Reviewerly/exHarmony huggingface dataset:
Generalization Properties of Adversarial Training for -ℓ<sub>0</sub> Bounded Adversarial Attacks

We have widely observed that neural networks are vulnerable to small additive perturbations to the input causing misclassification. In this paper, we focus on the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> -bounded adversarial attacks, and aim to theoretically characterize the performance of adversarial training for an important class of truncated classifiers. Such classifiers are shown to have strong performance empirically, as well as theoretically in the Gaussian mixture model, in the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> -adversarial setting. The main contribution of this paper is to prove a novel generalization bound for the binary classification setting with ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> -bounded adversarial perturbation that is distribution-independent. Deriving a generalization bound in this setting has two main challenges: (i) the truncated inner product which is highly non-linear; and (ii) maximization over the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> ball due to adversarial training is non-convex and highly non-smooth. To tackle these challenges, we develop new coding techniques for bounding the combinatorial dimension of the truncated hypothesis class.
Online Convex Optimization of Programmable Quantum Computers to Simulate Time-Varying Quantum Channels

Simulating quantum channels is a fundamental primitive in quantum computing, since quantum channels define general (trace-preserving) quantum operations. An arbitrary quantum channel cannot be exactly simulated using a finite-dimensional programmable quantum processor, making it important to develop optimal approximate simulation techniques. In this paper, we study the challenging setting in which the channel to be simulated varies adversarially with time. We propose the use of matrix exponentiated gradient descent (MEGD), an online convex optimization method, and analytically show that it achieves a sublinear regret in time. Through experiments, we validate the main results for time-varying dephasing channels using a programmable generalized teleportation processor.
Secure Distributed Gram Matrix Multiplication

The Gram matrix of a matrix A is defined as AA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sup> (or A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sup> A). Computing the Gram matrix is an important operation in many applications, such as linear regression with the least squares method, where the explicit solution formula includes the Gram matrix of the data matrix. Secure distributed matrix multiplication (SDMM) can be used to compute the product of two matrices using the help of worker servers. If a Gram matrix were computed using SDMM, the data matrix would need to be encoded twice, which causes an unnecessary overhead in the communication cost. We propose a new scheme for this purpose called secure distributed Gram matrix multiplication (SDGMM). It can leverage the advantages of computing a Gram matrix instead of a regular matrix product.
Permutation Invariant Individual Batch Learning

This paper considers the individual batch learning problem. Batch learning (in contrast to online) refers to the case where there is a "batch" of training data and the goal is to predict a test outcome. Individual learning refers to the case where the data (training and test) is arbitrary, individual. This batch individual setting poses a fundamental issue of defining a plausible criterion for a universal learner since in each experiment there is a single test sample. We propose a permutation invariant criterion that, intuitively, lets the individual training sequence manifest its empirical structure for predicting the test sample. This criterion is essentially a min-max regret, where the regret is based on a leave-one-out approach, minimized over the universal learner and maximized over the outcome sequences (thus agnostic). To show its plausibility, we analyze the criterion and its resulting learner for two cases: Binary Bernoulli and 1-D deterministic barrier. For both cases the regret behaves as O(c/N), N the size of the training and c = 1 for the Bernoulli case and log4 for the 1-D barrier. Interestingly, in the Bernoulli case, the regret in the stochastic setting behaves as O(1/2N) while here, in the individual setting, it has a larger constant.
Transfer Learning for Quantum Classifiers: An Information-Theoretic Generalization Analysis

A key component of a quantum machine learning model operating on classical inputs is the design of an embedding circuit mapping inputs to a quantum state. This paper studies a transfer learning setting in which classical-to-quantum embedding is carried out by an arbitrary parametric quantum circuit that is pre-trained based on data from a source task. At run time, a binary quantum classifier of the embedding is optimized based on data from the target task of interest. The average excess risk, i.e., the optimality gap, of the resulting classifier depends on how (dis)similar the source and target tasks are. We introduce a new measure of (dis)similarity between the binary quantum classification tasks via the trace distances. An upper bound on the optimality gap is derived in terms of the proposed task (dis)similarity measure, two Rényi mutual information terms between classical input and quantum embedding under source and target tasks, as well as a measure of complexity of the combined space of quantum embeddings and classifiers under the source task. The theoretical results are validated on a simple binary classification example.
An Algebraic Attack Against McEliece-like Cryptosystems Based on BCH codes

