Abraham Itzhak Weinberg

Teddy Lazebnik, Amit Somech, Abraham Itzhak Weinberg

Automated machine learning (AutoML) frameworks have become important tools in the data scientists' arsenal, as they dramatically reduce the manual work devoted to the construction of ML pipelines. Such frameworks intelligently search among millions of possible ML pipelines - typically containing feature engineering, model selection and hyper parameters tuning steps - and finally output an optimal pipeline in terms of predictive accuracy. However, when the dataset is large, each individual configuration takes longer to execute, therefore the overall AutoML running times become increasingly high. To this end, we present SubStrat, an AutoML optimization strategy that tackles the data size, rather than configuration space. It wraps existing AutoML tools, and instead of executing them directly on the entire dataset, SubStrat uses a genetic-based algorithm to find a small yet representative data subset which preserves a particular characteristic of the full data. It then employs the AutoML tool on the small subset, and finally, it refines the resulted pipeline by executing a restricted, much shorter, AutoML process on the large dataset. Our experimental results, performed on two popular AutoML frameworks, Auto-Sklearn and TPOT, show that SubStrat reduces their running times by 79% (on average), with less than 2% average loss in the accuracy of the resulted ML pipeline.

Abraham Itzhak Weinberg

In recent years, the threat facing airports from growing and increasingly sophisticated cyberattacks has become evident. Airports are considered a strategic national asset, so protecting them from attacks, specifically cyberattacks, is a crucial mission. One way to increase airports' security is by using Digital Twins (DTs). This paper shows and demonstrates how DTs can enhance the security mission. The integration of DTs with Generative AI (GenAI) algorithms can lead to synergy and new frontiers in fighting cyberattacks. The paper exemplifies ways to model cyberattack scenarios using simulations and generate synthetic data for testing defenses. It also discusses how DTs can be used as a crucial tool for vulnerability assessment by identifying weaknesses, prioritizing, and accelerating remediations in case of cyberattacks. Moreover, the paper demonstrates approaches for anomaly detection and threat hunting using Machine Learning (ML) and GenAI algorithms. Additionally, the paper provides impact prediction and recovery coordination methods that can be used by DT operators and stakeholders. It also introduces ways to harness the human factor by integrating training and simulation algorithms with Explainable AI (XAI) into the DT platforms. Lastly, the paper offers future applications and technologies that can be utilized in DT environments.

Abraham Itzhak Weinberg

We introduce \textbf{QuantFPFlow}, a reinforcement learning framework that integrates quantum amplitude estimation into the Fokker--Planck~(FP) formulation of stochastic policy optimisation. Classical continuous-space RL agents must estimate the FP partition function $Z = \int e^{-V(\mathbf{x})/D}\,d\mathbf{x}$ at cost $\calO(1/\varepsilon^{2})$; QuantFPFlow replaces this with a Grover-amplified amplitude estimator achieving $\calO(1/\varepsilon)$ -- a provable quadratic speedup. While the full quantum acceleration requires fault-tolerant hardware, the quantum-inspired classical simulation demonstrated here already exhibits the $\calO(1/\varepsilon)$ algorithmic structure. The estimated stationary distribution $\rhostar$ drives a theoretically grounded exploration bonus $\Raug = \Renv + α\log(1/\rhostar(s))$. This bonus steers the agent toward globally optimal regions of multimodal reward landscapes while simultaneously constraining policy variance through FP diffusion matching. On a continuous-control task specifically designed to expose local-optima failure, QuantFPFlow achieves mean reward $1{,}295.7 \pm 423.2$ versus $1{,}284.0 \pm 474.0$ for Soft Actor-Critic~(SAC), while discovering the global optimum \textbf{10.4\,\% more frequently} (33.9\,\% vs.\ 30.7\,\%). Policy entropy remains near $H(π)\approx 6.5$\,nats throughout training, whereas SAC collapses to $1.5$\,nats, confirming that FP diffusion matching actively prevents premature convergence. Dimensionality experiments further show computational scaling of $\calO(d^{0.35})$ for QuantFPFlow versus $\calO(d^{0.76})$ for classical FP estimation.

