Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Privacy for B5G/6G: New Privacy Goals, Challenges, and Research Directions

Abstract —Massive progress of mobile wireless telecommunication networks was achieved in the previous decades, with privacy enhancement in each. At present, mobile users are getting familiar with the latest 5G networks, and the discussion for the next generation of Beyond 5G (B5G)/6G networks has already been initiated. It is expected that B5G/6G will push the existing network capabilities to the next level, with higher speeds, enhanced reliability, and seamless connectivity. To make these expectations a reality, research is progressing on new technologies, architectures, and intelligence-based decision-making processes related to B5G/6G. Privacy considerations are a crucial aspect that needs further attention in such developments, as billions of people and devices will be transmitting their data through the upcoming network. This paper provides a comprehensive survey on privacy-related aspects for B5G/6G networks. First, it discusses a taxonomy of different privacy perspectives. Based on the taxonomy, the paper then conceptualizes a set of privacy goals for the B5G/6G and the challenges that appear as barriers to reaching these goals. Next, this work provides a set of solutions applicable to the proposed architecture of B5G/6G networks to mitigate the challenges. Additionally, this paper discusses the emerging ﬁeld of non-personal data privacy. It also provides an overview of standardization initiatives for privacy preservation. Finally, it concludes with a roadmap of future directions and upcoming trends containing privacy-related topics, which will be an arena for new research towards privacy-enhanced B5G/6G networks. This work provides a basis for privacy aspects that will signiﬁcantly impact peoples’ daily lives with future networks.
Residual Robot Learning for Object-Centric Probabilistic Movement Primitives

It is desirable for future robots to quickly learn new tasks and adapt learned skills to constantly changing environments. To this end, Probabilistic Movement Primitives (ProMPs) have shown to be a promising framework to learn generalizable trajectory generators from distributions over demonstrated trajectories. However, in practical applications that require high precision in the manipulation of objects, the accuracy of ProMPs is often insufficient, in particular when they are learned in cartesian space from external observations and executed with limited controller gains. Therefore, we propose to combine ProMPs with recently introduced Residual Reinforcement Learning (RRL), to account for both, corrections in position and orientation during task execution. In particular, we learn a residual on top of a nominal ProMP trajectory with Soft-Actor Critic and incorporate the variability in the demonstrations as a decision variable to reduce the search space for RRL. As a proof of concept, we evaluate our proposed method on a 3D block insertion task with a 7-DoF Franka Emika Panda robot. Experimental results show that the robot successfully learns to complete the insertion which was not possible before with using basic ProMPs.
Calculation of allowable maximum casing temperature to prevent tension failures in thermal wells. [Preprint]

A method of calculating the allowable maximum casing temperature to prevent tension failures in the threaded connections during cooling is described. The allowable temperature depends on the casing size, weight, grade, and connection design. Sufficient data are provided or referenced to permit the safe design or analysis of a string of API casing for steam injection service. (27 refs.)
Clustering and classification of low-dimensional data in explicit feature map domain: intraoperative pixel-wise diagnosis of adenocarcinoma of a colon in a liver

Application of artificial intelligence in medicine brings in highly accurate predictions achieved by complex models, the reasoning of which is hard to interpret. Their generalization ability can be reduced because of the lack of pixel wise annotated images that occurs in frozen section tissue analysis. To partially overcome this gap, this paper explores the approximate explicit feature map (aEFM) transform of low-dimensional data into a low-dimensional subspace in Hilbert space. There, with a modest increase in computational complexity, linear algorithms yield improved performance and keep interpretability. They remain amenable to incremental learning that is not a trivial issue for some nonlinear algorithms. We demonstrate proposed methodology on a very large-scale problem related to intraoperative pixel-wise semantic segmentation and clustering of adenocarcinoma of a colon in a liver. Compared to the results in the input space, logistic classifier achieved statistically significant performance improvements in micro balanced accuracy and F1 score in the amounts of 12.04% and 12.58%, respectively. Support vector machine classifier yielded the increase of 8.04% and 9.41%. For clustering, increases of 0.79% and 0.85% are obtained with ultra large-scale spectral clustering algorithm. Results are supported by a discussion of interpretability using Shapely additive explanation values for predictions of linear classifier in input space and aEFM induced space.
Topological phase transition in magnon bands in a honeycomb ferromagnet driven by sublattice symmetry breaking

