Reviewerly/exHarmony huggingface dataset:
Understanding the Micro, Meso, and Macro Worlds of User Experience

Overview: The article presents a framework for holistically understanding a product’s user experience (UX). Informed by the author’s experience as a UX researcher and manager at Google, it is intended to demystify the competencies that UX researchers can bring to organizations to help propel successful innovation. The framework outlines how users simultaneously inhabit three nested scale dimensions of experience with products (Worlds of UX) and demonstrates that engendering user-centered thinking within organizations requires integrating knowledge from each. The Micro World of UX comprises moment-by-moment experiences: direct sensory and motor phenomena, tasks, tools, and decision-making moments users encounter when using a product to accomplish lower-order goals. The Meso World of UX consists of user journeys: sequences of lower-order goals that form pathways toward higher-order goals. The Macro World of UX is the realm of user–product relationships: how users experience successive user journeys and how user segments orient toward products longitudinally.
Academician Alexander Konstantinovich Krylov as a teacher and educator

The subject of this article is the activities of Academician Alexander Konstantinovich Krylov - Academician of the Russian Academy of Arts, Honored Artist of the Russian Federation, painter, laureate of the International Prize named after N.K. Roerich, as a teacher and educator. This is a study of the contribution of Academician Alexander Konstantinovich Krylov to the development of education and science, his teaching methods, pedagogical and educational principles. The author examines in detail the scientific, teaching, social, and creative activities of A.K. Krylova and pays special attention to the educational activities of the academician. The author also presents a complete list of institutions that, according to the State Catalog of the Museum Fund of the Russian Federation and data provided by the artist himself, store his works. The research methodology is presented by the historical method, biographical method, and comparative method. Also, during the preparation of the article, a study was conducted on the basis of archival documents, publications, and pedagogical works of A.K. Krylov and his students. The main conclusions of the study are the creative portrait of the artist presented in this message, which has independent cultural and historical significance and expresses the deeply personal attitude of the author, who hopes that the information and characteristics he collected will be useful to future biographers of the outstanding Russian artist, academician of painting A. K. Krylov. The novelty of the research lies in the generalization of the creative materials of Academician A.K. Krylov, which can be in demand by aspiring artists, art historians, cultural experts, historians and everyone who cherishes the tragic and happy milestones of the steady spiritual growth of Russia - in the past, present and future.
Intelligent 3D garment system of the human body based on deep spiking neural network

Intelligent garments, a burgeoning class of wearable devices, have extensive applications in domains such as sports training and medical rehabilitation. Nonetheless, existing research in the smart wearables domain predominantly emphasizes sensor functionality and quantity, often skipping crucial aspects related to user experience and interaction. To address this gap, this study introduces a novel real-time 3D interactive system based on intelligent garments. The system utilizes lightweight sensor modules to collect human motion data and introduces a dual-stream fusion network based on pulsed neural units to classify and recognize human movements, thereby achieving real-time interaction between users and sensors. Additionally, the system in- corporates 3D human visualization functionality, which visualizes sensor data and recognizes human actions as 3D models in realtime, providing accurate and comprehensive visual feedback to help users better understand and analyze the details and features of human motion. This system has significant potential for applications in motion detection, medical monitoring, virtual reality, and other fields. The accurate classification of human actions con- tributes to the development of personalized training plans and injury prevention strategies. This study has substantial implications in the domains of intelligent garments, human motion monitoring, and digital twin visualization. The advancement of this system is expected to propel the progress of wearable technology and foster a deeper comprehension of human motion.
Importance-aware 3D volume visualization for medical content-based image retrieval-a preliminary study

A medical content-based image retrieval (CBIR) system is designed to retrieve images from large imaging repositories that are visually similar to a user′s query image. CBIR is widely used in evidence- based diagnosis, teaching, and research. Although the retrieval accuracy has largely improved, there has been limited development toward visualizing important image features that indicate the similarity of retrieved images. Despite the prevalence of3D volumetric data in medical imaging such as computed tomography (CT), current CBIR systems still rely on 2D cross-sectional views for the visualization of retrieved images. Such 2D visualization requires users to browse through the image stacks to confirm the similarity of the retrieved images and often involves mental reconstruction of 3D information, including the size, shape, and spatial relations of multiple structures. This process is time-consuming and reliant on users’ experience. In this study, we proposed an importance-aware 3D volume visualization method. The rendering parameters were automatically optimized to maximize the visibility of important structures that were detected and prioritized in the retrieval process. We then integrated the proposed visualization into a CBIR system, thereby complementing the 2D cross-sectional views for relevance feedback and further analyses. Our preliminary results demonstrate that 3D visualization can provide additional information using multimodal positron emission tomography and computed tomography (PET- CT) images of a non-small cell lung cancer dataset.
An improved quantum payment protocol based on group signature without entanglement

