Reviewerly/exHarmony huggingface dataset:
What motivates users to adopt cybersecurity practices? A survey experiment assessing protection motivation theory

Abstract Research Summary A 2 × 2 × 3 fully crossed factorial experiment is used to examine the linkages between key dimensions of protection motivation theory (PMT; perceived severity of risk, vulnerability to risk, and response costs) and the intentions to adopt information technology (IT) cybersecurity recommendations after being informed of degrees of risk in the vignettes. Data in this study consist of a nationwide sample of 720 American adults. Results from a series of fractional logistic regressions indicate support for many of the core mechanisms within PMT. Seventy percent of respondents indicated they were likely to follow IT recommendations to mitigate a cyber threat. Self‐efficacy and response costs affected intentions to do so. Policy Implications The study's findings have important implications for improving cybersecurity and reducing vulnerabilities to cyber threats. Current training programs need more effective communication strategies and engagement tools. Perceptions of users as security threats rather than potential contributors hinder progress in the ability of organizations to improve cybersecurity. Collaborative, user‐centered approaches can enhance users’ self‐efficacy and improve cybersecurity by aligning user and IT professional needs and capabilities. Strategies like gamified simulations and tailored interventions can create a more security‐focused culture and encourage user participation in defending an organization. Recognizing individual differences among users and further examining personal characteristics that may impact user interactions with technology and cybersecurity interventions is crucial. Overall, more personalized, adaptable approaches to cybersecurity policies and technical solutions, accounting for diverse user needs and characteristics, should be a priority for improving cybersecurity practices.
Model Mixing Using Bayesian Additive Regression Trees

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FDNet: Lightweight Train Image Fault Detection Network in Edge Computing Environments

The current train image fault detection model does not consider detection accuracy and speed, and its parameters and calculation amount are large. Consequently, the model cannot easily satisfy the requirements of the actual edge computing scenario. Therefore, this study proposes a lightweight train image fault detection network (FDNet) under an edge computing environment. FDNet makes three improvements based on YOLOX. First, FDNet designs dual-path enhanced channel attention (DECA) module and fuses the path aggregation network (PAN) to form a DECA-PAN module to suppress background interference, highlight the object, and improve the feature fusion ability of the model. Second, focal loss and alpha-complete intersection over union (Alpha-CIoU) loss are used to optimize the loss function to alleviate the problem of small image samples and uneven fault categories. Third, the receptive field fusion block is added to the FDNet detection head, and three groups of detection heads are devised to detect objects at different scales to improve the detection ability of the model on multiscale objects. Experimental results on the train image fault dataset demonstrate that the mean average precision (mAP) and speed of FDNet reach 94.82% and 103.4 frames per second (FPS), respectively, whereas its parameters and floating-point operations are less than one-sixth those of the well-known YOLOv4. The experimental results of FDNet, YOLOv5-s, YOLOX-s, YOLOv6-T, and YOLOv7-tiny in the public dataset COCO2017 show that FDNet achieves the highest mAP of 60.60% while maintaining a high detection speed among all lightweight models. The noise and illumination robustness tests show that FDNet has good anti-interference ability.
An array of microresonators as a photonic extreme learning machine

Machine learning technologies have found fertile ground in optics due to their promising features based on speed and parallelism. Feed-forward neural networks are one of the most widely used machine learning algorithms due to their simplicity and universal approximation capability. However, the typical training procedure, where all weights are optimized, can be time and energy consuming. An alternative approach is the Extreme Learning Machine, a feed-forward neural network in which only the output weights are trained, while the internal connections are random. Here we present an experimental implementation of a photonic extreme learning machine (PELM) in an integrated silicon chip. The PELM is based on the processing of the image of the scattered light by an array of 18 gratings coupled to microresonators. Light propagation in the microresonator array is a linear process while light detection by the video camera is a nonlinear process. Training is done offline by analyzing the recorded scattered light image with a linear classifier. We provide a proof-of-concept demonstration of the PELM by solving both binary and analog tasks, and show how the performance depends on the number of microresonators used in the readout procedure.
SAMStyler: Enhancing Visual Creativity With Neural Style Transfer and Segment Anything Model (SAM)

