate the effectiveness of our ReliCD framework.
Enabling Technologies for Web 3.0: A Comprehensive Survey
Venue: arXiv cs.CY 2023
Year: 2023

Web 3.0 represents the next stage of Internet evolution, aiming to empower users with increased autonomy, efficiency, quality, security, and privacy. This evolution can potentially democratize content access by utilizing the latest developments in enabling technologies. In this paper, we conduct an in-depth survey of enabling technologies in the context of Web 3.0, such as blockchain, semantic web, 3D interactive web, Metaverse, Virtual reality/Augmented reality, Internet of Things technology, and their roles in shaping Web 3.0. We commence by providing a comprehensive background of Web 3.0, including its concept, basic architecture, potential applications, and industry adoption. Subsequently, we examine recent breakthroughs in IoT, 5G, and blockchain technologies that are pivotal to Web 3.0 development. Following that, other enabling technologies, including AI, semantic web, and 3D interactive web, are discussed. Utilizing these technologies can effectively address the critical challenges in realizing Web 3.0, such as ensuring decentralized identity, platform interoperability, data transparency, reducing latency, and enhancing the system's scalability. Finally, we highlight significant challenges associated with Web 3.0 implementation, emphasizing potential solutions and providing insights into future research directions in this field.
Dissecting Bias of ChatGPT in College Major Recommendations
Venue: arXiv cs.CY 2023
Year: 2023

I investigate bias in terms of ChatGPT's college major recommendations for students with various profiles, looking at demographic disparities in factors such as race, gender, and socioeconomic status, as well as educational disparities such as score percentiles. By constructing prompts for the ChatGPT API, allowing the model to recommend majors based on high school student profiles, I evaluate bias using various metrics, including the Jaccard Coefficient, Wasserstein Metric, and STEM Disparity Score. The results of this study reveal a significant disparity in the set of recommended college majors, irrespective of the bias metric applied.
P not equal to NP
Venue: arXiv cs.CC 2023
Year: 2023

This article finds the answer to the question: for any problem from which a non-deterministic algorithm can be derived which verifies whether an answer is correct or not in polynomial time (complexity class NP), is it possible to create an algorithm that finds the right answer to the problem in polynomial time (complexity class P)? For this purpose, this article shows a decision problem and analyzes it to demonstrate that this problem does not belong to the complexity class P, but it belongs to the class NP; doing so it will be proved that it exists at least one problem that belongs to class NP but not to class P, which means that this article will prove that not all NP problems are P.
Revolutionizing Underwater Exploration of Autonomous Underwater Vehicles (AUVs) and Seabed Image Processing Techniques
Venue: arXiv cs.RO 2023
Year: 2023

The oceans in the Earth's in one of the last border lines on the World, with only a fraction of their depths having been explored. Advancements in technology have led to the development of Autonomous Underwater Vehicles (AUVs) that can operate independently and perform complex tasks underwater. These vehicles have revolutionized underwater exploration, allowing us to study and understand our oceans like never before. In addition to AUVs, image processing techniques have also been developed that can help us to better understand the seabed and its features. In this comprehensive survey, we will explore the latest advancements in AUV technology and seabed image processing techniques. We'll discuss how these advancements are changing the way we explore and understand our oceans, and their potential impact on the future of marine science. Join us on this journey to discover the exciting world of underwater exploration and the technologies that are driving it forward.
Choosing the Right Path for AI Integration in Engineering Companies: A Strategic Guide
Venue: arXiv cs.CY 2023
Year: 2023

