Reviewerly/exHarmony huggingface dataset:
Inverse design of plasma metamaterial devices with realistic elements

In an expansion of a previous study [1], we apply inverse design methods to produce two-dimensional plasma metamaterial devices with realistic plasma elements which incorporate quartz envelopes, collisionality (loss), non-uniform density profiles, and resistance to experimental error/perturbation. Backpropagated finite difference frequency domain simulations are used to design waveguides and demultiplexers operating under the transverse magnetic polarization. Optimal devices with realistic elements are compared to previous devices with idealized elements, and several parameter initialization schemes for the optimization algorithm are explored. Demultiplexing and waveguiding are demonstrated for microwave-regime devices composed of plasma elements with reasonable space-averaged plasma frequencies ~10 GHz and a collision frequency ~1 GHz, allowing for future in-situ training and experimental realization of these designs.
How sociotechnical imaginaries shape consumers’ experiences of and responses to commercial data collection practices

How is the ongoing “datafication” in society experienced by consumers? Critical discussions regarding the impact of datafication on consumers seldom study consumers’ actual experiences. Conversely, the studies that do exist of consumers and their experiences of datafication tend to take an individualistic approach, arguing that how consumers experience and respond to the ongoing datafication is the result of their individual psychological make-up or the result of processes of cost–benefit calculations. Against that background, this article will instead show that the ways in which consumers experience and respond to datafication is linked to a number of broader sociotechnical imaginaries. Based on in-depth user interviews and drawing on previous work on sociotechnical imaginaries, this article develops an analysis of consumers’ multiple imaginaries of data collection practices. Findings show that how consumers approach data collection operations is shaped by sociotechnical imaginaries that were both individually and collectively performed by consumers interacting with and using data-collecting devices.
Artificial neural network and dataset optimization for implementation of linear system models in resource‐constrained embedded systems

Abstract On‐board training of artificial neural network (ANN) is important in instances where real time data are required for model training. Provision of on‐board intelligence enables the developed systems to self‐recalibrate and enhances their efficiencies. In this work, investigations have been performed to determine optimized parameters of ANN model for linear systems. The performance parameters that is, model parameters, memory requirements, accuracy and processing time are chosen by considering the model to be installed on commercially available microcontrollers that have very limited on‐board memory. Minimum data requirements for training ANN models of linear systems are also explored for better performance. All dataset ranges are normalized in order to exclude the effects of range differences. It is shown that for linear systems, 1–3–1 architecture produces best results against ≤100 data points when Bayesian Regularization (BR) training function is used along with Log Sigmoid Activation function. Simulations for 1–3–1 architecture are then performed for datasets having 10, 25, 50 and 100 data points. The results show that training with 25 data points produces over‐all better performance than other datasets. A large dataset utilizes more training time and memory whereas a smaller dataset produces relatively lesser accuracy. The effects of clustered data and uniformly distributed data are also explored. It is found that total epochs in case of clustered data are significantly higher than uniformly distributed data. The combination of these optimized parameters that is, 1–3–1 architecture, with BR and Log function, for ≤100 data points can be used for the development and implementation of linear components or systems in resource‐constrained embedded systems.
Understanding and Adapting Bezel-to-Bezel Interactions for Circular Smartwatches in Mobile and Encumbered Scenarios

Supporting eyes-free interaction, mobility and encumbrance, while providing a broad set of commands on a smartwatch display is a difficult, yet important, task. Bezel-to-bezel (B2B) gestures are valuable for rapid command invocation during eyes-free operation, however we lack knowledge regarding B2B interactions on circular devices during common usage scenarios. We aim to improve our understanding of the dynamics of B2B interactions in these scenarios by conducting two studies and a third analysis: First, we explore the performance of B2B in a seated position; second, we explore the effect of mobility and encumbrance on the B2B interaction; finally, we improve on the B2B accuracies by calculating features and utilizing machine learning. With the limited interaction capabilities on smartwatches and the importance of the scenario of use, we conclude with applications and design guidelines for improved utilization of B2B that enables effective smartwatch control while in common, mobile and eyes-free scenarios.
AR Sightseeing: Comparing Information Placements at Outdoor Historical Heritage Sites using Augmented Reality