Neural Style Transfer (NST) is a popular technique of computer vision where the content of an image is blended with the style of another, which results in a fused image with certain properties of both original images. This approach has practical applications in various domains and has garnered significant attention in both industry and academia. An interesting application of this technique is segmented style transfer where a segmentation algorithm is used to locate objects within an image and then the style transfer method is performed locally, producing images with different styles for different objects. This approach opens up possibilities for creating visually striking compositions by seamlessly blending various artistic styles onto specific objects within an image, allowing for a new level of creative expression. This paper proposes a novel method that combines Segment Anything Model (SAM), a state-of-the-art vision transformer-based image segmentation model developed by Facebook, with style transfer. Our approach includes performing localized style transfer in selected segmentation regions of an image using classical style transfer algorithms. To ensure smooth transitions between the stylized and non-stylized border we also develop our loss function with a border smoothing technique. Experimental results demonstrate the robustness and effectiveness of the proposed methodology, including the ability to infuse multiple artistic styles into different objects within an image. The contributions of this work include integrating SAM with style transfer, proposing a novel loss function, evaluating the segmented style transfer in multiple content regions, comparing with state-of-the-art approaches, and experimenting with multiple style images for diverse stylization. Our primary focus centers on creating a model that serves as a digital painter across a wide range of image genres and artistic styles.
Exploring Visual Signifier Characteristics to Improve the Perception of Affordances of In-Place Touch Inputs

Touch screens supporting different inputs such as 'Tap', 'Dwell', 'Double Tap' and 'Force Press' are omnipresent in modern devices and yet this variety of interaction opportunities is rarely communicated to the user. Without visual signifiers, these potentially useful inputs remain unknown or underutilised. We propose a design space of visual signifier characteristics that may impact the perception of in-place one finger inputs. We generated 36 designs and investigated their perception in an online survey (N=32) and an interactive experiment (N=24). The results suggest that visual signifiers increase the perception of input possibilities beyond 'Tap', and reduce perceived mental effort for participants, who also prefer added visual signifiers over a baseline. Our work informs how future touch-based interfaces could be designed to better communicate in-place single finger input possibilities.
Optimal single-qubit tomography: Realization of locally optimal measurements on a quantum computer

The authors map qubit tomography to a quantum multiparameter estimation problem and find the optimal single-qubit measurements. Using a quantum computing demonstration they show that the optimal separable measurement bound can be saturated.
Modelling the spectral influence on photovoltaic device performance using the average photon energy and the depth of a water absorption band for improved forecasting

Accurate forecasts of solar panel performance can improve grid penetration, enable cost evaluation prior to project implementation, and improve fault detection during operation. Spectral correction functions (SCFs) used to model the influence of the solar spectrum in such forecasting models are typically based on either proxy representations of the spectrum, using parameters such as air mass, or parameters derived directly from the spectrum, such as the average photon energy (APE). Although the latter is more accurate, the APE is argued in some studies not to be a unique characteristic of the spectrum and to suffer from increased uncertainty when analysing spectra at longer wavelengths. This study first derives APE spectral correction function coefficients for three PV technologies — multicrystalline (mSi), triple junction amorphous silicon (aSi-T), and cadmium telluride (CdTe). Based on an analysis of uncertainty in the SCF for each of the three devices, this study proposes a new spectral correction function based on the average photon energy, φ, and the depth of a water absorption band, ɛ. The additional index enables spectra to be characterised by unique combinations of φ and ɛ. Several water absorption bands are tested and the 650–670 nm band is found to yield the most accurate SCF for all three PV devices. An optimal parameterisation of the SCF for each PV device, as well as a cost-accuracy-balanced parameterisation, is presented. Improvements in the prediction accuracy of up to 60% for both the mSi and aSi-T modules, and around 20% for the CdTe module, are achieved by the proposed model with respect to a comparable two-variable proxy SCF, namely the air mass and precipitable water function. Compared with the single-variable APE SCF, f(φ), the proposed model improves the prediction accuracy by around 10% for the aSi-T and mSi modules, and by around 2% for the CdTe module. No new data are required for the proposed model compared with f(φ) as the same spectra used to calculate the APE are used to calculate ɛ. The proposed spectral correction function can easily be integrated into wider photovoltaic performance models for improved forecasting.
Optimising the quantum/classical interface for efficiency and portability with a multi-level hardware abstraction layer for quantum computers

