Reviewerly/exHarmony huggingface dataset:
Learning in Pessiland via Inductive Inference

Pessiland is one of Impagliazzo’s five possible worlds in which NP is hard on average, yet no one-way function exists. This world is considered the most pessimistic because it offers neither algorithmic nor cryptographic benefits.In this paper, we develop a unified framework for constructing strong learning algorithms under the nonexistence of a one-way function, indicating a positive aspect of Pessiland. Using our framework, we improve the learning algorithm for adaptively changing distributions, which was introduced by Naor and Rothblum (ICML’06). Although the previous learner assumes the knowledge of underlying distributions, our learner is universal, i.e., does not assume any knowledge on distributions, and has better sample complexity. We also employ our framework to construct a strong agnostic learner with optimal sample complexity, which improves the previous PAC learner of Blum, Furst, Kearns, and Lipton (Crypto’93). Our learning algorithms are worst-case algorithms that run in exponential time with respect to computational depth, and as a by-product, we present the first characterization of the existence of a one-way function by the worst-case hardness of some promise problem in AM. As a corollary of our results, we establish the robustness of average-case learning, that is, the equivalence among various average-case learning tasks, such as (strong and weak) agnostic learning, learning adaptively changing distributions with respect to arbitrary unknown distributions, and weak learning with membership queries with respect to the uniform distribution.Our framework is based on the theory of Solomonoff’s inductive inference and the universal extrapolation algorithm of Impagliazzo and Levin (FOCS’90). Conceptually, the framework demonstrates that Pessiland is, in fact, a wonderland for machine learning in which various learning tasks can be efficiently solved by the generic algorithm of universal extrapolation.
The Bit Complexity of Efficient Continuous Optimization

We analyze the bit complexity of efficient algorithms for fundamental optimization problems, such as linear regression, p-norm regression, and linear programming (LP). State-of-the-art algorithms are iterative, and in terms of the number of arithmetic operations, they match the current time complexity of multiplying two n-by-n matrices (up to polylogarithmic factors). However, previous work has typically assumed infinite precision arithmetic, and due to complicated inverse maintenance techniques, the actual running times of these algorithms are unknown. To settle the running time and bit complexity of these algorithms, we demonstrate that a core common subroutine, known as inverse maintenance, is backward-stable. Additionally, we show that iterative approaches for solving constrained weighted regression problems can be accomplished with bounded-error preconditioners. Specifically, we prove that linear programs can be solved approximately in matrix multiplication time multiplied by polylog factors that depend on the condition number $\kappa$ of the matrix and the inner and outer radius of the LP problem. p-norm regression can be solved approximately in matrix multiplication time multiplied by polylog factors in $\kappa$. Lastly, linear regression can be solved approximately in input-sparsity time multiplied by polylog factors in $\kappa$. Furthermore, we present results for achieving lower than matrix multiplication time for p-norm regression by utilizing faster solvers for sparse linear systems.
Streaming Lower Bounds and Asymmetric Set-Disjointness

Frequency estimation in data streams is one of the classical problems in streaming algorithms. Following much research, there are now almost matching upper and lower bounds for the trade-off needed between the number of samples and the space complexity of the algorithm, when the data streams are adversarial. However, in the case where the data stream is given in a random order, or is stochastic, only weaker lower bounds exist. In this work we close this gap, up to logarithmic factors. In order to do so we consider the needle problem, which is a natural hard problem for frequency estimation studied in (Andoni et al. 2008, Crouch et al. 2016). Here, the goal is to distinguish between two distributions over data streams with t samples. The first is uniform over a large enough domain. The second is a planted model; a secret “needle“ is uniformly chosen, and then each element in the stream equals the needle with probability p, and otherwise is uniformly chosen from the domain. It is simple to design streaming algorithms that distinguish the distributions using space $s \approx 1 /\left(p^{2} t\right)$. It was unclear if this is tight, as the existing lower bounds are weaker. We close this gap and show that the trade-off is near optimal, up to a logarithmic factor. Our proof builds and extends classical connections between streaming algorithms and communication complexity, concretely multi-party unique set-disjointness. We introduce two new ingredients that allow us to prove sharp bounds. The first is a lower bound for an asymmetric version of multi-party unique set-disjointness, where players receive input sets of different sizes, and where the communication of each player is normalized relative to their input length. The second is a combinatorial technique that allows to sample needles in the planted model by first sampling intervals, and then sampling a uniform needle in each interval.
Navigating the Complexities of the Cloud-Native World: A Study of Developer Perspectives

