Reviewerly/exHarmony huggingface dataset:
An Impact-Centered, Sustainable, Positive Experience Design Model

The pursuit of sustainable well-being is one of the research objectives of positive experience design. Therefore, this paper aims to provide an impact-centered, sustainable, positive experience design model. First, the literature review method was used to define the research status and concept of impact-centered, sustainable design. Second, an impact-centered, sustainable, positive experience design model was constructed, and relevant formulas for concept generation and evaluation were proposed. Third, design verification was conducted through a workshop. Finally, the technology acceptance model questionnaire was used to evaluate and discuss the design model. An impact-centered sustainable positive design model was proposed, which included the important impact dimensions of sensory experience and meaningful experience on users’ qualities of life at different levels: healthy living (pleasure index and health behavior), harmonious community (social connectivity and social contribution), and livable environment (living environment and environmental contribution). Based on positive experience-related theory, this study takes long-term impacts as the starting point for a sustainable, positive experience design, which helps designers generate design concepts from a systematic and long-term perspective.
Using Graph Neural Networks for Social Recommendations

Recommender systems have revolutionized the way users discover and engage with content. Moving beyond the collaborative filtering approach, most modern recommender systems leverage additional sources of information, such as context and social network data. Such data can be modeled using graphs, and the recent advances in Graph Neural Networks have led to the prominence of a new family of graph-based recommender system algorithms. In this work, we propose the RelationalNet algorithm, which not only models user–item, and user–user relationships but also item–item relationships with graphs and uses them as input to the recommendation process. The rationale for utilizing item–item interactions is to enrich the item embeddings by leveraging the similarities between items. By using Graph Neural Networks (GNNs), RelationalNet incorporates social influence and similar item influence into the recommendation process and captures more accurate user interests, especially when traditional methods fall short due to data sparsity. Such models improve the accuracy and effectiveness of recommendation systems by leveraging social connections and item interactions. Results demonstrate that RelationalNet outperforms current state-of-the-art social recommendation algorithms.
Dog-whistling and democracy

Despite our fascination with dog-whistling, neither dog-whistling itself, nor the relationship between dog-whistling and democracy are well understood. This article separates the content from the technique of dog-whistling and develops a more precise conceptualisation of that phenomenon in order to untangle the ambiguity about it. Dog-whistling, it argues, should not be reduced to racism or verbal communication tactics but can be combined with various different worldviews, and can encompass a multiplicity of verbal and non-verbal communicative means which surreptitiously nudge or wink at a specific subgroup. Contra the prevalent conviction that dog-whistling is antithetical to democracy in toto, the article suggests that some manifestations of that phenomenon constitute a ‘lesser vice’ vis-à-vis a politics of zealous candour, and an ineluctable feature of democratic politics. This recognition has important implications for democratic theory, and recent lamentations that ours is an age of moral crisis, marked by the rise of post-truth politics.
A Quantitative Study on the Factors Influencing Implementation of Cybersecurity Laws and Regulations in Pakistan

The phenomenon of law implementation has received limited attention, despite the clear evidence that it is influenced by various factors prevalent in the country, and these factors can have an impact on and obstruct the effective implementation of legislation. The primary objective of this study was to analyze the critical factors that impact the implementation of cybersecurity laws in developing nations, such as Pakistan. The prevalence of corruption, a major hindrance to the implementation of cybersecurity laws and regulations, emerged as the most influential factor in Pakistan. Additionally, factors such as discrimination, illicit conduct, expertise, ambiguity, and public confidence significantly influenced the implementation of cybersecurity laws in Pakistan. A survey was conducted among managers from banking and IT firms to collect data samples on the factors that could potentially impact the implementation of the law. The findings from a sample of 172 respondents revealed that corruption, discrimination, illicit conduct, and ambiguity appeared to have a significant negative influence on cybersecurity law implementation, whereas expertise and public confidence emerged to have a significant positive influence on the implementation of cybersecurity laws in Pakistan. This study suggests that the government of Pakistan should consider various measures such as providing training, improving capacity building, fostering institutional cooperation, strengthening legislative conviction, and promoting global collaborations to enhance the implementation of cybersecurity.
Anomaly detection in sleep: detecting mouth breathing in children