With the popularization of E-payment, more and more electronic payment protocols have been proposed. In 2021, Lin et al. proposed a trusted third-party E-Payment protocol based on a set of three-particle local indistinguishable orthogonal product (X-LIOP) states. In this paper, we first point out that there is a security flaw where the merchant can forge the purchase goods information to sell something that the customer does not want to buy in Lin et al. protocol. Besides, the X-LIOP states used by Lin are not orthogonal and can be discriminated by Local Operation and Classical Communication (LOCC). Second, we propose a new electronic payment protocol that combines group signature and the three-particle X-LIOP states. Compared with Lin et al. protocol, customers can purchase goods anonymously in our protocol and the unforgeability of messages and signatures is guaranteed.
Semi-supervised Federated Learning for Digital Twin 6G-enabled IIoT: A Bayesian Estimated Approach

In recent years, the proliferation of Industrial Internet of Things (IIoT) devices has resulted in a substantial increase in data generation across various domains, including the nascent 6G networks. Digital Twins (DTs), serving as virtual replicas of physical entities, have gained popularity within the realm of IoT due to their capacity to simulate and optimize physical systems in a cost-effective manner. Nonetheless, the security of DTs and the safeguarding of the sensitive data they generate have emerged as paramount concerns. Fortunately, the Federated Fearning (FL) system has emerged as a promising solution to address the challenge of data privacy within DTs. Nonetheless, the requisite acquisition of a significant volume of labeled data for training purposes poses a formidable challenge, particularly in a DT environment that blends real and virtual data.To tackle this challenge, this study presents an innovative Semi-supervised FL (SSFL) framework designed to overcome the scarcity of labeled data through the strategic utilization of pseudo-labels.Specifically, our proposed SSFL algorithm, named SSFL-MBE, introduces a novel approach by combining Mix data augmentation and Bayesian Estimation consistency regularization loss, thereby integrating robust augmentation techniques to enhance model generalization. Furthermore, we introduce a Bayesian-estimated pseudo-label loss that leverages prior probabilistic knowledge to enhance model performance. Our investigation focuses particularly on a demanding scenario where labeled and unlabeled data are segregated across disparate locations, specifically, the server and various clients.Comprehensive evaluations conducted on CIFAR-10 and MNIST datasets conclusively demonstrate that our proposed algorithm consistently surpasses mainstream SSFL baseline models, exhibiting an enhancement in model performance ranging from 0.5% to 1.5%.Overall, this work contributes to the development of more efficient and secure approaches for model training in DT-empowered FL settings, which is crucial for the deployment of IIoTs in 6G-enabled environments.
Expanding the cloud-to-edge continuum to the IoT in serverless federated learning

Serverless computing enables greater flexibility and efficiency in the cloud-to-edge continuum. Artificial Intelligence and Machine Learning (AI/ML) applications benefit greatly from this paradigm, as they need to gather, preprocess, aggregate and analyze data at various scales. In such contexts, the increasing hardware/software resource availability of Internet of Things (IoT) devices provides the opportunity to exploit them not only as data sources in AI/ML infrastructures, but also as computational nodes for model training and inference; nevertheless, comprehensive frameworks are still mostly missing. This work introduces an innovative serverless computing architecture which expands the cloud-to-edge continuum toward IoT devices. The same functions can run on IoT, edge and cloud nodes with minimal to no code modification and they can be invoked through a uniform interface. A federated learning framework is defined based on the proposed architecture, exploiting an existing IoT-oriented ML algorithm in a novel way. Notably, IoT nodes are used for both federated training and local inference tasks. A full prototype implementation has been built with off-the-shelf technologies and devices. A case study on federated machine learning for activity recognition and experiments have been conducted to validate key elements of the proposal.
Multi-behavior recommendation with SVD graph neural networks

