OpenRaiser/Intern-Atlas huggingface dataset:
Addressing The Knapsack Challenge Through Cultural Algorithm Optimization
Venue: arXiv cs.NE 2023
Year: 2023

The "0-1 knapsack problem" stands as a classical combinatorial optimization conundrum, necessitating the selection of a subset of items from a given set. Each item possesses inherent values and weights, and the primary objective is to formulate a selection strategy that maximizes the total value while adhering to a predefined capacity constraint. In this research paper, we introduce a novel variant of Cultural Algorithms tailored specifically for solving 0-1 knapsack problems, a well-known combinatorial optimization challenge. Our proposed algorithm incorporates a belief space to refine the population and introduces two vital functions for dynamically adjusting the crossover and mutation rates during the evolutionary process. Through extensive experimentation, we provide compelling evidence of the algorithm's remarkable efficiency in consistently locating the global optimum, even in knapsack problems characterized by high dimensions and intricate constraints.
HPX -- An open source C++ Standard Library for Parallelism and Concurrency
Venue: arXiv cs.DC 2023
Year: 2023

To achieve scalability with today's heterogeneous HPC resources, we need a dramatic shift in our thinking; MPI+X is not enough. Asynchronous Many Task (AMT) runtime systems break down the global barriers imposed by the Bulk Synchronous Programming model. HPX is an open-source, C++ Standards compliant AMT runtime system that is developed by a diverse international community of collaborators called The Ste||ar Group. HPX provides features which allow application developers to naturally use key design patterns, such as overlapping communication and computation, decentralizing of control flow, oversubscribing execution resources and sending work to data instead of data to work. The Ste||ar Group comprises physicists, engineers, and computer scientists; men and women from many different institutions and affiliations, and over a dozen different countries. We are committed to advancing the development of scalable parallel applications by providing a platform for collaborating and exchanging ideas. In this paper, we give a detailed description of the features HPX provides and how they help achieve scalability and programmability, a list of applications of HPX including two large NSF funded collaborations (STORM, for storm surge forecasting; and STAR (OctoTiger) an astro-physics project which runs at 96.8% parallel efficiency on 643,280 cores), and we end with a description of how HPX and the Ste||ar Group fit into the open source community.
Generative Skill Chaining: Long-Horizon Skill Planning with Diffusion Models
Venue: arXiv cs.RO 2023
Year: 2023

Long-horizon tasks, usually characterized by complex subtask dependencies, present a significant challenge in manipulation planning. Skill chaining is a practical approach to solving unseen tasks by combining learned skill priors. However, such methods are myopic if sequenced greedily and face scalability issues with search-based planning strategy. To address these challenges, we introduce Generative Skill Chaining~(GSC), a probabilistic framework that learns skill-centric diffusion models and composes their learned distributions to generate long-horizon plans during inference. GSC samples from all skill models in parallel to efficiently solve unseen tasks while enforcing geometric constraints. We evaluate the method on various long-horizon tasks and demonstrate its capability in reasoning about action dependencies, constraint handling, and generalization, along with its ability to replan in the face of perturbations. We show results in simulation and on real robot to validate the efficiency and scalability of GSC, highlighting its potential for advancing long-horizon task planning. More details are available at: https://generative-skill-chaining.github.io/
Human evaluation of robotic grippers for berry picking
Venue: arXiv cs.RO 2023
Year: 2023

We describe the construction and evaluation of two robotic grippers for berry picking. Using a pneumatic cylinder drive, one was constructed from hard materials and the other from soft materials. A novel evaluation paradigm using a handle mechanism was developed, so the grippers could be directly op-erated by human participants. An artificial bush was also constructed and used for evaluation purposes. Overall, both grippers performed worse than the human hand, indicating that further development is needed.
Recognition of Cyber-Intrusion patterns in user cognitive behavioural characteristics for remote identification
Venue: arXiv cs.HC 2023
Year: 2023