Abstract Identifying mouth breathing during sleep in a reliable, non-invasive way is challenging and currently not included in sleep studies. However, it has a high clinical relevance in pediatrics, as it can negatively impact the physical and mental health of children. Since mouth breathing is an anomalous condition in the general population with only 2% prevalence in our data set, we are facing an anomaly detection problem. This type of human medical data is commonly approached with deep learning methods. However, applying multiple supervised and unsupervised machine learning methods to this anomaly detection problem showed that classic machine learning methods should also be taken into account. This paper compared deep learning and classic machine learning methods on respiratory data during sleep using a leave-one-out cross validation. This way we observed the uncertainty of the models and their performance across participants with varying signal quality and prevalence of mouth breathing. The main contribution is identifying the model with the highest clinical relevance to facilitate the diagnosis of chronic mouth breathing, which may allow more affected children to receive appropriate treatment.
Context-Aware Personalization: A Systems Engineering Framework

This study proposes a framework for a systems engineering-based approach to context-aware personalization, which is applied to e-commerce through the understanding and modeling of user behavior from their interactions with sales channels and media. The framework is practical and built on systems engineering principles. It combines three conceptual components to produce signals that provide content relevant to the users based on their behavior, thus enhancing their experience. These components are the ‘recognition and knowledge’ of the users and their behavior (persona); the awareness of users’ current contexts; and the comprehension of their situation and projection of their future status (intent prediction). The persona generator is implemented by leveraging an unsupervised machine learning algorithm to assign users into cohorts and learn cohort behavior while preserving their privacy in an ethical framework. The component of the users’ current context is fulfilled as a microservice that adopts novel e-commerce data interpretations. The best result of 97.3% accuracy for the intent prediction component was obtained by tokenizing categorical features with a pre-trained BERT (bidirectional encoder representations from transformers) model and passing these, as the contextual embedding input, to an LSTM (long short-term memory) neural network. Paired cohort-directed prescriptive action is generated from learned behavior as a recommended alternative to users’ shopping steps. The practical implementation of this e-commerce personalization framework is demonstrated in this study through the empirical evaluation of experimental results.
Investigating Participation Mechanisms in EU Code Week

Digital competence (DC) is a broad set of skills, attitudes, and knowledge for confident, critical and responsible use of digital technologies in every aspect of life. DC proves essential in the contemporary digital landscape, yet its diffusion is hindered by biases, misunderstandings, and limited awareness. Teaching Informatics in the educational curriculum is increasingly supported by the institutions but faces serious challenges, such as teacher upskilling and support. In response, grassroots movements promoting computing literacy in an informal setting have grown, including EU Code Week, whose vision is to develop computing skills while promoting diversity and raising awareness of the importance of digital skills. This study extensively analyses EU Code Week editions spanning 2014 to 2021 across European Union member states, pursuing three primary objectives: firstly, to evaluate the teacher engagement in the campaign in terms of penetration, retention, and spatial distribution; secondly, to characterise the multifaceted audience and themes embraced by these initiatives; and lastly, to investigate the influence of socio-economic factors on engagement. The investigation uncovers the underlying mechanisms fostering Code Week’s engagement, providing insights to campaign organisers for strategic planning and resource allocation in future editions. Moreover, the analysis reveals that the most engaged areas are characterised by lower income, as well as lower digital literacy, restricted access to technology, and a less established computer education, suggesting that Code Week thrives precisely where its impact is most needed.
Geomedia sensibility in media technologies

With the rapidly emerging development of technology-mediated tourism experiences and place-based digital services for visitors, there is a lack of awareness regarding the spatial implications of technological implementations. Based on a participatory action research method applied in this study on five research-based destination development projects in Sweden, we propose a spatially sensible approach when developing enhanced experiences for visitors through digital technology. This paper is motivated by the emerging criticism of how media technology is shaping and reshaping spaces. Drawing on the theoretical underpinnings of the growing field of geomedia studies, we propose “geomedia sensibility” as a way to offer a sensible approach in the co-creation of digital experiences at destinations.
A KRR-UKF robust state estimation method for distribution networks

State estimation is an integral component of energy management systems. Employing a state estimation methodology that is both accurate and resilient is essential for facilitating informed decision-making processes. However, the complex scenarios (unknown noise, low data redundancy, and reconfiguration) of the distribution network pose new challenges for state estimation. In the context of this study, we introduce a state estimation technique known as the kernel ridge regression and unscented Kalman filter. In normal conditions, the non-linear correlation among data and unknown noise increases the difficulty of modeling the distribution network. Thence, kernel ridge regression is developed to map the data into high-dimensional space that transforms the non-linear problem into linear formulations base on the data rather the complicate grid model, which improves model generalization performance and filters out unknown noises. In addition, with the unique prediction correction mechanism of the Kalman method, the kernel ridge regression-mapped state value can be revised by the measurement, which further enhances model accuracy and robustness. During abnormal operating conditions and taking into account the presence of faulty data within the measurement system, we initiate the use of a long short-term memory network and combined convolutional neural network (CNN) model, referred to as the ATT-CNN-GRU. This model is utilized for the prediction of pseudo-measurements. Subsequently, we use an outlier detection method known as ordering points to identify the clustering structure to effectively identify and substitute erroneous data points. Cases on the IEEE-33 bus system and 109-bus system from a city in China show that the method has superior accuracy and robustness.
Tilted magnetic anisotropy-tailored spin torque nano-oscillators for neuromorphic computing

