Reviewerly/exHarmony huggingface dataset:
Tactile renegotiations in actor training: what the pandemic taught us about touch

This article draws from the practice-research project under the umbrella title ‘From Haptic Deprivation to Haptic Possibilities’. The project began as a response to the first COVID-19 lockdown in the UK in March 2020 and the necessary transition to online interactions. As a practitioner-researcher who has been critically investigating tactile possibilities through somatically inspired methods within and outside actor training, I identified a ‘gap’ in how we could still embody relational potentialities of touch either working remotely or while practising physical distancing. Modified physical contact in my practice research originates in my work with actors in training and widens in online sessions and in-person workshops with non-actors. This article focuses on tactile renegotiations in actor training and critical observations regarding what touch can be, challenging universal and unified perceptions. Advancing two published TDPT blog posts on the project, the discussion directs attention to how necessary physical distantiation during the pandemic expanded the use of touch in my training practice and how these tactile renegotiations can be applied post-pandemically within and beyond actor training. Inspired by phenomenological and feminist theories of embodiment, touch is proposed as an ethical renegotiation between self and other that necessitates differentiation and distantiation in nearness.
Using Local Cache Coherence for Disaggregated Memory Systems

Disaggregated memory provides many cost savings and resource provisioning benefits for current datacenters, but software systems enabling disaggregated memory access result in high performance penalties. These systems require intrusive code changes to port applications for disaggregated memory or employ slow virtual memory mechanisms to avoid code changes. Such mechanisms result in high overhead page faults to access remote data and high dirty data amplification when tracking changes to cached data at page-granularity. In this paper, we propose a fundamentally new approach for disaggregated memory systems, based on the observation that we can use local cache coherence to track applications' memory accesses transparently, without code changes, at cache-line granularity. This simple idea (1) eliminates page faults from the application critical path when accessing remote data, and (2) decouples the application memory access tracking from the virtual memory page size, enabling cache-line granularity dirty data tracking and eviction. Using this observation, we implemented a new software runtime for disaggregated memory that improves average memory access time and reduces dirty data amplification1.
Saliency Object Detection Method Based on Real-Time Monitoring Image Information for Intelligent Driving

Reducing traffic accident occurrences and enhancing road safety can be achieved through the processing of real-time surveillance image information for saliency object detection.Although existing saliency object detection methods based on real-time monitoring image information for intelligent driving have yielded certain results, there remain some shortcomings.In complex road environments, distinguishing between background and salient targets with existing methods proves difficult, resulting in false and missed detections.Consequently, this study investigates a saliency object detection method based on real-time monitoring image information for intelligent driving.The Visual Geometry Group (VGG) network discriminator in Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) is modified, and techniques such as spectral normalization (SN) are employed to improve the dynamic stability of training.Pixel-level image size amplification and feature enhancement are conducted on the salient objects in the dataset, providing a richer data foundation for subsequent real-time monitoring of saliency target detection and defect classification.The YOLOv5s algorithm is utilized as the identification network, and the original YOLOv5s backbone network is replaced with the MobileNetV2 network, significantly reducing network complexity and enhancing identification efficiency.The algorithm's performance in recognizing salient targets in real-time monitoring images for intelligent driving is further improved through network optimizer optimization and clustering algorithm adoption.The efficacy of the proposed method is substantiated by experimental results.
Hidden in Plain Sight: Discreet User Interfaces

The Interactions website (interactions.acm.org) hosts a stable of bloggers who share insights and observations on HCI, often challenging current practices. Each issue we'll publish selected posts from some of the leading and emerging voices in the field.
Memory Disaggregation: Advances and Open Challenges

Compute and memory are tightly coupled within each server in traditional datacenters. Large-scale datacenter operators have identified this coupling as a root cause behind fleetwide resource underutilization and increasing Total Cost of Ownership (TCO). With the advent of ultra-fast networks and cache-coherent interfaces, memory disaggregation has emerged as a potential solution, whereby applications can leverage available memory even outside server boundaries. This paper summarizes the growing research landscape of memory disaggregation from a software perspective and introduces the challenges toward making it practical under current and future hardware trends. We also reflect on our seven-year journey in the SymbioticLab to build a comprehensive disaggregated memory system over ultra-fast networks. We conclude with some open challenges toward building next-generation memory disaggregation systems leveraging emerging cache-coherent interconnects.
Four vertex stabilizer amalgams for locally s-arc transitive graphs of Pushing Up Type

We find and characterize four vertex stabilizer amalgams that can occur for locally s-arc transitive graphs of Pushing Up Type with s≥4.
Hierarchical Over-the-Air Federated Learning with Differential Privacy