Ferromagnetic honeycomb systems are known to exhibit a magnonic topological phase under the existence of the next-nearest neighbor Dzyaloshinskii-Moriya interaction (DMI). Motivated by the recent progress in the sublattice-specific control of magnetic anisotropy, we study the topological phase of magnon bands of honeycomb ferromagnetic monolayer and bilayer with the sublattice symmetry breaking due to the different anisotropy energy in the presence of the DMI. We show that there is a topological phase transition between the topological magnon insulator and the topologically trivial magnon phase driven by the change of the relative size of the DMI and the anisotropy differences between the sublattices. The magnon thermal Hall conductivity is proposed as an experimental probe of the magnon topology.
Bots don’t Vote, but They Surely Bother!: A Study of Anomalous Accounts in a National Referendum

The Web contains several social media platforms for exchange of ideas and content publishing. These platforms are used by people, but also by distributed agents known as bots. Bots have existed for decades, with many of them being benevolent, although their influence in propagating and generating deceptive information has increased recently. Here we present a characterization of the discussion on Twitter about the 2020 Chilean constitutional referendum. Through a profile-oriented analysis that enables the isolation of anomalous content using machine learning, we obtain a characterization that matches national vote turnout, and we measure how anomalous accounts (some of which are automated bots) produce content and interact promoting (false) information.
Metal Wire Manipulation Planning for 3D Curving - How a Low Payload Robot Can Use a Bending Machine to Bend Stiff Metal Wire

This paper presents a combined task and motion planner for a robot arm to carry out 3D metal wire curving tasks by collaborating with a bending machine. We assume a collaborative robot that is safe to work in a human environment but has a weak payload to bend objects with large stiffness, and developed a combined planner for the robot to use a bending machine. Our method converts a 3D curve to a bending set and generates the feasible bending sequence, machine usage, robotic grasp poses, and pick-and-place arm motion considering the combined task and motion level constraints. Compared with previous deformable linear object shaping work that relied on forces provided by robotic arms, the proposed method is suitable for the material with high stiffness. We evaluate the system using different tasks. The results show that the proposed system is flexible and robust to generate robotic motion to corporate with the designed bending machine.
A Cauchy-type Hamilton-Jacobi Successive Approximation Scheme For Reachable Sets Computation

—Motivated by the scalability limitations of Eulerian methods for variational Hamilton-Jacobi-Isaacs (HJI) formulations that provide a least restrictive controller in problems that involve state or input constraints under a worst-possible disturbance, we introduce a second-order, successive sweep algorithm for computing the zero sublevel sets of a popular reachability value functional. Under sufﬁcient HJI partial differential equation regularity and continuity assumption throughout the state space, we show that with state feedback control under the worst-possible disturbance, we can compute the state set that are reachable within a prescribed veriﬁcation time bound.
Human Biometric Signals Monitoring based on WiFi Channel State Information using Deep Learning

—In this paper, we ﬁrst present a single-input, multiple-output convolutional neural network that can estimate both heart rate and respiration rate simultaneously by exploiting the underlying link between heart rate and respiration rate. The inputs to the neural network are the amplitude and phase of channel state information collected by a pair of WiFi devices. Our WiFi-based technique addresses privacy concerns and is adapt- able to a variety of settings. This system’s overall accuracy for the heart and respiration rate estimation can reach 99 . 109% and 98 . 581% , respectively. Furthermore, we developed and analyzed two deep learning-based neural network classiﬁcation algorithms for categorizing four types of sleep stages: wake, rapid eye movement (REM) sleep, non-rapid eye movement (NREM) light sleep, and NREM deep sleep. This system’s overall classiﬁcation accuracy can reach 95 . 925% .
Fusion-Correction Network for Single-Exposure Correction and Multi-Exposure Fusion

—The photographs captured by digital cameras usually suffer from over-exposure or under-exposure problems. The Single-Exposure Correction (SEC) and Multi-Exposure Fusion (MEF) are two widely studied image processing tasks for image exposure enhancement. However, current SEC and MEF methods ignore the internal correlation between SEC and MEF, and are proposed under distinct frameworks. What’s more, most MEF methods usually fail at processing a sequence containing only under-exposed or over-exposed images. To alleviate these problems, in this paper, we develop an integrated framework to simultaneously tackle the SEC and MEF tasks. Built upon the Laplacian Pyramid (LP) decomposition, we propose a novel Fusion-Correction Network (FCNet) to fuse and correct an image sequence sequentially in a multi-level scheme. In each LP level, the image sequence is feed into a Fusion block and a Correction block for consecutive image fusion and exposure correction. The corrected image is upsampled and re-composed with the high-frequency detail components in next-level, producing the base sequence for the next-level blocks. Experiments on the benchmark dataset [2] demonstrate that our FCNet is effective on both the SEC and MEF tasks. The code will be publicly released.
Aggregate Queries on Knowledge Graphs: Fast Approximation with Semantic-aware Sampling

