Reviewerly/exHarmony huggingface dataset:
Multi-Dimensional Fingerprints-based Multi-Attacker Detection for 6G Systems

The future 6G systems are expected to achieve intelligent connection and interaction between various heterogeneous terminals, increasing the fragility for spoofing attacks. Due to the high security and energy efficiency, Physical Layer Authentication (PLA) has been regarded as a powerful method to verify the identity of devices. Nevertheless, due to the inaccurate identifying fingerprints caused by the imperfect estimation and variations of the limited fingerprints, most of the state-of-the-art PLA schemes have low reliability and robustness in low Signal-Noise Ratio (SNR) environments. Besides, most PLA schemes rely on the prior knowledge of attackers to establish authentication models, thus reducing the feasibility of actual communications. To address the first challenge, we propose a multi-attacker detection architecture based on multi-dimensional fingerprints, which can provide more robust identifiable spatial attributes for devices by using fingerprints observed by receivers in multi-locations. Upon the designed detection architecture, to tackle the second issue, we propose four clustering-based PLA schemes without requiring their training fingerprint sets. Considering that the aforementioned schemes can divide fingerprints from different transmitters into several disjoint clusters but can not precisely identify forged fingerprints, we further propose the graph learning-based PLA approaches with only a few labeled fingerprints. The simulation results on real industrial outdoor and indoor datasets demonstrate the superiority of the designed detection system in Adjusted Mutual Information (AMI) and authentication accurate rate (AucRate) over the single observation-based PLA schemes.
Direct Tracking of a Wireless Transmitter Based on Rao–Blackwellized Point Mass Filter

In this paper, we address the problem of direct tracking of a wireless transmitter. That is, the inputs given to the Bayesian filter are the received baseband signals instead of pre-computed ranges or angles. We first propose to use the Rao-Blackwellized Point Mass Filter (RBPMF) to solve such a tracking problem. As such, the resulting tracking solution is still computationally expensive. Therefore, we propose an approach for reducing the computational cost of the RBPMF. More precisely, we replace the prediction step by the one of the Linear Kalman Filter (LKF). This combination helps to avoid expensive operations such as the weight convolution in the prediction step. In addition, it also allows complexity reductions in the correction step. As a result, the complexity is reduced by one order of magnitude compared to the original RBPMF. We compare our approach to representative direct-tracking methods, based on Iterative Extended Kalman Filter (IEKF) and Particle Filter (PF). The proposed solution has lower and comparable localization error compared to IEKF and PF, respectively. In addition, the proposed solution is of slightly less complexity than PF. However, the complexity reduction is significant compared to the conventional RBPMF.
Permutation Resemblance

Motivated by the problem of constructing bijective maps with low differential uniformity, we introduce the notion of permutation resemblance of a function, which looks to measure the distance a given map is from being a permutation. We prove several results concerning permutation resemblance and show how it can be used to produce low differentially uniform bijections. We also study the permutation resemblance of planar functions, which over fields of odd characteristic are known not to be bijections and to have the optimal differential uniformity.
Research on Modulation Recognition Algorithm Based on Channel and Spatial Self-Attention Mechanism

In the harsh electromagnetic environment with strong interference, the prior information of the received signal can not be fully obtained, and considering the complex and variable modulation modes, the modulation recognition of radio signals has brought great trouble.In this paper, we propose a method for automatic modulation recognition based on deep convolutional neural network for channel and spatial self-attention mechanism by combining the architecture of feature autonomous learning of deep learning.The correlation of input vectors in channel and spatial dimensions is enhanced by a self-attentive mechanism, and the number of layers of network structure, connection method, pooling method, and hyperparameters are optimized, to enhance the overall fitting ability of the network and improve the accuracy and robustness of modulation recognition.On the RadioML2016.10Adata set, the proposed method is compared with the previous baseline method at different signal-to-noise ratios.The experimental results show that the performance of this paper's method is better in the identification of 16QAM versus 64QAM.
Proactive Resource Autoscaling Scheme Based on SCINet for High-Performance Cloud Computing

