Reviewerly/exHarmony huggingface dataset:
A Scenario Approach to Robust Simulation-based Path Planning

We consider a prototypical path planning problem on a graph with uncertain cost of mobility on its edges. At a given node, the planning agent can access the true cost for edges to its neighbors and uses a noisy simulator to estimate the cost-to-go from the neighboring nodes. The objective of the planning agent is to select a neighboring node such that, with high probability, the cost-to-go is minimized for the worst possible realization of uncertain parameters in the simulator. By modeling the cost-to-go as a Gaussian process (GP) for every realization of the uncertain parameters, we apply a scenario approach in which we draw fixed independent samples of the uncertain parameter. We present a scenario-based iterative algorithm using the upper confidence bound (UCB) of the fixed independent scenarios to compute the choice of the neighbor to go to. We characterize the performance of the proposed algorithm in terms of a novel notion of regret defined with respect to an additional draw of the uncertain parameter, termed as scenario regret under re-draw. In particular, we characterize a high probability upper bound on the regret under re-draw for any finite number of iterations of the algorithm, and show that this upper bound tends to zero asymptotically with the number of iterations. We supplement our analysis with numerical results.
Development and Preliminary Evaluation of a RANSAC Algorithm for Dynamical Model Identification in the Presence of Unmodeled Dynamics

This paper reports a novel Random Sample Consensus (RANSAC) algorithm for robust identification of second-order plant dynamical model parameters in the presence of unmodeled plant dynamics and noisy experimental data. Accurate plant dynamical models are essential to model-based control system design and for accurate numerical simulation of plant response. Studies of RANSAC approaches for plant model identification have been extremely limited and have not explored performance improvements in the presence of unmodeled dynamics. The performance of the proposed approach, evaluated in a preliminary simulation study of a planar aerial rotorcraft model, is found to be significantly more robust to the effects of unmodeled vehicle dynamics and outlier noise than conventional least squares parameter identification. We conjecture that the proposed approach may be broadly applicable to robust model parameter identification for a wide variety of plants that exhibit noisy sensor data and/or unmodeled dynamics.
Optimizing Exponent Bias for Sub-8bit Floating-Point Inference of Fine-tuned Transformers

The Transformer-based fine-tuned neural networks have demonstrated remarkable success in natural language processing (NLP) at the cost of a substantial computational burden. Post-training quantization (PTQ) is a promising technique to reduce the computational cost without expensive re-training. But prior works either demand complex calibration or suffer noticeable accuracy degradation. This paper proposes a practical method for sub-8bit floating-point (FP) PTQ. The proposed method optimizes the exponent bias to minimize quantization error in terms of signal-to-quantization noise ratio (SQNR) progressively like stochastic gradient descent. We evaluate that the proposed method achieves close to full-precision model accuracy for 6 to 8 bit FP PTQ of fine-tuned BERT on GLUE and SQuAD tasks with negligible run-time overhead.
A low power neural network training processor with 8-bit floating point with a shared exponent bias and fused multiply add trees

This demonstration showcases a neural network training processor implemented in silicon through 40nm LPCMOS technology. Based on custom 8-bit floating point and efficient tree-based processing schemes and dataflow, we achieve 2.48× higher energy efficiency than a prior low-power neural network training processor.
Bin-Specific Quantization in Spectral-Domain Convolutional Neural Network Accelerators

Spectral-domain convolution engines can effectively reduce the computational complexity of convolution operations. In these engines, however, element-wise multiplications of the spectral representations dominate the multiply and accumulate (MAC) operations. In light of this, we propose bin-specific quantization (BSQ), which is to judiciously allocate varying bit width to each spectral bin in overlap-save. This allows efficient computation of the Hadamard product since the magnitude of the high-frequency components in image features is significantly smaller than that of the low-frequency counterparts. Using the statistics from spectral representations of feature maps, we also delineate methods for properly allocating bit precision to those spectral bins. When BSQ is applied, the average bit precisions of the arithmetic operators in spectral-domain convolvers, without the requirement of network re-training, were lowered by 24 % (AlexNet), 20% (VGG-16), and 22% (ResNet-18) while having no significant reduction (< 1%) on classification accuracy on the ImageNet dataset.
Accelerate Model Parallel Deep Learning Training Using Effective Graph Traversal Order in Device Placement

Modern neural networks require long training to reach decent performance on massive datasets. One common approach to speed up training is model parallelization, where large neural networks are split across multiple devices. However, different device placements of the same neural network lead to different training times. Most of the existing device placement solutions treat the problem as sequential decision-making by traversing neural network graphs and assigning their neurons to different devices. This work studies the impact of neural network graph traversal orders on device placement. In particular, we empirically study how different graph traversal orders of neural networks lead to different device placements, which in turn affects the training time of the neural network. Our experiment results show that the best graph traversal order depends on the type of neural networks and their computation graphs features. In this work, we also provide recommendations on choosing effective graph traversal orders in device placement for various neural network families to improve the training time in model parallelization.
Practical Convex Formulations of One-hidden-layer Neural Network Adversarial Training