Abraham Itzhak Weinberg

Modern cloud-native environments present a fundamentally different exfiltration threat surface than traditional file-based scenarios. Attackers targeting AWS, GCP, Azure, and OCI steal S3 presigned URLs, container images, Kubernetes secrets, Terraform state modules, and IAM role tokens -- artefacts that existing honeytoken and beacon frameworks do not address. We present \textbf{CLOUDBURST}, the first formal taxonomy and measurement framework for cloud-native passive beacons, comprising six vector classes across four major cloud providers. We introduce the \textit{Cloud Attribution Score} (CAS), a four-component metric that explicitly models ephemeral infrastructure penalty ($E_p$), IAM coverage depth ($I_c$), and multi-cloud correlation bonus ($M_b$) -- dimensions absent from all prior attribution quality metrics. Experiments across $21$ deployed beacons, $205$ simulated callbacks, and three attacker sophistication levels yield four principal findings. First, IAM Canary Roles achieve the highest CAS (mean $0.450$) and Detection Resistance (DR $= 0.873$), making them the most deployable vector. Second, S3 Presigned URLs achieve the highest detection resistance (DR $= 0.890$), surviving all three cloud-native scanner models (AWS Macie, Checkov/tfsec, Prisma Cloud/Wiz). Third, ephemeral infrastructure churn degrades CAS from $\approx 0.79$ at deployment to $\approx 0.18$--$0.22$ at $48$ hours for all vectors ($p < 0.001$), establishing the first quantitative model of attribution decay in containerised environments. Fourth, Serverless Function Triggers exhibit the worst detection resistance (DR $= 0.611$) due to their explicit outbound HTTP callback pattern, motivating covert callback channel design as future work. No significant CAS difference is observed across cloud providers ($H = 1.99$, $p = 0.57$), confirming that CLOUDBURST is provider-agnostic in its effectiveness.

Abraham Itzhak Weinberg

We present \textbf{ORCHID} (\textit{Orchestrated Reduction Consensus for Hash-based Integrity in Distributed Ledgers}), a novel bio-inspired consensus protocol that maps the neuroscientific \emph{binding problem} -- how the brain integrates distributed neural oscillations into a unified conscious percept -- onto the distributed systems \emph{consensus problem}, how blockchain nodes agree on a single ledger state under Byzantine faults. Grounded in the Penrose--Hameroff Orchestrated Objective Reduction (Orch~OR) hypothesis and the Kuramoto synchronisation model, ORCHID equips each node with a quantum-noisy phase oscillator; consensus is triggered when the network's order parameter $r(t)$ crosses a \emph{binding threshold} $θ_b$, mirroring the gamma-band binding event in conscious perception. ORCHID is further strengthened by a coherence-weighted Quantum Secret Sharing (QSS) layer, extending the survey framework of Weinberg to a concrete consensus application. Simulation results on Watts--Strogatz small-world networks ($n=10$--$150$) demonstrate: (i)~the Kuramoto order parameter reaches $r_{\max}=0.988$ under coupling $K=3.0$, well above the theoretical critical coupling $K_c \approx 1.41$; (ii)~a sharp QSS fidelity phase transition at coherence $c^*\approx 0.82$, confirming Theorem~2; (iii)100\% consensus rate at all tested Byzantine fractions (0\%--40\%), with median convergence under 4~s for $n=30$; and (iv)~ORCHID achieves $O(n{\cdot}k)$ message complexity, outperforming PBFT's $O(n^2)$ at $n\geq150$. These results establish ORCHID as a scalable, biologically plausible, and quantum-augmented consensus mechanism for post-quantum distributed ledgers.

Abraham Itzhak Weinberg

We present a hybrid classical-quantum framework for portfolio construction and rebalancing. Asset selection is performed using Ledoit-Wolf shrinkage covariance estimation combined with hierarchical correlation clustering to extract n = 10 decorrelated stocks from the S&P 500 universe without survivorship bias. Portfolio weights are optimised via an entropy-regularised Genetic Algorithm (GA) accelerated on GPU, alongside closed-form minimum-variance and equal-weight benchmarks. Our primary contribution is the formulation of the portfolio rebalancing schedule as a Quadratic Unconstrained Binary Optimisation (QUBO) problem. The resulting combinatorial optimisation task is solved using the Quantum Approximate Optimisation Algorithm (QAOA) within a walk-forward framework designed to eliminate lookahead bias. This approach recasts dynamic rebalancing as a structured binary scheduling problem amenable to variational quantum methods. Backtests on S&P 500 data (training: 2010-2024; out-of-sample test: 2025, n = 249 trading days) show that the GA + QAOA strategy attains a Sharpe ratio of 0.588 and total return of 10.1%, modestly outperforming the strongest classical baseline (GA with 10-day periodic rebalancing, Sharpe 0.575) while executing 8 rebalances versus 24, corresponding to a 44.5% reduction in transaction costs. Multi-restart QAOA (4096 measurement shots per run) exhibits concentrated probability mass on high-quality schedules, indicating stable convergence of the variational procedure. These findings suggest that hybrid classical-quantum architectures can reduce turnover in portfolio rebalancing while preserving competitive risk-adjusted performance, providing a structured testbed for near-term quantum optimisation in financial applications.