Graph Neural Networks (GNNs) have been extensively employed in the field of recommendation systems, offering users personalized recommendations and yielding remarkable outcomes. Recently, GNNs incorporating contrastive learning have demonstrated promising performance in handling the sparse data problem of recommendation systems. However, existing contrastive learning methods still have limitations in resisting noise interference, especially for multi-behavior recommendation. To mitigate the aforementioned issues, this paper proposes a GNN-based multi-behavior recommendation model called MB-SVD that utilizes Singular Value Decomposition (SVD) graphs to enhance model performance. In particular, MB-SVD considers user preferences across different behaviors, improving recommendation effectiveness. First, MB-SVD integrates the representation of users and items under different behaviors with learnable weight scores, which efficiently considers the influence of different behaviors. Then, MB-SVD generates augmented graph representation with global collaborative relations. Next, we simplify the contrastive learning framework by directly contrasting original representation with the enhanced representation using the InfoNCE loss. Through extensive experimentation, the remarkable performance of our proposed MB-SVD approach in multi-behavior recommendation endeavors across diverse real-world datasets is exhibited.
Two-step ensemble under-sampling algorithm for massive imbalanced data classification

Imbalanced data classification is a challenging problem in the field of machine learning. Class imbalance, class overlap, and large data volume significantly affect classification performance. Focusing on the impact of class overlap on classification effectiveness, we propose a two-step ensemble under-sampling algorithm based on boundary information mining (TSSE-BIM) with the goal of reducing the information loss from under-sampling methods on large-scale imbalanced data. In the first stage, the proposed method applies an improved equalization under-sampling strategy to mine sample contribution information and quickly obtains the distribution information of data relative to the decision boundary. In the second stage, based on the boundary information, a weighted boundary sampling is performed to remove noisy and highly overlapping samples. It is easy to retain samples with high contribution and effectively suppress the information loss caused by under-sampling. Then, the overall framework is designed based on a serial ensemble similar to boosting, where the weights of each base classifier are assigned to achieve a more powerful performance based on the false positive rate and false negative rate on the original data. Finally, extensive experiments indicate that TSSE-BIM outperforms state-of-the-art methods and ranks first on average under four metrics, especially F1 and MMC.
Non-iterative reversible information hiding in the secret sharing domain

Long running time due to multiple iterations is the main drawback of existing information hiding in the sharing domain (IHSD) algorithm. To address this problem, we propose non-iterative reversible information hiding in the secret sharing domain (NIIHSD). We calculate the coefficients of the polynomial that make the least significant bit of the shadow pixel equal to ‘0’ or ‘1’ and store them in two forms (A0 and A1). According to the information to be embedded, coefficients are randomly selected in A0 and A1 to generate qualified shares by once calculation. The comparison with IHSD demonstrates our scheme improves the running speed of the algorithm by a factor of 2 to 4 for different (k,n) thresholds and secret image with different sizes. The coefficient forms only need to be created once to be reused. Our scheme has a greater advantage when sharing a large number of images.
High-speed encrypted traffic classification by using payload features

Traffic encryption techniques facilitate cyberattackers to hide their presence and activities. Traffic classification is an important method to prevent network threats. However, due to the tremendous traffic volume and limitations of computing, most existing traffic classification techniques are inapplicable to the high-speed network environment. In this paper, we propose a High-speed Encrypted Traffic Classification (HETC) method containing two stages. First, to efficiently detect whether traffic is encrypted, HETC focuses on randomly sampled short flows and extracts aggregation entropies with chi-square test features to measure the different patterns of the byte composition and distribution between encrypted and unencrypted flows. Second, HETC introduces binary features upon the previous features and performs fine-grained traffic classification by combining these payload features with a Random Forest model. The experimental results show that HETC can achieve a 94% F-measure in detecting encrypted flows and a 85%–93% F-measure in classifying fine-grained flows for a 1-KB flow-length dataset, outperforming the state-of-the-art comparison methods. Meanwhile, HETC does not need to wait for the end of the flow and can extract mass computing features. The average time for HETC to process each flow is only 2 or 16 ms, which is lower than the flow duration in most cases, making it a good candidate for high-speed traffic classification.
Deepfake face discrimination based on self-attention mechanism