The container resource autoscaling technique provides scalability to cloud services composed of microservice architecture in a cloud-native computing environment. However, the service efficiency is reduced as the scaling is delayed because dynamic loads occur with various workload patterns. Furthermore, estimating the efficient resource size for the workload is difficult, resulting in resource waste and overload. Therefore, this study proposes high-performance resource management (HiPerRM), which stably and elastically manages container resources to ensure service scalability and efficiency even under rapidly changing dynamic loads. HiPerRM forecasts future workloads using a sample convolutional and interaction network (SCINet) model applied with the reversible instance normalization (RevIN) method. HiPerRM generates a resource request with an elastic size based on the forecasted CPU and memory usage, and then efficiently adjusts the pod's resource request and the number of replicas via HiPerRM's VPA (Hi-VPA) and HiPerRM's HPA (Hi-HPA). As a result of evaluating the performance of HiPerRM, the average resource utilization was improved by approximately 3.96–34.06% compared to conventional autoscaling techniques, even when the resource size was incorrectly estimated for various workloads, and there were relatively fewer overloads.
AMBITION: Ambient Temperature Aware VM Allocation for Edge Data Centers

Edge data centers are increasingly deployed to improve response time of intelligent services. Due to the high computing demands for such services, edge data centers consume a considerable amount of power, generating excessive heat. To mitigate thermal problems with a smaller cooling power, edge data centers usually trigger software-based thermal management techniques along with the air cooling systems. Unfortunately, the ambient temperature of servers often has a surge due to the consolidation of VMs and heat propagation among components (e.g., CPU, GPU, memory unit, disk, etc.). Higher ambient temperature further increases the on-chip temperature, invoking more frequent thermal throttling. To resolve thermal problems deteriorated by the ambient temperature, in this paper, we propose an ambient temperature aware VM allocation technique, called AMBITION. Considering the performance impact of ambient temperature, AMBITION estimates the actual computing capacity of servers. Based on the computing demands of VMs, AMBITION finds an appropriate server which has sufficient ambient-aware computing capacity to run the VM; it allocates computation-intensive VMs to the servers with the higher ambient-aware computing capacity, and distributes memory-intensive VMs to the individual servers as much as possible. In our experiments on an edge data center, AMBITION shows the execution time speedup of 50.3%, on average (up to 73.8%), compared to a conventional VM allocation technique while saving system-wide energy by 5.9% (up to 13.6%). At the expense of 5.8% speedup (from 50.3% to 44.5%), AMBITION further saves cooling power by 84.3%, leading to 29.3% of total edge data center energy saving.
Accelerating Stereo Image Simulation for Automotive Applications Using Neural Stereo Super Resolution

Camera image simulation is integral to the virtual validation of autonomous vehicles and robots that use visual perception to understand their environment. It also has applications in creating image datasets for training learning-based vision models. As camera image simulation takes into account a wide variety of external and internal parameters, achieving a high-fidelity simulation is a computationally expensive process. Recently, several neural network-based techniques have been proposed to reduce the computational complexity of image rendering, a critical element of the camera simulation pipeline. However, the existing methods are tailored for monocular camera images and are not optimised for stereo images, which are widely used in autonomous driving applications. To address this, we propose a technique based on Stereo Super Resolution (SSR) to speed up the simulation of stereo images. The proposed method first simulates stereo images at a lower resolution, then super-resolves them to their original resolution using our introduced SSR model, ETSSR. We evaluated the performance of our technique using the CARLA driving simulator and created our own synthetic dataset for training ETSSR. The evaluations indicate that our approach can speed up stereo image simulation by a factor of up to 2.57 over various resolutions. Moreover, it shows that our ETSSR achieves on-par or superior performance compared to the state-of-the-art models, using significantly fewer parameters and FLOPs. We have made our source code and dataset available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hamedhaghighi/ETSSR</uri> .
Median Autoregressive Graph Filters

