OpenRaiser/Intern-Atlas huggingface dataset:
Hardware-aware training of models with synaptic delays for digital event-driven neuromorphic processors
Venue: arXiv cs.NE 2024
Year: 2024

Configurable synaptic delays are a basic feature in many neuromorphic neural network hardware accelerators. However, they have been rarely used in model implementations, despite their promising impact on performance and efficiency in tasks that exhibit complex (temporal) dynamics, as it has been unclear how to optimize them. In this work, we propose a framework to train and deploy, in digital neuromorphic hardware, highly performing spiking neural network models (SNNs) where apart from the synaptic weights, the per-synapse delays are also co-optimized. Leveraging spike-based back-propagation-through-time, the training accounts for both platform constraints, such as synaptic weight precision and the total number of parameters per core, as a function of the network size. In addition, a delay pruning technique is used to reduce memory footprint with a low cost in performance. We evaluate trained models in two neuromorphic digital hardware platforms: Intel Loihi and Imec Seneca. Loihi offers synaptic delay support using the so-called Ring-Buffer hardware structure. Seneca does not provide native hardware support for synaptic delays. A second contribution of this paper is therefore a novel area- and memory-efficient hardware structure for acceleration of synaptic delays, which we have integrated in Seneca. The evaluated benchmark involves several models for solving the SHD (Spiking Heidelberg Digits) classification task, where minimal accuracy degradation during the transition from software to hardware is demonstrated. To our knowledge, this is the first work showcasing how to train and deploy hardware-aware models parameterized with synaptic delays, on multicore neuromorphic hardware accelerators.
Exploring Post Quantum Cryptography with Quantum Key Distribution for Sustainable Mobile Network Architecture Design
Venue: arXiv cs.CR 2024
Year: 2024

The proliferation of mobile networks and their increasing importance to modern life, combined with the emerging threat of quantum computing, present new challenges and opportunities for cybersecurity. This paper addresses the complexity of protecting these critical infrastructures against future quantum attacks while considering operational sustainability. We begin with an overview of the current landscape, identify the main vulnerabilities in mobile networks, and evaluate existing security solutions with new post-quantum cryptography (PQC) methods. We then present a quantum-secure architecture with PQC and Quantum Key Distribution (QKD) tailored explicitly for sustainable mobile networks and illustrate its applicability with several use cases that emphasize the need for advanced protection measures in this new era. In addition, a comprehensive analysis of PQC algorithm families is presented, focusing on their suitability for integration in mobile environments, with particular attention to the trade-offs between energy consumption and security improvements. Finally, recommendations for strengthening mobile networks against quantum threats are provided through a detailed examination of current challenges and opportunities.
Efficient Parking Search using Shared Fleet Data
Venue: arXiv cs.AI 2024
Year: 2024

Finding an available on-street parking spot is a relevant problem of day-to-day life. In recent years, cities such as Melbourne and San Francisco deployed sensors that provide real-time information about the occupation of parking spots. Finding a free parking spot in such a smart environment can be modeled and solved as a Markov decision process (MDP). The problem has to consider uncertainty as available parking spots might not remain available until arrival due to other vehicles also claiming spots in the meantime. Knowing the parking intention of every vehicle in the environment would eliminate this uncertainty. Unfortunately, it does currently not seem realistic to have such data from all vehicles. In contrast, acquiring data from a subset of vehicles or a vehicle fleet appears feasible and has the potential to reduce uncertainty. In this paper, we examine the question of how useful sharing data within a vehicle fleet might be for the search times of particular drivers. We use fleet data to better estimate the availability of parking spots at arrival. Since optimal solutions for large scenarios are infeasible, we base our method on approximate solutions, which have been shown to perform well in single-agent settings. Our experiments are conducted on a simulation using real-world and synthetic data from the city of Melbourne. The results indicate that fleet data can significantly reduce search times for an available parking spot.
Navigating the Serious Game Design Landscape: A Comprehensive Reference Document
Venue: arXiv cs.HC 2024
Year: 2024

Within the evolving field of digital intervention, serious games emerge as promising tools for evidence-based interventions. Research indicates that gamified therapy, whether employed independently or in conjunction with online psychoeducation or traditional programs, proves more efficacious in delivering care to patients. As we navigate the intricate realm of serious game design, bridging the gap between therapeutic approaches and creative design proves complex. Professionals in clinical and research roles demonstrate innovative thinking yet face challenges in executing engaging therapeutic serious games due to the lack of specialized design skills and knowledge. Thus, a larger question remains: How might we aid and educate professionals in clinical and research roles the importance of game design to support their innovative therapeutic approaches? This study examines potential solutions aimed at facilitating the integration of gamification design principles into clinical study protocols, a pivotal aspect for aligning therapeutic practices with captivating narratives in the pursuit of innovative interventions. We propose two solutions, a flow chart framework for serious games or a comprehensive reference document encompassing gamification design principles and guidelines for best design practices. Through an examination of literature reviews, it was observed that selected design decisions varied across studies. Thus, we propose that the second solution, a comprehensive reference design guide, is more versatile and adaptable.
The Simultaneous Interval Number: A New Width Parameter that Measures the Similarity to Interval Graphs
Venue: arXiv cs.DM 2024
Year: 2024

