Reviewerly/exHarmony huggingface dataset:
No Forking Way: Detecting Cloning Attacks on Intel SGX Applications

Forking attacks against TEEs like Intel SGX can be carried out either by rolling back the application to a previous state, or by cloning the application and by partitioning its inputs across the cloned instances. Current solutions to forking attacks require Trusted Third Parties (TTP) that are hard to find in real-world deployments. In the absence of a TTP, many TEE applications rely on monotonic counters to mitigate forking attacks based on rollbacks; however, they have no protection mechanism against forking attack based on cloning. In this paper, we analyze 72 SGX applications and show that approximately 20% of those are vulnerable to forking attacks based on cloning—including those that rely on monotonic counters.
The Day-After-Tomorrow: On the Performance of Radio Fingerprinting over Time

The performance of Radio Frequency (RF) Fingerprinting (RFF) techniques is negatively impacted when the training data is not temporally close to the testing data. This can limit the practical implementation of physical-layer authentication solutions. To circumvent this problem, current solutions involve collecting training and testing datasets at close time intervals—this being detrimental to the real-life deployment of any physical-layer authentication solution. We refer to this issue as the Day-After-Tomorrow (DAT) effect, being widely attributed to the temporal variability of the wireless channel, which masks the physical-layer features of the transmitter, thus impairing the fingerprinting process.
Secure Softmax/Sigmoid for Machine-learning Computation

Softmax and sigmoid, composing exponential functions (ex) and division (1/x), are activation functions often required in training. Secure computation on non-linear, unbounded 1/x and ex is already challenging, let alone their composition. Prior works aim to compute softmax by its exact formula via iteration (CrypTen, NeurIPS ’21) or with ASM approximation (Falcon, PoPETS ’21). They fall short in efficiency and/or accuracy. For sigmoid, existing solutions such as ABY2.0 (Usenix Security ’21) compute it via piecewise functions, incurring logarithmic communication rounds.
Prompting a Large Language Model to Generate Diverse Motivational Messages: A Comparison with Human-Written Messages

Large language models (LLMs) are increasingly capable and prevalent, and can be used to produce creative content. The quality of content is influenced by the prompt used, with more specific prompts that incorporate examples generally producing better results. On from this, it could be seen that using instructions written for crowdsourcing tasks (that are specific and include examples to guide workers) could prove effective LLM prompts. To explore this, we used a previous crowdsourcing pipeline that gave examples to people to help them generate a collectively diverse corpus of motivational messages. We then used this same pipeline to generate messages using GPT-4, and compared the collective diversity of messages from: (1) crowd-writers, (2) GPT-4 using the pipeline, and (3 & 4) two baseline GPT-4 prompts. We found that the LLM prompts using the crowdsourcing pipeline caused GPT-4 to produce more diverse messages than the two baseline prompts. We also discuss implications from messages generated by both human writers and LLMs.
Co-Designing with a Social Robot Facilitator: Effects of Robot Mood Expression on Human Group Dynamics

Social robots can be designed to support the facilitation of co-design sessions. Facilitators regulate group dynamics to promote effective collaboration among stakeholders. Group dynamics are sensitive to mood expressions: Positive mood expressions by the facilitator promote cooperation in the group, whereas negative expressions promote conflict. However, whether mood expressions by a social robot facilitator also influence human group dynamics is an open scientific and practical question. To learn more, an experiment (N = 98) was conducted where small groups engaged in a co-design session led by a social robot facilitator. The robot displayed positive, neutral, or negative mood expressions throughout the session. The results showed that positive robot expressions, compared to neutral or negative expressions, increased perceived robot valence. Perceived robot valence increased cooperation and decreased conflict in the human groups. These findings contribute novel insight into how social robots can be used to innovate how co-design is facilitated.
Cost Analysis of Cloud Migration for Small Businesses

In this comprehensive research article, we delve into the intricate world of cloud migration for small businesses. Cloud migration has become a pivotal strategy for businesses of all sizes, offering flexibility, scalability, and cost-efficiency. This article conducts a thorough cost analysis of cloud migration, exploring its benefits, challenges, methodologies, results, and discussions. By the end, you'll have a deep understanding of how small businesses can leverage cloud technology while keeping their budget in check. The transformative power of cloud migration is reshaping the competitive landscape for small businesses. As digitalization becomes increasingly essential, embracing the cloud can mean the difference between thriving and merely surviving in today's fast-paced business environment. This article takes you on a journey through the complex realm of cloud migration, with a focus on cost analysis and its profound implications for small enterprises.
Computer‐Vision Based Gesture‐Metasurface Interaction System for Beam Manipulation and Wireless Communication