Federated learning (FL) is a burgeoning field that examines the cooperative interaction of machine learning (ML) models with users, enabling the training of a global model while each user retains its data locally. With differential privacy (DP), FL also becomes an enabler for training ML models in a more private manner. While there has been a growing body of work exploring various aspects of FL, most studies, especially in the context of hierarchical federated learning (HFL), treat different levels of the hierarchy as a composition of two DP mechanisms. In this paper, we introduce a DP based privacy preserving method with hierarchical over-the-air FL and address both communication and privacy aspects in an end-to-end fashion.
Thinking style and strategies of informational behaviour of internet users

Background The development of digital reality sets the task of studying the thinking styles and strategies of working with information of higher school students. The style of thinking is understood by the authors as a kind of combination of thinking functions aimed at the perception, analysis and processing of information. The aim of the research is to study the features of students’ thinking styles and strategies of their informational behaviour in the context of Internet interaction.Methods The study was carried out using the author’s questionnaire ‘Style of thinking’, the methodology ‘Strategies of informational behaviour’ (Abakumova, Grishina, and Zvezdina 2022 Abakumova, I., A. Grishina, and G. Zvezdina. 2022. “Psychological Predictors of Students’ Behavior in Internet Under Information Uncertainty.” International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE) 10 (3): 17–26. https://doi.org/10.23947/2334-8496-2022-10-3-17-26.[Crossref] , [Google Scholar]). The sample included 115 students of both genders aged 18–21 years. The study was conducted on the basis of Kalmyk State University (Elista, Kalmykia, Russia).Results Groups of students with dominant styles of thinking were identified. It was shown that the severity and ratio of informational behaviour strategies differs among students with different dominant thinking styles. Significant relationships between the styles of thinking and strategies of informational behaviour of students were revealed.Conclusions Thinking styles and strategies of information behaviour of students have significant relationships.
Magnum: Tackling high-dimensional structures with self-organization

A big challenge in dealing with real-world problems is scalability. In fact, this is partially the reason behind the success of deep learning over other learning paradigms. Here, we tackle the scalability of a novel learning paradigm proposed in 2021 based solely on self-organizing principles. This paradigm consists of only dynamical equations which self-organize with the input to create attractor-repeller points that are related to the patterns found in data. To achieve scalability for such a system, we propose the Magnum algorithm, which utilizes many self-organizing subsystems (Inertia-SyncMap) each with subsets of the problem’s variables. The main idea is that by merging Inertia-SyncMaps, Magnum builds over time a variable correlation by consensus, capable of accurately predicting the structure of large groups of variables. Experiments show that Magnum surpasses or ties with other unsupervised algorithms in all of the high-dimensional chunking problems, each with distinct types of shapes and structural features. Moreover, Inertia-SyncMap alone outperforms or ties with other unsupervised algorithms in six out of seven basic chunking problems. Thus, this work sheds light on how self-organization learning paradigms can be scaled up to deal with high-dimensional structures and compete with current learning paradigms.
Exploring Adversarial Attacks on Learning-based Localization

We investigate the robustness of a convolutional neural network (CNN) RF transmitter localization model in the face of adversarial actors which may poison or spoof sensor data to disrupt or defeat the algorithm. We train the CNN to estimate transmitter locations based on sensor coordinates and received signal strength (RSS) measurements from a real-world dataset. We consider attacks from adversaries with varying capabilities to include naive, random attacks and omniscient, worst-case attacks. We apply countermeasures based on statistical outlier approaches and train the CNN against adversarial attacks to improve performance. Adversarial training is shown to completely neutralize some attacks and improve accuracy by up to 65% in other cases. Our evaluation of countermeasures indicates that a combination of statistical techniques and adversarial training can provide more robust defense against adversarial attacks.
PROSE: Multi-round fair coflow scheduling without prior knowledge