Abraham Itzhak Weinberg

Honeytokens, decoy digital assets planted to detect and attribute unauthorised access, are a well-established primitive in cyber deception. Existing generation tools produce static, template-based tokens that lack organisational specificity and are identifiable by statistical, syntactic, and semantic analysis. We introduce PHANTOM (Polymorphic Honeytoken Adaptation with Narrative-Tailored Organisational Mimicry), a framework that generates contextually convincing honeytokens by encoding organisation-specific knowledge: domain names, service naming conventions, technology-stack idioms, and realistic secret-value distributions, into a multi-component generation pipeline. We formalise honeytoken quality through a four-component Believability Score that captures syntactic validity, semantic coherence, statistical plausibility, and human acceptance. We use this metric to evaluate PHANTOM across 8 token types and 4 organisational contexts against a template-based baseline. PHANTOM achieves B = 0.778 +/- 0.057 versus B = 0.576 +/- 0.058 for templates (Delta = +0.203, t = 14.07, p < 0.001, Cohen's d = 3.52). Human-evaluator acceptance rises from 6.2% to 100%, and detection resistance (DR = 1 - Pd) improves from 0.609 to 0.870 across three simulated scanner models (regex, entropy analysis, and ML classifier), each with p < 0.001. The semantic coherence gap (Delta Sc = +0.309, d = 4.52) is the dominant driver, confirming the hypothesis that organisational context is the critical missing ingredient in current approaches. All results are reproduced without external API calls, making the pipeline fully deployable in air-gapped environments.

Abraham Itzhak Weinberg

Folk meteorological proverbs encode centuries of empirical observation by agricultural communities. Two Hebrew proverbs link lunar calendar anchor days to monthly winter rainfall: (i) "If Rosh Chodesh is rainy, the whole month is rainy" and (ii) "If it rains on Shabbat Mevarechim, the whole month is rainy." Shabbat Mevarechim is the last Saturday before each new Hebrew month, preceding Rosh Chodesh by one to seven days. The first proverb is widely known; the second circulates in Hasidic oral tradition with no identified written source. Both have never been formally tested. We analyse 75 years (1950-2024) of daily precipitation data from seven Israeli cities across three climatic regions, comprising 191,758 station-days and 2,422 Hebrew-month observations during the winter rainy season (Marcheshvan-Adar). A rainy Rosh Chodesh increases the probability of a rainy month from 22.2% to 38.6% (lift +16.4 percentage points; chi-square = 57.8, p = 2.9e-14; Bayes factor 1.81). A rainy Shabbat Mevarechim produces a similar effect (lift +16.5 percentage points, p = 8.0e-13), despite preceding Rosh Chodesh by up to seven days. The effect decays with lag and mirrors daily rainfall autocorrelation (r = 0.35-0.44 at lag 1; ~0 at lag 7), consistent with Mediterranean cyclone persistence. A bootstrap permutation test (p < 1e-4) and a 15-year rolling analysis show declining predictive power (-0.20 percentage points per year, p < 0.001), consistent with shortening precipitation events under warming climate conditions. Both proverbs encode real but probabilistic meteorological signals whose reliability is decreasing over time.

Abraham Itzhak Weinberg

Current cyber attribution approaches typically operate on a per-incident basis, leaving open whether aggregating evidence across campaigns improves adversary identification. We investigate whether cross-campaign attribution reduces ambiguity or whether structural limits persist under longitudinal data. We model adversary fingerprints as multi-dimensional feature vectors encoding behavioral, infrastructural, and temporal characteristics derived from covert beacon interactions. We introduce ARCANE (Attacker Re-identification via Cross-campaign Attribution Network), a probabilistic framework that aggregates passive telemetry across campaigns and organizations to construct persistent adversary fingerprints. These fingerprints are updated using a Bayesian belief network that integrates new evidence over time. A time-decayed confidence metric captures accumulated similarity across campaigns. Evaluation on a synthetic dataset of multiple threat profiles shows that intra-actor similarity consistently exceeds inter-actor similarity. However, separation between distinct actors remains limited due to shared operational practices among sophisticated adversaries. Results indicate that cross-campaign aggregation alone does not resolve attribution ambiguity. Performance is constrained by a structural ceiling in feature space, where inter-actor similarity remains high even without evasion. Attribution accuracy remains stable under increasing evasion, suggesting the main limitation is feature indistinguishability rather than adversarial adaptation. These findings highlight the need for additional signal classes, such as targeting patterns, temporal coordination, and infrastructure relationships, to improve attribution reliability.

