OpenRaiser/Intern-Atlas huggingface dataset:
Digital Twins for Autonomous Driving: A Comprehensive Implementation and Demonstration
Venue: arXiv cs.NI 2023
Year: 2023

The concept of a digital twin (DT) plays a pivotal role in the ongoing digital transformation and has achieved significant strides for various wireless applications in recent years. In particular, the field of autonomous vehicles is a domain that is ripe for exploiting the concept of DT. Nevertheless, there are many challenges that include holistic consideration and integration of hardware, software, communication methods, and collaboration of edge/cloud computing. In this paper, an end-to-end (E2E) real-world smart mobility DT is designed and implemented for the purpose of autonomous driving. The proposed system utilizes roadside units (RSUs) and edge computing to capture real-world traffic information, which is then processed in the cloud to create a DT model. This DT model is then exploited to enable route planning services for the autonomous vehicle to avoid heavy traffic. Real-world experimental results show that the system reliability can reach 99.53% while achieving a latency that is 3.36% below the 3GPP recommended value of 100 ms for autonomous driving. These results clearly validate the effectiveness of the system according to practical 3GPP standards for sensor and state map sharing (SSMS) and information sharing.
Tumbug: A pictorial, universal knowledge representation method
Venue: arXiv cs.AI 2023
Year: 2023

Since the key to artificial general intelligence (AGI) is commonly believed to be commonsense reasoning (CSR) or, roughly equivalently, discovery of a knowledge representation method (KRM) that is particularly suitable for CSR, the author developed a custom KRM for CSR. This novel KRM called Tumbug was designed to be pictorial in nature because there exists increasing evidence that the human brain uses some pictorial type of KRM, and no well-known prior research in AGI has researched this KRM possibility. Tumbug is somewhat similar to Roger Schank's Conceptual Dependency (CD) theory, but Tumbug is pictorial and uses about 30 components based on fundamental concepts from the sciences and human life, in contrast to CD theory, which is textual and uses about 17 components (= 6 Primitive Conceptual Categories + 11 Primitive Acts) based mainly on human-oriented activities. All the Building Blocks of Tumbug were found to generalize to only five Basic Building Blocks that exactly correspond to the three components {O, A, V} of traditional Object-Attribute-Value representation plus two new components {C, S}, which are Change and System. Collectively this set of five components, called "SCOVA," seems to be a universal foundation for all knowledge representation.
Optimal games in Room 25 solo and coop modes
Venue: arXiv cs.GT 2023
Year: 2023

We study the problem of optimal games for the solo and coop modes of the board game Room 25 (season 1). We show that the game cannot be won in a single turn for any starting configuration, but that it can be done in two for some configurations. We introduce an opening that wins in two turns with enough luck, while having a low probability of losing immediately. We then show that the game can be won in a single turn if the game's rules are slightly modified, although the probability of winning then becomes substantially lower than in the two-turn strategy. At last, we show that if the players are maximally unlucky, they will lose regardless of their strategy.
Dynamic Routing for Integrated Satellite-Terrestrial Networks: A Constrained Multi-Agent Reinforcement Learning Approach
Venue: arXiv cs.NI 2023
Year: 2023

The integrated satellite-terrestrial network (ISTN) system has experienced significant growth, offering seamless communication services in remote areas with limited terrestrial infrastructure. However, designing a routing scheme for ISTN is exceedingly difficult, primarily due to the heightened complexity resulting from the inclusion of additional ground stations, along with the requirement to satisfy various constraints related to satellite service quality. To address these challenges, we study packet routing with ground stations and satellites working jointly to transmit packets, while prioritizing fast communication and meeting energy efficiency and packet loss requirements. Specifically, we formulate the problem of packet routing with constraints as a max-min problem using the Lagrange method. Then we propose a novel constrained Multi-Agent reinforcement learning (MARL) dynamic routing algorithm named CMADR, which efficiently balances objective improvement and constraint satisfaction during the updating of policy and Lagrange multipliers. Finally, we conduct extensive experiments and an ablation study using the OneWeb and Telesat mega-constellations. Results demonstrate that CMADR reduces the packet delay by a minimum of 21% and 15%, while meeting stringent energy consumption and packet loss rate constraints, outperforming several baseline algorithms.
Non-Terrestrial Network (NTN): a Novel Alternate Fractional Programming for the Downlink Channels Power Allocation
Venue: arXiv cs.IT 2023
Year: 2023