The Engineering, Procurement and Construction (EPC) businesses operating within the energy sector are recognizing the increasing importance of Artificial Intelligence (AI). Many EPC companies and their clients have realized the benefits of applying AI to their businesses in order to reduce manual work, drive productivity, and streamline future operations of engineered installations in a highly competitive industry. The current AI market offers various solutions and services to support this industry, but organizations must understand how to acquire AI technology in the most beneficial way based on their business strategy and available resources. This paper presents a framework for EPC companies in their transformation towards AI. Our work is based on examples of project execution of AI-based products development at one of the biggest EPC contractors worldwide and on insights from EPC vendor companies already integrating AI into their engineering solutions. The paper covers the entire life cycle of building AI solutions, from initial business understanding to deployment and further evolution. The framework identifies how various factors influence the choice of approach toward AI project development within large international engineering corporations. By presenting a practical guide for optimal approach selection, this paper contributes to the research in AI project management and organizational strategies for integrating AI technology into businesses. The framework might also help engineering companies choose the optimum AI approach to create business value.
No More Trade-Offs. GPT and Fully Informative Privacy Policies
Venue: arXiv cs.CY 2023
Year: 2023

The paper reports the results of an experiment aimed at testing to what extent ChatGPT 3.5 and 4 is able to answer questions regarding privacy policies designed in the new format that we propose. In a world of human-only interpreters, there was a trade-off between comprehensiveness and comprehensibility of privacy policies, leading to the actual policies not containing enough information for users to learn anything meaningful. Having shown that GPT performs relatively well with the new format, we provide experimental evidence supporting our policy suggestion, namely that the law should require fully comprehensive privacy policies, even if this means they become less concise.
Integrating ChatGPT in a Computer Science Course: Students Perceptions and Suggestions
Venue: arXiv cs.CY 2023
Year: 2023

The integration of artificial intelligence tools such as ChatGPT in the education system has gained attention in recent years. This experience report explores students' perceptions and suggestions for integrating ChatGPT in a computer science course. Following a ChatGPT activity which includes code completion and analysis, seven students participated in in-depth interviews. Findings from the transcribed interviews suggest that ChatGPT has the potential to enhance learning experience including programming. They highlighted the tool's ability to respond immediately to queries and supporting personalised learning. However, they raise concerns that heavy reliance on ChatGPT may adversely affect students' critical thinking and problem-solving skills. These findings show the importance of carefully balancing using ChatGPT in computer science courses. The findings of this research have significant implications for educators, curriculum designers and policymakers as they explore integrating AI tools into educational contexts.
VR-CAD Framework for Parametric Data Modification with a 3D Shape-based Interaction
Venue: arXiv cs.GR 2023
Year: 2023

In this poster, we present a new VR-CAD framework, allowing user to modify parametric CAD data with 3D interaction in an immersive environment. With this framework, users can implicitly modify parameter values of CAD data with co-localized 3D shape-based interaction. This poster describes the system architecture and the interaction technique based on it.
Circuit-centric Genetic Algorithm (CGA) for Analog and Radio-Frequency Circuit Optimization
Venue: arXiv cs.NE 2023
Year: 2023

This paper presents an automated method for optimizing parameters in analog/high-frequency circuits, aiming to maximize performance parameters of a radio-frequency (RF) receiver. The design target includes a reduction of power consumption and noise figure and an increase in conversion gain. This study investigates the use of an artificial algorithm for the optimization of a receiver, illustrating how to fulfill the performance parameters with diverse circuit parameters. To overcome issues observed in the traditional Genetic Algorithm (GA), the concept of the Circuit-centric Genetic Algorithm (CGA) is proposed as a viable approach. The new method adopts an inference process that is simpler and computationally more efficient than the existing deep learning models. In addition, CGA offers significant advantages over manual design of finding optimal points and the conventional GA, mitigating the designer's workload while searching for superior optimum points.
Algorithms for Learning Kernels Based on Centered Alignment
Venue: arXiv cs.LG 2024
Year: 2024