Abraham Itzhak Weinberg, Alessio Faccia

Financial crimes fast proliferation and sophistication require novel approaches that provide robust and effective solutions. This paper explores the potential of quantum algorithms in combating financial crimes. It highlights the advantages of quantum computing by examining traditional and Machine Learning (ML) techniques alongside quantum approaches. The study showcases advanced methodologies such as Quantum Machine Learning (QML) and Quantum Artificial Intelligence (QAI) as powerful solutions for detecting and preventing financial crimes, including money laundering, financial crime detection, cryptocurrency attacks, and market manipulation. These quantum approaches leverage the inherent computational capabilities of quantum computers to overcome limitations faced by classical methods. Furthermore, the paper illustrates how quantum computing can support enhanced financial risk management analysis. Financial institutions can improve their ability to identify and mitigate risks, leading to more robust risk management strategies by exploiting the quantum advantage. This research underscores the transformative impact of quantum algorithms on financial risk management. By embracing quantum technologies, organisations can enhance their capabilities to combat evolving threats and ensure the integrity and stability of financial systems.

Abraham Itzhak Weinberg

As Multi Agent Reinforcement Learning systems are used in safety critical applications. Understanding why agents make decisions and how they achieve collective behavior is crucial. Existing explainable AI methods struggle in multi agent settings. They fail to attribute collective outcomes to individuals, quantify emergent behaviors, or capture complex interactions. We present MACIE Multi Agent Causal Intelligence Explainer, a framework combining structural causal models, interventional counterfactuals, and Shapley values to provide comprehensive explanations. MACIE addresses three questions. First, each agent's causal contribution using interventional attribution scores. Second, system level emergent intelligence through synergy metrics separating collective effects from individual contributions. Third, actionable explanations using natural language narratives synthesizing causal insights. We evaluate MACIE across four MARL scenarios: cooperative, competitive, and mixed motive. Results show accurate outcome attribution, mean phi_i equals 5.07, standard deviation less than 0.05, detection of positive emergence in cooperative tasks, synergy index up to 0.461, and efficient computation, 0.79 seconds per dataset on CPU. MACIE uniquely combines causal rigor, emergence quantification, and multi agent support while remaining practical for real time use. This represents a step toward interpretable, trustworthy, and accountable multi agent AI.

Abraham Itzhak Weinberg, Cristiano Premebida, Diego Resende Faria

Causality has become a fundamental approach for explaining the relationships between events, phenomena, and outcomes in various fields of study. It has invaded various fields and applications, such as medicine, healthcare, economics, finance, fraud detection, cybersecurity, education, public policy, recommender systems, anomaly detection, robotics, control, sociology, marketing, and advertising. In this paper, we survey its development over the past five decades, shedding light on the differences between causality and other approaches, as well as the preconditions for using it. Furthermore, the paper illustrates how causality interacts with new approaches such as Artificial Intelligence (AI), Generative AI (GAI), Machine and Deep Learning, Reinforcement Learning (RL), and Fuzzy Logic. We study the impact of causality on various fields, its contribution, and its interaction with state-of-the-art approaches. Additionally, the paper exemplifies the trustworthiness and explainability of causality models. We offer several ways to evaluate causality models and discuss future directions.

Abraham Itzhak Weinberg, Alessio Faccia

Triple Entry (TE) is an accounting method that utilizes three accounts or 'entries' to record each transaction, rather than the conventional double-entry bookkeeping system. Existing studies have found that TE accounting, with its additional layer of verification and disclosure of inter-organizational relationships, could help improve transparency in complex financial and supply chain transactions such as blockchain. Machine learning (ML) presents a promising avenue to augment the transparency advantages of TE accounting. By automating some of the data collection and analysis needed for TE bookkeeping, ML techniques have the potential to make this more transparent accounting method scalable for large organizations with complex international supply chains, further enhancing the visibility and trustworthiness of financial reporting. By leveraging ML algorithms, anomalies within distributed ledger data can be swiftly identified, flagging potential instances of fraud or errors. Furthermore, by delving into transaction relationships over time, ML can untangle intricate webs of transactions, shedding light on obscured dealings and adding an investigative dimension. This paper aims to demonstrate the interaction between TE and ML and how they can leverage transparency levels.

