Reviewerly/exHarmony huggingface dataset:
Comparative relation mining of customer reviews based on a hybrid CSR method

Online reviews contain comparative opinions that reveal the competitive relationships of related products, help identify the competitiveness of products in the marketplace, and influence consumers’ purchasing choices. The Class Sequence Rule (CSR) method, which is previously commonly used to identify the comparative relations of reviews, suffers from low recognition efficiency and inaccurate generation of rules. In this paper, we improve on the CSR method by proposing a hybrid CSR method, which utilises dependency relations and the part-of-speech to identify frequent sequence patterns in customer reviews, which can reduce manual intervention and reinforce sequence rules in the relation mining process. Such a method outperforms CSR and other CSR-based models with an F-value of 84.67%. In different experiments, we find that the method is characterised by less time-consuming and efficient in generating sequence patterns, as the dependency direction helps to reduce the sequence length. In addition, this method also performs well in implicit relation mining for extracting comparative information that lacks obvious rules. In this study, the optimal CSR method is applied to automatically capture the deeper features of comparative relations, thus improving the process of recognising explicit and implicit comparative relations.
The Future of Generative AI: How GenAI Would Change Human-Computer Co-creation in the Next 10 to 15 Years

The past few years have witnessed a remarkable advancement in the field of Generative Artificial Intelligence (GenAI), a technology capable of generating new content based on input prompts and existing knowledge. This technology has the potential to revolutionize the way of human-computer co-creation. However, existing research on GenAI has primarily focused on technical aspects, and more research is needed from a design perspective, mainly through speculative and critical design. Therefore, this study aims to explore how GenAI would transform human-computer co-creation in the next 10 to 15 years by means of design fiction and playful critical design. The study will involve (co-)speculative workshops utilizing design fiction, followed by focus groups to gather insights. In addition, this research will examine the user experience issues of interacting with functional GenAI prototypes through playful critical design.
LEVI: Exploring Possibilities for an Adaptive Board Game System

Visual impairment can create accessibility barriers for players in board games because information about the gameplay is often communicated through visuals. We present LEVI, a standalone adaptive board game system (ABGs) using RFID technology to adapt the gameplay experience for board games for players with or without visual impairment. As many hybrid board game systems often require costly resources (digital elements) which may not be accessible to the user, we considered low-cost alternatives to our solution. Given the various gameplay styles in board games, we mainly focus on audio adaptation for this game system. We present an evaluation of our current proof-of-concept game system and future possibilities of using our adaptive board game system.
Player-Centric Procedural Content Generation: Enhancing Runtime Customization by Integrating Real-Time Player Feedback

Game content creation poses significant challenges, particularly for indie developers and small teams, as it presents difficulties in scaling to meet diverse player preferences. Adaptable procedural content generation (PCG) provides a promising solution to this issue. Extensive literature exists on adaptable PCG techniques, encompassing both offline (during development) and online (during gameplay) approaches. Building upon this foundation, we propose a novel extension called Player-Centric Procedural Content Generation (PCPCG) as an additional tool for creating unique game experiences based on player preferences. In contrast to runtime adaptable PCG methods, which adapt or learn based solely on in-game data, PCPCG actively involves players in the learning loop by soliciting their feedback. Additionally, it shifts the focus from designers (as in mixed initiative (MI) and co-creative PCG) to the players as providers of learning data, thus operating during gameplay rather than during development. PCPCG possesses three key qualities: 1) real-time operation during gameplay, 2) active participation of players (not designers) in the learning loop, and 3) online learning from player feedback to create engaging and personalized content. It is important to differentiate PCPCG from content creation aids and in-game data adaptable PCGs. While PCPCG falls under the umbrella of adaptable PCG, it goes beyond relying solely on in-game data by incorporating valuable player feedback as a vital information source for content generation. PCPCG introduces a novel and promising approach to runtime procedural content generation by leveraging player feedback to create adaptive and personalized game content. While our proof of concept demonstrates the viability of PCPCG in a Pac-Man domain, further research is required to explore its limitations as the complexity of the possibility space increases.
Gaia data processing. SEAPipe: The source environment analysis pipeline