This paper presents new and effective algorithms for learning kernels. In particular, as shown by our empirical results, these algorithms consistently outperform the so-called uniform combination solution that has proven to be difficult to improve upon in the past, as well as other algorithms for learning kernels based on convex combinations of base kernels in both classification and regression. Our algorithms are based on the notion of centered alignment which is used as a similarity measure between kernels or kernel matrices. We present a number of novel algorithmic, theoretical, and empirical results for learning kernels based on our notion of centered alignment. In particular, we describe efficient algorithms for learning a maximum alignment kernel by showing that the problem can be reduced to a simple QP and discuss a one-stage algorithm for learning both a kernel and a hypothesis based on that kernel using an alignment-based regularization. Our theoretical results include a novel concentration bound for centered alignment between kernel matrices, the proof of the existence of effective predictors for kernels with high alignment, both for classification and for regression, and the proof of stability-based generalization bounds for a broad family of algorithms for learning kernels based on centered alignment. We also report the results of experiments with our centered alignment-based algorithms in both classification and regression.
Modeling and performance evaluation of computer systems security operation
Venue: arXiv cs.CR 2024
Year: 2024

A model of computer system security operation is developed based on the fork-join queueing network formalism. We introduce a security operation performance measure, and show how it may be used to performance evaluation of actual systems.
Design of Minimum Correlated, Maximal Clique Sets of One-Dimensional Uni-polar (Optical) Orthogonal Codes
Venue: arXiv cs.IT 2024
Year: 2024

This paper proposes an algorithm to search a family of multiple sets of minimum correlated one dimensional uni-polar (optical) orthogonal codes (1-DUOC) or optical orthogonal codes (OOC) with fixed as well as variable code parameters. The cardinality of each set is equal to upper bound. The codes within a set can be searched for general values of code length, code weight, auto-correlation constraint and cross-correlation constraint. Each set forms a maximal clique of the codes within given range of correlation properties . These one-dimensional uni-polar orthogonal codes can find their application as signature sequences for spectral spreading purpose in incoherent optical code division multiple access (CDMA) systems.
The calculation of air filtration efficiency through the visual basic programming language
Venue: arXiv cs.CE 2024
Year: 2024

The present article describes the development of a software which was written in visual basic programming language. The software calculates the particle collection efficiency and penetration of a fibrous filter medium for given values of particle diameter, fiber diameter filter thickness, volume fraction etc, during the process of air filtration. The progress of the development of software is divided into two steps, the design of graphical user interface (GUI) and the code writing. The code is mainly based on the mathematical model of particle collection efficiency of fibrous filters media.
Deep Reinforcement Learning in Parameterized Action Space
Venue: arXiv cs.AI 2024
Year: 2024

Recent work has shown that deep neural networks are capable of approximating both value functions and policies in reinforcement learning domains featuring continuous state and action spaces. However, to the best of our knowledge no previous work has succeeded at using deep neural networks in structured (parameterized) continuous action spaces. To fill this gap, this paper focuses on learning within the domain of simulated RoboCup soccer, which features a small set of discrete action types, each of which is parameterized with continuous variables. The best learned agent can score goals more reliably than the 2012 RoboCup champion agent. As such, this paper represents a successful extension of deep reinforcement learning to the class of parameterized action space MDPs.
Compressed Hypothesis Testing: To Mix or Not to Mix?
Venue: arXiv cs.IT 2024
Year: 2024

In this paper, we study the problem of determining $k$ anomalous random variables that have different probability distributions from the rest $(n-k)$ random variables. Instead of sampling each individual random variable separately as in the conventional hypothesis testing, we propose to perform hypothesis testing using mixed observations that are functions of multiple random variables. We characterize the error exponents for correctly identifying the $k$ anomalous random variables under fixed time-invariant mixed observations, random time-varying mixed observations, and deterministic time-varying mixed observations. For our error exponent characterization, we introduce the notions of inner conditional Chernoff information and outer conditional Chernoff information. It is demonstrated that mixed observations can strictly improve the error exponents of hypothesis testing, over separate observations of individual random variables. We further characterize the optimal sensing vector maximizing the error exponents, which leads to explicit constructions of the optimal mixed observations in special cases of hypothesis testing for Gaussian random variables. These results show that mixed observations of random variables can reduce the number of required samples in hypothesis testing applications. In order to solve large-scale hypothesis testing problems, we also propose efficient algorithms - LASSO based and message passing based hypothesis testing algorithms.
GuStL - An Experimental Guarded States Language
Venue: arXiv cs.DC 2024
Year: 2024