Abstract Hand gesture plays an important role in many circumstances, which is one of the most common interactive methods in daily life, especially for disabled people. Human–machine interaction is another popular research topic to realize direct and efficient control, making machines intelligent and maneuverable. Here, a special human–machine interaction system is proposed and namedas computer‐vision (CV) based gesture‐metasurface interaction (GMI) system, which can be used for both direct beam manipulations and real‐time wireless communications. The GMI system first needs to select its working mode according to the gesture command to determine whether to perform beam manipulations or wireless communications, and then validate the permission for further operation by recognizing unlocking gesture to ensure security. Both beam manipulation and wireless communication functions are validated experimentally, which show that the GMI system can not only realize real‐time switching and remote control of different beams through gesture command, but also communicate with a remote computer in real time by translating the gesture language to text message. The proposed non‐contact GMI system has the advantages of good interactivity, high flexibility, and multiple functions, which can find potential applications in community security, gesture‐command smart home, barrier‐free communications, and so on.
Validity Arguments for Automated Essay Scoring of Young Students’ Writing Traits

ABSTRACTMachines have a long-demonstrated ability to find statistical relationships between qualities of texts and surface-level linguistic indicators of writing. More recently, unlocked by artificial intelligence, the potential of using machines to identify content-related writing trait criteria has been uncovered. This development is significant, especially in formative assessment contexts where feedback is key. Yet the extent to which writing traits can be validly scored by machines remains under-researched, especially in the K-12 context. The present study investigated the validity of machine learning (ML) models designed for students in grades 3–6 to score three writing traits: task fulfillment, organization and coherence, and vocabulary and expression. The study utilized an argument-based approach, focusing on two primary inferences: evaluation and explanation. The evaluation inference investigated human-machine score alignment, the ability for the models to detect off-topic and gibberish responses, and the consistency of human-machine score alignment across grades and language backgrounds. The explanation inference investigated the relevance of features used in the models. Results indicated that human-machine score alignment was sufficient for all writing traits; however, validity concerns were raised regarding the models’ performances detecting off-topic and gibberish responses and the consistency across sub-groups. Implications for language assessment professionals and other educators were discussed. Disclosure statementNo potential conflict of interest was reported by the author(s).EthicsThis research was approved by the social sciences, humanities, and education research ethics board of the University of Toronto, reference number 34,203.Additional informationFundingThe work was supported by the Social Sciences and Humanities Research Council of Canada [486987].
Theoretical Quantum Modeling of Improvisation in Networked Music Performances to Regulate the Behaviour of Artificial Musicians

During collective musical performances over the network that are characterized by improvisation, a performer may face the problem of what connected musician to follow most in order to direct his/her own improvisation. This choice may be taken on the basis of different factors related to the state of the network and of the kind of the received musical signal. In this paper we investigate the possibility to adopt the mathematical formalism of Quantum Mechanics to describe some interaction phenomena occurring during improvised networked music performances. We propose a decision-making system, having a quantum circuit in its core, where the approximated decision by performers is modeled with state superposition and probability amplitudes used in quantum computing. The model considers the levels of signal clarity (i.e., audio quality related to packet losses), latency, and musical novelty (e.g., melodic or harmonic variation with respect to a previous musical sequence) as the factors that affect the decision of a performer to select another connected performer to follow. This model may be exploited to regulate the behaviour of an artificial intelligent agent playing the role of a virtual musician in the networked ensemble (via generative music techniques). This allows to create mixed human-artificial ensembles and even fully artificial ensembles in networked contexts.
The History of Quantum Games

In this paper, we explore the historical development of playable quantum physics related games (quantum games). For the purpose of this examination, we have collected over 260 quantum games ranging from commercial games, applied and serious games, and games that have been developed at quantum themed game jams and educational courses. We provide an overview of the journey of quantum games across three dimensions: the perceivable dimension of quantum physics, the dimension of scientific purposes, and the dimension of quantum technologies. We then further reflect on the definition of quantum games and its implications. While motivations behind developing quantum games have typically been educational or academic, themes related to quantum physics have begun to be more broadly utilised across a range of commercial games. In addition, as the availability of quantum computer hardware has grown, entirely new variants of quantum games have emerged to take advantage of these machines’ inherent capabilities, quantum computer games.
SimpleMapping: Real-Time Visual-Inertial Dense Mapping with Deep Multi-View Stereo