Aims. Our goal is to describe two potential options for the Source Environment Analysis pipeline (SEAPipe) for the Gaia mission. This pipeline will enable the discovery of sources that are new to Gaia , in the sense that they were not found by the on-board detection algorithm. These additional sources (secondaries) are discoverable in the vicinity of those Gaia sources (primaries) that were found by the on-board detection. Methods. The main algorithmic steps required are described: the two dimensional image reconstruction of one dimensional transit data; the analysis of these images to find the additional sources present, and the determination of the mean positions; proper motions, parallaxes, and brightness of these sources. Additionally, the Monte Carlo simulations used to characterise the performance of the pipelines are described. Results. The performance of the two options for SEAPipe, the vanilla and image-subtraction versions, are compared. Their selection functions were computed in terms of the magnitude of the secondary sources and their angular separations from their corresponding primary source. The completeness and purity of the resultant catalogue of secondary sources as found by each of the pipelines, given the expected magnitude distribution of the primary sources and the magnitude and angular separation distributions of the secondary sources, is also presented. The image-subtraction pipeline is shown to outperform the vanilla pipeline.
Relational topologies in the learning activity spaces: operationalising a sociomaterial approach

Abstract Technology-mediated interactions and datafication are increasingly central in contemporary social dynamics and institutions, including teaching and learning processes. In order to fully understand the complex entanglements of human and non-human actants that emerge in postdigital education, it is essential to imagine new methodological approaches that are sensitive to the multidimensional nature of education—as a socially and materially-situated phenomenon that increasingly takes place across distributed contexts. The overall goal of this paper is to propose and operationalise a new methodological approach for the study of technology in education. It draws on the notion of relational topologies to improve our understanding of educational settings and, ultimately, how learning unfolds. The proposed approach relies on a multi-paradigm enquiry strategy, based on the idea of using “topologies of digital data practices” in combination with the three dimensions that articulate design-for-learning processes according to the Activity-Centred Analysis and Design (ACAD) framework: epistemic, social and set designs. While the article focuses on presenting the elements of the approach from a theoretical perspective, we illustrate its application through the data collected in a small case study that will serve as a testbed. The topologies of relations we present in this article show uses of technology—as described by participants in their own learning experience—that involve different spaces, devices, and personal situations. In doing so, we reveal how humans and non-humans are entangled in hybrid, unstable and generative ways. The article concludes with some remarks on the value of the proposed approach for studying technology in education and its potential to explore the state-of-the-actual in this field, with the ultimate goal of helping inform educational research, practice and decision-making.
Maliciously Secure MPC From Semi-Honest 2 PC in the Server-Aided Model

Secure multi-party computation (MPC) provides provable security guarantees for many privacy critical applications. The semi-honest MPC protocols are secure against semi-honest adversaries who can only observe the protocol execution, while the maliciously secure MPC protocols are secure against malicious adversaries who can deviate from the protocol description arbitrarily. Many security sensitive applications tend to use semi-honest MPC protocols because malicious security comes with huge communication and/or computation costs. In this work, we show how to efficiently transform generic semi-honest two-party protocols into maliciously secure multi-party protocol in the server-aided setting. We further propose an optimized constant-round server-aided MPC protocol. The proposed protocols are secure when all but one parties are maliciously corrupted, while the remaining party and the server are corrupted by semi-honest and non-colluding adversaries. We implement and evaluate our constant-round protocol. For the 2-party case, our protocol is only 1.11× slower than the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">semi-honest</i> Yao's Garbled Circuits protocol, and it is 9.16× faster than the maliciously secure authenticated garbling protocol and 4.96× faster than the state-of-the-art maliciously secure server-aided protocol of Wu <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> For the 8-party case, our protocol is 103.29× faster than the authenticated garbling protocol and 17.03× faster than the protocol of Wu <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i>
Improving model fairness in image-based computer-aided diagnosis

Abstract Deep learning has become a popular tool for computer-aided diagnosis using medical images, sometimes matching or exceeding the performance of clinicians. However, these models can also reflect and amplify human bias, potentially resulting inaccurate missed diagnoses. Despite this concern, the problem of improving model fairness in medical image classification by deep learning has yet to be fully studied. To address this issue, we propose an algorithm that leverages the marginal pairwise equal opportunity to reduce bias in medical image classification. Our evaluations across four tasks using four independent large-scale cohorts demonstrate that our proposed algorithm not only improves fairness in individual and intersectional subgroups but also maintains overall performance. Specifically, the relative change in pairwise fairness difference between our proposed model and the baseline model was reduced by over 35%, while the relative change in AUC value was typically within 1%. By reducing the bias generated by deep learning models, our proposed approach can potentially alleviate concerns about the fairness and reliability of image-based computer-aided diagnosis.
Context-Aware Adaptive Anomaly Detection in IoT Systems