The cloud-native ecosystem has witnessed remarkable growth in recent years, resulting in a multitude of new frameworks tailored to address the changing needs of developers and operators. This expansion has brought forth a myriad of challenges, including complexity, security, adoption barriers, and developer experience. Gaining insight into developer perspectives is crucial for unlocking the full potential of cloud-native technologies and optimizing the various components of a cloud-native deployment. In this paper, we present the first comprehensive analysis of the challenges and opportunities within the cloud-native deployments, drawing from multiple surveys involving over 1000 participants. We have devised vital research questions to guide our analysis, addressing developers’ concerns regarding containerization, preferred workloads for autoscaling scenarios, workload isolation methods, challenges faced when integrating observability tools, and preferences, use cases, and obstacles encountered when adopting WebAssembly (WASM) in the cloud-native ecosystem. Our findings strive to bridge the knowledge gap between the research community and the industry, fostering future research directions in the dynamic and constantly evolving cloud-native domain.
Dynamic “Succincter”

Augmented B-trees (aB-trees) are a broad class of data structures. The seminal work “succincter” by Pǎtraşcu [1] showed that any aB-tree can be stored using only two bits of redundancy, while supporting queries to the tree in time proportional to its depth. It has been a versatile building block for constructing succinct data structures, including rank/select data structures, dictionaries, locally decodable arithmetic coding, storing balanced parenthesis, etc.In this paper, we show how to “dynamize” an aB-tree. Our main result is the design of dynamic aB-trees (daB-trees) with branching factor two using only three bits of redundancy (with the help of lookup tables that are of negligible size in applications), while supporting updates and queries in time polynomial in its depth. As an application, we present a dynamic rank/select data structure for n-bit arrays, also known as a dynamic fully indexable dictionary (FID) [2]. It supports updates and queries in $O(\log n / \log \log n)$ time, and when the array has m ones, the \begin{equation*}\log \begin{pmatrix}n \\m\end{pmatrix}+On / 2^{\log 0.199} n\end{equation*}bits. Note that the update and query times are optimal even without space constraints due to a lower bound by Fredman and Saks [3]. Prior to our work, no dynamic FID with near-optimal update and query times and redundancy $o(n / \log n)$ was known. We further show that a dynamic sequence supporting insertions, deletions and rank/select queries can be maintained in (optimal) $O(\log n / \log \log n)$ time and with $O\left(n \cdot \operatorname{poly} \log \log n / \log ^{2} n\right)$ bits of redundancy.
Extracting Randomness from Samplable Distributions, Revisited

Randomness extractors provide a generic way of converting sources of randomness that are merely unpredictable into almost uniformly random bits. While in general, deterministic randomness extraction is impossible, it is possible if the source has some structural constraints.While much of the literature on deterministic extraction has focused on sources with strong independence properties, a natural class where deterministic extraction is possible is sources that can sampled by a polynomial size circuit, Levin [SIAM J Comp’86]. Trevisan and Vadhan [FOCS’00] explicitly constructed deterministic randomness extractors for this class of sources, assuming very strong circuit lower bounds.We suggest that there is perhaps an even more reasonable model of natural sources of randomness than Levin’s: sources sampled by polynomial size quantum circuits. Under a suitable circuit lower bound, we show that Trevisan and Vadhan’s extractor indeed works for this class.Along the way, we substantially improve their analysis in the classical case, showing that a circuit lower bound against NP-circuits suffice in the classical case (as opposed to a lower bounds on $\Sigma_{5}$-circuits, as shown by Trevisan and Vadhan). Moreover, we show that under this assumption, it is possible to handle sources sampled by postselecting circuits (a variant of nondeterministic circuits). We show that this model is sufficient to capture randomness extraction in the presence of efficiently computable leakage.
Yardstick Competition Regulation for Incentive Mechanisms in Federated Learning: Balancing Cost Optimization and Fairness

Federated learning (FL) is a global machine-learning model that is trained using several participating nodes, where data is kept private. In this paper, we propose a cost-effective dynamic joint load balancing and pricing algorithm (CELBP) for heterogeneous workers that utilizes the Yardstick Competition Regulation (YCR) to encourage workers to disclose their costs honestly and provide incentives based on relative contributions. In the dynamic case, we aim to minimize the total cost of the server, considering the positive value of subtasks assigned to workers and their limited full load, and a new cost function for workers that accounts for their efficiency level and total communication and computation time required to complete assigned tasks. We show that CELBP outperforms other schemes in terms of accuracy and training time while reducing the server's total cost, using experiments with the MNIST and CIFAR-10 datasets. Additionally, we show that using the proposed online algorithms improves accuracy and reduces latency when compared to other algorithms mentioned in the paper.
Navigating the Low-Code Landscape: A Comparison of Development Platforms