We present a real-time visual-inertial dense mapping method capable of performing incremental 3D mesh reconstruction with high quality using only sequential monocular images and inertial measurement unit (IMU) readings. 6-DoF camera poses are estimated by a robust feature-based visual-inertial odometry (VIO), which also generates noisy sparse 3D map points as a by-product. We propose a sparse point aided multi-view stereo neural network (SPA-MVSNet) that can effectively leverage the informative but noisy sparse points from the VIO system. The sparse depth from VIO is firstly completed by a single-view depth completion network. This dense depth map, although naturally limited in accuracy, is then used as a prior to guide our MVS network in the cost volume generation and regularization for accurate dense depth prediction. Predicted depth maps of keyframe images by the MVS network are incrementally fused into a global map using TSDF-Fusion. We extensively evaluate both the proposed SPA-MVSNet and the entire dense mapping system on several public datasets as well as our own dataset, demonstrating the system’s impressive generalization capabilities and its ability to deliver high-quality 3D reconstruction online. Our proposed dense mapping system achieves a 39.7% improvement in F-score over existing systems when evaluated on the challenging scenarios of the EuRoC dataset.
The Work Avatar Face-Off: Knowledge Worker Preferences for Realism in Meetings

While avatars have grown in popularity in social settings, their use in the workplace is still debatable. We conducted a large-scale survey to evaluate knowledge worker sentiment towards avatars, particularly the effects of realism on their acceptability for work meetings. Our survey of 2509 knowledge workers from multiple countries rated five avatar styles for use by managers, known colleagues and unknown colleagues. In all scenarios, participants favored higher realism, but fully realistic avatars were sometimes perceived as uncanny. Less realistic avatars were rated worse when interacting with an unknown colleague or manager, as compared to a known colleague. Avatar acceptability varied by country, with participants from the United States and South Korea rating avatars more favorably. We supplemented our quantitative findings with a thematic analysis of open-ended responses to provide a comprehensive understanding of factors influencing work avatar choices. In conclusion, our results show that realism had a significant positive correlation with acceptability. Non-realistic avatars were seen as fun and playful, but only suitable for occasional use.
Opportunities and Challenges of Hybrid User Interfaces for Optimization of Mixed Reality Interfaces

Current research highlights the importance of adaptive mixed reality interfaces, as increased adoption leads to increasingly diverse, complex and unconstrained interaction scenarios. An interesting approach for adaptation, is the optimization of interface layout and behaviour. We thereby consider three distinct types of context to which the interface adapts: the user, the activity, and the environment. The latter of these includes a myriad of interactive devices surrounding the user, the capabilities of which we propose to take advantage of by integrating them in a hybrid user interface. Hybrid user interfaces offer many opportunities to address distinct usability issues, such as visibility, reachability, and ergonomics. However, considering additional interactive devices for optimizing mixed reality interfaces introduces a number of additional challenges, such as detecting available and suitable devices and modeling the respective interaction costs. Moreover, using different devices potentially introduces a switching cost e.g., in terms of cognitive load and time. In this paper, we aim to discuss different opportunities and challenges of using hybrid user interfaces for the optimization of mixed reality interfaces and thereby highlight directions for future work.
Remote Monitoring and Teleoperation of Autonomous Vehicles—Is Virtual Reality an Option?

While the promise of autonomous vehicles has led to significant scientific and industrial progress, fully automated, SAE level 5 conform cars will likely not see mass adoption anytime soon. Instead, in many applications, human supervision, such as remote monitoring and teleoperation, will be required for the foreseeable future. While Virtual Reality (VR) has been proposed as one potential interface for teleoperation, its benefits and drawbacks over physical monitoring and teleoperation solutions have not been thoroughly investigated. To this end, we contribute three user studies, comparing and quantifying the performance of and subjective feedback for a VR-based system with an existing monitoring and teleoperation system, which is in industrial use today. Through these three user studies, we contribute to a better understanding of future virtual monitoring and teleoperation solutions for autonomous vehicles. The results of our first user study (n= 16) indicate that a VR interface replicating the physical interface does not outperform the physical interface. It also quantifies the negative effects that combined monitoring and teleoperating tasks have on users irrespective of the interface being used. The results of the second user study (n= 24) indicate that the perceptual and ergonomic issues caused by VR outweigh its benefits, like better concentration through isolation. The third follow-up user study (n= 24) specifically targeted the perceptual and ergonomic issues of VR; the subjective feedback of this study indicates that newer-generation VR headsets have the potential to catch up with the current physical displays.
A novel Explainable Artificial Intelligence and secure Artificial Intelligence asset sharing platform for the manufacturing industry