With the rapid progress of deepfake technology, the improper use of manipulated images and videos presenting synthetic faces has arisen as a noteworthy concern, thereby posing threats to both daily life and national security. While numerous CNN based deepfake face detection methods were proposed, most of the existing approaches encounter challenges in effectively capturing the image contents across different scales and positions. In this paper, we present a novel two-branch structural network, referred to as the Self-Attention Deepfake Face Discrimination Network (SADFFD). Specifically, a branch incorporating cascaded multi self-attention mechanism (SAM) modules, is parallelly integrated with EfficientNet-B4 (EffB4). The multi SAM branch supplies additional features that concentrate on image regions essential for discriminating between real and fake. The EffB4 network is adopted because of its efficiency by adjusting the resolution, depth, and width of the network. According to our comprehensive experiments conducted on FaceForensics++, Celeb-DF, and our self-constructed SAMGAN3 datasets, the proposed SADFFD demonstrated the highest detection accuracy, averaging 99.01% in FaceForensics++, 98.65% in Celeb-DF, and an impressive 99.99% in SAMGAN3, surpassing other state-of-the-art (SOTA) methods.
Continual learning for adaptive social network identification

The popularity of social networks as primary mediums for sharing visual content has made it crucial for forensic experts to identify the original platform of multimedia content. Various methods address this challenge, but the constant emergence of new platforms and updates to existing ones often render forensic tools ineffective shortly after release. This necessitates the regular updating of methods and models, which can be particularly cumbersome for techniques based on neural networks which cannot quickly adapt to new classes without sacrificing performance on previously learned ones – a phenomenon known as catastrophic forgetting. Recently, researchers aimed at mitigating this problem via a family of techniques known as continual learning. In this paper we study the applicability of continual learning techniques to the social network identification task by evaluating two relevant forensic scenarios: Incremental Social Platform Classification, for handling newly introduced social media platforms, and Incremental Social Version Classification, for addressing updated versions of a set of existing social networks. We perform an extensive experimental evaluation of a variety of continual learning approaches applied to these two scenarios. Experimental results demonstrate that, although Continual Social Network Identification remains a difficult problem, catastrophic forgetting can be significantly mitigated in both scenarios by retaining only a fraction of the image patches from past task training samples or by employing previous tasks prototypes.
Space Limited Linear-Time Graph Algorithms on Big Data

We study algorithms for graph problems in which the graphs are of extremely large size N so that super-linear time ω(N) or linear space Θ(N) would become impractical. We use a parameter k to characterize the computational power of a normal computer that can provide additional time and space bounded by polynomials of k in dealing with the large graphs. In particular, we are interested in strict linear-time algorithms using space O(kO(1)). In our case studies, as examples, we present (1) a randomized greedy algorithm of time O(N) and space O(k2) for a parameterized version of the Maximal Matching problem; and (2) randomized kernelization algorithms of time O(N) and space O(kO(1)) for a number of well-known NP-hard problems. Our kernelization algorithms have their kernel sizes match the best kernel sizes by known polynomial-time kernelization algorithms with no space constraints for the problems. We also study the relationship between our proposed model and the streaming model. This study motivates a new streaming kernelization algorithm for the famous Vertex Cover problem that has an optimal update time complexity while matches the best known space complexity of streaming algorithms for the problem.
Cultivating connectedness: Effects of an app-based compassion meditation course on changes in global identity, nature connectedness, and pro-environmental behaviour

Individual behaviour change and political measures are required to solve pressing global environmental crises such as climate change. Identification with an ingroup that includes all humanity (i.e., a global identity) was positively related to pro-environmental engagement in various studies, but little is known about how to foster a global identity. We argue that meditation with a focus on cultivating compassion might increase global identity because connectedness with people all over the world is a traditional goal of such practice. In a randomised controlled intervention study with pre-post design (N = 164, Germany), participants received an app-based 7-day meditation course. Controlling for pre-survey levels, compared to the waiting control group, the meditation group reported stronger changes in global identity and pro-environmental behaviour but not climate policy support. We found that participating in the meditation course indirectly predicted changes in pro-environmental behaviour through a strengthened global self-investment, as one dimension of global identity. Moreover, we also found stronger changes in nature connectedness in the meditation group compared to the control group. Hence, a 7-day app-based compassion meditation course indeed seems to cultivate connectedness – with both humans and the broader natural world. Future research should compare different course curricula. Moreover, it should aim at diverse samples, longitudinal assessments, and investigate reasons for self-selecting in and dropping out of meditation-related studies.