Cyber-attacks keep threatening global networks and information infrastructures. The threat is getting more and more destructive and hard to counter day by day as the global networks continue to enlarge exponentially with limited security counter-measures. As this fact requires more sophisticated methods and techniques in urgency, a multidisciplinary remote cognitive observation technique is proposed in this paper to meet today's cybersecurity needs. The proposed method introduces a non-traditional Cognitive Psychology and Artificial Intelligence (AI) based remote threat identification which can be considered during the cyber security system design. It also enables to access the cognitive behavioural parameters of an intruder/hacker remotely without any physical contact via online connection, disregarding the distance of the thread. The ultimate goal of this work is to develop a supplementary cognitive cyber security tool for next generations secure online banking, finance or trade systems.
Semantic Draw Engineering for Text-to-Image Creation
Venue: arXiv cs.HC 2023
Year: 2023

Text-to-image generation is conducted through Generative Adversarial Networks (GANs) or transformer models. However, the current challenge lies in accurately generating images based on textual descriptions, especially in scenarios where the content and theme of the target image are ambiguous. In this paper, we propose a method that utilizes artificial intelligence models for thematic creativity, followed by a classification modeling of the actual painting process. The method involves converting all visual elements into quantifiable data structures before creating images. We evaluate the effectiveness of this approach in terms of semantic accuracy, image reproducibility, and computational efficiency, in comparison with existing image generation algorithms.
Dataset Optimization for Chronic Disease Prediction with Bio-Inspired Feature Selection
Venue: arXiv cs.NE 2023
Year: 2023

In this study, we investigated the application of bio-inspired optimization algorithms, including Genetic Algorithm, Particle Swarm Optimization, and Whale Optimization Algorithm, for feature selection in chronic disease prediction. The primary goal was to enhance the predictive accuracy of models streamline data dimensionality, and make predictions more interpretable and actionable. The research encompassed a comparative analysis of the three bio-inspired feature selection approaches across diverse chronic diseases, including diabetes, cancer, kidney, and cardiovascular diseases. Performance metrics such as accuracy, precision, recall, and f1 score are used to assess the effectiveness of the algorithms in reducing the number of features needed for accurate classification. The results in general demonstrate that the bio-inspired optimization algorithms are effective in reducing the number of features required for accurate classification. However, there have been variations in the performance of the algorithms on different datasets. The study highlights the importance of data pre-processing and cleaning in ensuring the reliability and effectiveness of the analysis. This study contributes to the advancement of predictive analytics in the realm of chronic diseases. The potential impact of this work extends to early intervention, precision medicine, and improved patient outcomes, providing new avenues for the delivery of healthcare services tailored to individual needs. The findings underscore the potential benefits of using bio-inspired optimization algorithms for feature selection in chronic disease prediction, offering valuable insights for improving healthcare outcomes.
Graph-Network-Based Predictive Modeling for Highly Cross-Linked Polymer Systems
Venue: arXiv cs.CE 2023
Year: 2023

In this study, a versatile methodology for initiating polymerization from monomers in highly cross-linked materials is investigated. As polymerization progresses, force-field parameters undergo continuous modification due to the formation of new chemical bonds. This dynamic process not only impacts the atoms directly involved in bonding, but also influences the neighboring atomic environment. Monitoring these complex changes in highly cross-linked structures poses a challenge. To address this issue, we introduce a graph-network-based algorithm that offers both rapid and accurate predictions. The algorithm merges polymer construction protocols with LAMMPS, a large-scale molecular dynamics simulation software. The adaptability of this code has been demonstrated by its successful application to various amorphous polymers, including porous polymer networks (PPNs), and epoxy-resins, while the algorithm has been employed for additional tasks, such as implementing pore-piercing deformations and calculating material properties.
Towards Practical Cell-Free 6G Network Deployments: An Open-Source End-to-End Ray Tracing Simulator
Venue: arXiv cs.NI 2023
Year: 2023