As neural networks become more prevalent in safety-critical systems, ensuring their robustness against adversaries becomes essential. "Adversarial training" is one of the most common methods for training robust networks. Current adversarial training algorithms solve highly non-convex bi-level optimization problems. These algorithms suffer from the lack of convergence guarantees and can exhibit unstable behaviors. A recent work has shown that the standard training formulation of a one-hidden-layer, scalar-output fully-connected neural network with rectified linear unit (ReLU) activations can be reformulated as a finite-dimensional convex program, addressing the aforementioned issues for training non-robust networks. In this paper, we leverage this "convex training" framework to tackle the problem of adversarial training. Unfortunately, the scale of the convex training program proposed in the literature grows exponentially in the data size. We prove that a stochastic approximation procedure that scales linearly yields high-quality solutions. With the complexity roadblock removed, we derive convex optimization models that train robust neural networks. Our convex methods provably produce an upper bound on the global optimum of the adversarial training objective and can be applied to binary classification and regression. We demonstrate in experiments that the proposed method achieves a superior robustness compared with the existing methods.
Distributed State Estimation for Nonlinear Systems with Unknown Parameters

We present a distributed observer for nonlinear systems, which can be used to estimate the state of a central plant with unknown parameters using a network of sensors that measure noisy linear combinations of the state. Our state estimator is based on the unscented Kalman filter and the dual Kalman filter method, which runs two Kalman filters in parallel for both state and parameter estimation. This allows us to improve estimates of the central plants parameters in order to improve state estimates, and vice versa. Furthermore, using results from stochastic stability analysis, we prove that under certain conditions the expected value of the proposed algorithm’s estimation error is bounded. This result also shows us that as t goes to infinity, the error bound scales as a function of the largest eigenvalue of the sensor noise covariance matrix. Lastly, we demonstrate the ability of our state estimator to accurately track the state of nonlinear systems through simulations.
Data-Assisted Vision-Based Hybrid Control for Robust Stabilization with Obstacle Avoidance via Learning of Perception Maps

We study the problem of target stabilization with robust obstacle avoidance in robots and vehicles that have access only to vision-based sensors for the purpose of real-time localization. This problem is particularly challenging due to the topological obstructions induced by the obstacle, which preclude the existence of smooth feedback controllers able to achieve simultaneous stabilization and robust obstacle avoidance. To overcome this issue, we develop a vision-based hybrid controller that switches between two different feedback laws depending on the current position of the vehicle using a hysteresis mechanism and a data-assisted supervisor. The main innovation of the paper is the incorporation of suitable perception maps into the hybrid controller. These maps can be learned from data obtained from cameras in the vehicles and trained via convolutional neural networks (CNN). Under suitable assumptions on this perception map, we establish theoretical guarantees for the trajectories of the vehicle in terms of convergence and obstacle avoidance. Moreover, the proposed vision-based hybrid controller is numerically tested under different scenarios, including noisy data, sensors with failures, and cameras with occlusions.
Black-Box Anonymous Commit-and-Prove

Commit-and-prove is a building block that allows a party to commit to a secret input and then later prove something about it. This is a pillar of many cryptographic protocols and especially the ones underlying anonymous systems. In anonymous systems, often there is a set of public commitments, and a prover wants to prove a property about one of the inputs committed in the set, while hiding which one. This latter property gives the prover anonymity within the set. Currently, there are numerous commit-and-prove protocols in the anonymous setting from various computational and setup assumptions. However, all such approaches are non-black-box in the cryptographic primitive. In fact, there exists no anonymous black-box construction of commit-and-prove protocols, under any computational or setup assumption. This is despite the fact that, when anonymity is not required, black-box commit-and-prove protocols are well known. Is this inherent in the anonymous setting? In this paper we provide a partial answer to the above question by constructing the first (one-time) black-box commit-and-prove protocol in the anonymous setting. We do so by first introducing a new primitive that we call Partially Openable Commitment (POC), and instantiating it in a black-box way from a Random Oracle. Next we show a black-box commit-and-prove protocol based on POC. From a theoretical standpoint, our result reduces the gap between known black-box feasibility results in the non-anonymous setting and the anonymous setting. From a practical standpoint, we show that our protocol can be very efficient for certain relations of interest.
Anonymous Authenticated Communication