Graph filters are fundamental for extracting and processing signals defined over irregular domains. Most graph filters in the literature are linear, and are usually based on polynomials of the graph shift matrix. Autoregressive Moving Average (ARMA) graph filters are a generalization of polynomial filters, but are still linear. In this work, we propose a nonlinear graph filter that is an extension of the order one ARMA graph filter, to give the Median Autoregressive Graph Filter (MAF). The proposed MAF has a similar localization property to the linear counterpart, and can be implemented in a distributive manner. Though strictly nonlinear, the MAF has some linear-like properties, which will be formally proven in this work. Application of the proposed MAF to the denoising of real-world sensor network datasets will be presented. Comparisons with the linear counterpart will also be made.
Toward More Practical Causal Epidemiology and Health Risk Assessment Using Causal Artificial Intelligence

Population attributable fraction (PAF), probability of causation, burden of disease, and related quantities derived from relative risk ratios are widely used in applied epidemiology and health risk analysis to quantify the extent to which reducing or eliminating exposures would reduce disease risks. This causal interpretation conflates association with causation. It has sometimes led to demonstrably mistaken predictions and ineffective risk management recommendations. Causal artificial intelligence (CAI) methods developed at the intersection of many scientific disciplines over the past century instead use quantitative high-level descriptions of networks of causal mechanisms (typically represented by conditional probability tables or structural equations) to predict the effects caused by interventions. This chapter summarizes these developments and discusses how CAI methods can be applied to realistically imperfect data and knowledge—e.g., to datasets with unobserved (latent) variables, missing data, measurement errors, interindividual heterogeneity in exposure-response functions, and model uncertainty. The chapter argues that CAI methods can help to improve the conceptual foundations and practical value of epidemiological calculations by replacing association-based attributions of risk to exposures or other risk factors with causal predictions of the changes in health effects caused by interventions.
Causally Explainable Decision Recommendations Using Causal Artificial Intelligence

For an AI agent to make trustworthy decision recommendations under uncertainty on behalf of human principals, it should be able to explain why its recommended decisions make preferred outcomes more likely and what risks they entail. Such rationales use causal models to link potential courses of action to resulting outcome probabilities. They reflect an understanding of possible actions, preferred outcomes, effects of action on outcome probabilities, and acceptable risks and trade-offs—the standard ingredients of normative theories of decision-making under uncertainty, such as expected utility theory. Competent AI advisory systems should also notice changes that might affect a user’s plans and goals. In response, they should apply both learned patterns for quick response (analogous to fast, intuitive “System 1” decision-making in human psychology) and also slower causal inference and simulation, decision optimization, and planning algorithms (analogous to deliberative “System 2” decision-making in human psychology) to decide how best to respond to changing conditions. Concepts of conditional independence, conditional probability tables (CPTs) or models, causality, heuristic search for optimal plans, uncertainty reduction, and value of information (VoI) provide a rich, principled framework for recognizing and responding to relevant changes and features of decision problems via both learned and calculated responses. This chapter reviews how these and related concepts can be used to identify probabilistic causal dependencies among variables, detect changes that matter for achieving goals, represent them efficiently to support responses on multiple time scales, and evaluate and update causal models and plans in light of new data. The resulting causally explainable decisions make efficient use of available information to achieve goals in uncertain environments.
Towards Zero-Shot Multi-Speaker Multi-Accent Text-to-Speech Synthesis

This paper presents a framework towards multi-accent neural text-to-speech synthesis for zero-shot multi-speaker, which employs an encoder-decoder architecture and an accent classifier to control the pronunciation variation from the encoder. The encoder and decoder are pre-trained on a large-scale multi-speaker corpus. The accent-informed encoder outputs are taken by the attention-based decoder to generate accented prosody. This framework allows for fine-tuning with limited training data from multiple accents, and is able to generate accented speech for unseen speakers. Both objective and subjective evaluations confirm the effectiveness of the proposed framework.
Demonstration of Fibercuit: Techniques to Prototype High-Resolution Flexible and Kirigami Circuits with a Fiber Laser Engraver

