Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Feasibility of Data-Driven Emotion Modeling for Human Digital Twins

Human Digital Twins (HDTs) are specifically designed to represent humans virtually. Despite their recency, HDTs are considered to be powerful tools for enhancing personalized healthcare, early disease detection, improving patient outcomes, and optimizing lifestyle. Building HDTs is challenging especially due to the complexity of modeling human behavior. To unravel such complexity, in this paper we focus on specific aspects of human behavior and emotions (valence and arousal) to enable a more informed HTD behavior modeling. We assessed the feasibility and performance of our data collection infrastructure with N=112 participants. During a science festival, our participants wore a smartwatch to self-report their emotions while simultaneously their physiological data were collected through smartwatch sensors. We explored predictive modeling possibilities once with all the collected data and then with only the sensor-related data. The former performed best with 25 features, modeled with the Quadratic Discriminant Analysis classifier, resulting in 72.4% accuracy, however, with only 4 features derived from the self-reports, the K-Nearest Neighbors Classifier was able to achieve an accuracy of 71.6%. The latter used 17 sensor-related features and the Quadratic Discriminant Analysis classifier estimator reaching a 51.3% accuracy.
A model to analyze age-related differences in human-generated head-tail sequences

The generation of random-like sequences is a common task for assessing high-level cognitive abilities, such as inhibition, sustained attention and working memory. In general, many studies have shown a detrimental effect of aging on pseudo-random productions. The performance of participants in random generation tasks has typically been assessed by measures of randomness such as, among others, entropy and algorithmic complexity that are calculated from the series of responses produced by the subject. We focus on analyzing the mental model of randomness that people implicitly use when producing random series. We propose a novel latent class model based on Markov chains that aims to classify individuals into homogeneous classes according to the way they generate head-tail series. Our results reveal that there are significant age-related differences in the way individuals produce random-like sequences. Specifically, the group of healthy adults implicitly uses a simpler mental mechanism, in terms of memory requirements, compared to the group of younger participants. Author summary It is well known that, in general, people deviate from randomness as they attempt to mentally generate head-tail sequences as randomly as possible. The extensive literature on this topic has shown that human-generated head-tail series tend to have more alternations than would be expected by chance. However, it seems unrealistic to suppose that all individuals generate sequences based on the same random mental model. We conducted an experiment in which 331 individuals were asked to mentally simulate a fair coin: 69 healthy older adults with an age ≥ 60 and 262 Biology students with an age between 18 and 20. We found that the way in which random sequences are generated varies between subjects. A similar approach could be used to analyze differences in random generation tasks between subjects with different disorders and healthy subjects.
Predictive Modeling of Evoked Intracranial EEG Response to Medial Temporal Lobe Stimulation in Patients with Epilepsy

Despite over a decade of promising results, closed-loop neurostimulation for the treatment of drug-resistant epilepsy (DRE) still relies on manual parameter tuning and yields unpredictably variable outcomes. Fully automated algorithms with predictable outcomes have remained a theoretical possibility, mainly due to a lack of generalizable models of the brain’s dynamic network response to stimulation under varying parameters. In this work, we study predictive dynamical models of human intracranial EEG (iEEG) response under parametrically-rich neurostimulation and develop models that are predictively accurate and biologically interpretable. Using data from n = 10 subjects, we show that evoked iEEG dynamics are best explained by stimulation-triggered switched-linear models with approximately 300ms of causal historical dependence. These models are highly consistent across stimulation amplitudes and frequencies, including STIM OFF durations, which allows for learning a single generalizable model from abundant STIM OFF and limited STIM ON data. Despite significant heterogeneity among subjects, we observed a consistent distance-dependent pattern whereby stimulation impacts the actuation site and nearby regions (≲ 20mm) directly with little or no network mediation, reaches medium-distance regions (20 ∼100mm) indirectly through network interactions and hardly reaches more distal areas (≳ 100mm). Peak involvement of network interactions occurs at about 60-80mm from the stimulation site. Due to their predictive accuracy and mechanistic interpretability, these models have far-reaching applications in model-based seizure forecasting and stimulation design for closed-loop neurostimulation.
Deep learning reduces data requirements and allows real-time measurements in Imaging Fluorescence Correlation Spectroscopy

Imaging Fluorescence Correlation Spectroscopy (Imaging FCS) is a powerful tool to extract information on molecular mobilities, actions and interactions in live cells, tissues and organisms. Nevertheless, several limitations restrict its applicability. First, FCS is data hungry, requiring 50,000 frames at 1 ms time resolution to obtain accurate parameter estimates. Second, the data size makes evaluation slow. Thirdly, as FCS evaluation is model-dependent, data evaluation is significantly slowed unless analytic models are available. Here we introduce two convolutional neural networks (CNNs) – FCSNet and Im-FCSNet – for correlation and intensity trace analysis, respectively. FCSNet robustly predicts parameters in 2D and 3D live samples. ImFCSNet reduces the amount of data required for accurate parameter retrieval by at least one order of magnitude and makes correct estimates even in moderately defocused samples. Both CNNs are trained on simulated data, are model-agnostic, and allow autonomous, real-time evaluation of Imaging FCS measurements.
A Large-Scale Measurement of Website Login Policies