The advent of 6G wireless communication marks a transformative era in technological connectivity, bringing forth challenges and opportunities alike. This paper unveils an innovative, open-source simulator, meticulously crafted for cell-free 6G wireless networks. This simulator is not just a tool but a gateway to the future, blending cutting-edge channel models with the simulation of both physical propagation effects and intricate system-level protocols. It stands at the forefront of technological advancement by integrating LIS and MIMO technologies, harnessing the power of the Unity game engine for efficient ray-tracing and GPU-accelerated computations. The unparalleled flexibility in scenario configuration, coupled with its unique ability to dynamically simulate interactions across network layers, establishes this simulator as an indispensable asset in pioneering &G systems' research and development.
The Conquest of Quantum Genetic Algorithms: The Adventure to Cross the Valley of Death
Venue: arXiv cs.NE 2023
Year: 2023

In recent years, the emergence of the first quantum computers at a time when AI is undergoing a fruitful era has led many AI researchers to be tempted into adapting their algorithms to run on a quantum computer. However, in many cases the initial enthusiasm has ended in frustration, since the features and principles underlying quantum computing are very different from traditional computers. In this paper, we present a discussion of the difficulties arising when designing a quantum version of an evolutionary algorithm based on Darwin's evolutionary mechanism, the so-called genetic algorithms. The paper includes the code in both Python and QISKIT of the quantum version of one of these evolutionary algorithms allowing the reader to experience the setbacks arising when translating a classical algorithm to its quantum version. The algorithm studied in this paper, termed RQGA (Reduced Quantum Genetic Algorithm), has been chosen as an example that clearly shows these difficulties, which are common to other AI algorithms.
An Efficient Dynamic Transaction Storage Mechanism for Sustainable High Throughput Bitcoin
Venue: arXiv cs.NI 2023
Year: 2023

As coin-based rewards dwindle, transaction fees play an important role as mining incentives in Bitcoin. In this paper, we propose a novel mechanism called Efficient Dynamic Transaction Storage (EDTS) for dynamically allocating transactions among blocks to achieve efficient storage utilization. By leveraging a combination of Cuckoo Filter and Dynamic Transaction Storage (DTS) strategies, EDTS is able to improve the scalability while remaining sustainable even after the Bitcoin enters a transaction-fee regime. In addition to preventing deviant mining behaviors under the transaction-fee regime, EDTS can also provide differentiated transmission priorities based on transaction fees while allowing the investors to engage in pledging more transaction fees. In EDTS, we applied the multi-objective optimization algorithm U-NSGA-III to find the best DTS strategy and its corresponding attributes. Experimental results show that the EDTS mechanism together with the optimized DTS strategy can achieve a throughput of 325.3 TPS. The experimental results reveal that the scalability improvement of EDTS is superior to the performance of Bitcoin NG, which is the best known on-chain scaling solution, while maintaining the sustainability under the transaction-fee regime.
An Integrated Imitation and Reinforcement Learning Methodology for Robust Agile Aircraft Control with Limited Pilot Demonstration Data
Venue: arXiv cs.AI 2023
Year: 2023

In this paper, we present a methodology for constructing data-driven maneuver generation models for agile aircraft that can generalize across a wide range of trim conditions and aircraft model parameters. Maneuver generation models play a crucial role in the testing and evaluation of aircraft prototypes, providing insights into the maneuverability and agility of the aircraft. However, constructing the models typically requires extensive amounts of real pilot data, which can be time-consuming and costly to obtain. Moreover, models built with limited data often struggle to generalize beyond the specific flight conditions covered in the original dataset. To address these challenges, we propose a hybrid architecture that leverages a simulation model, referred to as the source model. This open-source agile aircraft simulator shares similar dynamics with the target aircraft and allows us to generate unlimited data for building a proxy maneuver generation model. We then fine-tune this model to the target aircraft using a limited amount of real pilot data. Our approach combines techniques from imitation learning, transfer learning, and reinforcement learning to achieve this objective. To validate our methodology, we utilize real agile pilot data provided by Turkish Aerospace Industries (TAI). By employing the F-16 as the source model, we demonstrate that it is possible to construct a maneuver generation model that generalizes across various trim conditions and aircraft parameters without requiring any additional real pilot data. Our results showcase the effectiveness of our approach in developing robust and adaptable models for agile aircraft.
Parametric Constraints for Bayesian Knowledge Tracing from First Principles
Venue: arXiv cs.CY 2023
Year: 2023