Spin torque nano-oscillators (STNOs) hold significant promise for communication and bio-inspired computing applications. However, their modulation capability is constrained by a dilemma between frequency window and linewidth reduction, particularly in hypercritical conditions like the presence of an external magnetic field. This poses a notable challenge in the practical application of STNOs. Here, we report a unique type of all-electrical compact STNOs that employ the tilted magnetic anisotropy (TMA), which can efficiently promote the linewidth Δf reduction and precisely modulate oscillation frequency ranging from 495 to 556 MHz. The developed STNOs consist of a ferromagnetic reference layer with tunable TMA, wherein the spin transfer torque along the tilted spin polarization direction elaborates a self-oscillation of magnetic moments in the free layer without application of magnetic field. The free layer equips in a magnetic droplet oscillation mode, and the oscillation frequency can be modulated either synergistically or independently by varying the current intensity and/or the TMA angle. Nevertheless, the TMA angle primarily governs the deformation of the magnetic droplet and the corresponding oscillation frequency and linewidth. Moreover, a unique 4 × 4 STNO array with optimized input current and TMA configuration is proposed to execute the reservoir computing hardware training based on nonlinear dynamic oscillation phase-coupling characteristics, promising a diverse synchronization map with high kernel quality and low generation rank for highly reliable pattern classification implementation. The developed STNOs possess a simple structure, nonlinearity, high frequency tunability, and compatibility with CMOS processes, enabling them a fundamental component for large-scale integration of advanced hardware in neuromorphic computing.
(Un)likelihood Training for Interpretable Embedding

Cross-modal representation learning has become a new normal for bridging the semantic gap between text and visual data. Learning modality agnostic representations in a continuous latent space, however, is often treated as a black-box data-driven training process. It is well known that the effectiveness of representation learning depends heavily on the quality and scale of training data. For video representation learning, having a complete set of labels that annotate the full spectrum of video content for training is highly difficult, if not impossible. These issues, black-box training and dataset bias, make representation learning practically challenging to be deployed for video understanding due to unexplainable and unpredictable results. In this article, we propose two novel training objectives, likelihood and unlikelihood functions, to unroll the semantics behind embeddings while addressing the label sparsity problem in training. The likelihood training aims to interpret semantics of embeddings beyond training labels, while the unlikelihood training leverages prior knowledge for regularization to ensure semantically coherent interpretation. With both training objectives, a new encoder-decoder network, which learns interpretable cross-modal representation, is proposed for ad-hoc video search. Extensive experiments on TRECVid and MSR-VTT datasets show that the proposed network outperforms several state-of-the-art retrieval models with a statistically significant performance margin.
URLink: Using Names As Sole Internet Addresses to Tackle Scanning Attacks in IoT

The Internet adopts a layered architecture where IP addresses are used to identify endpoints and port numbers serves as application multiplexers over a single host. Nowadays, names are usually used to expose a service to public access. However, even with the current DNS architecture, nodes must still know what the running host's IP address and service's port number are to access the service. In fact, any node can directly contact a publicly available node, sometimes for other purposes than accessing its public services. This is specially a challenge in IoT as highlighted by numerous high-profile DDoS attacks which leverage Internet scanning to find vulnerable IoT nodes. Defending against this is often a challenge for service operators. This paper questions this current architecture and calls for an alternative called URLink, where names are used as the sole identifier and access door towards a service. Through a new network abstraction called URLSocket, clients are no longer aware of the public service's IP address and port number. We argue that such an approach is beneficial for IoT networks, as it can be used to address various security and privacy issues in these network. While such an architecture calls for changes in the client application stacks, existing applications (e.g. those running on an IoT node) can still leverage the proposed system in the current Internet.
Retrieval-Augmented GPT-3.5-Based Text-to-SQL Framework with Sample-Aware Prompting and Dynamic Revision Chain