In this demonstration, we showcase Fibercuit [15], a set of rapid prototyping techniques to fabricate high-resolution, flexible, and kirigami circuits using a fiber laser engraver. A set of examples will be presented in this demo, including custom dice, flex cables, custom end-stop switches, electromagnetic coils, LED earrings and a kirigami crane circuit designed and fabricated using our techniques.
Block Double-Submission Attack: Block Withholding Can Be Self-Destructive

Proof-of-Work (PoW) is a Sybil control mechanism adopted in blockchain-based cryptocurrencies. It prevents the attempt of malicious actors to manipulate distributed ledgers. Bitcoin has successfully suppressed double-spending by accepting the longest PoW chain. Nevertheless, PoW encountered several major security issues surrounding mining competition. One of them is a Block WithHolding (BWH) attack that can exploit a widespread and cooperative environment called a mining pool. This attack takes advantage of untrustworthy relationships between mining pools and participating agents. Moreover, detecting or responding to attacks is challenging due to the nature of mining pools. In this paper, however, we suggest that BWH attacks also have a comparable trust problem. Because a BWH attacker cannot have complete control over BWH agents, they can betray the belonging mining pool and seek further benefits by trading with victims. We prove that this betrayal is not only valid in all attack parameters but also provides double benefits; finally, it is the best strategy for BWH agents. Furthermore, our study implies that BWH attacks may encounter self-destruction of their own revenue, contrary to their intention.
D-KODE: Distributed Mechanism to Manage a Billion Discrete-log Keys

We focus on two emerging key-management issues in the blockchain space: (i) allowing a blockchain system to airdrop/send tokens to a potential client Bob, who is yet to set up the required cryptographic key, and (ii) creating a cross-chain bridge that allows users to securely send tokens from one blockchain to another. The existing solutions for the first problem need Bob to either generate and maintain public-private key pairs locally for the first time in his life---a usability bottleneck---or place trust in third-party custodial services---a privacy and censorship nightmare. Whereas, most existing solutions for the second require the users to trust a custodial service to realize the bridge with their keys. Towards solving this issue of trust via decentralization, distributed key generation (DKG) based solutions are being actively considered; here, a set of servers generate the discrete log keys in a distributed manner and link them to the users/accounts. Nevertheless, these solutions introduce computation and communication overhead that is linear in the number of generated account keys and do not scale well even for a million keys, especially as the set of DKG servers evolves.
Implementing and Optimizing Matrix Triples with Homomorphic Encryption

In today’s interconnected world, data has become a valuable asset, leading to a growing interest in protecting it through techniques such as privacy-preserving computation. Two well-known approaches are multi-party computation and homomorphic encryption with use cases such as privacy-preserving machine learning evaluating or training neural networks. For multi-party computation, one of the fundamental arithmetic operations is the secure multiplication in the malicious security model and by extension the multiplication of matrices which is expensive to compute in the malicious model. Transferring the problem of secure matrix multiplication to the homomorphic domain enables savings in communication complexity, reducing the main bottleneck.
Faster TFHE Bootstrapping with Block Binary Keys

Fully Homomorphic Encryption over the Torus (TFHE) is a homomorphic encryption scheme which supports efficient Boolean operations over encrypted bits. TFHE has a unique feature in that the evaluation of each binary gate is followed by a bootstrapping procedure to refresh the noise of a ciphertext. In particular, this gate bootstrapping involves two algorithms called the blind rotation and key-switching.
Jujutsu: A Two-stage Defense against Adversarial Patch Attacks on Deep Neural Networks

