Reviewerly/exHarmony huggingface dataset:
A Quantitative Information Flow Analysis of the Topics API

Third-party cookies have been a privacy concern since cookies were first developed in the mid 1990s, but more strict cookie policies were only introduced by Internet browser vendors in the early 2010s. More recently, due to regulatory changes, browser vendors have started to completely block third-party cookies, with both Firefox and Safari already compliant. The Topics API is being proposed by Google as an additional and less intrusive source of information for interest-based advertising (IBA), following the upcoming deprecation of third-party cookies. Initial results published by Google estimate the probability of a correct re-identification of a random individual would be below 3% while still supporting IBA. In this paper, we analyze the re-identification risk for individual Internet users introduced by the Topics API from the perspective of Quantitative Information Flow (QIF), an information- and decision-theoretic framework. Our model allows a theoretical analysis of both privacy and utility aspects of the API and their trade-off, and we show that the Topics API does have better privacy than third-party cookies. We leave the utility analyses for future work.
Extending Browser Extension Fingerprinting to Mobile Devices

Browser extensions are tools that extend basic browser features to enhance web experience. It has been shown that extensions can be exploited to fingerprint users and even infer personal information about them. However, as browser extensions have been limited to desktops previously, no prior work has explored fingerprintability of extensions on mobile devices, despite the increasing extension support for mobile browsers. This paper aims to fill this gap by extending extension fingerprinting techniques, traditionally performed on desktops, to mobile phones. Out of the 16 chosen extensions, we discover that 6 extensions are uniquely identifiable by their client-side modifications. We present our experimental results through our evaluation of variable interactions between various browsers, devices, and extension lists, and investigate how shifting the attention from the list of installed extensions to the actual modification data can help attackers discriminate users better.
Maybenot: A Framework for Traffic Analysis Defenses

In light of the increasing ubiquity of end-to-end encryption and the use of technologies such as Tor and VPNs, analyzing communications metadata---traffic analysis---is a last resort for network adversaries. Traffic analysis attacks are more effective thanks to improvements in deep learning, raising the importance of deploying defenses. This paper introduces Maybenot, a framework for traffic analysis defenses. Maybenot is an evolution and generalization of the Tor Circuit Padding Framework by Perry and Kadianakis, designed to support a wide range of protocols and use cases. Defenses are probabilistic state machines that trigger padding and blocking actions based on events. A lightweight simulator enables rapid development and testing of defenses. In addition to describing the Maybenot framework, machines, and simulator, we implement and thoroughly evaluate the state-of-the-art website fingerprinting defenses FRONT and RegulaTor as Maybenot machines. Our evaluation identifies challenges associated with state machine-based frameworks as well as possible enhancements that will further improve Maybenot's support for effective defenses moving forward.
TIGER: Tor Traffic Generator for Realistic Experiments

Tor is the most widely adopted anonymity network, helping safeguard the privacy of Internet users, including journalists and human rights activists. However, effective attacks aimed at deanonymizing Tor users' remains a significant threat. Unfortunately, evaluating the impact such attacks by collecting realistic Tor traffic without gathering real users' data poses a significant challenge.
Granular IoT Device Identification Using TF-IDF and Cosine Similarity

Internet of things (IoT) devices are becoming more prevalent in home environments and are shown to be generally insecure. There have been many previous studies looking to identify unknown IoT devices on networks. To truly secure a network however, there is a need to identify unknown devices down to the granularity of firmware version; a problem previous studies have failed to solve. As devices change versions, it is expected that there would be subtle differences in the on-wire signatures that would be hard for a human analyst to notice, but easy for an NLP technique to identify. In this paper we extract keywords from both encrypted and unencrypted network traffic and first use UMAP with K-Means clustering to visualise the data and show that natural clusters form across our test dataset of 18 devices covering 61 versions. This analysis suggests that there are underlying patterns in the extracted keywords that could be detected by machine learning techniques. We then show that these patterns can be detected by proposing a novel technique using TF-IDF and cosine similarity that follows the clustering results to identify IoT devices down to the level of firmware version. We show that our chosen features are strong enough to work accurately across a range of device types, manufacturers, models and versions, and note the main observations found when trying to identify devices down to a firmware version. This approach to get granularity down to device version level achieves an accuracy of 67% without being to the detriment of identifying device models, where we achieve an accuracy of 90%.
A First Look at Generating Website Fingerprinting Attacks via Neural Architecture Search

An adversary can use website fingerprinting (WF) attacks to breach the privacy of users who access the web through encrypted tunnels like Tor. These attacks have increasingly relied on the use of deep neural networks (DNNs) to build powerful classifiers that can match the traffic of a target user to the specific traffic pattern of a website.
Falkor: Federated Learning Secure Aggregation Powered by AESCTR GPU Implementation