Abstract Steady progress is being made in the development of quantum computing platforms based on different types of qubit technologies. Each platform requires bespoke strategies to maximise the efficiency of the quantum/classical interface when operating close to the qubits. At a higher level, however, a shared interface allowing portability of quantum algorithms across all the available quantum platforms is preferred. Striking the right balance between portability and performance of the algorithm as implemented on quantum hardware remains a major challenge for this field. Here, we propose a quantum hardware abstraction layer (QHAL) providing a multi-level intermediate representation of the quantum stack. A collaborative effort between software specialists and quantum hardware developers operating on four major qubit technologies (photonics, silicon, superconducting and trapped ions) led to the identification of a minimum common set of instructions and metadata allowing the QHAL to interact efficiently with multiple platforms. Access to the stack from the higher levels increases latency yet minimises the amount of hardware architecture parameters to be handled by the algorithm developer, thus simplifying code development and reducing security threats from misuse or malicious access for hardware developers. Access to the stack from the lowest—closest to the qubits—level provides the highest hardware responsiveness, suitable for algorithms requiring minimum latency for data and instruction transfer. With respect to existing quantum assembly languages, the QHAL extends further down in the stack by defining an application-binary interface to interact with the quantum hardware. By defining a standard representation of the quantum stack, a common reference framework is provided to both software and hardware developers which would ensure future integration of their R&amp;D efforts.
Evo-MAML: Meta-Learning with Evolving Gradient

How to rapidly adapt to new tasks and improve model generalization through few-shot learning remains a significant challenge in meta-learning. Model-Agnostic Meta-Learning (MAML) has become a powerful approach, with offers a simple framework with excellent generality. However, the requirement to compute second-order derivatives and retain a lengthy calculation graph poses considerable computational and memory burdens, limiting the practicality of MAML. To address this issue, we propose Evolving MAML (Evo-MAML), an optimization-based meta-learning method that incorporates evolving gradient within the inner loop. Evo-MAML avoids the second-order information, resulting in reduced computational complexity. Experimental results show that Evo-MAML exhibits higher generality and competitive performance when compared to existing first-order approximation approaches, making it suitable for both few-shot learning and meta-reinforcement learning settings.
The great crypto crash in September 2018: why did the cryptocurrency market collapse?

Abstract The cryptocurrency crash on the 5th of September, 2018, resulted in price decreases in 95 of the 100 leading digital currencies. We obtained millisecond data of some of the more prominent cryptocurrencies–bitcoin, ethereum, ripple, bitcoin cash and eos–and some of the smaller cryptocurrencies–neo, nem, omg, tezos and lisk–that were most affected in the crash and investigated what caused the digital market to collapse. We find that the behaviour of the more prominent cryptocurrencies and bitcoin, in particular, was the dominant factor behind the crash. We also find that smaller cryptocurrencies followed the behaviour of the larger ones in the crash. Furthermore, our empirical findings show that the trading behaviour of cryptocurrency traders (CTs) did not trigger the digital market crash. We propose the introduction of a single-cryptocurrency circuit breaker most prominent largest cryptocurrency–bitcoin–that will halt trading during market disruptions.
Range-Restricted and Horn Interpolation through Clausal Tableaux

Abstract We show how variations of range-restriction and also the Horn property can be passed from inputs to outputs of Craig interpolation in first-order logic. The proof system is clausal tableaux, which stems from first-order ATP. Our results are induced by a restriction of the clausal tableau structure, which can be achieved in general by a proof transformation, also if the source proof is by resolution/paramodulation. Primarily addressed applications are query synthesis and reformulation with interpolation. Our methodical approach combines operations on proof structures with the immediate perspective of feasible implementation through incorporating highly optimized first-order provers.
The Social Route to Abstraction: Interaction and Diversity Enhance Performance and Transfer in a Rule‐Based Categorization Task

Capacities for abstract thinking and problem-solving are central to human cognition. Processes of abstraction allow the transfer of experiences and knowledge between contexts helping us make informed decisions in new or changing contexts. While we are often inclined to relate such reasoning capacities to individual minds and brains, they may in fact be contingent on human-specific modes of collaboration, dialogue, and shared attention. In an experimental study, we test the hypothesis that social interaction enhances cognitive processes of rule-induction, which in turn improves problem-solving performance. Through three sessions of increasing complexity, individuals and groups were presented with a problem-solving task requiring them to categorize a set of visual stimuli. To assess the character of participants' problem representations, after each training session, they were presented with a transfer task involving stimuli that differed in appearance, but shared relations among features with the training set. Besides, we compared participants' categorization behaviors to simulated agents relying on exemplar learning. We found that groups performed superior to individuals and agents in the training sessions and were more likely to correctly generalize their observations in the transfer phase, especially in the high complexity session, suggesting that groups more effectively induced underlying categorization rules from the stimuli than individuals and agents. Crucially, variation in performance among groups was predicted by semantic diversity in members' dialogical contributions, suggesting a link between social interaction, cognitive diversity, and abstraction.
Towards Sustainability-aware Recommender Systems: Analyzing the Trade-off Between Algorithms Performance and Carbon Footprint