Coflow scheduling plays a crucial role in enhancing network-level performance in datacenter applications. Existing works on coflow scheduling predominantly concentrate on clairvoyant schedulers, which assume prior knowledge of coflow sizes before transmission. However, this assumption is often unrealistic in practical scenarios like pipelined computation. Consequently, research on non-clairvoyant schedulers has garnered increasing attention. Most studies on non-clairvoyant schedulers focus on either improving efficiency or promoting fairness, with limited emphasis on striking a balance between the two objectives. Additionally, for non-cooperative environments like multi-tenant datacenter networks, it is essential to ensure strategy-proofness in coflow scheduling. Without adequate measures, tenants may manipulate their reported requirements to gain an unfair advantage in resource allocation. Ideally, in non-cooperative environments, a coflow scheduler should achieve efficiency, fairness, and strategy-proofness, all without prior knowledge of coflow sizes. However, few existing efforts have managed to simultaneously achieve these objectives. To fill this gap, we develop PROSE, a non-clairvoyant scheduler for non-cooperative environments. PROSE divides the scheduling time into multiple rounds and employs a two-stage scheduling mechanism based on these rounds. In the first stage, PROSE estimates the sizes of the coflows with unknown sizes by transmitting the probe flows, using a policy that combines coflow competition with batch processing. Once the information about coflow sizes is obtained, PROSE prioritizes coflows among tenants for bandwidth allocation in the second stage, aiming to accelerate coflow completion within each round. Furthermore, PROSE ensures optimal isolation guarantee by fairly limiting the total data transfer for each tenant and dynamically adjusts the scheduling order of coflows in a round-robin manner across multiple rounds. PROSE prevents tenants from attempting to consume excessive network resources through manipulative actions that deceive the scheduler. Trace-driven simulations demonstrate that PROSE outperforms the fair scheduler, achieving up to a 2.37× reduction in average coflow completion time, indicating substantial efficiency improvements.
Verifying List Swarm Attestation Protocols

Swarm attestation protocols extend remote attestation by allowing a verifier to efficiently measure the integrity of software code running on a collection of heterogeneous devices across a network. Many swarm attestation protocols have been proposed for a variety of system configurations. However, these protocols are currently missing explicit specifications of the properties guaranteed by the protocol and formal proofs of correctness. In this paper, we address this gap in the context of list swarm attestation protocols, a category of swarm attestation protocols that allow a verifier to identify the set of healthy provers in a swarm. We describe the security requirements of swarm attestation protocols. We focus our work on the SIMPLE+ protocol, which we model and verify using the Tamarin prover. Our proofs enable us to identify two variations of SIMPLE+: (1) we remove one of the keys used by SIMPLE+ without compromising security, and (2) we develop a more robust design that increases the resilience of the swarm to device compromise. Using Tamarin, we demonstrate that both modifications preserve the desired security properties.
EMI-LiDAR: Uncovering Vulnerabilities of LiDAR Sensors in Autonomous Driving Setting using Electromagnetic Interference

Autonomous Vehicles (AVs) using LiDAR-based object detection systems are rapidly improving and becoming an increasingly viable method of transportation. While effective at perceiving the surrounding environment, these detection systems are shown to be vulnerable to attacks using lasers which can cause obstacle misclassifications or removal. These laser attacks, however, are challenging to perform, requiring precise aiming and accuracy. Our research exposes a new threat in the form of Intentional Electro-Magnetic-Interference (IEMI), which affects the time-of-flight (TOF) circuits that make up modern LiDARs. We show that these vulnerabilities can be exploited to force the AV Perception system to misdetect, misclassify objects, and perceive non-existent obstacles. We evaluate the vulnerability in three AV perception modules (PointPillars, PointRCNN, and Apollo) and show how the classification rate drops below 50%. We also analyze the impact of the IEMI injection on two fusion models (AVOD and Frustum-ConvNet) and in real-world scenarios. Finally, we discuss potential countermeasures and propose two strategies to detect signal injection.
Never Let Me Down Again: Bidding-Down Attacks and Mitigations in 5G and 4G

Bidding-down attacks reduce the security of a mobile network connection. Weaker encryption algorithms or even downgrades to prior network generations enable an adversary to exploit numerous attack vectors and harm the users of a network. The problem of bidding-down attacks has been known for generations, and various mitigations are integrated into the latest 4G and 5G specifications. However, current research lacks a systematic identification and analysis of the variety of potential attack vectors. In this work, we classify an extensive set of bidding-down attack vectors and mitigations and analyze their specification and implementation in phones and networks. Our results demonstrate vulnerabilities for all attacks and devices, including the latest mobile generation 5G and recent flagship phones. To further prove how the identified attack vectors can be exploited in sophisticated attacks, we conduct two case studies in which we apply a full downgrade attack from 5G SA to 2G and bid down a 5G NSA connection by enforcing null encryption. Again, we find a majority of systems vulnerable. With this paper, we hope to improve the state of bidding-down mitigations in the specification and implementation.
SoK: An Analysis of End-to-End Encryption and Authentication Ceremonies in Secure Messaging Systems