Low-code development is a software-development paradigm that can reduce the conceptual gap between application domain problem expertise and software engineering solution expertise. Hence, it might be a vital means to reduce the impact of the world-wide scarcity of software-developers. Consequently, many different low-code development platforms (LCDPs) have surfaced in the last 5–10 years, which domain experts must understand to chose the best-possible LCDP for their applications. This choice is not made restricting to front-end features of these LCDPs, but also to back-end features, which the domain experts are usually less familiar with. To support domain experts in making this decision, we have investigated popular LCDPs for features relevant to domain experts. Our findings aim to guide these domain experts, but also researchers in better understanding the landscape of LCDPs.
Model-Driven Optimization: Towards Performance-Enhancing Low-Level Encodings

In Model-Driven Optimisation, meta-heuristic opti-mization algorithms are applied to models to solve optimization problems. A meta-model is used to describe a modelling language which defines the search space. Exploration operators (e.g., mutation) are usually expressed as model transformations. During the search space exploration, transformations as well as model copying can become a performance bottleneck, significantly slowing down performance. In this paper, as a first step towards solving this issue, we contribute a low-level encoding of models that does not replace, but compliments them. The encoding stores information about the mutable parts of the model in a way that is inexpensive to change and copy, whereas other operations (e.g., querying of non-mutable parts) are still performed on the actual model. We include a formal framework for expressing what such an encoding looks like, together with an implementation on top of MDEOptimiser, an existing tool for Model-Driven Optimization. In a performance evaluation on two scenarios, we find improved performance in one, and new, clearly identified performance challenges in a second scenario.
Federated Learning for Improved Automatic Modulation Classification: Data Heterogeneity and Low SNR Accuracy

This research introduces novel federated learning (FL)-based methods, namely FedAvgAMC, FedPerAMC, and FedAvgAMCAug, to enhance automatic modulation classification (AMC) in communication systems. By addressing data heterogeneity, incorporating higher-order modulation schemes, and exploring personalized models for clients, these methods overcome challenges associated with centralized learning and data leakage. Simulation results demonstrate significant improvements in accuracy, particularly at low and moderate signal-to-noise ratios (SNRs). FedAvgAMCAug, a privacy-preserving method, achieves superior performance, outperforming CentA MC by 18% in accuracy at an SNR of 5 dB and consistently surpassing existing learning techniques across all clients and SNR values. This research highlights the potential of federated learning to enhance AMC accuracy in real-world scenarios, paving the way for future optimizations and exploration of techniques to address data uncertainty for even more robust performance.
David and Goliath: Asymmetric Advantage in MIoT

Modern battlefields are experiencing an increased dependency on smart devices that can sense, process, and communicate data, also known as the Military Internet of Things (MIoT) architecture. While MIoT supports and enhances situation awareness, it opens up new cyber attacks that, when properly designed and deployed, can affect the safety of military operators.In this work, we discuss the trade-off between two opposite phenomena caused by the massive deployment of MIoT in modern battlefields: situational awareness and asymmetric advantage. We highlight how a resource-constrained adversary can resort to simple and effective tactics to deploy cyber attacks that can have either short-term effects or be detrimental to strategic military objectives.
Automated Mitigation of Frame Problem in UML Class Diagram Verification

The validation and verification of UML class diagrams are essential for ensuring the correctness of complex software systems. However, existing approaches have limitations, such as the inability to automatically deal with the frame problem. The frame problem occurs when operation specifications are incomplete, which can lead to unintended system behavior. This paper proposes an automated approach to specify frame conditions for class diagram verification. Frame conditions are operation contracts that explicitly define the effects an operation may have on the system to mitigate the frame problem. The proposed approach analyzes the behavioral specification of a class diagram to identify relevant information and specify frame conditions. To evaluate the approach, we used the approach to automatically specify frame conditions for different UML diagrams. We then simulated different execution scenarios and analyzed them to evaluate the effectiveness of the specified frame conditions in preventing unintended system behavior resulting from the frame problem. The approach has been implemented and put into practice in the Temporal Property Validator (TPV) tool.
Towards Robust Deep Neural Networks Against Design-Time and Run-Time Failures