The deployment of Internet-of-Things (IoT) devices in cyber-physical applications has introduced a new set of vulnerabilities. These security and reliability challenges require a holistic solution due to the cross-domain, cross-layer, and interdisciplinary nature of IoT systems. However, most works presented in the literature primarily focus on the cyber aspect, including the network and application layers, and the physical layer is overlooked. In this chapter, we utilize IoT sensors that capture the physical properties of the system to ensure the integrity of IoT sensor data and identify anomalous incidents in the environment. We propose an adaptive context-aware anomaly detection method optimized for fog computing. In this approach (Yasaei et al., IoT-CAD: context-aware adaptive anomaly detection in IoT systems through sensor association. In: 2020 IEEE/ACM International Conference on Computer Aided Design (ICCAD), pp. 1–9. IEEE, Piscataway (2020)) (Copyright Ⓒ2020 IEEE), we devise a novel sensor association algorithm that generates fingerprints of sensors, clusters them, and extracts the context of the system. Based on the contextual information, our predictor model, which comprises a long short-term memory (LSTM) neural network and Gaussian estimator, detects anomalies, and a consensus algorithm identifies the anomaly source. Furthermore, our model updates itself to adapt to the variation in the environment and system. The results demonstrate that our model detects the anomaly with 92.0% precision in 532ms, which meets the real-time constraint of the system under test.
A Survey of Embedded Machine Learning for Smart and Sustainable Healthcare Applications

Recent advances in machine learning algorithms (ML) and low-power edge devices enable novel wearable applications. Embedded machine learning organically combines these concepts, leading to new application areas. Specifically, machine learning algorithms offer reliable decision-making, classification, and regression performance, while embedded devices allow these algorithms to run at the edge with limited computational power. New embedded devices usually integrate powerful dedicated processors with multiple sensors, such as inertial measurement units. For example, an Nvidia Jetson Nano developer kit is equipped with a quad-core ARM Cortex-A57 processor, 128 CUDA cores with 4 GB memory, and thermal sensors. Hence, embedded machine learning allows performing machine learning directly on devices used in the field, thus leading to numerous novel applications. Promising target applications include health-related applications such as health monitoring, human activity recognition, human pose estimation, and service applications such as energy management in mobile devices.
On the Vulnerability of Deep Reinforcement Learning to Backdoor Attacks in Autonomous Vehicles

Recent work has shown that the introduction of autonomous vehicles can help solve critical transportation issues, such as reducing traffic jams. Deep learning has shown advanced capabilities in complex tasks and has been applied to autonomous vehicles, e.g., deep neural networks for detection and classification of pedestrians and vehicles, deep reinforcement learning for steering and acceleration control, etc. However, security of autonomous vehicles is critical, especially in the context of human-related tasks. Backdoor attacks in neural networks are an emerging attack vector, aiming to deliberately compromise a model by inserting a backdoor and produce the malicious attacker-chosen outputs when certain triggers are encountered. In this chapter, we first introduce related work on deep learning in autonomous vehicles and discuss respective applications. Afterward, we present the backdoor attack literature, focusing on autonomous vehicle controllers employing deep reinforcement learning models. Finally, we introduce backdoor defenses and analyze their effectiveness.
Machine Learning for Efficient Perception in Automotive Cyber-Physical Systems

In emerging semiautonomous vehicles, accurate environmental perception with advanced driver assistance systems (ADAS) is critical to achieving safety and performance goals. Enabling robust perception for vehicles with ADAS requires solving multiple complex problems related to the selection and placement of sensors, object detection, and sensor fusion. Current methods address these problems in isolation, which leads to inefficient solutions. We present PASTA, a novel framework for global co-optimization of deep learning and sensing for ADAS-based vehicle perception. Experimental results with the Audi-TT and BMW-Minicooper vehicles show how PASTA can intelligently traverse the perception design space to find robust, vehicle-specific solutions.
Machine Learning Components for Autonomous Navigation Systems

Autonomous cyber-physical systems, such as autonomous vehicles, drones etc., are artificial systems that interact with the physical world without any human supervision. Such systems have several intermediate tasks such as sensor data fusion, perception, planning, etc., that are critical for safe operation in the real world. Traditional CPS systems used model-based algorithms for such tasks that required domain knowledge, but in recent years machine learning algorithms have been proposed that are more accurate as they automatically extract pertinent information from large volumes of data. In this chapter, we explore the recent machine learning algorithms that have been proposed to perform the various tasks within an autonomous system.
Digitally-Induced Altered States of Consciousness and Playful HCI: Future Research Agenda of a Novel Perspective