A knowledge graph (KG) manages large-scale and real-world facts as a big graph in a schema-flexible manner. Aggregate query is a fundamental query over KGs, e.g., “what is the average price of cars produced in Germany? ”. Despite its importance, answering aggregate queries on KGs has received little attention in the literature. Aggregate queries can be supported based on factoid queries, e.g., “find all cars produced in Germany”, by applying an additional aggregate operation on factoid queries' answers. However, this straightforward method is challenging because both the accuracy and efficiency of factoid query processing will seriously impact the performance of aggregate queries. In this paper, we propose a “sampling-estimation” model to answer aggregate queries over KGs, which is the first work to provide an approximate aggregate result with an effective accuracy guarantee, and without relying on factoid queries. Specifically, we first present a semantic-aware sampling to collect a high-quality random sample through a random walk based on knowledge graph embedding. Then, we propose unbiased estimators for COUNT, SUM, and a consistent estimator for AVG to compute the approximate aggregate results based on the random sample, with an accuracy guarantee in the form of confidence interval. We extend our approach to support iterative improvement of accuracy, and more complex queries with filter, GROUP-BY, and different graph shapes, e.g., chain, cycle, star, flower. Extensive experiments over real-world KGs demonstrate the effectiveness and efficiency of our approach.
BYOTee: Towards Building Your Own Trusted Execution Environments Using FPGA

In recent years, we have witnessed unprecedented growth in using hardware-assisted Trusted Execution Environments (TEE) or enclaves to protect sensitive code and data on commodity devices thanks to new hardware security features, such as Intel SGX and Arm TrustZone. Even though the proprietary TEEs bring many benefits, they have been criticized for lack of transparency, vulnerabilities, and various restrictions. For example, existing TEEs only provide a static and fixed hardware Trusted Computing Base (TCB), which cannot be customized for different applications. Existing TEEs time-share a processor core with the Rich Execution Environment (REE), making execution less efficient and vulnerable to cache side-channel attacks. Moreover, TrustZone lacks hardware support for multiple TEEs, remote attestation, and memory encryption. In this paper, we present BYOTee (Build Your Own Trusted Execution Environments), which is an easy-to-use infrastructure for building multiple equally secure enclaves by utilizing commodity Field Programmable Gate Arrays (FPGA) devices. BYOTee creates enclaves with customized hardware TCBs, which include softcore CPUs, block RAMs, and peripheral connections, in FPGA on demand. Additionally, BYOTee provides mechanisms to attest the integrity of the customized enclaves' hardware and software stacks, including bitstream, firmware, and the Security-Sensitive Applications (SSA) along with their inputs and outputs to remote verifiers. We implement a BYOTee system for the Xilinx System-on-Chip (SoC) FPGA. The evaluations on the low-end Zynq-7000 system for four SSAs and 12 benchmark applications demonstrate the usage, security, effectiveness, and performance of the BYOTee framework.
Robustly-reliable learners under poisoning attacks

Data poisoning attacks, in which an adversary corrupts a training set with the goal of inducing specific desired mistakes, have raised substantial concern: even just the possibility of such an attack can make a user no longer trust the results of a learning system. In this work, we show how to achieve strong robustness guarantees in the face of such attacks across multiple axes. We provide robustly-reliable predictions, in which the predicted label is guaranteed to be correct so long as the adversary has not exceeded a given corruption budget, even in the presence of instance targeted attacks, where the adversary knows the test example in advance and aims to cause a specific failure on that example. Our guarantees are substantially stronger than those in prior approaches, which were only able to provide certificates that the prediction of the learning algorithm does not change, as opposed to certifying that the prediction is correct, as we are able to achieve in our work. Remarkably, we provide a complete characterization of learnability in this setting, in particular, nearly-tight matching upper and lower bounds on the region that can be certified, as well as efficient algorithms for computing this region given an ERM oracle. Moreover, for the case of linear separators over logconcave distributions, we provide efficient truly polynomial time algorithms (i.e., non-oracle algorithms) for such robustly-reliable predictions. We also extend these results to the active setting where the algorithm adaptively asks for labels of specific informative examples, and the difficulty is that the adversary might even be adaptive to this interaction, as well as to the agnostic learning setting where there is no perfect classifier even over the uncorrupted data.
A Mean-Field Analysis of a Network Behavioral–Epidemic Model

The spread of an epidemic disease and the population’s collective behavioral response are deeply intertwined, influencing each other’s evolution. Such a co-evolution typically has been overlooked in mathematical models, limiting their real-world applicability. To address this gap, we propose and analyse a behavioral–epidemic model, in which a susceptible–infected–susceptible epidemic model and an evolutionary game-theoretic decision-making mechanism concerning the use of self-protective measures are coupled. Through a mean-field approach, we characterize the asymptotic behavior of the system, deriving conditions for global convergence to a disease-free equilibrium and characterizing the endemic equilibria of the system and their (local) stability properties. Interestingly, for a certain range of the model parameters, we prove global convergence to a limit cycle, characterized by periodic epidemic outbreaks and collective behavioral response.
Policy-Based Bayesian Experimental Design for Non-Differentiable Implicit Models