We propose a novel way of generalizing the class of interval graphs, via a graph width parameter called the simultaneous interval number. This parameter is related to the simultaneous representation problem for interval graphs and defined as the smallest number $d$ of labels such that the graph admits a $d$-simultaneous interval representation, that is, an assignment of intervals and label sets to the vertices such that two vertices are adjacent if and only if the corresponding intervals, as well as their label sets, intersect. We show that this parameter is $\mathsf{NP}$-hard to compute and give several bounds for the parameter, showing in particular that it is sandwiched between pathwidth and linear mim-width. For classes of graphs with bounded parameter values, assuming that the graph is equipped with a simultaneous interval representation with a constant number of labels, we give $\mathsf{FPT}$ algorithms for the clique, independent set, and dominating set problems, and hardness results for the independent dominating set and coloring problems. The $\mathsf{FPT}$ results for independent set and dominating set are for the simultaneous interval number plus solution size. In contrast, both problems are known to be $\mathsf{W}[1]$-hard for linear mim-width plus solution size.
Network architecture search of X-ray based scientific applications
Venue: arXiv cs.LG 2024
Year: 2024

X-ray and electron diffraction-based microscopy use bragg peak detection and ptychography to perform 3-D imaging at an atomic resolution. Typically, these techniques are implemented using computationally complex tasks such as a Psuedo-Voigt function or solving a complex inverse problem. Recently, the use of deep neural networks has improved the existing state-of-the-art approaches. However, the design and development of the neural network models depends on time and labor intensive tuning of the model by application experts. To that end, we propose a hyperparameter (HPS) and neural architecture search (NAS) approach to automate the design and optimization of the neural network models for model size, energy consumption and throughput. We demonstrate the improved performance of the auto-tuned models when compared to the manually tuned BraggNN and PtychoNN benchmark. We study and demonstrate the importance of the exploring the search space of tunable hyperparameters in enhancing the performance of bragg peak detection and ptychographic reconstruction. Our NAS and HPS of (1) BraggNN achieves a 31.03\% improvement in bragg peak detection accuracy with a 87.57\% reduction in model size, and (2) PtychoNN achieves a 16.77\% improvement in model accuracy and a 12.82\% reduction in model size when compared to the baseline PtychoNN model. When inferred on the Orin-AGX platform, the optimized Braggnn and Ptychonn models demonstrate a 10.51\% and 9.47\% reduction in inference latency and a 44.18\% and 15.34\% reduction in energy consumption when compared to their respective baselines, when inferred in the Orin-AGX edge platform.
Dynamic Frequency-Based Fingerprinting Attacks against Modern Sandbox Environments
Venue: arXiv cs.CR 2024
Year: 2024

The cloud computing landscape has evolved significantly in recent years, embracing various sandboxes to meet the diverse demands of modern cloud applications. These sandboxes encompass container-based technologies like Docker and gVisor, microVM-based solutions like Firecracker, and security-centric sandboxes relying on Trusted Execution Environments (TEEs) such as Intel SGX and AMD SEV. However, the practice of placing multiple tenants on shared physical hardware raises security and privacy concerns, most notably side-channel attacks. In this paper, we investigate the possibility of fingerprinting containers through CPU frequency reporting sensors in Intel and AMD CPUs. One key enabler of our attack is that the current CPU frequency information can be accessed by user-space attackers. We demonstrate that Docker images exhibit a unique frequency signature, enabling the distinction of different containers with up to 84.5% accuracy even when multiple containers are running simultaneously in different cores. Additionally, we assess the effectiveness of our attack when performed against several sandboxes deployed in cloud environments, including Google's gVisor, AWS' Firecracker, and TEE-based platforms like Gramine (utilizing Intel SGX) and AMD SEV. Our empirical results show that these attacks can also be carried out successfully against all of these sandboxes in less than 40 seconds, with an accuracy of over 70% in all cases. Finally, we propose a noise injection-based countermeasure to mitigate the proposed attack on cloud environments.
Computing Inductive Invariants of Regular Abstraction Frameworks
Venue: arXiv cs.FL 2024
Year: 2024