Augmented Reality (AR) has influenced the presentation of historical information to tourists and museum visitors by making the information more immersive and engaging. Since smartphones and AR glasses are the primary devices to present AR information to users, it is essential to understand how the information about a historical site can be presented effectively and what type of device is best suited for information placements. In this paper, we investigate the placement of two types of content, historical images and informational text, for smartphones and AR glasses in the context of outdoor historical sites. For this, we explore three types of placements: (1) on-body, (2) world, and (3) overlay. To evaluate all nine combinations of text and image placements for smartphone and AR glasses, we conducted a controlled experiment (N = 18) at outdoor historical landmarks. We discovered that on-body image and text placements were the most convenient compared to overlay and world for both devices. Furthermore, participants found themselves more successful in exploring historical sites using a smartphone than AR glasses. Although interaction with a smartphone was more convenient, participants found exploring AR content using AR glasses more fun.
CNN Multi-Position Wearable Sensor Human Activity Recognition Used in Basketball Training

With the development trend of artificial intelligence technology and the popularization of wearable sensors, human activity recognition based on sensor data information has received widespread attention and has great application value. In order to better optimize the network structure and reduce the total number of main training parameters in the convolutional layer, a convolutional network entity model based on shared resources of main parameters is clearly proposed. We analyzed the CNN multi-position wearable sensor human activity recognition used in basketball training. According to the entity model of the main parameters of shared resources, the effectiveness of the entity model is verified from both the total number of sensors and the accuracy of single-class recognition. In addition to maintaining the actual effect of recognition, the main training parameters are also reduced. The simulation results verify the actual effect of the SVM algorithm and motion simulation of the convolutional network entity model. On this basis, scientific research physical exercise methods are selected to reasonably ensure the smooth progress of appropriate physical exercise at a certain level, improve the quality of training and the actual effect.
An Approach for Curating Collections of Historical Documents with the Use of Topic Detection Technologies

Digital curation of materials available in large online repositories is required to enable the reuse of Cultural Heritage resources in specific activities like education or scientific research. The digitization of such valuable objects is an important task for making them accessible through digital platforms such as Europeana, therefore ensuring the success of transcription campaigns via the Transcribathon platform is highly important for this goal. Based on impact assessment results, people are more engaged in the transcription process if the content is more oriented to specific themes, such as First World War. Currently, efforts to group related documents into thematic collections are in general hand-crafted and due to the large ingestion of new material they are difficult to maintain and update. The current solutions based on text retrieval are not able to support the discovery of related content since the existing collections are multi-lingual and contain heterogeneous items like postcards, letters, journals, photographs etc. Technological advances in natural language understanding and in data management have led to the automation of document categorization and via automatic topic detection. To use existing topic detection technologies on Europeana collections there are several challenges to be addressed: (1) ensure representative and qualitative training data, (2) ensure the quality of the learned topics, and (3) efficient and scalable solutions for searching related content based on the automatically detected topics, and for suggesting the most relevant topics on new items. This paper describes in more details each such challenge and the proposed solutions thus offering a novel perspective on how digital curation practices can be enhanced with the help of machine learning technologies.
Knowledge graph of alpine skiing events: A focus on meteorological conditions

Alpine skiing, as an outdoor winter sport, is particularly vulnerable to the variation of meteorological conditions. Scattered and multi-source big data cannot be fully utilized to conduct effective decision analyses by conventional data analysis methods. Presently, knowledge graphs are the most advanced organization form of knowledge base, which can make explicit the complex relationships among different objects. Thus, introducing knowledge graph to the event management of alpine skiing is significant to improve the ability of risk prediction and decision-making. In this research, we analyze the components and dynamic characteristics of alpine skiing, and construct an "Object-Characteristic-Relation" representation model to express multi-level knowledge. Moreover, we propose a "Characteristic-value- Relationship" representation method based on the multi-source data, to construct the knowledge graph of alpine skiing. With the proposed method, comprehensive relationships between meteorological conditions and alpine skiing can be represented clearly, and support further knowledge reasoning for the event management under meteorological conditions. We have tested the utility of the proposed method in a case study of 2018 Winter Olympics in PyeongChang. The case study realizes an semi-automatic construction of knowledge graph for alpine skiing, provides decision supports for event risk managements, according to different meteorological conditions, and grounds a foundation for future knowledge graph construction of other large-scale sport events.
The Italian epistemic marker mi sa [to me it knows] compared to so [I know], non so [I don’t know], non so se [I don’t know whether], credo [I believe], penso [I think]