Anonymity and authenticity are apparently conflicting goals. Anonymity means hiding a party’s identity whereas authenticity means proving a party’s identity. So how can a set of senders authenticate their messages without revealing their identity? Despite the paradoxical nature of this problem, there exist many cryptographic schemes designed to achieve both goals simultaneously, in some form. This paper provides a composable treatment of communication channels that achieve different forms of anonymity and authenticity. More specifically, three channel functionalities for many senders and one receiver are introduced which provide some trade-off between authenticity and anonymity (of the senders). For each of them, composably realizing it is proved to corresponds to the use of a certain type of cryptographic scheme, namely (1) a new type of scheme which we call bilateral signatures (syntactically related to designated verifier signatures), (2) partial signatures, and (3) ring signatures. This treatment hence provides composable semantics for (game-based) security definitions for these types of schemes. The results of this paper can be interpreted as the dual of the work by Kohlweiss et al. (PETS 2013), where composable notions for anonymous confidential communication were introduced and related to the security definitions of certain types of public-key encryption schemes, and where the treatment of anonymous authenticated communication was stated as an open problem.
Cumulatively All-Lossy-But-One Trapdoor Functions from Standard Assumptions

Chakraborty, Prabhakaran, and Wichs (PKC’20) recently introduced a new tag-based variant of lossy trapdoor functions, termed cumulatively all-lossy-but-one trapdoor functions (CALBO-TDFs). Informally, CALBO-TDFs allow defining a public tag-based function with a (computationally hidden) special tag, such that the function is lossy for all tags except when the special secret tag is used. In the latter case, the function becomes injective and efficiently invertible using a secret trapdoor. This notion has been used to obtain advanced constructions of signatures with strong guarantees against leakage and tampering, and also by Dodis, Vaikunthanathan, and Wichs (EUROCRYPT’20) to obtain constructions of randomness extractors with extractor-dependent sources. While these applications are motivated by practical considerations, the only known instantiation of CALBO-TDFs so far relies on the existence of indistinguishability obfuscation. In this paper, we propose the first two instantiations of CALBO-TDFs based on standard assumptions. Our constructions are based on the LWE assumption with a sub-exponential approximation factor and on the DCR assumption, respectively, and circumvent the use of indistinguishability obfuscation by relying on lossy modes and trapdoor mechanisms enabled by these assumptions.
RAMus- A New Lightweight Block Cipher for RAM Encryption

Over the past decades, there has been a dramatic increase of the attacks recovering the data from the RAM memory. These have heightened the need for new solutions and primitives suitable for the encryption of this information. In this paper we introduce RAMus, a new tweakable lightweight block cipher whose properties support its usage for securing the RAM memory. In this sense, RAMus attains all the requirements provided by the (German) Federal Office of Information Security (BSI) in the domain of encryption algorithms suitable for RAM and memory encryption. The design strategy of RAMus is inspired from the LS-approach. Compared to the literature, in our proposal the linear layer is replaced by a second Sbox layer. In RAMus, the diffusion is ensured by the Sbox layers, which use Sboxes with a non-trivial branch number.
GCMK: Detecting Spam Movie Review Based on Graph Convolutional Network Embedding Movie Background Knowledge

In recent years, the movie industry is booming and many consumers regard movie reviews as an important reference for choosing movies. In the mean time, more and more movie marketing teams glorify their movies and suppress rival movies of the same period by hiring spammers to publish massive misleading reviews. It results in the existence of a large number of spam movie reviews on the online movie review platforms, which greatly misleads consumers and seriously undermines the healthy development of the movie industry. At present, there is little research on spam movie reviews, whose method of detecting spam movie reviews mainly relies on the text and statistical features of reviews, ignoring the significance of movie background knowledge such as movie characters and plots. In this paper, we propose a novel method for detecting spam movie reviews, which uses a graph convolutional neural network embedding movie background knowledge (GCMK). Specifically, we firstly construct a directed heterogeneous knowledge graph by using the movie synopses and the high-quality long comments of movies. Then we use the graph convolutional neural network to obtain the embedded features of the movie background knowledge, use BERT (Bidirectional Encoder Representations from Transformers) model to extract the text features of reviews, and obtain the correlation vectors between these reviews and the corresponding movies by comparing the embedded features of the movie background knowledge and the text features of reviews. Finally, we fuse text features, user statistical features and correlation vectors to construct the detection model. The experimental results demonstrate our proposed GCMK method is more effective than the other state-of-the-art baselines, with an F1-score of 84.94%.
Measuring the Effects of Confidants on Privacy in Smart Contracts