Deep Neural Networks (DNNs) have gained widespread adoption, but they also exhibit post-deployment failures that pose risks to property and life. Consequently, enhancing DNN robustness in safety-critical areas is crucial. This paper improves the robustness of DNN models against failures that may arise during either design time or run time. For design failures, we introduce TestRank, an efficient method for identifying DNN design issues. Constrained by test resources, TestRank selects high-quality test cases leveraging intrinsic and contextual attributes of test samples. HybridRepair then offers effective failure repair by selectively annotating failure regions and utilizing semi-supervised learning techniques. To counteract run-time fault injection attacks, we propose D2NN and DeepDyve, which introduce the dual modular redundancy concept to models for protection at the neuron and system levels, respectively. This delicate redundancy achieves lightweight protection for DNN-based systems. Evaluation performed on various image classification datasets demonstrates the effectiveness of our approaches.
Towards SysML v2 as a Variability Modeling Language

Systems engineers seek ways to reuse system models to accelerate the initial design development process on new complex projects. Despite its promise, one such solution ‐‐ the deployment of Model-based Product Line Engineering (MBPLE) ‐‐ presents challenges to practitioners. Not only is deployment costly, but an overwhelming number of variability modeling language options also lack interoperability. While research efforts work towards creating a universal variability modeling language and developing transformations between existing languages, this paper proposes incorporating variability modeling concepts into a system modeling language for a centralized management of product lines. In this work, we highlight SysML v2 as a promising language to use for variability modeling, arguing that it can orthogonally model systems and their variability. Further, we propose a novel metamodel that describes a SysML v2-based approach to modeling conceptual and asset variability spaces. We exemplify the deployment of this metamodel on a simple yet representative example found in a complex development environment. Ultimately, we hope the proposed metamodel will motivate SysML v2 tool vendors to support variability modeling by incorporating instantiation and constraint definition mechanisms and visualization capabilities in future tool releases.
Email Filtering based on Artificial Neural Network: Activation functions and Learning rate analysis

The information and communication technologies evolution, along with the development of the Internet, has meant that networks and information systems now play a crucial role in the society, whose citizens security is not marginalized. Most successful cyberattacks start with a well-known vector, which is email using social engineering techniques as spam and phishing. Email attacks are more and more frequent, therefore it is necessary to know how we can protect the confidential information. This paper aims to detect phishing and spam emails using high-accuracy machine learning techniques using the ANN algorithm with data preprocessing, while changing the learning rate for each activation function to find the one that gives the minimum error in terms of RMSE for each function.
IoT and Blockchain Integration Security

The Internet of Things (IoT) is already playing an important role in our daily lives by integrating smart devices. Whether it is smart homes, smart cities, or smart transportation, these devices communicate with each other to provide high-quality services to their customers These devices operate together to create a vast network that allows for the production, transmission, and sharing of enormous amounts of data. The fundamental issue is how to maintain the security and integrity of such a complicated network to guarantee the long-term viability and dependability of these Internet of Things-based solutions. Blockchain is an emerging technology from its decentralized properties, proving its distinctive features and capabilities for various problems and application areas, including IoT systems. In this paper, we present a review of blockchain adaptation in the context of the Internet of Things, and how it might address the integration and security issues of devices connected to IoT systems. Discuss IoT data security and the use of blockchain.
An improved Actor-based programming framework for simultaneous management of multiple heterogeneous robot swarms

Robotics has been widely used in our daily life in recent years. However, programming and management for heterogeneous robot swarms are still very challenging. In this paper, we present an Actor-based programming framework for simultaneous management of multiple heterogeneous robot swarms. Our framework encapsulates robotic behavior as the ‘Actor’ by using the OODA (observation, orientation, decision, action) bus-plugin mechanism, and takes ‘Actor’ as the basic control unit. Based on the proposed ‘Actor’, we further designed a domain-specific language (DSL) to decouple the development into task development and algorithm development. Besides, we implement a novel communication middleware and the backbone of a multi-layer Actor model to support heterogeneous robot swarms. We implement our proposed framework in C++ and evaluate it on heterogeneous swarm platforms. The experimental results show that our framework can support the development and management of heterogeneous robot swarms.
FastVL: Fast Moving 3D Indoor Reconstruction System Based on SE(2) Constraint for Visual-Laser Fusion

Three-dimensional reconstruction methods face challenges in computational power and robustness when dealing with fast-moving scenes. This paper proposes a fast moving three-dimensional (3D) indoor reconstruction system based on SE(2) constraint for visual-laser fusion. The system utilizes the fusion of visual and 2D laser data to acquire both color texture and more accurate camera pose information. To reduce the complexity of multi-dimensional motion computation, this paper introduces ground constraint to reduce the dimensionality from SE(3) to SE(2). The moving Truncated Signed Distance Function (TSDF) method effectively eliminates interference from discrete points in the model. By deploying a RGB-D camera and a 2D LiDAR on a differential drive platform, the high-precision 3D reconstruction performance of the proposed algorithm in fast-moving scenes is validated through comparisons with real-world datasets.