Authenticating on a website using a password involves a multi-stage login process, where each stage entails critical policy and implementation decisions that impact login security and usability. While the security community has identified best practices for each stage of the login workflow, we currently lack a broad understanding of website login policies in practice. Prior work relied upon manual inspection of websites, producing evaluations of only a small population of sites skewed towards the most popular ones. In this work, we seek to provide a more comprehensive and systematic picture of real-world website login policies. We develop an automated method for inferring website login policies and apply it to domains across the Google CrUX Top 1 Million. We successfully evaluate the login policies on between 18K and 359K sites (varying depending on the login stage considered), providing characterization of a population two to three orders of magnitude larger than previous studies. Our findings reveal the extent to which insecure login policies exist and identify some underlying causes. Ultimately, our study provides the most comprehensive empirical grounding to date on the state of website login security, shedding light on directions for improving online authentication.
This paper is included in the Proceedings of the 32nd USENIX Security Symposium.

Deep learning models are widely used in many applications. Despite their impressive performance, the security aspect of these models has raised serious concerns. Universal adversarial patch attack is one of the security problems in deep learning, where an attacker can generate a patch trigger on pre-trained models using gradient information. Whenever the trigger is pasted on an input, the model will misclassify it to a target label. Existing attacks are realized with access to the model’s gradient or its output conﬁdence. In this paper, we propose a novel attack method H ARD B EAT that generates universal adversarial patches with access only to the predicted label. It utilizes historical data points during the search for an optimal patch trigger and performs focused/directed search through a novel importance-aware gradient approximation to explore the neighborhood of the current trigger. The evaluation is conducted on four popular image datasets with eight models and two online commercial services. The experimental results show H ARD B EAT is signiﬁcantly more effective than eight baseline attacks, having more than twice high-ASR (attack success rate) patch triggers (>90%) on local models and 17.5% higher ASR on online services. Three existing advanced defense techniques fail to defend against H ARD B EAT .
Synchronization Storage Channels (S2C): Timer-less Cache Side-Channel Attacks on the Apple M1 via Hardware Synchronization Instructions

Shared caches have been a prime target for mounting cross-process/core side-channel attacks. Fundamentally, these attacks require a mechanism to accurately observe changes in cache state. Most cache attacks rely on timing measurements to indirectly infer cache state changes, and attack success hinges on the reliability/availability of accurate timing sources. Far fewer techniques have been proposed to directly observe cache state changes without reliance on timers. Further, none of said ‘timer-less’ techniques are accessible to userspace attackers targeting modern CPUs. This paper proposes a novel technique for mounting timer-less cache attacks targeting Apple M1 CPUs named Synchronization Storage Channels (S 2 C). The key observation is that the implementation of synchronization instructions, specifically Load-Linked/Store-Conditional (LL/SC), makes architectural state changes when L1 cache evictions occur. This by itself is a useful starting point for attacks, however faces multiple technical challenges when being used to perpetrate cross-core cache attacks. Specifically, LL/SC only observes L1 evictions (not shared L2 cache evictions). Further, each attacker thread can only simultaneously monitor one address at a time through LL/SC (as opposed to many). We propose a suite of techniques and reverse engineering to overcome these limitations, and demonstrate how a single-threaded userspace attacker can use LL/SC to simultaneously monitor multiple (up to 11) victim L2 sets and succeed at standard cache-attack applications, such as breaking cryptographic implementations and constructing covert channels.
Pass2Edit: A Multi-Step Generative Model for Guessing Edited Passwords

While password stufﬁng attacks (that exploit the direct pass-word reuse behavior) have gained considerable attention, only a few studies have examined password tweaking attacks, where an attacker exploits users’ indirect reuse behaviors (with edit operations like insertion, deletion, and substitution). For the ﬁrst time , we model the password tweaking attack as a multi-class classiﬁcation problem for characterizing users’ password edit/modiﬁcation processes, and propose a generative model coupled with the multi-step decision-making mechanism, called P ASS 2 EDIT , to accurately characterize users’ password reuse/modiﬁcation behaviors. We demonstrate the effectiveness of P ASS 2 EDIT through extensive experiments, which consist of 12 practical attack scenarios and employ 4.8 billion real-world passwords. The experimental results show that P ASS 2 EDIT and its variant signiﬁcantly improve over the prior art. More speciﬁcally, when the victim’s password at site A (namely pw A ) is known, within 100 guesses, the cracking success rate of P ASS 2 EDIT in guessing her password at site B ( pw B (cid:2) = pw A ) is 24.2% (for common users) and 11.7% (for security-savvy users), respectively, which is 18.2%-33.0% higher than its foremost counter-parts. Our results highlight that password tweaking is a much more damaging threat to password security than expected.
"I'm going to trust this until it burns me" Parents' Privacy Concerns and Delegation of Trust in K-8 Educational Technology