Non-terrestrial network (NTN) communication has garnered considerable attention from government entities, industries, and academia in recent times. NTN networks encompass a variety of systems, including Low Earth Orbit (LEO) satellites, Medium Earth Orbit (MEO) satellites, Geostationary Earth Orbit (GEO) satellites, High Altitude Platforms (HAPS), and Low Altitude Platforms (LAPS). Furthermore, the deployment of high-throughput satellites (HTS/VHTS) in the GEO space has gained momentum. While LEO and MEO satellites offer advantages such as low latency and reduced launching costs compared to GEO satellites, this study focuses on GEO satellites due to their stationary nature and broader coverage. In traditional cellular networks, each user equipment (UE) is allocated at least one resource block (RB), which is not shared with other UEs. However, in NTN communications, where the coverage area is extensive, dedicating an RB to only one UE is an inefficient utilization of radio resources. To address this challenge, fractional programming (FP), cognitive radio, and rate splitting multiple access (RSMA) are existing technologies. This paper aims to maximize spectral efficiency, average RBG rate, and sum rate for GEO satellite systems. However, achieving this objective involves dealing with a non-convex, NP-hard problem, as it requires the logarithmic sum of different fractions. Finding a global solution to such an NP-hard problem presents significant challenges. This paper introduces a novel alternate fractional programming algorithm specifically designed to tackle these complex NP-hard problems in the context of GEO NTN cellular networks. By employing this innovative approach, the study seeks to contribute to the optimization of NTN communication systems, enabling efficient resource allocation and improved network performance.
Curriculum Design Helps Spiking Neural Networks to Classify Time Series
Venue: arXiv cs.NE 2023
Year: 2023

Spiking Neural Networks (SNNs) have a greater potential for modeling time series data than Artificial Neural Networks (ANNs), due to their inherent neuron dynamics and low energy consumption. However, it is difficult to demonstrate their superiority in classification accuracy, because current efforts mainly focus on designing better network structures. In this work, enlighten by brain-inspired science, we find that, not only the structure but also the learning process should be human-like. To achieve this, we investigate the power of Curriculum Learning (CL) on SNNs by designing a novel method named CSNN with two theoretically guaranteed mechanisms: The active-to-dormant training order makes the curriculum similar to that of human learning and suitable for spiking neurons; The value-based regional encoding makes the neuron activity to mimic the brain memory when learning sequential data. Experiments on multiple time series sources including simulated, sensor, motion, and healthcare demonstrate that CL has a more positive effect on SNNs than ANNs with about twice the accuracy change, and CSNN can increase about 3% SNNs' accuracy by improving network sparsity, neuron firing status, anti-noise ability, and convergence speed.
Automatic Recognition of Learning Resource Category in a Digital Library
Venue: arXiv cs.DL 2023
Year: 2023

Digital libraries often face the challenge of processing a large volume of diverse document types. The manual collection and tagging of metadata can be a time-consuming and error-prone task. To address this, we aim to develop an automatic metadata extractor for digital libraries. In this work, we introduce the Heterogeneous Learning Resources (HLR) dataset designed for document image classification. The approach involves decomposing individual learning resources into constituent document images (sheets). These images are then processed through an OCR tool to extract textual representation. State-of-the-art classifiers are employed to classify both the document image and its textual content. Subsequently, the labels of the constituent document images are utilized to predict the label of the overall document.
Transforming Agriculture with Intelligent Data Management and Insights
Venue: arXiv cs.DB 2023
Year: 2023

Modern agriculture faces grand challenges to meet increased demands for food, fuel, feed, and fiber with population growth under the constraints of climate change and dwindling natural resources. Data innovation is urgently required to secure and improve the productivity, sustainability, and resilience of our agroecosystems. As various sensors and Internet of Things (IoT) instrumentation become more available, affordable, reliable, and stable, it has become possible to conduct data collection, integration, and analysis at multiple temporal and spatial scales, in real-time, and with high resolutions. At the same time, the sheer amount of data poses a great challenge to data storage and analysis, and the \textit{de facto} data management and analysis practices adopted by scientists have become increasingly inefficient. Additionally, the data generated from different disciplines, such as genomics, phenomics, environment, agronomy, and socioeconomic, can be highly heterogeneous. That is, datasets across disciplines often do not share the same ontology, modality, or format. All of the above make it necessary to design a new data management infrastructure that implements the principles of Findable, Accessible, Interoperable, and Reusable (FAIR). In this paper, we propose Agriculture Data Management and Analytics (ADMA), which satisfies the FAIR principles. Our new data management infrastructure is intelligent by supporting semantic data management across disciplines, interactive by providing various data management/analysis portals such as web GUI, command line, and API, scalable by utilizing the power of high-performance computing (HPC), extensible by allowing users to load their own data analysis tools, trackable by keeping track of different operations on each file, and open by using a rich set of mature open source technologies.
Informal Safety Guarantees for Simulated Optimizers Through Extrapolation from Partial Simulations
Venue: arXiv cs.LG 2023
Year: 2023