Itay Etelis, Avi Rosenfeld, Abraham Itzhak Weinberg et al.

In recent years, transformer models have revolutionized Natural Language Processing (NLP), achieving exceptional results across various tasks, including Sentiment Analysis (SA). As such, current state-of-the-art approaches for SA predominantly rely on transformer models alone, achieving impressive accuracy levels on benchmark datasets. In this paper, we show that the key for further improving the accuracy of such ensembles for SA is to include not only transformers, but also traditional NLP models, despite the inferiority of the latter compared to transformer models. However, as we empirically show, this necessitates a change in how the ensemble is constructed, specifically relying on the Hierarchical Ensemble Construction (HEC) algorithm we present. Our empirical studies across eight canonical SA datasets reveal that ensembles incorporating a mix of model types, structured via HEC, significantly outperform traditional ensembles. Finally, we provide a comparative analysis of the performance of the HEC and GPT-4, demonstrating that while GPT-4 closely approaches state-of-the-art SA methods, it remains outperformed by our proposed ensemble strategy.

Abraham Itzhak Weinberg

Emergent communication in multi-agent systems typically occurs through independent learning, resulting in slow convergence and potentially suboptimal protocols. We introduce TSLEC (Trust-Based Social Learning with Emergent Communication), a framework where agents explicitly teach successful strategies to peers, with knowledge transfer modulated by learned trust relationships. Through experiments with 100 episodes across 30 random seeds, we demonstrate that trust-based social learning reduces episodes-to-convergence by 23.9% (p < 0.001, Cohen's d = 1.98) compared to independent emergence, while producing compositional protocols (C = 0.38) that remain robust under dynamic objectives (Phi > 0.867 decoding accuracy). Trust scores strongly correlate with teaching quality (r = 0.743, p < 0.001), enabling effective knowledge filtering. Our results establish that explicit social learning fundamentally accelerates emergent communication in multi-agent coordination.

Abraham Itzhak Weinberg

Generative Artificial Intelligence (GenAI) presents transformative opportunities for organizations, yet both midsize organizations and larger enterprises face distinctive adoption challenges. Midsize organizations encounter resource constraints and limited AI expertise, while enterprises struggle with organizational complexity and coordination challenges. Existing technology adoption frameworks, including TAM (Technology Acceptance Model), TOE (Technology Organization Environment), and DOI (Diffusion of Innovations) theory, lack the specificity required for GenAI implementation across these diverse contexts, creating a critical gap in adoption literature. This paper introduces FAIGMOE (Framework for the Adoption and Integration of Generative AI in Midsize Organizations and Enterprises), a conceptual framework addressing the unique needs of both organizational types. FAIGMOE synthesizes technology adoption theory, organizational change management, and innovation diffusion perspectives into four interconnected phases: Strategic Assessment, Planning and Use Case Development, Implementation and Integration, and Operationalization and Optimization. Each phase provides scalable guidance on readiness assessment, strategic alignment, risk governance, technical architecture, and change management adaptable to organizational scale and complexity. The framework incorporates GenAI specific considerations including prompt engineering, model orchestration, and hallucination management that distinguish it from generic technology adoption frameworks. As a perspective contribution, FAIGMOE provides the first comprehensive conceptual framework explicitly addressing GenAI adoption across midsize and enterprise organizations, offering actionable implementation protocols, assessment instruments, and governance templates requiring empirical validation through future research.

Abraham Itzhak Weinberg

Image retrieval remains a challenging task due to the complex interaction between human visual perception, memory, and computational processes. Current image search engines often struggle to efficiently retrieve images based on natural language descriptions, as they rely on time-consuming preprocessing, tagging, and machine learning pipelines. This paper introduces the Human-Oriented Retrieval Search Engine for Images (HORSE), a novel approach that leverages neuro-symbolic indexing to improve image retrieval by focusing on human-oriented indexing. By integrating cognitive science insights with advanced computational techniques, HORSE enhances the retrieval process, making it more aligned with how humans perceive, store, and recall visual information. The neuro-symbolic framework combines the strengths of neural networks and symbolic reasoning, mitigating their individual limitations. The proposed system optimizes image retrieval, offering a more intuitive and efficient solution for users. We discuss the design and implementation of HORSE, highlight its potential applications in fields such as design error detection and knowledge management, and suggest future directions for research to further refine the system's metrics and capabilities.