Bayesian Knowledge Tracing (BKT) is a probabilistic model of a learner's state of mastery corresponding to a knowledge component. It considers the learner's state of mastery as a "hidden" or latent binary variable and updates this state based on the observed correctness of the learner's response using parameters that represent transition probabilities between states. BKT is often represented as a Hidden Markov Model and the Expectation-Maximization (EM) algorithm is used to infer these parameters. However, this algorithm can suffer from several issues including producing multiple viable sets of parameters, settling into a local minima, producing degenerate parameter values, and a high computational cost during fitting. This paper takes a "from first principles" approach to deriving constraints that can be imposed on the BKT parameter space. Starting from the basic mathematical truths of probability and building up to the behaviors expected of the BKT parameters in real systems, this paper presents a mathematical derivation that results in succinct constraints that can be imposed on the BKT parameter space. Since these constraints are necessary conditions, they can be applied prior to fitting in order to reduce computational cost and the likelihood of issues that can emerge from the EM procedure. In order to see that promise through, the paper further introduces a novel algorithm for estimating BKT parameters subject to the newly defined constraints. While the issue of degenerate parameter values has been reported previously, this paper is the first, to our best knowledge, to derive the constrains from first principles while also presenting an algorithm that respects those constraints.
Empowering Africa: An In-depth Exploration of the Adoption of Artificial Intelligence Across the Continent
Venue: arXiv cs.CY 2023
Year: 2023

This paper explores the dynamic landscape of Artificial Intelligence (AI) adoption in Africa, analysing its varied applications in addressing socio-economic challenges and fostering development. Examining the African AI ecosystem, the study considers regional nuances, cultural factors, and infrastructural constraints shaping the deployment of AI solutions. Case studies in healthcare, agriculture, finance, and education highlight AI's transformative potential for efficiency, accessibility, and inclusivity. The paper emphasizes indigenous AI innovations and international collaborations contributing to a distinct African AI ecosystem. Ethical considerations, including data privacy and algorithmic bias, are addressed alongside policy frameworks supporting responsible AI implementation. The role of governmental bodies, regulations, and private sector partnerships is explored in creating a conducive AI development environment. Challenges such as digital literacy gaps and job displacement are discussed, with proposed strategies for mitigation. In conclusion, the paper provides a nuanced understanding of AI in Africa, contributing to sustainable development discussions and advocating for an inclusive and ethical AI ecosystem on the continent.
What's my role? Modelling responsibility for AI-based safety-critical systems
Venue: arXiv cs.CY 2023
Year: 2023

AI-Based Safety-Critical Systems (AI-SCS) are being increasingly deployed in the real world. These can pose a risk of harm to people and the environment. Reducing that risk is an overarching priority during development and operation. As more AI-SCS become autonomous, a layer of risk management via human intervention has been removed. Following an accident it will be important to identify causal contributions and the different responsible actors behind those to learn from mistakes and prevent similar future events. Many authors have commented on the "responsibility gap" where it is difficult for developers and manufacturers to be held responsible for harmful behaviour of an AI-SCS. This is due to the complex development cycle for AI, uncertainty in AI performance, and dynamic operating environment. A human operator can become a "liability sink" absorbing blame for the consequences of AI-SCS outputs they weren't responsible for creating, and may not have understanding of. This cross-disciplinary paper considers different senses of responsibility (role, moral, legal and causal), and how they apply in the context of AI-SCS safety. We use a core concept (Actor(A) is responsible for Occurrence(O)) to create role responsibility models, producing a practical method to capture responsibility relationships and provide clarity on the previously identified responsibility issues. Our paper demonstrates the approach with two examples: a retrospective analysis of the Tempe Arizona fatal collision involving an autonomous vehicle, and a safety focused predictive role-responsibility analysis for an AI-based diabetes co-morbidity predictor. In both examples our primary focus is on safety, aiming to reduce unfair or disproportionate blame being placed on operators or developers. We present a discussion and avenues for future research.
High-Performance Computing in Battery Development: From Pore Scale to Continuum
Venue: arXiv cs.CE 2023
Year: 2023