Regular transition systems (RTS) are a popular formalism for modeling infinite-state systems in general, and parameterised systems in particular. In a CONCUR 22 paper, Esparza et al. introduce a novel approach to the verification of RTS, based on inductive invariants. The approach computes the intersection of all inductive invariants of a given RTS that can be expressed as CNF formulas with a bounded number of clauses, and uses it to construct an automaton recognising an overapproximation of the reachable configurations. The paper shows that the problem of deciding if the language of this automaton intersects a given regular set of unsafe configurations is in $\textsf{EXPSPACE}$ and $\textsf{PSPACE}$-hard. We introduce $\textit{regular abstraction frameworks}$, a generalisation of the approach of Esparza et al., very similar to the regular abstractions of Hong and Lin. A framework consists of a regular language of $\textit{constraints}$, and a transducer, called the $\textit{interpretation}$, that assigns to each constraint the set of configurations of the RTS satisfying it. Examples of regular abstraction frameworks include the formulas of Esparza et al., octagons, bounded difference matrices, and views. We show that the generalisation of the decision problem above to regular abstraction frameworks remains in $\textsf{EXPSPACE}$, and prove a matching (non-trivial) $\textsf{EXPSPACE}$-hardness bound. $\textsf{EXPSPACE}$-hardness implies that, in the worst case, the automaton recognising the overapproximation of the reachable configurations has a double-exponential number of states. We introduce a learning algorithm that computes this automaton in a lazy manner, stopping whenever the current hypothesis is already strong enough to prove safety. We report on an implementation and show that our experimental results improve on those of Esparza et al.
A Systematic Survey of the Gemini Principles for Digital Twin Ontologies
Venue: arXiv cs.ET 2024
Year: 2024

Ontologies are widely used for achieving interoperable Digital Twins (DTws), yet competing DTw definitions compound interoperability issues. Semantically linking these differing twins is feasible through ontologies and Cognitive Digital Twins (CDTws). However, it is often unclear how ontology use bolsters broader DTw advancements. This article presents a systematic survey following the PRISMA method, to explore the potential of ontologies to support DTws to meet the Centre for Digital Built Britain's Gemini Principles and aims to link progress in ontologies to this framework. The Gemini Principles focus on common DTw requirements, considering: Purpose for 1) Public Good, 2) Value Creation, and 3) Insight; Trustworthiness with sufficient 4) Security, 5) Openness, and 6) Quality; and appropriate Functionality of 7) Federation, 8) Curation, and 9) Evolution. This systematic literature review examines the role of ontologies in facilitating each principle. Existing research uses ontologies to solve DTw challenges within these principles, particularly by connecting DTws, optimising decisionmaking, and reasoning governance policies. Furthermore, analysing the sectoral distribution of literature found that research encompassing the crossover of ontologies, DTws and the Gemini Principles is emerging, and that most innovation is predominantly within manufacturing and built environment sectors. Critical gaps for researchers, industry practitioners, and policymakers are subsequently identified.
Privacy Can Arise Endogenously in an Economic System with Learning Agents
Venue: arXiv cs.GT 2024
Year: 2024

We study price-discrimination games between buyers and a seller where privacy arises endogenously--that is, utility maximization yields equilibrium strategies where privacy occurs naturally. In this game, buyers with a high valuation for a good have an incentive to keep their valuation private, lest the seller charge them a higher price. This yields an equilibrium where some buyers will send a signal that misrepresents their type with some probability; we refer to this as buyer-induced privacy. When the seller is able to publicly commit to providing a certain privacy level, we find that their equilibrium response is to commit to ignore buyers' signals with some positive probability; we refer to this as seller-induced privacy. We then turn our attention to a repeated interaction setting where the game parameters are unknown and the seller cannot credibly commit to a level of seller-induced privacy. In this setting, players must learn strategies based on information revealed in past rounds. We find that, even without commitment ability, seller-induced privacy arises as a result of reputation building. We characterize the resulting seller-induced privacy and seller's utility under no-regret and no-policy-regret learning algorithms and verify these results through simulations.
Authenticity in Authorship: The Writer's Integrity Framework for Verifying Human-Generated Text
Venue: arXiv cs.CR 2024
Year: 2024