Blockchain Systems provide highly welcome properties such as immutability, observability, availability, and distribution for implementing smart contracts without the need for intermediaries. While the smart contract goals of observability and enforceability can easily be achieved on blockchains, the goal of privity is much harder to tackle. In the context of smart contracts on blockchains, privity aims in limiting the spread of knowledge to the participants with a contractual need-to-know. However, limiting access to data can limit the possible degrees of proactive enforcement of correct decisions and it can negatively impact their availability. Therefore, it can be required to find a proper balance between privity and enforceability or availability requirements. Designers may be forced to include additional participants (confidants) in the decision process only for the sake of enforceability or availability. In this paper, we introduce measures for assessing the impact of confidants for decisions within smart contracts on privacy. We model smart contracts in form of inter-organizational business processes and provide modeling constructs for privity requirements and the inclusion of confidants.
Challenges and Opportunities of Blockchain for Auditable Processes in the Healthcare Sector

Blockchain technologies (BT) promise to offer exciting research directions for improving various aspects of business processes, in particular in cross-organizational settings where participants do not fully trust each other. However, while blockchain may readily provide transparency and immutability for the processes recorded on a shared ledger, these very characteristics can be problematic in regard to privacy and data protection requirements. In this paper, we address the challenges and opportunities of using BT to secure distributed processes where participants may have an incentive to make false claims or subvert pre-agreed compliance rules in their private processes. Specifically, our analysis is based on a real-world use case, namely how BT can secure (privacy preserving) commitments to processing steps that facilitate federated machine learning (FL) in the healthcare sector. Thereby, an immutable audit trail is created that can be used to detect deviations in retrospect. Hereby, we place a particular focus on the management of patient consent for accessing their data in FL. Our approach draws inspiration from the domain of Self-Sovereign Identity (SSI) where BT is also relied upon to enable the creation and management of decentralized identifiers while focusing on data minimization. The results of our work are not constrained to the particular use case and can be applicable to other emerging research areas of BPM, such as federated process mining.
A Systematic Local Fork Management Framework for Blockchain Sandbox Environments

Blockchain technology presently permeates multiple industries, resulting in an increasing number of agents (primarily end-users and engineers) interacting with it in a variety of contexts. This, in turn, has introduced the practical need for a sufficient tooling ecosystem for blockchain solutions testing and evaluation under secure environment conditions. To that end, this study presents a robust framework for the creation and management of customisable, persistent, private, scalable blockchain environments that fulfill precisely the need for live sandbox platforms with on-chain interaction capabilities. We extend the concept of local chain forking, emphasising the limitations of existing tooling and methodologies and propose how our framework mitigates identified weaknesses and bridges some of the resulting gaps in desirable non-functional attributes. We offer a reference implementation for our framework and discuss how it can be applicable to a broad array of DevOps and security-oriented Use Cases. Finally, emerging challenges are discussed and potential directions for further research are drawn.
An Argumentation-Based Approach for Goal Reasoning and Explanations Generation

Abstract Explainable Artificial Intelligence systems, including intelligent agents, are expected to explain their internal decisions, behaviors and reasoning that produce their choices to the humans (or to other systems) with which they interact. Given this context, the aim of this article is to introduce a practical reasoning agent framework that supports generation of explanations about the goals the agent committed to. Firstly, we present an argumentation-based formalization for supporting goal reasoning. This is based on the belief-based goal processing model proposed by Castelfranchi and Paglieri, which is a more granular and refined model than the Beliefs–Desires–Intentions model. We focus on the dynamics of goals since they are desires until they become intentions, including the conditions under which a goal can be cancelled. We use formal argumentation reasoning to support the passage of the goals from their initial state until their final state. Secondly, in order that agents based on the proposed formalization be able to generate explanations about the goals they decided to commit to, we endow them with a mechanism for generating both complete and partial explanations. Finally, we use a scenario of rescue robots in order to illustrate the performance of our proposal, for which a simulator was developed to support the agents goal reasoning.
Effective ML-Block and Weighted IoU Loss for Object Detection

In computer vision tasks, better performance of the transformer model is due to self-attention mechanism and learning of global information. However, it also largely increases parameters and calculations. In this paper, we propose the following problems. (1) How to build a lighter module that integrates CNN and Transformer? We propose the ML-block module in this paper. Especially, for one thing, reducing the number of channels after the convolution module; for another, spatial attention is introduced after the ML-block input layer to reduce the loss caused by information fusion. (2) Small object detection problem in one-stage object detector. We propose weighted IoU loss in this paper. According to the object size, it adaptively weighs the IoU loss to improve the performance of the detector. We adopt YOLOX-s as the baseline through sufficient experiments on PASCAL VOC data sets and COCOmini data sets to demonstrate the effectiveness of our methods, and AP increases by 1.1 $$\%$$ and 3.3 $$\%$$ .