We propose a novel protocol, Falkor, for secure aggregation for Federated Learning in the multi-server scenario based on masking of local models via a stream cipher based on AES in counter mode and accelerated by GPUs running on the aggregating servers. The protocol is resilient to client dropout and has reduced clients/servers communication cost by a factor equal to the number of aggregating servers (compared to the naïve baseline method). It scales simultaneously in the two major complexity aspects: 1) large number of clients; 2) highly complex machine learning models such as CNNs, RNNs, Transformers, etc. The AES-CTR-based masking function in our aggregation protocol is built on the concept of counterbased cryptographically-secure pseudorandom number generators (csPRNGs) as described in [32] and subsequently used by Facebook for their torchcsprng csPRNG. We improve upon torchcsprng by careful use of shared memory on the GPU device, a recent idea of Cihangir Tezcan [38] and obtain 100x speedup in the masking function compared to a single CPU core.
High-precision RNS-CKKS on fixed but smaller word-size architectures: theory and application

A prevalent issue in the residue number system (RNS) variant of the Cheon-Kim-Kim-Song (CKKS) homomorphic encryption (HE) scheme is the challenge of efficiently achieving high precision on hardware architectures with a fixed, yet smaller, word-size of bit length W , especially when the scaling factor satisfies log Δ > W
ART-Owen Scrambling

We present a novel algorithm for implementing Owen-scrambling, combining the generation and distribution of the scrambling bits in a single self-contained compact process. We employ a context-free grammar to build a binary tree of symbols, and equip each symbol with a scrambling code that affects all descendant nodes. We nominate the grammar of adaptive regular tiles (ART) derived from the repetition-avoiding Thue-Morse word, and we discuss its potential advantages and shortcomings. Our algorithm has many advantages, including random access to samples, fixed time complexity, GPU friendliness, and scalability to any memory budget. Further, it provides two unique features over known methods: it admits optimization, and it is in-vertible, enabling screen-space scrambling of the high-dimensional Sobol sampler.
Vision-Based Interfaces for Character-Based Text Entry: Comparison of Errors and Error Correction Properties of Eye Typing and Head Typing

We examined two vision-based interfaces (VBIs) for performance and user experience during character-based text entry using an on-screen virtual keyboard. Head-based VBI uses head motion to steer the computer pointer and mouth-opening gestures to select the keyboard keys. Gaze-based VBI utilizes gaze for pointing at the keys and an adjustable dwell for key selection. The results showed that after three sessions (45 min of typing in total), able-bodied novice participants (N = 34) typed significantly slower yet yielded significantly more accurate text with head-based VBI with gaze-based VBIs. The analysis of errors and corrective actions relative to the spatial layout of the keyboard revealed a difference in the error correction behavior of the participants when typing using both interfaces. We estimated the error correction cost for both interfaces and suggested implications for the future use and improvement of VBIs for hands-free text entry.
Combination of Reinforcement Learning Models Towards considerate motion planning for multiple pedestrians

This paper addresses a human in the loop reinforcement learning(HiL-RL) model to realize compassionate behavior where humans and robots co-exist in a simulation environment. A composition method of RL models is proposed to deal with multiple pedestrian and scalability issues. In HiL-RL a single human pedestrian gives a reward to the robot corresponding to the robots behaviour. To deal with multiple pedestrians and improve scalability, trained RL models for each pedestrian are combined instead of executing Hil-RL with multiple pedestrians. Three combination methods, one where the Q values for a given state and action pair are combined and the two more where the velocities for a given state are combined but using slightly different methods are proposed. The robots behaviour is then determined and compared for each of these models in an environment where two and four pedestrians are present. The robot sucessfully chnages its motion in order to the avoid collision with nearby pedstrians.
Feature Descriptor based on Differential Evolution for Visual Navigation in Dynamic Environments

This work proposes to use a teach-and-repeat method to navigate in an outdoor environment. Instead of using state-of-the-art feature point descriptors to match the current images to the database, we rely on the Generated Binary Robust Independent Elementary Features (GRIEF), a model robust to changes in illumination and weather. These descriptors are obtained via a training process employing evolutionary methods and binary comparison tests. The objective is to obtain visual feature descriptors designed for a given task. For the teach-and-repeat task, they are trained to optimize the estimate of the robot heading error with respect to a learned path. In this work, we propose to investigate how Differential Evolution (DE) can improve the performance of the GRIEF in terms of the convergence rate during the training phase and of fitness score. The new approach, named GRIEF-DE, is trained with the Michigan data set, made of pictures of an outdoor environment with significant changes in appearance caused by seasonal weather variations and illumination, and compared with the original GRIEF. The proposed model was applied to 4 different data sets and the experimental results suggest that the use of Differential Evolution improves the training performance as well as the estimation of the robot orientation with respect to the path of reference.
Interpretability Is in the Mind of the Beholder: A Causal Framework for Human-Interpretable Representation Learning