We present an algebraic attack on a McEliece-like scheme based on BCH codes (BCH-McEliece), where the Goppa code is replaced by a suitably permuted BCH code. Our attack continues the line of work devising attacks against McEliece-like schemes with Goppa-like codes, with the goal of getting a better understanding of why Goppa codes are so intractable. Our starting point is the work of Faugère, Perret and Portzamparc (Asiacrypt 2014). We take their algebraic model and adapt and improve their attack algorithm so that it can handle BCH-McEliece. We demonstrate experimentally that our attack is practical for high rate codes over non-prime fields for parameters where generic attacks suggest cryptographic security.
A Vision-Based Autonomous UAV Inspection Framework for Unknown Tunnel Construction Sites With Dynamic Obstacles

Tunnel construction using the drill-and-blast method requires the 3D measurement of the excavation front to evaluate underbreak locations. Considering the inspection and measurement task's safety, cost, and efficiency, deploying lightweight autonomous robots, such as unmanned aerial vehicles (UAV), becomes more necessary and popular. Most of the previous works use a prior map for inspection viewpoint determination and do not consider dynamic obstacles. To maximally increase the level of autonomy, this letter proposes a vision-based UAV inspection framework for dynamic tunnel environments without using a prior map. Our approach utilizes a hierarchical planning scheme, decomposing the inspection problem into different levels. The high-level decision maker first determines the task for the robot and generates the target point. Then, the mid-level path planner finds the waypoint path and optimizes the collision-free static trajectory. Finally, the static trajectory will be fed into the low-level local planner to avoid dynamic obstacles and navigate to the target point. Besides, our framework contains a novel dynamic map module that can simultaneously track dynamic obstacles and represent static obstacles based on an RGB-D camera. After inspection, the Structure-from-Motion (SfM) pipeline is applied to generate the 3D shape of the target. To our best knowledge, this is the first time autonomous inspection has been realized in unknown and dynamic tunnel environments. Our flight experiments in a real tunnel prove that our method can autonomously inspect the tunnel excavation front surface.
Predicting Class Distribution Shift for Reliable Domain Adaptive Object Detection

Unsupervised Domain Adaptive Object Detection (UDA-OD) uses unlabelled data to improve the reliability of robotic vision systems in open-world environments. Previous approaches to UDA-OD based on self-training have been effective in overcoming changes in the general appearance of images. However, shifts in a robot's deployment environment can also impact the likelihood that different objects will occur, termed class distribution shift. Motivated by this, we propose a framework for explicitly addressing class distribution shift to improve pseudo-label reliability in self-training. Our approach uses the domain invariance and contextual understanding of a pre-trained joint vision and language model to predict the class distribution of unlabelled data. By aligning the class distribution of pseudo-labels with this prediction, we provide weak supervision of pseudo-label accuracy. To further account for low quality pseudo-labels early in self-training, we propose an approach to dynamically adjust the number of pseudo-labels per image based on model confidence. Our method outperforms state-of-the-art approaches on several benchmarks, including a 4.7 mAP improvement when facing challenging class distribution shift.
Low-Visibility Vehicle-Road Environment Perception Based on the Multi-Modal Visual Features Fusion of Polarization and Infrared

As a key component of the automatic driving assistant system, vehicle-road environment perception technology is the basis of decision-making and the guarantee of safe driving. However, the existing vehicle-road environment perception technology based on vision mainly focus on the conditions of good illumination and weather and rely on visible light imaging equipment, which is not highly adaptable to special environments. In this paper, the key technologies of low-visibility vehicle-road environment perception are deeply studied by combining multi-mode visual imaging and deep learning, and a model of Multi-Branch Input Encoding-Decoding Network (MBINEDN) is proposed. In this model, the polarized degree image and infrared image of the same road scene are input simultaneously in the coding stage, and two branch networks are used to extract the multi-mode features. The lightweight network MobileNetV2 is used as the feature extractor to reduce the amount of model parameters, so as to retain the polarization and infrared feature of the image to the maximum extent. Finally, the two are combined and output in the decoding stage. Experiments show that using MBINEDN model to process two images can effectively improve the segmentation effect. At the same time, it shows that the effect of vehicle-road environment semantic segmentation based on multi-modal visual features is better than that of single modal features. Therefore, the application of multi-mode visual images to the segmentation of low-visibility vehicle-road scenes can provide a strong guarantee for the visual perception technology of vehicle safety driving assistance system.
DTHNet: Dual-Stream Network Based on Transformer and High-Resolution Representation for Shadow Extraction from Remote Sensing Imagery