Over the past couple of years, implementations of Artificial Intelligence (AI) have significantly risen in numerous platforms, tools and applications around the world, impacting a broad range of industries such as manufacturing towards Smart Factories and Industry 4.0, in general. Nevertheless, despite industrial AI being the driving force for smart factories, there is strong reluctance in its adoption by manufacturers due to the lack of transparency of the black-box AI models and trust behind the decisions taken, as well as the awareness of where and how it should be incorporated in their processes and products. This paper introduces the Explainable AI platform of XMANAI which takes advantage of the latest AI advancements and technological breakthroughs in Explainable AI (XAI) in order to build "glass box" AI models that are explainable to a "human-in-the-loop" without the decrease of AI performance. The core of the platform consists of a catalogue of hybrid and graph AI models which are built, fine-tuned and validated either as baseline AI models that will be reusable to address any manufacturing problem or trained AI models that have been fine-tuned for solving concrete manufacturing problems in a trustful manner through value-based explanations that are easily and effectively interpreted by humans.
Hypericons for interpretability: decoding abstract concepts in visual data

Abstract In an era of information abundance and visual saturation, the need for resources to organise and access the vast expanse of visual data is paramount. Abstract concepts-such as comfort , power , or freedom -emerge as potent instruments to index and manage visual data, particularly in contexts like Cultural Heritage (CH). However, the variance and disparity in the visual signals that evoke a single abstract concept challenge conventional approaches to automatic visual management rooted in the Computer Vision (CV) field. This paper critically engages with the prevalent trend of automating high-level visual reasoning while placing exclusive reliance on visual signals, prominently featuring Convolutional Neural Networks (CNNs). We delve into this trend, scrutinising the knowledge sought by CNNs and the knowledge they ultimately encapsulate. In this endeavour, we accomplish three main objectives: (1) introduction of ARTstract, an extensive dataset encompassing cultural images that evoke specific abstract concepts; (2) presentation of baseline model performances on ARTstract to elucidate the intricate nuances of image classification based on abstract concepts; and, critically, (3) utilization of ARTstract as a case study to explore both traditional and non-traditional avenues of visual interpretability, a trajectory inspired by Offert and Bell (2021). To more comprehensively understand how CNNs assimilate and reflect cultural meanings, and to discern the echoes reverberating within these visions, we unveil SD-AM, a novel approach to explainability that collapses visuals into hypericon images through a fusion of feature visualization techniques and Stable Diffusion denoising. Overall, this study critically addresses abstract concept image classification’s challenges within the CNN paradigm. By embracing innovative methodologies and providing comprehensive analyses of explainability techniques, we make a substantial contribution to the broader discourse surrounding automatic high-level visual understanding, its interpretability, and the ensuing implications for comprehending culture within the digital era. Through our exploration, we illuminate the multifaceted trends, complexities, and opportunities that underlie the fusion of high-level visual reasoning and computer vision.
RealTHASC—a cyber-physical XR testbed for AI-supported real-time human autonomous systems collaborations

Today’s research on human-robot teaming requires the ability to test artificial intelligence (AI) algorithms for perception and decision-making in complex real-world environments. Field experiments, also referred to as experiments “in the wild,” do not provide the level of detailed ground truth necessary for thorough performance comparisons and validation. Experiments on pre-recorded real-world data sets are also significantly limited in their usefulness because they do not allow researchers to test the effectiveness of active robot perception and control or decision strategies in the loop. Additionally, research on large human-robot teams requires tests and experiments that are too costly even for the industry and may result in considerable time losses when experiments go awry. The novel Real-Time Human Autonomous Systems Collaborations (RealTHASC) facility at Cornell University interfaces real and virtual robots and humans with photorealistic simulated environments by implementing new concepts for the seamless integration of wearable sensors, motion capture, physics-based simulations, robot hardware and virtual reality (VR). The result is an extended reality (XR) testbed by which real robots and humans in the laboratory are able to experience virtual worlds, inclusive of virtual agents, through real-time visual feedback and interaction. VR body tracking by DeepMotion is employed in conjunction with the OptiTrack motion capture system to transfer every human subject and robot in the real physical laboratory space into a synthetic virtual environment, thereby constructing corresponding human/robot avatars that not only mimic the behaviors of the real agents but also experience the virtual world through virtual sensors and transmit the sensor data back to the real human/robot agent, all in real time. New cross-domain synthetic environments are created in RealTHASC using Unreal Engine™, bridging the simulation-to-reality gap and allowing for the inclusion of underwater/ground/aerial autonomous vehicles, each equipped with a multi-modal sensor suite. The experimental capabilities offered by RealTHASC are demonstrated through three case studies showcasing mixed real/virtual human/robot interactions in diverse domains, leveraging and complementing the benefits of experimentation in simulation and in the real world.