Research on Explainable Artificial Intelligence has recently started exploring the idea of producing explanations that, rather than being expressed in terms of low-level features, are encoded in terms of interpretable concepts learned from data. How to reliably acquire such concepts is, however, still fundamentally unclear. An agreed-upon notion of concept interpretability is missing, with the result that concepts used by both post hoc explainers and concept-based neural networks are acquired through a variety of mutually incompatible strategies. Critically, most of these neglect the human side of the problem: a representation is understandable only insofar as it can be understood by the human at the receiving end. The key challenge in human-interpretable representation learning (hrl) is how to model and operationalize this human element. In this work, we propose a mathematical framework for acquiring interpretable representations suitable for both post hoc explainers and concept-based neural networks. Our formalization of hrl builds on recent advances in causal representation learning and explicitly models a human stakeholder as an external observer. This allows us derive a principled notion of alignment between the machine's representation and the vocabulary of concepts understood by the human. In doing so, we link alignment and interpretability through a simple and intuitive name transfer game, and clarify the relationship between alignment and a well-known property of representations, namely disentanglement. We also show that alignment is linked to the issue of undesirable correlations among concepts, also known as concept leakage, and to content-style separation, all through a general information-theoretic reformulation of these properties. Our conceptualization aims to bridge the gap between the human and algorithmic sides of interpretability and establish a stepping stone for new research on human-interpretable representations.
Fused filament fabrication in CAD education: A closed-loop approach

Integrating low-cost fused filament fabrication 3D printing as a foundation for learning 3D modelling is explored. This method blends traditional computer aided design (CAD) instruction with additive manufacturing possibilities. Experimental results demonstrate increased comprehension speed and reduced learning time. This hands-on approach empowers students by enabling direct engagement with the modelling process. Analogous to reverse engineering, the strategy instructs engineering students from final product to model creation, closing the gap between theory and practice. Incorporating 3D printing bridges this divide, enhancing understanding, creativity and problem-solving. The study underscores technology's influence on learning strategies, aligning with the surge of 3D printing in education. Results link advanced design technology usage to improved student performance, with 3D-printed materials yielding 45% higher grades and 30% faster task completion. This study advocates curricular advancement for design-focused careers through enhanced technology integration and favourable 3D printing model reception.
Virtual Reality Aggression Prevention Training for People with Mild Intellectual Disabilities: A Feasibility Study

Introduction Problems with adequately regulating aggression are comon in people with mild to borderline intellectual disabilities (MBID). Learning through experience has been shown to be effective in MBID. Therefore, we adapted an aggression prevention training that uses virtual reality (VR), which was initially made for a forensic psychiatric population. We aimed to assess the feasibility of this VR aggression training for MBID.
The Probabilistic Image Encryption Algorithm Based on Galois Field GF(257)

Image encryption technology is an essential measure to protect image information from illegal access. A new probabilistic image encryption algorithm with 480-bit external secret key was proposed in this paper. A chaotic pseudo-random generator is employed to produce the equivalent keys. Then the plain image is covered by the equivalent keys under exclusive or operation to obtain a pending sequence. The pending sequence is transformed into a cipher sequence by arithmetic operations based on GF(257). Finally, the cipher sequence is also covered by the equivalent keys to obtain the cipher image. In the transformation, a 1024-bit pseudo-random number sequence is introduced as the base numbers for the power operation to assist the encryption. The proposed image encryption algorithm has the characteristic of probabilistic encryption. Encrypting the same plain image with the same secret key will result in completely different cipher images. Experiments show that the proposed algorithm has the low time complexity (9.67pi/bit) and space complexity (13.07bit/bit), and high sensitivities on the auxiliary sequence (49.94%), secret key (BACI 26.7698% typically), and plain image (BACI 26.7845% typically), and is a preferred image encryption scheme for commercial and personal image information security.
Surveillance without ‘Baddies’

Abstract The Covid-19 pandemic raises new questions regarding the moral permissibility of surveillance—in this context there is no human aggressor and, thus, the antagonistic relationship between the surveillant and the surveilled, which is commonly assumed to obtain in much of the literature on surveillance ethics, does not exist. This raises the question of the extent to which existing frameworks for ethical surveillance apply to the surveillance activities aiming at controlling virus transmission. In this chapter, we will address the question of whether and in what form liability should play a role in surveillance ethics in the context of pandemics. Can persons who are not responsible for catching Covid-19 and do not intend to harm or wrong others by transmitting the virus, be liable to being surveilled on account of their posing a threat of virus transmission to others? If so, are they innocent or culpable threats?
Ethical Requirements for Digital Systems for Contact Tracing in Pandemics