Shadow extraction from remote sensing images is critical work. However, the inter-class similarity of shadows with dark water, trees, and roads and the dependence on other objects make accurate shadow extraction still challenging. This letter proposes a dual-stream network based on the transformer and high-resolution representation (DTHNet) for multi-scale shadow extraction. The DTHNet utilizes two streams: the high-resolution representation stream extracts deep features while maintaining detail information, and the clustering representation stream provides clustering feature constraints to enhance the ability to distinguish between foreground and background. Additionally, the designed multi-scale auxiliary predictor and hybrid loss aid in extracting multi-scale shadows. We evaluated our proposed method on the AISD dataset and compared it against six state-of-the-art generic semantic segmentation models and shadow extraction methods. The experimental results demonstrate that the DTHNet outperforms the existing methods.
The New Lantern Laws: Facial Recognition on the Entry/Exit System

The European Commission, with the agreement of the European Parliament and the Council, established the Entry/Exit System (‘EES’) for stronger and smarter EU borders aimed at third-country nationals crossing the external borders of Member States within the Schengen Area, i.e., the Hungarian-Serbian or the Polish-Belarusian border. In essence, the EES captures facial images along with other biometric data to associate with a person’s name or type of travel document. Although the aim of this technology is to save on costs and improve efficiency within border control and management authorities, there is a long-standing precedent showing these types of technology tend to perpetuate direct and indirect forms of discrimination regarding people with a non-Western appearance, in the form of blacklists and self-learning algorithms. On top of this, facial recognition systems tend to inject bias into their performance through the databases sustaining them, in terms of race as well as gender.As a result, the trade-off for strengthened efficiency in border control comes in as an increased possibility of false positives and negatives on law enforcement. On one side, false positives can trigger great threats to the fundamental rights of third-country nationals, who normally migrate due to conflicts in their home countries and grave discrimination on the basis of sex, gender, or religion. On the other side, false negatives may come at a cost of the ultimate objective pursued by the EES; to secure the Union’s exterior borders through an automated system, which will facilitate the detection of fraud and unnoticed over-stayers.In this context, the paper intends to analyse Regulation (EU) 2017/2226 and its future implementation starting from September 2022. In this sense, we will compare both the EES System with Proposal for a Regulation laying down harmonised rules of AI intelligence, with regard to the safeguards implemented by the EES system, although facial recognition systems are categorised, pursuant to the same Proposal for a Regulation on Artificial Intelligence as high-risk AI systems. The possible conflicting legal problems stemming from discrimination and the feasibility of law enforcement will also be examined. Considering the foregoing, we will draw out the policy implications of the EES system in light of the current developments of the Union in terms of regulating artificial intelligence.
Active Privacy-Utility Trade-Off Against Inference in Time-Series Data Sharing

Internet of things devices have become highly popular thanks to the services they offer. However, they also raise privacy concerns since they share fine-grained time-series user data with untrusted third parties. We model the users personal information as the secret variable, to be kept private from an honest-but-curious service provider, and the useful variable, to be disclosed for utility. We consider an active learning framework, where one out of a finite set of measurement mechanisms is chosen at each time step, each revealing some information about the underlying secret and useful variables, albeit with different statistics. The measurements are taken such that the correct value of useful variable can be detected quickly, while the confidence on the secret variable remains below a predefined level. For privacy measure, we consider both the probability of correctly detecting the secret variable value and the mutual information between the secret and released data. We formulate both problems as partially observable Markov decision processes, and numerically solve by advantage actor-critic deep reinforcement learning. We evaluate the privacy-utility trade-off of the proposed policies on both the synthetic and real-world time-series datasets.
Lightweight Polynomial Multiplication Accelerator for NTRU Using Shared SRAM

Established in 1996, NTRU was standardized according to IEEE P1363.1 and ANSI X9.98 and has been in use for a long time. This brief presents a lightweight accelerator which supports NTRU’s main operation, polynomial multiplication. The proposed accelerator is embedded within the slave wrapper of the static random-access memory (SRAM) rather than using a dedicated memory and can directly access the SRAM, eliminating data transfer and data redundancy between the SRAM and the accelerator. This helps in reducing the latency and the required resources. In this brief, we focus on the NTRU version submitted to the National Institute of Standards and Technology (NIST) post-quantum cryptography standardization process. This version contains four parameter sets, which are all supported by the proposed accelerator. The accelerator can be synthesized at a clock frequency of 270 MHz, requires only 17.3 k gate counts, and can perform encryption and decryption within 0.1 and 1.5 ms, respectively. The proposed accelerator is expected to be implemented in resource-constrained applications such as Internet of Things.
A Cryogenic 12 GHz Frequency Doubler With Temperature Compensation for Trapped-Ion Quantum Computer