The "Writer's Integrity" framework introduces a paradigm shift in maintaining the sanctity of human-generated text in the realms of academia, research, and publishing. This innovative system circumvents the shortcomings of current AI detection tools by monitoring the writing process, rather than the product, capturing the distinct behavioral footprint of human authorship. Here, we offer a comprehensive examination of the framework, its development, and empirical results. We highlight its potential in revolutionizing the validation of human intellectual work, emphasizing its role in upholding academic integrity and intellectual property rights in the face of sophisticated AI models capable of emulating human-like text. This paper also discusses the implementation considerations, addressing potential user concerns regarding ease of use and privacy, and outlines a business model for tech companies to monetize the framework effectively. Through licensing, partnerships, and subscriptions, companies can cater to universities, publishers, and independent writers, ensuring the preservation of original thought and effort in written content. This framework is open source and available here, https://github.com/sanadv/Integrity.github.io
Intelligent Message Behavioral Identification System
Venue: arXiv cs.SI 2024
Year: 2024

On social media platforms, the act of predicting reposting is seen as a challenging issue related to Short Message Services (SMS). This study examines the issue of predicting picture reposting in SMS and forecasts users' behavior in sharing photographs on Twitter. Several research vary. The paper introduces a network called Image Retweet Modeling (IRM) that models heterogeneous image retransmission. It considers the user's previous reposting of the image tweet, the next contact in the SMS, and the preferences of the reposted person. Three aspects connected to content. A text-guided multimodal neural network is developed to create a novel multi-faceted attention ranking network methodology. This allows for learning the joint image Twitter representation and user preference representation in the prediction job. Multiple experiments conducted on extensive data sets demonstrate that our approach outperforms current methods on Social Network platforms.
Decoupled Weight Decay for Any $p$ Norm
Venue: arXiv cs.LG 2024
Year: 2024

With the success of deep neural networks (NNs) in a variety of domains, the computational and storage requirements for training and deploying large NNs have become a bottleneck for further improvements. Sparsification has consequently emerged as a leading approach to tackle these issues. In this work, we consider a simple yet effective approach to sparsification, based on the Bridge, or $L_p$ regularization during training. We introduce a novel weight decay scheme, which generalizes the standard $L_2$ weight decay to any $p$ norm. We show that this scheme is compatible with adaptive optimizers, and avoids the gradient divergence associated with $0<p<1$ norms. We empirically demonstrate that it leads to highly sparse networks, while maintaining generalization performance comparable to standard $L_2$ regularization.
Trackable Agent-based Evolution Models at Wafer Scale
Venue: arXiv cs.NE 2024
Year: 2024

Continuing improvements in computing hardware are poised to transform capabilities for in silico modeling of cross-scale phenomena underlying major open questions in evolutionary biology and artificial life, such as transitions in individuality, eco-evolutionary dynamics, and rare evolutionary events. Emerging ML/AI-oriented hardware accelerators, like the 850,000 processor Cerebras Wafer Scale Engine (WSE), hold particular promise. However, practical challenges remain in conducting informative evolution experiments that efficiently utilize these platforms' large processor counts. Here, we focus on the problem of extracting phylogenetic information from agent-based evolution on the WSE platform. This goal drove significant refinements to decentralized in silico phylogenetic tracking, reported here. These improvements yield order-of-magnitude performance improvements. We also present an asynchronous island-based genetic algorithm (GA) framework for WSE hardware. Emulated and on-hardware GA benchmarks with a simple tracking-enabled agent model clock upwards of 1 million generations a minute for population sizes reaching 16 million agents. We validate phylogenetic reconstructions from these trials and demonstrate their suitability for inference of underlying evolutionary conditions. In particular, we demonstrate extraction, from wafer-scale simulation, of clear phylometric signals that differentiate runs with adaptive dynamics enabled versus disabled. Together, these benchmark and validation trials reflect strong potential for highly scalable agent-based evolution simulation that is both efficient and observable. Developed capabilities will bring entirely new classes of previously intractable research questions within reach, benefiting further explorations within the evolutionary biology and artificial life communities across a variety of emerging high-performance computing platforms.
Incubating Text Classifiers Following User Instruction with Nothing but LLM
Venue: arXiv cs.CL 2024
Year: 2024

In this paper, we aim to generate text classification data given arbitrary class definitions (i.e., user instruction), so one can train a small text classifier without any human annotation or raw corpus. Compared with pioneer attempts, our proposed Incubator is the first framework that can handle complicated and even mutually dependent classes (e.g., "TED Talk given by Educator" and "Other"). Specifically, Incubator is an LLM firstly tuned on the instruction-to-data mappings that we obtained from classification datasets and descriptions on HuggingFace together with in-context augmentation by GPT-4. We then refine Incubator by learning on the cluster centers of semantic textual embeddings to emphasize the uniformity and semantic diversity in generations. We compare Incubator on various classification tasks with strong baselines such as direct LLM-based inference and training data generation by prompt engineering. Experiments show Incubator is able to (1) perform well on traditional benchmarks, (2) take label dependency and user preference into consideration, and (3) enable logical text mining by incubating multiple classifiers.