The two studies presented in this paper concern the Italian epistemic marker mi sa [lit. to me it knows], which seems to have no equivalent in other European languages and has received very little attention in the literature. No analysis of the occurrences of mi sa in contemporary spoken corpora can be found (first gap) as well as no investigation on the epistemic relationship between mi sa and (1) the other modal expressions that use the verb sapere [to know] in the first person singular of the simple present, i.e., so [I know], non so [I do not know], non so se [I do not know whether] as well as (2) its supposed synonyms credo [I believe] and penso [I think] (second gap). The two studies are closely intertwined, the first being an exploratory, qualitative pilot study for the second. Study 1 aims to fill the first gap through the analysis of the contemporary Italian spoken corpus KIParla. The quantitative and qualitative analyses revealed five types of occurrences (theoretically reducible to two main ones), the most numerous of which are 'mi sa che + proposition'. Study 2 aims to fill the second gap through a questionnaire administered online. The quantitative and statistical results showed the epistemic relationships between the six markers: for the majority of the participants, in the epistemic continuum that goes from unknowledge to uncertainty and then to knowledge, (1) non so refers to unknowledge; non so se, mi sa, credo and penso refer to uncertainty; so refers to knowledge; (2) mi sa, credo, penso confirm to be synonyms; (3) non so se is evaluated as much more uncertain than mi sa, credo, penso. These four epistemic markers seem to occupy a different position along the uncertainty continuum ranging between two poles: doubt (high uncertainty) and belief (low uncertainty).
OMASGAN: Out-of-distribution Minimum Anomaly Score GAN for Anomaly Detection

Generative models trained in an unsupervised manner may set high likelihood and low reconstruction loss to Out-of-Distribution (OoD) samples. This leads to failures to detect anomalies, overall decreasing Anomaly Detection (AD) performance. In addition, AD models underperform due to the rarity of anomalies. To address these limitations, we develop the OoD Minimum Anomaly Score GAN (OMASGAN) which performs retraining by including the proposed minimum-anomaly-score OoD samples. These OoD samples are generated on the boundary of the support of the normal class data distribution in a proposed self-supervised learning manner. Our OMASGAN retraining algorithm leads to more accurate estimation of the underlying data distribution including multimodal supports and also disconnected modes. For inference, for AD, we devise a discriminator which is trained with negative and positive samples either generated (negative or positive) or real (only positive). The evaluation of OMASGAN on image data using the leave-one-out method shows that it achieves an improvement of at least 0.24 and 0.07 points in AUROC on average on the MNIST and CIFAR-10 datasets, respectively, over other benchmark models for AD.
Electronic Brainstorming With a Chatbot Partner: A Good Idea Due to Increased Productivity and Idea Diversity

Brainstorming is a creative technique that fosters collaboration to enhance idea generation. The occurrence of evaluation apprehension, a fear of being evaluated negatively by others, however, can stymy brainstorming. How the advantages of collaboration can be leveraged while evaluation apprehension is prevented is an open scientific and practical problem. In this brief research report, it is proposed that chatbots could provide a solution. Chatbots can be designed to share ideas with their users, facilitating inspiration. Compared to human beings, chatbots are also perceived as possessing limited agency and evaluative capacity. This could reduce evaluation apprehension. Given that chatbots are often embedded in a textual chat interface, social cues (picture, name, and description) can reinforce the perceived chatbot identity, enhancing its alleged effects on evaluation apprehension and subsequently on brainstorming performance. These conjectures were tested in an online 2 × 2 between-subjects experiment (n = 120) where people were instructed to brainstorm with a partner that was framed as either a chatbot or human being (but followed the same automated script), with or without the presence of social cues. The results showed that brainstorming with a chatbot led participants to produce more ideas, with more diversity than brainstorming with an alleged human being. Social cues enhanced the effect on idea diversity, but only with the chatbot. No significant effects on evaluation apprehension were found. The contribution of this study is therefore that chatbots can be used for effective human-machine teaming during brainstorming, but this enhancement is not explained by its effects on evaluation apprehension.
MixCT: Mixing Confidential Transactions from Homomorphic Commitment

Mixing protocols serve as a promising solution to the unlinkability in blockchains. They work by hiding one transaction among a set of transactions and enjoy the advantage of high compatibility with the underlying system. However, due to the inherent public addresses of the blockchains built on the account-based model, the unlinkability is highly restricted to non-confidential transactions. In this paper, we propose MixCT, a mixing service for confidential payment systems built from homomorphic commitment in the account-based model. We provide an efficient instantiation of MixCT by the Pedersen commitment and the one-out-of-many proof. The evaluation results show that MixCT introduces a small cost for its users while being highly compatible with the underlying blockchain.
Efficient Hash-Based Redactable Signature for Smart Grid Applications