Due to the increasing availability and efficiency of digital technologies humanity has reached a point where digitally altering consciousness might become ubiquitous, echoing in all areas of the functioning of society. In addition to the already familiar functions in terms of conveying information, enabling experiences, and extending our realities, there is an emerging field of digitally-induced altered states of consciousness (DIAL). Precursors of the societal impact of DIAL technologies include various examples from binaural beats to video games that provide invaluable insights into forthcoming DIAL technologies.
A Systematic Evaluation of Backdoor Attacks in Various Domains

Deep learning found its place in various real-world applications, where many also have security requirements. Unfortunately, as these systems become more pervasive, understanding how they fail becomes more challenging. While there are multiple failure modes in machine learning, one category received significant attention in the last few years—backdoor attacks. Backdoor attacks aim to make a model misclassify some of its inputs to a preset-specific label while other classification results would behave normally. While many works investigate various backdoor attacks and defenses for different domains, no works aim to provide a systematic comparison of backdoor attacks for different scenarios. This work considers backdoor attacks in image, sound, text, and graph domains and provides a comparative analysis of their respective strengths.
Adversarial ML for DNNs, CapsNets, and SNNs at the Edge

Recent studies have shown that Machine Learning (ML) algorithm suffers from several vulnerability threats. Among them, adversarial attacks represent one of the most critical issues. This chapter provides an overview of the ML vulnerability challenges, with a focus on the security threats for Deep Neural Networks, Capsule Networks, and Spiking Neural Networks. Moreover, it discusses the current trends and outlooks on the methodologies for enhancing the ML models’ robustness.
Mitigating Backdoor Attacks on Deep Neural Networks

This chapter considers backdoor attacks on deep neural networks and discusses two defense approaches against such attacks. One approach aims to remove backdoors. Specifically, an attacker imitator function is found by solving an optimization problem. The attacker imitator function converts clean samples into samples that are functionally similar to poisoned samples. Then, the backdoors are removed by making the neural network not sensitive to the samples generated by the attacker imitator function. The other method aims to identify poisoned inputs and reject the corresponding outputs from the backdoored network. In this method, two off-line novelty detection models are first trained to collect samples that are potentially poisoned. Then, a binary classifier is trained with the collected samples and clean validation samples. The binary classifier detects on-line poisoned samples with high accuracy. A wide range of illustrative examples with various types of triggers is considered, such as invisible triggers, triggers with real-world meaning, and dynamic triggers. The chapter ends with a discussion of potential benign applications of the backdoor phenomena and a discussion of potential future directions for study on backdoor attacks and defenses.
On the Challenge of Hardware Errors, Adversarial Attacks and Privacy Leakage for Embedded Machine Learning

Machine Learning deployment in Embedded Systems and Edge devices offer interesting advantages compared with the Cloud-based approaches, especially from a power consumption and environmental impact perspective. However, two principal problems need to be addressed towards trustworthy Embedded ML; first, Robustness to errors: several sources of faults can jeopardize ML systems integrity; be it hardware failures, as well as malicious fault injection. Second, Security and Privacy: this includes adversarial attacks and information leakage. In this chapter, we investigate these issues with an exploratory study on inherent fault tolerance of deep neural networks, as well as an overview on Embedded Systems-friendly defenses against adversarial attacks. Moreover, we provide an overview on privacy issues and discuss open problems we think the community needs to investigate.
Development and validation of the simplified Chinese Author Recognition Test: Evidence from eye movements of Chinese adults in Mainland China

Background It is well evident that individuals' levels of print exposure are significantly correlated with their reading ability across languages, and an author recognition test is commonly used to measure print exposure objectively. For the first time, the current work developed and validated a Simplified Chinese Author Recognition Test (SCART) and examined its role in explaining Chinese online reading. Methods In Study 1, we constructed the SCART for readers of simplified Chinese and validated the test using data collected from 203 young adults in Mainland China. Participants were measured on the SCART and three self‐report tasks about their reading experiences and habits. Study 2 recruited additional 68 young adults in Mainland and measured their print exposure (with the same tasks used in Study 1), reading‐related cognitive ability (working memory, rapid automatic naming, Chinese character reading, and vocabulary knowledge), and Chinese online reading via an eye‐tracking passage reading task. Results Results of Study 1 support the high reliability and validity of the SCART. Results of Study 2 indicate that SCART scores significantly predicted participants' online reading processing while controlling for subjective reading experiences and habits, and reading‐related cognitive abilities. Across two studies, we found converging evidence that the in‐depth recognition of the authors (i.e., participants have read the books written by these authors) appears to be a better indicator of print exposure than the superficial recognition of the author names. Conclusions Taken together, this work filled in the gap in the literature by providing an evidence‐based, objective print exposure measure for simplified Chinese and contributes to a broader understanding of print exposure and online reading processing across different writing systems.