Abstract The goal of this chapter is to provide ethical requirements for digital tracking and tracing systems for Covid-19. Moreover, the chapter also identifies risks in guidelines: that temporary measures become permanent; that temporary measures affect information distribution norms, creating a new normal which may not be desirable; and that measures in one country negatively influence policies in another country. Recognizing these risks, the chapter proposes a solution to the inherent limits of developing a guideline with a check-list function, or other forms of easily determinable guidelines that can be used by a non-expert.
Digital Contact Tracing Applications (DCTAs)

Abstract Digital Contact Tracing Applications (DCTAs) can collect large amounts of personally identifiable information. The approach taken by public health officials in using DCTAs could impact individual rights such as autonomy and the right to informed consent. Approaches that lean towards mandatory use of DCTAs, could impact autonomy and informed consent more than other approaches. This chapter argues that given the state of emergency and extreme circumstances during a pandemic the scale of Covid-19, we ought to follow a public health ethics approach even if it impacts individual autonomy and informed consent to some extent, until such time when the risks are not as high and more information is available.
Technofixing Surveillance

Abstract Covid-19 has led to mass surveillance of populations at a global scale. However, surveillance does not engender trust and indeed frequently speaks to a pre-existing lack of trust in the person under surveillance. This chapter examines the relationship between consent and coercion in a liberal society, and the relevance of moral trust in authorities to that relationship. In particular, it draws on earlier work, coupled with recent concerns from Snowden to Cambridge Analytica, to argue that moral trust is important to provide the authority to conduct surveillance in any circumstance. In states of emergency, though, the chapter argues that pre-existing trust is essential. The squandering of that trust in times of non-emergency is therefore dangerous and diminishes the state’s ability to adequately protect its citizens. Trust could be developed through increasing transparency of government processes and decision-making, especially when ‘fighting’ an ‘enemy’ which lacks semantic understanding.
Restoring a Triangle on a Plane by Three Projections

In this paper, we consider the following problem. On the plane, a triangle ΔABC and three straight lines l1, l2, and l3 in the general position are given; the locations of the straight lines are unknown. One must restore the triangle ΔABC by the known lengths of the sides of the triangles ΔA1B1C1, ΔA2B2C2, and ΔA3B3C3, which are the projections of the triangle ΔABC onto the lines l1, l2, and l3. Similar problems and their multidimensional generalizations are of interest in the theory of computer images.
A corpus of Persian literary text

Abstract Persian poetry has profoundly affected all periods of Persian literature and the literature of other countries as well. It is a fundamental vehicle for expressing Persian culture and political opinion. This paper presents a corpus of Persian literary text mainly focusing on poetry, covering the ninth to twenty-first century annotated for century and style, with additional partial annotation of rhetorical figures. Our resource is the largest and the most diverse corpus available in Persian literary text, with a particularly broad temporal scope. This allows us to conduct several computational experiments to analyze poetic styles, authors and time periods, as well as context shifts over time, for which we rely both on supervised models and on Persian poetry-specific heuristics. The corpus, the tools, and experiments described in this paper can be used not only for digital humanities studies of Persian literature but also for processing Persian texts in general, as well as in other broader cross-linguistic applications.
Pluricentricity from a quantitative point of view

Abstract This chapter illustrates how the distributional semantic lectometric workflow can be applied to Spanish language data in a large-scale lectometric study of the pluricentricity of Spanish. Starting with a dataset of 142 concepts, the analysis explores the distances between Spanish in Spain, Argentina, Peru, Colombia, Mexico, and the United States. The lectometric workflow presented so far is enriched with a number of methodological add-ons: a multidimensional scaling analysis to visualize patterns of lectal relations, a restriction to non-uniform concepts to bring out differences more clearly, and a focus on richly modelled concepts to increase methodological stability. Also, comparing distributional models to both a disambiguated set of tokens and the full sample from which the modelled tokens were drawn serves as a way to evaluate the success of the distributional approach.
Parameters and procedures for token-based distributional semantics

Abstract Whereas Chapter 2 introduced the basic concepts behind the distributional approach to modelling lexical semantics on the level of individual occurrences in an informal way, we now discuss this procedure in technical detail. We introduce the distributional models that are used in this volume and offer an overview of the different parameters that play a role in building them. Distributional modelling indeed implies making a number of choices, from the source of the data and the unit of analysis to the definition of what counts as context and the selection of metrics and algorithms. Accordingly, the chapter focuses on the choices and options that we have included in our workflow, and that shape the models that will appear in following chapters.