This brief presents a frequency doubler (FD) for a microwave-control system intended for an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{171}\text {Yb}^{+}$ </tex-math></inline-formula> ion-trap-based quantum computer. The circuit is optimized for frequency multiplication from 11GHz to 13 GHz with a ≥29 dBc fundamental- and third harmonic rejection over the entire frequency range and peak HRR1/HRR3 of 37 dBc/ 39 dBc. This enables the microwave frequency generation to drive the hyperfine transitions in the electronic ground state of an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{171}\text {Yb}^{+}$ </tex-math></inline-formula> ion at 12.6 GHz. Cryogenic measurements of the FD down to 4.5 K enable circuit functionality verification for the intended low-temperature operation. Additionally, insights into the cryogenic temperature effects of the employed BiCMOS technology are obtained and used to derive a biasing control methodology for constant performance in the temperature range of 300K to 4.5K. The proposed circuit is silicon-proven and fabricated in a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {0.13~ \mu \text {m} }$ </tex-math></inline-formula> SiGe BiCMOS process, consuming a core area of only 0.034 mm2.
A Novel Temple Clamping Force Measurement Method for Eyeglasses Design

The temple clamping force is an important factor affecting the fit and comfort of most eyeglasses. The temple height is usually very small (1.0~6.0 mm) and the clamping force is very low (0.0~1.0 N). However, the diameters of the effective area of available force sensors are usually larger than the temple height, and their lowest measurable force is sometimes larger than the clamping forces. This causes difficulty in measuring the clamping force using force sensors. Hence, we proposed a novel temple clamping force measurement method for eyeglasses, which only uses a digital tension meter. In this method, the displacement of the ear attachment points (EAPs) at the temple was computed based on the static and wearing expanded lateral distance of EAPs; and then the tensile force was measured based on the temple displacement by using a digital tension meter. The method has been applied to investigate the relationship between the measured clamping forces and the perceived fit scores of the temples. The experimental results are consistent with the previous report, which demonstrates the reliability of our proposed method.
Making Systemic Risk Assessments Work: How the DSA Creates a Virtuous Loop to Address the Societal Harms of Content Moderation

The EU’s Digital Services Act (DSA) introduces a new regulatory approach to address the societal harms of online platforms: systemic risk assessments. While a core component of the DSA, the regulation only outlines the standards and processes governing systemic risk assessments in broad strokes. It is still unclear what they will entail in practice. This article is the first to articulate how systemic risk assessments should be implemented. It situates systemic risk assessments as a critical step toward platform accountability as they address societal harms, while existing approaches, such as remedy mechanisms, only protect individual rights. Engaging with intangible harms and regulating speech and public discourse, risk assessments also entail significant challenges. Conventional reference points for content moderation regulation, such as terms and conditions, contractual freedom, fundamental rights and expertise, do not provide practical and legitimate bases to concretize risk assessment obligations. Public actors, such as the Commission, should refrain from defining substantive standards, too, as they are directly bound by freedom of expression guarantees. Instead, the article argues, the Commission should foster a procedural framework, a “virtuous loop”, which empowers civil society and allows to specify and refine the standards governing systemic risks over time. Developing this framework, the article explains how systemic risk assessment can fix “multistakeholderism”, and “multistakeholderism”, in turn, can help make systemic risk assessments work.
An Efficient CNN Inference Accelerator Based on Intra- and Inter-Channel Feature Map Compression

Deep convolutional neural networks (CNNs) generate intensive inter-layer data during inference, which results in substantial on- chip memory size and off-chip bandwidth. To solve the memory constraint, this paper proposes an accelerator adopting a compression technique that can reduce the inter-layer data by removing both intra- and inter-channel redundant information. Principal component analysis (PCA) is utilized in the compression process to concentrate inter-channel information. The spatial differences, truncation, and reconfigurable bit-width coding are implemented inside every feature map to eliminate the intra-channel data redundancy. Moreover, a particular data arrangement is introduced to enhance data continuity to optimize PCA analysis and improve compression performance. A CNN accelerator with the proposed compression technique is designed to support the on- the-fly compression process by pipelining the reconstruction, CNN computation, and compression operation. The prototype accelerator is implemented using 28-nm CMOS technology. It achieves 819.2GOPS peak throughput and 3.75TOPS/W energy efficiency with 218.5mW. Experiments show that the proposed compression technique achieves compression ratios of 21.5% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim $ </tex-math></inline-formula> 43.0% (8-bit mode) and 9.8% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim $ </tex-math></inline-formula> 19.3% (16-bit mode) on state-of-the-art CNNs with a negligible accuracy loss.