Self-supervised learning is the backbone of state of the art language modeling. It has been argued that training with predictive loss on a self-supervised dataset causes simulators: entities that internally represent possible configurations of real-world systems. Under this assumption, a mathematical model for simulators is built based in the Cartesian frames model of embedded agents, which is extended to multi-agent worlds through scaling a two-dimensional frame to arbitrary dimensions, where literature prior chooses to instead use operations on frames. This variant leveraging scaling dimensionality is named the Cartesian object, and is used to represent simulations (where individual simulacra are the agents and devices in that object). Around the Cartesian object, functions like token selection and simulation complexity are accounted for in formalizing the behavior of a simulator, and used to show (through the Löbian obstacle) that a proof of alignment between simulacra by inspection of design is impossible in the simulator context. Following this, a scheme is proposed and termed Partial Simulation Extrapolation aimed at circumventing the Löbian obstacle through the evaluation of low-complexity simulations.
Deploying ADVISER: Impact and Lessons from Using Artificial Intelligence for Child Vaccination Uptake in Nigeria
Venue: arXiv cs.CY 2023
Year: 2023

More than 5 million children under five years die from largely preventable or treatable medical conditions every year, with an overwhelmingly large proportion of deaths occurring in underdeveloped countries with low vaccination uptake. One of the United Nations' sustainable development goals (SDG 3) aims to end preventable deaths of newborns and children under five years of age. We focus on Nigeria, where the rate of infant mortality is appalling. In particular, low vaccination uptake in Nigeria is a major driver of more than 2,000 daily deaths of children under the age of five years. In this paper, we describe our collaboration with government partners in Nigeria to deploy ADVISER: AI-Driven Vaccination Intervention Optimiser. The framework, based on an integer linear program that seeks to maximize the cumulative probability of successful vaccination, is the first successful deployment of an AI-enabled toolchain for optimizing the allocation of health interventions in Nigeria. In this paper, we provide a background of the ADVISER framework and present results, lessons, and success stories of deploying ADVISER to more than 13,000 families in the state of Oyo, Nigeria.
Penetration Testing and Legacy Systems
Venue: arXiv cs.SE 2023
Year: 2023

As per Adusumilli (2015),'70% of corporate business systems today are legacy applications. Recent statistics prove that over 60% of IT budget is spent on maintaining these Legacy systems, showing the rigidity and the fragile nature of these systems.' Usually, testing is included during the software development cycle, using testing techniques such as unit testing, integration testing, and system testing before releasing the product. After the software product is released to production, no additional testing is done; the testing process is back to the table only when modifications are made. Techniques such as regression testing are included to ensure the changes do not affect existing functionality, but testing nonfunctional features that are rarely included in such regression tests' scope. Schrader (2021) affirms that 'legacy systems are often maintained only to ensure function,' and IT organizations may fail to consider the cybersecurity perspective to remain secure. Legacy systems are a high-risk component for the organization that must be carefully considered when structuring a cyber security strategy. This paper aims to help the reader understand some measures that can be taken to secure legacy systems, explaining what penetration testing is and how this testing technique can help secure legacy systems. Keywords: Testing, legacy, security, risks, prevention, mitigation, pentesting.
Taxonomy-based CheckList for Large Language Model Evaluation
Venue: arXiv cs.CL 2023
Year: 2023

As large language models (LLMs) have been used in many downstream tasks, the internal stereotypical representation may affect the fairness of the outputs. In this work, we introduce human knowledge into natural language interventions and study pre-trained language models' (LMs) behaviors within the context of gender bias. Inspired by CheckList behavioral testing, we present a checklist-style task that aims to probe and quantify LMs' unethical behaviors through question-answering (QA). We design three comparison studies to evaluate LMs from four aspects: consistency, biased tendency, model preference, and gender preference switch. We probe one transformer-based QA model trained on SQuAD-v2 dataset and one autoregressive large language model. Our results indicate that transformer-based QA model's biased tendency positively correlates with its consistency, whereas LLM shows the opposite relation. Our proposed task provides the first dataset that involves human knowledge for LLM bias evaluation.
A recursive linear time modular decomposition algorithm via LexBFS
Venue: arXiv cs.DM 2024
Year: 2024

A module of a graph G is a set of vertices that have the same set of neighbours outside. Modules of a graphs form a so-called partitive family and thereby can be represented by a unique tree MD(G), called the modular decomposition tree. Motivated by the central role of modules in numerous algorithmic graph theory questions, the problem of efficiently computing MD(G) has been investigated since the early 70's. To date the best algorithms run in linear time but are all rather complicated. By combining previous algorithmic paradigms developed for the problem, we are able to present a simpler linear-time that relies on very simple data-structures, namely slice decomposition and sequences of rooted ordered trees.
Dichotic harmony for the musical practice
Venue: arXiv cs.SD 2024
Year: 2024