Programming a parallel computing system that consists of several thousands or even up to a million message passing processing units may ask for a language that supports waiting for and sending messages over hardware channels. As programs are looked upon as state machines, the language provides syntax to implement a main event driven loop. The language presented herewith surely will not serve as a generic programming language for any arbitrary task. Its main purpose is to allow for a prototypical implementation of a dynamic software system as a proof of concept.
Linear-time logics -- a coalgebraic perspective
Venue: arXiv cs.LO 2024
Year: 2024

We describe a general approach to deriving linear-time logics for a wide variety of state-based, quantitative systems, by modelling the latter as coalgebras whose type incorporates both branching and linear behaviour. Concretely, we define logics whose syntax is determined by the type of linear behaviour, and whose domain of truth values is determined by the type of branching behaviour, and we provide two semantics for them: a step-wise semantics akin to that of standard coalgebraic logics, and a path-based semantics akin to that of standard linear-time logics. The former semantics is useful for model checking, whereas the latter is the more natural semantics, as it measures the extent with which qualitative properties hold along computation paths from a given state. Our main result is the equivalence of the two semantics. We also provide a semantic characterisation of a notion of logical distance induced by these logics. Instances of our logics support reasoning about the possibility, likelihood or minimal cost of exhibiting a given linear-time property.
A set-theoretical approach for ABox reasoning services (Extended Version)
Venue: arXiv cs.LO 2024
Year: 2024

In this paper we consider the most common ABox reasoning services for the description logic $\mathcal{DL}\langle \mathsf{4LQS^{R,\!\times}}\rangle(\mathbf{D})$ ($\mathcal{DL}_{\mathbf{D}}^{4,\!\times}$, for short) and prove their decidability via a reduction to the satisfiability problem for the set-theoretic fragment \flqsr. The description logic $\mathcal{DL}_{\mathbf{D}}^{4,\!\times}$ is very expressive, as it admits various concept and role constructs, and data types, that allow one to represent rule-based languages such as SWRL. Decidability results are achieved by defining a generalization of the conjunctive query answering problem, called HOCQA (Higher Order Conjunctive Query Answering), that can be instantiated to the most wide\-spread ABox reasoning tasks. We also present a \ke\space based procedure for calculating the answer set from $\mathcal{DL}_{\mathbf{D}}^{4,\!\times}$ knowledge bases and higher order $\mathcal{DL}_{\mathbf{D}}^{4,\!\times}$ conjunctive queries, thus providing means for reasoning on several well-known ABox reasoning tasks. Our calculus extends a previously introduced \ke\space based decision procedure for the CQA problem.
The duality structure gradient descent algorithm: analysis and applications to neural networks
Venue: arXiv cs.LG 2024
Year: 2024

The training of machine learning models is typically carried out using some form of gradient descent, often with great success. However, non-asymptotic analyses of first-order optimization algorithms typically employ a gradient smoothness assumption (formally, Lipschitz continuity of the gradient) that is too strong to be applicable in the case of deep neural networks. To address this, we propose an algorithm named duality structure gradient descent (DSGD) that is amenable to non-asymptotic performance analysis, under mild assumptions on the training set and network architecture. The algorithm can be viewed as a form of layer-wise coordinate descent, where at each iteration the algorithm chooses one layer of the network to update. The decision of what layer to update is done in a greedy fashion, based on a rigorous lower bound on the improvement of the objective function for each choice of layer. In the analysis, we bound the time required to reach approximate stationary points, in both the deterministic and stochastic settings. The convergence is measured in terms of a parameter-dependent family of norms that is derived from the network architecture and designed to confirm a smoothness-like property on the gradient of the training loss function. We empirically demonstrate the behavior of DSGD in several neural network training scenarios.