Text-to-SQL aims at generating SQL queries for the given natural language questions and thus helping users to query databases. Prompt learning with large language models (LLMs) has emerged as a recent approach, which designs prompts to lead LLMs to understand the input question and generate the corresponding SQL. However, it faces challenges with strict SQL syntax requirements. Existing work prompts the LLMs with a list of demonstration examples (i.e. question-SQL pairs) to generate SQL, but the fixed prompts can hardly handle the scenario where the semantic gap between the retrieved demonstration and the input question is large. In this paper, we propose a retrieval-augmented prompting method for an LLM-based Text-to-SQL framework, involving sample-aware prompting and a dynamic revision chain. Our approach incorporates sample-aware demonstrations, which include the composition of SQL operators and fine-grained information related to the given question. To retrieve questions sharing similar intents with input questions, we propose two strategies for assisting retrieval. Firstly, we leverage LLMs to simplify the original questions, unifying the syntax and thereby clarifying the users' intentions. To generate executable and accurate SQLs without human intervention, we design a dynamic revision chain that iteratively adapts fine-grained feedback from the previously generated SQL. Experimental results on three Text-to-SQL benchmarks demonstrate the superiority of our method over strong baseline models.
Introducing Semantic-Based Receptive Field into Semantic Segmentation via Graph Neural Networks

Current semantic segmentation models typically use deep learning models as encoders. However, these models have a fixed receptive field, which can cause mixed information within the receptive field and lead to confounding effects during neural network training. To address these limitations, we propose the "semantic-based receptive field" based on our analysis in current models. This approach seeks to improve the segmentation performance by aggregate image patches with similar representation rather than their physical location, aiming to enhance the interpretability and accuracy of semantic segmentation models. For implementation, we utilize Graph representation learning (GRL) approaches into current semantic segmentation models. Specifically, we divide the input image into patches and construct them into graph-structured data that expresses semantic similarity. Our Graph Convolution Receptor block uses graph-structured data purpose-built from image data and adopt a node-classification-like perspective to address the problem of semantic segmentation. Our GCR module models the relationship between semantic relative patches, allowing us to mitigate the adverse effects of confounding information and improve the quality of feature representation. By adopting this approach, we aim to enhance the accuracy and robustness of the semantic segmentation task. Finally, we evaluated our proposed module on multiple semantic segmentation models and compared its performance to baseline models on multiple semantic segmentation datasets. Our empirical evaluations demonstrate the effectiveness and robustness of our proposed module, as it consistently outperformed baseline models on these datasets.
Learning to Match Features with Geometry-Aware Pooling

Finding reliable and robust correspondences across images is a fundamental and crucial step for many computer vision tasks, such as 3D-reconstruction and virtual reality. However, previous studies still struggle in challenging cases, including large view changes, repetitive pattern and textureless regions, due to the neglect of geometric constraint in the process of feature encoding. Accordingly, we propose a novel GPMatcher, which is designed to introduce geometric constraints and guidance in the feature encoding process. To achieve this goal, we compute camera poses with the corresponding features in each attention layer and adopt a geometry-aware pooling to reduce the redundant information in the next layer. By these means, an iterative geometry-aware pooing and pose estimation pipeline is constructed, which avoids the updating of redundant features and reduces the impact of noise. Experiments conducted on a range of evaluation benchmarks demonstrate that our method improves the matching accuracy and achieves the state-of-the-art performance.
IIHT: Medical Report Generation with Image-to-Indicator Hierarchical Transformer

Automated medical report generation has become increasingly important in medical analysis. It can produce computer-aided diagnosis descriptions and thus significantly alleviate the doctors' work. Inspired by the huge success of neural machine translation and image captioning, various deep learning methods have been proposed for medical report generation. However, due to the inherent properties of medical data, including data imbalance and the length and correlation between report sequences, the generated reports by existing methods may exhibit linguistic fluency but lack adequate clinical accuracy. In this work, we propose an image-to-indicator hierarchical transformer (IIHT) framework for medical report generation. It consists of three modules, i.e., a classifier module, an indicator expansion module and a generator module. The classifier module first extracts image features from the input medical images and produces disease-related indicators with their corresponding states. The disease-related indicators are subsequently utilised as input for the indicator expansion module, incorporating the "data-text-data" strategy. The transformer-based generator then leverages these extracted features along with image features as auxiliary information to generate final reports. Furthermore, the proposed IIHT method is feasible for radiologists to modify disease indicators in real-world scenarios and integrate the operations into the indicator expansion module for fluent and accurate medical report generation. Extensive experiments and comparisons with state-of-the-art methods under various evaluation metrics demonstrate the great performance of the proposed method.