The dichotic method of hearing sound adapts in the region of musical harmony. The algorithm of the separation of the being dissonant voices into several separate groups is proposed. For an increase in the pleasantness of chords the different groups of voices are heard out through the different channels of headphones. Is created two demonstration program for PC. Keywords: music, harmony, chord, dichotic listening, dissonance, consonance, headphones, pleasantness, midi.
A Simplified and Improved Free-Variable Framework for Hilbert's epsilon as an Operator of Indefinite Committed Choice
Venue: arXiv cs.AI 2024
Year: 2024

Free variables occur frequently in mathematics and computer science with ad hoc and altering semantics. We present the most recent version of our free-variable framework for two-valued logics with properly improved functionality, but only two kinds of free variables left (instead of three): implicitly universally and implicitly existentially quantified ones, now simply called "free atoms" and "free variables", respectively. The quantificational expressiveness and the problem-solving facilities of our framework exceed standard first-order and even higher-order modal logics, and directly support Fermat's descente infinie. With the improved version of our framework, we can now model also Henkin quantification, neither using quantifiers (binders) nor raising (Skolemization). We propose a new semantics for Hilbert's epsilon as a choice operator with the following features: We avoid overspecification (such as right-uniqueness), but admit indefinite choice, committed choice, and classical logics. Moreover, our semantics for the epsilon supports reductive proof search optimally.
Showcasing straight-line programs with memory via matrix Bruhat decomposition
Venue: arXiv cs.DS 2024
Year: 2024

We suggest that straight-line programs designed for algebraic computations should be accompanied by a comprehensive complexity analysis that takes into account both the number of fundamental algebraic operations needed, as well as memory requirements arising during evaluation. We introduce an approach for formalising this idea and, as illustration, construct and analyse straight-line programs for the Bruhat decomposition of $d\times d$ matrices with determinant $1$ over a finite field of order $q$ that have length $O(d^2\log(q))$ and require storing only $O(\log(q))$ matrices during evaluation.
Controlling Recurrent Neural Networks by Conceptors
Venue: arXiv cs.NE 2024
Year: 2024

The human brain is a dynamical system whose extremely complex sensor-driven neural processes give rise to conceptual, logical cognition. Understanding the interplay between nonlinear neural dynamics and concept-level cognition remains a major scientific challenge. Here I propose a mechanism of neurodynamical organization, called conceptors, which unites nonlinear dynamics with basic principles of conceptual abstraction and logic. It becomes possible to learn, store, abstract, focus, morph, generalize, de-noise and recognize a large number of dynamical patterns within a single neural system; novel patterns can be added without interfering with previously acquired ones; neural noise is automatically filtered. Conceptors help explaining how conceptual-level information processing emerges naturally and robustly in neural systems, and remove a number of roadblocks in the theory and applications of recurrent neural networks.
Cluster Before You Hallucinate: Approximating Node-Capacitated Network Design and Energy Efficient Routing
Venue: arXiv cs.DS 2024
Year: 2024

We consider the following node-capacitated network design problem. The input is an undirected graph, set of demands, uniform node capacity and arbitrary node costs. The goal is to find a minimum node-cost subgraph that supports all demands concurrently subject to the node capacities. We consider both single and multi-commodity demands, and provide the first poly-logarithmic approximation guarantees. For single-commodity demands (i.e., all request pairs have the same sink node), we obtain an $O(\log^2 n)$ approximation to the cost with an $O(\log^3 n)$ factor violation in node capacities. For multi-commodity demands, we obtain an $O(\log^4 n)$ approximation to the cost with an $O(\log^{10} n)$ factor violation in node capacities. We use a variety of techniques, including single-sink confluent flows, low-load set cover, random sampling and cut-sparsification. We also develop new techniques for clustering multicommodity demands into (nearly) node-disjoint clusters, which may be of independent interest. Moreover, this network design problem has applications to energy-efficient virtual circuit routing. In this setting, there is a network of routers that are speed scalable, and that may be shutdown when idle. We assume the standard model for power: the power consumed by a router with load (speed) $s$ is $σ+ s^α$ where $σ$ is the static power and the exponent $α> 1$. We obtain the first poly-logarithmic approximation algorithms for this problem when speed-scaling occurs on nodes of a network.
Simulating dynamic systems using Linear Time Calculus theories
Venue: arXiv cs.LO 2024
Year: 2024

To appear in Theory and Practice of Logic Programming (TPLP). Dynamic systems play a central role in fields such as planning, verification, and databases. Fragmented throughout these fields, we find a multitude of languages to formally specify dynamic systems and a multitude of systems to reason on such specifications. Often, such systems are bound to one specific language and one specific inference task. It is troublesome that performing several inference tasks on the same knowledge requires translations of your specification to other languages. In this paper we study whether it is possible to perform a broad set of well-studied inference tasks on one specification. More concretely, we extend IDP3 with several inferences from fields concerned with dynamic specifications.