Instant-messaging (IM) and voice over IP (VoIP) applications like WhatsApp, Zoom, and Skype have made people extremely reliant on online communications for their audio, video, and text conversations. Since more people are using these platforms to talk to each other and share sensitive information, many ongoing concerns have been raised about how the government and law enforcement monitor these platforms. Due to these concerns, the need for a method to secure confidential messages and electronic conversations has grown. This solution could be achieved by implementing an end-to-end encryption (E2EE) system without relying on any first or third parties, such as an online service or a centralized infrastructure like a public key infrastructure (PKI), which may be attacked, malicious, or coerced by law enforcement and government surveillance programs. In this systematization of knowledge paper, we first introduce the most popular E2EE apps, including their underlying E2EE messaging protocols. Then, based on the existing research literature, we investigate and systematize their E2EE features, including their underlying authentication ceremonies. Even though many research studies have examined some messaging services, we analyze and evaluate a broader set of the most popular E2EE apps and their underlying authentication ceremonies. Based on our evaluation, we have determined that all current E2EE apps, particularly when operating in opportunistic E2EE mode, are incapable of repelling active man-in-the-middle (MitM) attacks. In addition, we find that none of the current E2EE apps provide better and more usable authentication ceremonies, resulting in insecure E2EE communications against active MitM attacks. The conclusions of this systematization paper could influence future research in the field, including any improvements to the implementation of E2EE systems and authentication ceremonies that provide powerful protections against eavesdropping and MitM attacks.
Strategies for strengthening UX competencies and cultivating corporate UX in a large organisation developing robots

ABSTRACTABSTRACTIntegrating UX into industry practices is a well-researched topic particularly for software companies. However, little is known about how UX integration and adoption ‘works’ within the robotics industry. This study identifies behaviours impeding UX adoption at the individual, team, and organisational level within a company developing robots. We carried out a one-year long Action Research study in a large, international company during design and development of a service robot. Based on a UX maturity assessment, we carried out six interventions, where the researcher and practitioners worked deliberately with UX competence-development and the company's UX culture. Together we identified six barriers: achieving appropriate confidence levels, trust in UX, commitment and support structures, trade-offs in UX, handover practices, and UX management. To address these barriers, we developed and tested 21 strategies to help foster good UX practices and promote a ‘UX-friendly’ culture that empowers non-UX professionals to drive user-involved initiatives themselves.KEYWORDS: UX strategyaction researchUX of robotscorporate UXUX in software developmentrobot development‌ AcknowledgmentsThe authors, in particular the researcher embedded in the company, would like to extend gratitude to the case company and all internal and external stakeholders involved for making this research possible; it has been a true pleasure collaborating with you. We would also like thank our Research Assistant who contributed to the initial coding; thank you for your perseverance.Disclosure statementNo potential conflict of interest was reported by the author(s).
Circumventing the Defense against Modulation Classification Attacks

Modulation classification (MC) has a wide range of applications in spectrum sharing, management, and enforcement and can also be used by an adversary to launch traffic analysis or selective jamming. While recent modulation obfuscation techniques show promising results in mitigating MC attacks, in this paper we develop a novel convolution neural network (CNN)-based model to attack those defenses and successfully identify the true modulation scheme. Our extensive simulation and over-the-air experiments using show that our classification technique achieves around 85-99% accuracy for SNR levels 0 dB and above. Furthermore, our results demonstrate that the proposed model can effectively differentiate between obfuscated and non-obfuscated symbols, even when a transmitter switches between them as a new defense mechanism, achieving an accuracy of 95%.
SoK: A Comprehensive Evaluation of 2FA-based Schemes in the Face of Active Concurrent Attacks from User Terminal

Malware-infected terminals pose a pervasive threat to authentication systems. As password-only authentication cannot adequately protect against malware on terminals, the literature proposes several authentication methods claiming to provide security in the presence of significant security threats, including infected terminals. Most methods incorporate a password-independent factor in the authentication process to mitigate these threats. According to the community view in the literature, 2FA-oriented methods appear to be secure in the presence of malware on the authentication terminal. In this work, we systematize these 2FA-based academic schemes' threat models and authentication procedures to examine how they ensure security at every step of the authentication process. Additionally, we present an active concurrent attack framework named CSI(Concurrent Session Injection) and have done a comprehensive analysis of studied academic authentication systems against it. Furthermore, we systematize secure authentication systems from the literature that claim to provide protection against user terminal malware and concurrent attacks and point out their potential vulnerabilities. Our research emphasizes the significance of taking proper security measures against such threats and creates the opportunity to design more secure authentication systems in future research.