After COVID-19 restrictions forced an almost overnight transition to distance learning for students of all ages, education software became a target for data breaches, with incidents like the Illuminate data breach affecting millions of students nationwide and over 820,000 current and former students in New York City (NYC) alone. Despite a general return to in-person schooling, some schools continue to rely on remote-learning technologies, with NYC even using remote learning during weather-related closures or “snow days. ” Given the ongoing use of these classroom technologies, we sought to understand parents’ awareness of their security and privacy risks. We also wanted to know what concerns parents had around their childrens’ use of these tools, and what informed these concerns. To answer these questions, we interviewed 18 NYC parents with children in grades K-8. We found that though the COVID-19 pandemic was the first exposure to remote learning technologies for many children and some parents, there was insufficient guidance and training around them provided for children, parents, and educators. We also found that participating parents implicitly trusted schools and the Department of Education (DOE) to keep their children - and their children’s data - safe, and therefore rarely reported privacy and security concerns about classroom technologies. At the same time, however, they described many situations that indicated privacy and security risks with respect to classroom technologies.
Content-Type: multipart/oracle - Tapping into Format Oracles in Email End-to-End Encryption

S/MIME and OpenPGP use cryptographic constructions re-peatedly shown to be vulnerable to format oracle attacks in protocols like TLS, SSH, or IKE. However, format oracle attacks in the End-to-End Encryption (E2EE) email setting are considered impractical as victims would need to open many attacker-modified emails and communicate the decryption result to the attacker. But is this really the case? In this paper, we survey how an attacker may remotely learn the decryption state in email E2EE. We analyze the interplay of MIME and IMAP and describe side-channels emerging from network patterns that leak the decryption status in Mail User Agents (MUAs). Concretely, we introduce specific MIME trees that produce decryption-dependent network patterns when opened in a victim’s email client. We survey 19 OpenPGP-and S/MIME-enabled email clients and four cryptographic libraries and uncover a side-channel leaking the decryption status of S/MIME messages in one client. Further, we discuss why the exploitation in the other clients is impractical and show that it is due to missing feature support and implementation quirks. These unintended defenses create an unfortunate conflict between usability and security. We present more rigid countermeasures for MUA developers and the standards to prevent exploitation.
Improving Logging to Reduce Permission Over-Granting Mistakes

Access control conﬁgurations are gatekeepers to block un-welcome access to sensitive data. Unfortunately, system administrators (sysadmins) sometimes over-grant permissions when resolving unintended access-deny issues reported by legitimate users, which may open up security vulnerabilities for attackers. One of the primary reasons is that modern software does not provide informative logging to guide sysadmins to understand the reported problems. This paper makes one of the ﬁrst attempts (to the best of our knowledge) to help developers improve log messages in order to help sysadmins correctly understand and ﬁx access-deny issues without over-granting permissions. First, we conducted an observation study to understand the current practices of access-deny logging in the server software. Our study shows that many access-control program locations do not have any log messages; and a large percentage of existing log messages lack useful information to guide sysadmins to correctly understand and ﬁx the issues. On top of our observations, we built S EC L OG , which uses static analysis to automatically help developers ﬁnd missing access-deny log locations and identify relevant information at the log location. We evaluated S EC L OG with ten widely deployed server applications. Overall, S EC L OG identiﬁed 380 new log statements for access-deny cases, and also enhanced 550 existing access-deny log messages with diagnostic information. We have reported 114 log statements to the developers of these applications, and so far 70 have been accepted into their main branches . We also conducted a user study with sysad-mins (n=32) on six real-world access-deny issues. S EC L OG can reduce the number of insecure ﬁxes from 27 to 1, and also improve the diagnosis time by 64.2% on average.
Erebus: Access Control for Augmented Reality Systems