In this paper, we present a comparative analysis of the trade-off between the performance of state-of-the-art recommendation algorithms and their environmental impact. In particular, we compared 18 popular recommendation algorithms in terms of both performance metrics (i.e., accuracy and diversity of the recommendations) as well as in terms of energy consumption and carbon footprint on three different datasets. In order to obtain a fair comparison, all the algorithms were run based on the implementations available in a popular recommendation library, i.e., RecBole, and used the same experimental settings. The outcomes of the experiments showed that the choice of the optimal recommendation algorithm requires a thorough analysis, since more sophisticated algorithms often led to tiny improvements at the cost of an exponential increase of carbon emissions. Through this paper, we aim to shed light on the problem of carbon footprint and energy consumption of recommender systems, and we make the first step towards the development of sustainability-aware recommendation algorithms.
Large Language Model Augmented Narrative Driven Recommendations

Narrative-driven recommendation (NDR) presents an information access problem where users solicit recommendations with verbose descriptions of their preferences and context, for example, travelers soliciting recommendations for points of interest while describing their likes/dislikes and travel circumstances. These requests are increasingly important with the rise of natural language-based conversational interfaces for search and recommendation systems. However, NDR lacks abundant training data for models, and current platforms commonly do not support these requests. Fortunately, classical user-item interaction datasets contain rich textual data, e.g., reviews, which often describe user preferences and context – this may be used to bootstrap training for NDR models. In this work, we explore using large language models (LLMs) for data augmentation to train NDR models. We use LLMs for authoring synthetic narrative queries from user-item interactions with few-shot prompting and train retrieval models for NDR on synthetic queries and user-item interaction data. Our experiments demonstrate that this is an effective strategy for training small-parameter retrieval models that outperform other retrieval and LLM baselines for narrative-driven recommendation.
Goal-Oriented Multi-Modal Interactive Recommendation with Verbal and Non-Verbal Relevance Feedback

Interactive recommendation enables users to provide verbal and non-verbal relevance feedback (such as natural-language critiques and likes/dislikes) when viewing a ranked list of recommendations (such as images of fashion products), in order to guide the recommender system towards their desired items (i.e. goals) across multiple interaction turns. Such a multi-modal interactive recommendation (MMIR) task has been successfully formulated with deep reinforcement learning (DRL) algorithms by simulating the interactions between an environment (i.e. a user) and an agent (i.e. a recommender system). However, it is typically challenging and unstable to optimise the agent to improve the recommendation quality associated with implicit learning of multi-modal representations in an end-to-end fashion in DRL. This is known as the coupling of policy optimisation and representation learning. To address this coupling issue, we propose a novel goal-oriented multi-modal interactive recommendation model (GOMMIR) that uses both verbal and non-verbal relevance feedback to effectively incorporate the users' preferences over time. Specifically, our GOMMIR model employs a multi-task learning approach to explicitly learn the multi-modal representations using a multi-modal composition network when optimising the recommendation agent. Moreover, we formulate the MMIR task using goal-oriented reinforcement learning and enhance the optimisation objective by leveraging non-verbal relevance feedback for hard negative sampling and providing extra goal-oriented rewards to effectively optimise the recommendation agent. Following previous work, we train and evaluate our GOMMIR model by using user simulators that can generate natural-language feedback about the recommendations as a surrogate for real human users. Experiments conducted on four well-known fashion datasets demonstrate that our proposed GOMMIR model yields significant improvements in comparison to the existing state-of-the-art baseline models.
STRec: Sparse Transformer for Sequential Recommendations

With the rapid evolution of transformer architectures, researchers are exploring their application in sequential recommender systems (SRSs) and presenting promising performance on SRS tasks compared with former SRS models. However, most existing transformer-based SRS frameworks retain the vanilla attention mechanism, which calculates the attention scores between all item-item pairs. With this setting, redundant item interactions can harm the model performance and consume much computation time and memory. In this paper, we identify the sparse attention phenomenon in transformer-based SRS models and propose Sparse Transformer for sequential Recommendation tasks (STRec) to achieve the efficient computation and improved performance. Specifically, we replace self-attention with cross-attention, making the model concentrate on the most relevant item interactions. To determine these necessary interactions, we design a novel sampling strategy to detect relevant items based on temporal information. Extensive experimental results validate the effectiveness of STRec, which achieves the state-of-the-art accuracy while reducing 54% inference time and 70% memory cost. We also provide massive extended experiments to further investigate the property of our framework.