An application for high-performance computing (HPC) is shown that is relevant in the field of battery development. Simulations of electrolyte wetting and flow are conducted using pore network models (PNM) and the lattice Boltzmann method (LBM), while electrochemical simulations are conducted using the tool BEST. All aforementioned software packages show an appropriate scaling behavior. A workflow for optimizing battery performance by improving the filling of battery components is presented. A special focus is given to the unwanted side effect of gas entrapment encountered during filling. It is also known to adversely affect the electrochemical performance of batteries and can be partially prevented by appropriate microstructure design such as electrode perforation.
Holonic Learning: A Flexible Agent-based Distributed Machine Learning Framework
Venue: arXiv cs.DC 2023
Year: 2023

Ever-increasing ubiquity of data and computational resources in the last decade have propelled a notable transition in the machine learning paradigm towards more distributed approaches. Such a transition seeks to not only tackle the scalability and resource distribution challenges but also to address pressing privacy and security concerns. To contribute to the ongoing discourse, this paper introduces Holonic Learning (HoL), a collaborative and privacy-focused learning framework designed for training deep learning models. By leveraging holonic concepts, the HoL framework establishes a structured self-similar hierarchy in the learning process, enabling more nuanced control over collaborations through the individual model aggregation approach of each holon, along with their intra-holon commitment and communication patterns. HoL, in its general form, provides extensive design and flexibility potentials. For empirical analysis and to demonstrate its effectiveness, this paper implements HoloAvg, a special variant of HoL that employs weighted averaging for model aggregation across all holons. The convergence of the proposed method is validated through experiments on both IID and Non-IID settings of the standard MNISt dataset. Furthermore, the performance behaviors of HoL are investigated under various holarchical designs and data distribution scenarios. The presented results affirm HoL's prowess in delivering competitive performance particularly, in the context of the Non-IID data distribution.
Concrete Problems in AI Safety, Revisited
Venue: arXiv cs.CY 2023
Year: 2023

As AI systems proliferate in society, the AI community is increasingly preoccupied with the concept of AI Safety, namely the prevention of failures due to accidents that arise from an unanticipated departure of a system's behavior from designer intent in AI deployment. We demonstrate through an analysis of real world cases of such incidents that although current vocabulary captures a range of the encountered issues of AI deployment, an expanded socio-technical framing will be required for a more complete understanding of how AI systems and implemented safety mechanisms fail and succeed in real life.
The lower energy consumption in cryptocurrency mining processes by SHA-256 Quantum circuit design used in hybrid computing domains
Venue: arXiv cs.ET 2023
Year: 2023

Cryptocurrency mining processes always lead to a high energy consumption at considerably high production cost, which is nearly one-third of cryptocurrency (e.g. Bitcoin) price itself. As the core of mining process is based on SHA-256 cryptographic hashing function, by using the alternative quantum computers, hybrid quantum computers or more larger quantum computing devices like quantum annealers, it would be possible to reduce the mining energy consumption with a quantum hardware's low-energy-operation characteristics. Within this work we demonstrated the use of optimized quantum mining facilities which would replace the classical SHA-256 and high energy consuming classical hardware in near future.