Augmented Reality (AR) is widely considered the next evolution in personal devices, enabling seamless integration of the digital world into our reality. Such integration, however, often requires unfettered access to sensor data, causing significant overprivilege for applications that run on these platforms. Through analysis of 17 AR systems and 45 popular AR applications, we explore existing mechanisms for access control in AR platforms, identify key trends in how AR applications use sensor data, and pinpoint unique threats users face in AR environments. Using these findings, we design and implement Erebus, an access control framework for AR platforms that enables fine-grained control over data used by AR applications. Erebus achieves the principle of least privileged through the creation of a domain-specific language (DSL) for permission control in AR platforms, allowing applications to specify data needed for their functionality. Using this DSL, Erebus further enables users to customize app permissions to apply under specific user conditions. We implement Erebus on Google’s AR-Core SDK and port five existing AR applications to demonstrate the capability of Erebus to secure various classes of apps. Performance results using these applications and various microbenchmarks show that Erebus achieves its security goals while being practical,introducing negligible performance overhead to the AR system.
On the Complexity of k-DQBF

Recently Dependency Quantified Boolean Formula (DQBF) has attracted a lot of attention in the SAT community. Intuitively, a DQBF is a natural extension of quantified boolean formula where for each existential variable, one can specify the set of universal variables it depends on. It has been observed that a DQBF with k existential variables – henceforth denoted by k -DQBF – is essentially a k -CNF formula in succinct representation. However, beside this and the fact that the satisfiability problem is NEXP -complete, not much is known about DQBF. In this paper we take a closer look at k -DQBF and show that a number of well known classical results on k -SAT can indeed be lifted to k -DQBF, which shows a strong resemblance between k -SAT and k -DQBF. More precisely, we show the following.
Hey Kimya, Is My Smart Speaker Spying on Me? Taking Control of Sensor Privacy Through Isolation and Amnesia

Although smart speakers and other voice assistants are becoming increasingly ubiquitous, their always-standby nature continues to prompt significant privacy concerns. To address these, we propose K IMYA , a hardening framework that allows device vendors to provide strong data-privacy guarantees. Concretely, K IMYA guarantees that microphone data can only be used for local processing, and is immediately discarded unless a user-auditable notification is generated. K IMYA thus makes devices accountable for their data-retention behavior. Moreover, K IMYA is not limited to voice assistants, but is applicable to all devices with always-standby, event-triggered sensors. We implement K IMYA for ARM Cortex-M, and apply it to a wake-word detection engine. Our evaluation shows that K IMYA introduces low overhead, can be used in constrained environments, and does not require hardware modifications.
Forming Faster Firmware Fuzzers

A recent trend for assessing the security of an embedded system’s firmware is rehosting , the art of running the firmware in a virtualized environment, rather than on the original hardware platform. One significant use case for firmware rehosting is fuzzing to dynamically uncover security vulnerabilities. However, state-of-the-art implementations suffer from high emulator-induced overhead, leading to less-than-optimal execution speeds. Instead of emulation, we propose near-native rehosting: running embedded firmware as a Linux userspace process on a high-performance system that shares the instruction set family with the targeted device. We implement this approach with S AFIRE F UZZ , a throughput-optimized rehost-ing and fuzzing framework for ARM Cortex-M firmware. S AFIRE F UZZ takes monolithic binary-only firmware images and uses high-level emulation (HLE) and dynamic binary rewriting to run them on far more powerful hardware with low overhead. By replicating experiments of HALucinator, the state-of-the-art HLE-based rehosting system for binary firmware, we show that S AFIRE F UZZ can provide a 690x throughput increase on average during 24-hour fuzzing cam-paigns while covering up to 30% more basic blocks.
Understanding User Awareness and Behaviors Concerning Encrypted DNS Settings

Recent developments to encrypt the Domain Name System (DNS) have resulted in major browser and operating system vendors deploying encrypted DNS functionality, often enabling various configurations and settings by default. In many cases, default encrypted DNS settings have implications for performance and privacy; for example, Firefox’s default DNS setting sends all of a user’s DNS queries to Cloudflare, potentially introducing new privacy vulnerabilities. In this paper, we confirm that most users are unaware of these developments—with respect to the rollout of these new technologies, the changes in default settings, and the ability to customize encrypted DNS configuration to balance user preferences between privacy and performance. Our findings suggest several important implications for the designers of interfaces for encrypted DNS functionality in both browsers and operating systems, to help improve user awareness concerning these settings, and to ensure that users retain the ability to make choices that allow them to balance tradeoffs concerning DNS privacy and performance.
Co-design of Anytime Computation and Robust Control ( Supplimental )

@TECHREPORT{UPenn-ESE-15-324, AUTHOR = {Yash Vardhan Pant and Kartik Mohta and Houssam Abbas and Truong X. Nghiem and Joseph Devietti and Rahul Mangharam}, TITLE = {Co-design of Anytime Computation and Robust Control (Supplemental)}, NUMBER = {UPennESE-15-324}, INSTITUTION = {Department of Electrical and Systems Engineering, University of Pennsylvania, ADDRESS = {Philadelphia, PA},