For applications in healthcare, physics, energy, robotics, and many other fields, designing maximally informative experiments is valuable, particularly when experiments are expensive, time-consuming, or pose safety hazards. While existing approaches can sequentially design experiments based on prior observation history, many of these methods do not extend to implicit models, where simulation is possible but computing the likelihood is intractable. Furthermore, they often require either significant online computation during deployment or a differentiable simulation system. We introduce Reinforcement Learning for Deep Adaptive Design (RL-DAD), a method for simulation-based optimal experimental design for non-differentiable implicit models. RL-DAD extends prior work in policy-based Bayesian Optimal Experimental Design (BOED) by reformulating it as a Markov Decision Process with a reward function based on likelihood-free information lower bounds, which is used to learn a policy via deep reinforcement learning. The learned design policy maps prior histories to experiment designs offline and can be quickly deployed during online execution. We evaluate RL-DAD and find that it performs competitively with baselines on three benchmarks.
You Cannot Always Win the Race: Analyzing the LFENCE/JMP Mitigation for Branch Target Injection

LFENCE/JMP is an existing software mitigation option for Branch Target Injection (BTI) and similar transient execution attacks stemming from indirect branch predictions, which is commonly used on AMD processors. However, the effectiveness of this mitigation can be compromised by the inherent race condition between the speculative execution of the predicted target and the architectural resolution of the intended target, since this can create a window in which code can still be transiently executed. This work investigates the potential sources of latency that may contribute to such a speculation window. We show that an attacker can"win the race", and thus that this window can still be sufficient to allow exploitation of BTI-style attacks on a variety of different x86 CPUs, despite the presence of the LFENCE/JMP mitigation.
Towards Flexible Anonymous Networks

Anonymous Communication designs such as Tor build their security upon distributing the trust in many volunteers running relays in many locations globally. In practice, it leads to a heterogeneous network in which many versions of the same Tor software exist, with a different set of protocol features. Because of the heterogeneous aspect of the network, the maintainers employ forward-compatible protocol design strategies to maintain network extensibility. These strategies aim to guarantee that different versions of the Tor software interact without unrecoverable errors. In this work, we cast the protocol tolerance enabled with forward-compatible protocol considerations as a fundamental security issue. Despite being beneficial for the developers, we argue that protocol tolerance is the cause of many strong attacks against Tor in the past fifteen years. To address this issue, we propose FAN for Flexible Anonymous Network, a new software architecture for volunteer-based distributed networks that shifts the dependence away from protocol tolerance without losing the ability for the developers to ensure the continuous evolution of their software. We realize an implementation, evaluate the overheads and, experiment with several of FAN's benefits to defend against a severe attack still applicable to Tor today.
Risk-based path planning for autonomous vehicles

In this paper, a risk map-based path planning algorithm is introduced for autonomous vehicles. Multivariate B-splines are implemented to generate a risk map, which measures the risk of colliding with different objects. In the following step, a two-level optimal control problem is designed. At the first level, an overall lowest risk trajectory is found. Then in the second level, among the paths whose risk value is the lowest, the one with the minimum steering effort is determined. Finally, numerical simulations are carried out with the optimal control software OCPID-DAE1. Results show that this method is useful and meaningful to find a path of minimum risk for an autonomous vehicle.
E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation

Contour-based instance segmentation methods have developed rapidly recently but feature rough and hand-crafted front-end contour initialization, which restricts the model performance, and an empirical and fixed backend predicted-label vertex pairing, which contributes to the learning difficulty. In this paper, we introduce a novel contour-based method, named E2EC, for high-quality instance segmentation. Firstly, E2EC applies a novel learnable contour initialization architecture instead of hand-crafted contour initialization. This consists of a contour initialization module for constructing more explicit learning goals and a global contour deformation module for taking advantage of all of the vertices' features better. Secondly, we propose a novel label sampling scheme, named multi-direction alignment, to reduce the learning difficulty. Thirdly, to improve the quality of the boundary details, we dynamically match the most appropriate predicted-ground truth vertex pairs and propose the corresponding loss function named dynamic matching loss. The experiments showed that E2EC can achieve a state-of-the-art performance on the KITTI INStance (KINS) dataset, the Semantic Boundaries Dataset (SBD), the Cityscapes and the COCO dataset. E2EC is also efficient for use in real-time applications, with an inference speed of 36 fps for $512\times 512$ images on an NVIDIA A6000 GPU. Code will be released at https://github.com/zhang-tao-whu/e2ec.