Adversarial patch attacks create adversarial examples by injecting arbitrary distortions within a bounded region of the input to fool deep neural networks (DNNs). These attacks are robust (i.e., physically-realizable) and universally malicious, and hence represent a severe security threat to real-world DNN-based systems.
PSI with computation or Circuit-PSI for Unbalanced Sets from Homomorphic Encryption

Circuit-based Private Set Intersection (circuit-PSI) refers to cryptographic protocols that let two parties with input set X and Y compute a function f over the intersection set X ∩ Y, without revealing any other information. The research efforts for circuit-PSI mainly focus on the case where input set sizes |X| and |Y| are similar so far, and they scale poorly for extremely unbalanced set sizes |X| ≫ |Y|. Recently, Lepoint et al. (ASIACRYPT'21) proposed the first dedicated solutions for this problem, which has online cost only linear in the small set size |Y|. However, it requires an expensive setup phase that requires huge storage of about O(|X|) on the small set holder side, which can be problematic in applications where the small set holder is assumed to have restricted equipment.
ZEKRA: Zero-Knowledge Control-Flow Attestation

To detect runtime attacks against programs running on a remote computing platform, Control-Flow Attestation (CFA) lets a (trusted) verifier determine the legality of the program’s execution path, as recorded and reported by the remote platform (prover). However, besides complicating scalability due to verifier complexity, this assumption regarding the verifier’s trustworthiness renders existing CFA schemes prone to privacy breaches and implementation disclosure attacks under “honest-but-curious” adversaries. Thus, to suppress sensitive details from the verifier, we propose to have the prover outsource the verification of the attested execution path to an intermediate worker of which the verifier only learns the result. However, since a worker might be dishonest about the outcome of the verification, we propose a purely cryptographical solution of transforming the verification of the attested execution path into a verifiable computational task that can be reliably outsourced to a worker without relying on any trusted execution environment. Specifically, we propose to express a program-agnostic execution path verification task inside an arithmetic circuit whose correct execution can be verified by untrusted verifiers in zero knowledge.
CacheFX: A Framework for Evaluating Cache Security

Over the last two decades, the danger of sharing resources between programs has been repeatedly highlighted. Multiple side-channel attacks, which seek to exploit shared components for leaking information, have been devised, mostly targeting shared caching components. In response, the research community has proposed multiple cache designs that aim at curbing the source of side channels.
Deepfake CAPTCHA: A Method for Preventing Fake Calls

Deep learning technology has made it possible to generate realistic content of specific individuals. These ‘deepfakes’ can now be generated in real-time which enables attackers to impersonate people over audio and video calls. Moreover, some methods only need a few images or seconds of audio to steal an identity. Existing defenses perform passive analysis to detect fake content. However, with the rapid progress of deepfake quality, this may be a losing game.
IGA : An Improved Genetic Algorithm to Construct Weightwise (Almost) Perfectly Balanced Boolean Functions with High Weightwise Nonlinearity

The Boolean functions satisfying secure properties on the restricted sets of inputs are studied recently due to their importance in the framework of the FLIP stream cipher. However, finding Boolean functions with optimal cryptographic properties is an open research problem in the cryptographic community. This paper presents an Improved Genetic Algorithm (IGA) with the directed changes that keep the weightwise balancedness of Boolean functions. A cross-protection strategy is proposed to ensure that the offspring has the same weightwise balancedness characteristics of the parents while implementing crossover. Then, a large number of weightwise (almost) perfectly balanced (W(A)PB) functions with a good nonlinearity profile are obtained based on IGA. Finally, we make comparisons between our constructions and relevant works. The comparisons show that IGA has a significant advantage for reaching the W(A)PB functions with high weightwise nonlinearity. Moreover, it is the first time to obtain the 8-variable WPB functions with the weightwise nonlinearity of 28 in the restricted sets of inputs with Hamming weight of 4, and list the statistical indicators of the weightwise nonlinearity for W(A)PB functions for input size n = 9, 10.