The sharing of energy usage data in smart grids is becoming increasingly popular because it not only allows different entities to access fine-grained energy consumption data but also improves the effectiveness of smart grid technologies. How to ensure both verifiability and privacy of the shared data is a vital issue. Most existing privacy-preserving authentication schemes greatly hinder the flexibility of sharing data among multiple parties due to vulnerability and inefficiency reasons. The customer-centric energy usage data management framework based on redactable signature (RS) technology can be seen as an effective solution. It offers customers the flexibility to remove parts of privacy-sensitive data depending on different data usage demands, and ensures data verifiability for third party service providers. However, existing RS schemes are computationally inefficient for constrained devices such as smart meters. Besides, it is said that quantum computers are expected to break all traditional public-key primitives. In this regard, almost all existing RS schemes are vulnerable to quantum attacks. To address the above concerns, in this work, we propose a hash-based RS scheme HRSS based on a variant of the Goldreich-Goldwasser-Micali tree, a length-doubling pseudorandom generator, and an underlying SPHINCS $$^+$$ framework. Our HRSS is the first quantum-safe RS scheme, where the security depends only on the security of underlying hash functions. We instantiate and evaluate the performance of our design. Theoretical and experimental comparisons with recent works show that HRSS is practical for smart grids.
Trigonometric words ranking model for spam message classification

Abstract The significant increase in the volume of fake (spam) messages has led to an urgent need to develop and implement a robust anti‐spam method. Several of the current anti‐spam systems depend mainly on the word order of the message in determining the spam message, which results in the system's inability to predict the correct type of message when the word order changes. In this paper, a new framework is proposed for anti‐spam filtering that does not depend on the word's position in the message, called the Trigonometric Words Ranking Model (TWRM). The proposed TWRM is based on restricting spammers over the network by measuring a theta angle, which is a relationship between message weight and spam. TWRM classifies messages by calculating the rank of each word that places the corresponding message in the correct class. The rank of words is derived from their frequency in the entire data category. The proposed method is applied to three datasets of spam messages: UCI spam email, Enron spam, and TREC spam data. The proposed model is proven as more efficient than the Minhash and vector space models. Moreover, the TWRM performance provided better retrieval time and defence, which is reflected in the accuracy of (99.64%), which is higher than that of Minhash (88.79%) and vector space (92.59%).
Precise Extraction of Deep Learning Models via Side-Channel Attacks on Edge/Endpoint Devices

With growing popularity, deep learning (DL) models are becoming larger-scale, and only the companies with vast training datasets and immense computing power can manage their business serving such large models. Most of those DL models are proprietary to the companies who thus strive to keep their private models safe from the model extraction attack (MEA), whose aim is to steal the model by training surrogate models. Nowadays, companies are inclined to offload the models from central servers to edge/endpoint devices. As revealed in the latest studies, adversaries exploit this opportunity as new attack vectors to launch side-channel attack (SCA) on the device running victim model and obtain various pieces of the model information, such as the model architecture (MA) and image dimension (ID). Our work provides a comprehensive understanding of such a relationship for the first time and would benefit future MEA studies in both offensive and defensive sides in that they may learn which pieces of information exposed by SCA are more important than the others. Our analysis additionally reveals that by grasping the victim model information from SCA, MEA can get highly effective and successful even without any prior knowledge of the model. Finally, to evince the practicality of our analysis results, we empirically apply SCA, and subsequently, carry out MEA under realistic threat assumptions. The results show up to 5.8 times better performance than when the adversary has no model information about the victim model.
FLMJR: Improving Robustness of Federated Learning via Model Stability

Federated Learning (FL) is vulnerable to model poisoning attacks that hurt the joint training global model by sending malicious updates. Existing defenses rely heavily on restrictions on clients’ model updates to defend against attacks. However, the global model can be attacked by elaborate malicious perturbation under defensive restriction due to the sensitivity of the model to perturbations, which leads the model to be vulnerable. Therefore, in this work, we investigate the defense against attacks from a novel perspective of the model stability towards perturbation on parameters. We propose a new method named Federated Learning with Model Jacobian Regularization (FLMJR) to enhance the robustness of FL. Considering prediction volatility of the model is determined by the model-output Jacobian, we reduce the Jacobian regularization to improve model stability towards model perturbations while maintaining the model’s accuracy. We conduct extensive experiments under both IID and NonIID settings to evaluate the defense against state-of-the-art model poisoning attacks, which demonstrates that our method not only has superior fidelity and robustness, but can also be easily integrated to further improve the robustness of existing server-based robust aggregation approaches (e.g., Fedavg, Trimean, Median, Bulyan, and FLTrust).