Reviewerly/exHarmony huggingface dataset:
Quantum Secure Multi-Party Summation with Identity Authentication Based on Commutative Encryption

In quantum secure multi-party summation protocols, some attackers can impersonate legitimate participants in the summation process, and easily steal the summation results from the participants. This is often overlooked for existing secure multi-party summation protocols, thus rendering them insecure. Based on commutative encryption, a quantum secure multi-party summation protocol with identity authentication is proposed in this paper. In the protocol, each participant encodes a secret integer on photons via unitary operations. At the same time, a one-way hash function technique with a key is utilized to perform identity authentication operations for each participant. Finally, the summation is calculated with the help of a semi-trusted third party. The analysis of the protocol shows that the proposed protocol is correct and resistant to common and impersonation attacks. Compared to related protocols, the use and measurement of single photons makes the protocol easier to implement into existing technology. Furthermore, the simulation experiments on the IBM Q Experience cloud platform demonstrate the effectiveness of the presented protocol.
Been There, Seen That: Visualization of Movement and 3D Eye Tracking Data from Real‐World Environments

Abstract The distribution of visual attention can be evaluated using eye tracking, providing valuable insights into usability issues and interaction patterns. However, when used in real, augmented, and collaborative environments, new challenges arise that go beyond desktop scenarios and purely virtual environments. Toward addressing these challenges, we present a visualization technique that provides complementary views on the movement and eye tracking data recorded from multiple people in real‐world environments. Our method is based on a space‐time cube visualization and a linked 3D replay of recorded data. We showcase our approach with an experiment that examines how people investigate an artwork collection. The visualization provides insights into how people moved and inspected individual pictures in their spatial context over time. In contrast to existing methods, this analysis is possible for multiple participants without extensive annotation of areas of interest. Our technique was evaluated with a think‐aloud experiment to investigate analysis strategies and an interview with domain experts to examine the applicability in other research fields.
<i>Teru Teru Bōzu</i>: Defensive Raincloud Plots

Univariate visualizations like histograms, rug plots, or box plots provide concise visual summaries of distributions. However, each individual visualization may fail to robustly distinguish important features of a distribution, or provide sufficient information for all of the relevant tasks involved in summarizing univariate data. One solution is to juxtapose or superimpose multiple univariate visualizations in the same chart, as in Allen et al.'s "raincloud plots." In this paper I examine the design space of raincloud plots, and, through a series of simulation studies, explore designs where the component visualizations mutually "defend" against situations where important distribution features are missed or trivial features are given undue prominence. I suggest a class of "defensive" raincloud plot designs that provide good mutual coverage for surfacing distributional features of interest.
Mapping subsurface geological structures in the Birimian Supergroup, Ghana using airborne magnetic and radiometric data: Implications for gold exploration

Rarity of outcrops is a typical feature of considerable parts of the Birimian Supergroup in Ghana as a result of deep weathering which leads to the formation of thick surficial cover and laterization. This hampers the precise mapping of lithological boundaries and mafic dykes. These boundaries are tectonic contacts that may have the potential to host orogenic gold deposits. This study aims to map these subsurface geological features using regional airborne geophysical data. Derivatives of magnetic data such as analytic signal, vertical derivative, horizontal gradient and the ternary map of the radiometric data were employed to interpret subsurface geology. The general correlation of the magnetic and radiometric maps with the existing geological map confirms their suitability as geological mapping tools. Tectonostratigraphic contacts of several geological domains within the Birimian Supergroup were delineated. These zones host world-class gold deposits and are potential corridors to focus exploration activities to ensure efficient fund utilisation and minimisation of exploration risks. Additionally, SSE-NNW, WSW-ENE and WNW-ESE trending dolerite dyke swarms were outlined.
Analysis of the influence of population distribution characteristics on swarm intelligence optimization algorithms

The distribution characteristic of populations is one of the main characteristics of population. The pattern of distribution characteristics determines the probability law of individual values. At the same time, the initialization of the population lays a foundation for the iterative process of the swarm intelligence optimization algorithm. To reveal the influence of population distribution characteristics on the swarm intelligence optimization algorithm, this paper proposes a variety of search strategies based on the populations with different distribution characteristics and analyzes the influence of population distribution characteristics on optimization process by comparing the test results of the optimization algorithm after implementing these strategies. First, a new population generator is designed that can transform the same initial population into a population with uniform and central peaking distributions. On this basis, the two kinds of populations are applied to the global and local search stages of the optimization process, and four different search strategies are formed. Among them, the global and local search strategies based on a uniformly distributed population are the traditional methods. Finally, the performance of the optimization algorithm using different search strategies is evaluated through 29 test problems from the CEC2017 benchmark for 10, 30, 50 and 100 dimensions. In addition, the algorithms are applied to solve the TSP problem. For CEC2017 in 100D, only 13 of the 29 test functions achieve the best optimization effect by using the traditional method, while the other 16 test functions achieve better search results by using the other three search strategies. The analysis shows that the population distribution characteristics have a great influence on the population optimization algorithm. The performance of the algorithm with different population distribution combination strategies is statistically superior to the traditional algorithm with a uniform distribution population, as revealed in the test functions ranging from 38.7% to 62.9% for different dimensions. By reconstructing populations with different distribution characteristics, the overall efficiency of the swarm intelligence optimization algorithm can be greatly improved.
Uncertainty Modelling

Organizations attempt to create value for their customers while managing different uncertainties. Uncertainties in supply chains offer both opportunities and challenges to decision-makers. We start this chapter by reviewing three uncertainty types (i.e. truth, epistemological and ontological uncertainty) and discuss why the ontological uncertainty deserves special attention. We study the uncertainty modelling approach along two dimensions to address the challenges of managing the ontological uncertainty. First, we introduce the additive and multiplicative demand models to capture the evolutionary dynamics of uncertainty. Second, we focus on advanced methods, such as the fast Fourier transformation (FFT) and demand regularization, to model complex demand dynamics when demand is formed as combination of different uncertain elements.
Graph structure learning layer and its graph convolution clustering application

To learn the embedding representation of graph structure data corrupted by noise and outliers, existing graph structure learning networks usually follow the two-step paradigm, i.e., constructing a "good" graph structure and achieving the message passing for signals supported on the learned graph. However, the data corrupted by noise may make the learned graph structure unreliable. In this paper, we propose an adaptive graph convolutional clustering network that alternatively adjusts the graph structure and node representation layer-by-layer with back-propagation. Specifically, we design a Graph Structure Learning layer before each Graph Convolutional layer to learn the sparse graph structure from the node representations, where the graph structure is implicitly determined by the solution to the optimal self-expression problem. This is one of the first works that uses an optimization process as a Graph Network layer, which is obviously different from the function operation in traditional deep learning layers. An efficient iterative optimization algorithm is given to solve the optimal self-expression problem in the Graph Structure Learning layer. Experimental results show that the proposed method can effectively defend the negative effects of inaccurate graph structures. The code is available at https://github.com/HeXiax/SSGNN.
Pipelined Key Switching Accelerator Architecture for CKKS-Based Fully Homomorphic Encryption

The increasing ubiquity of big data and cloud-based computing has led to increased concerns regarding the privacy and security of user data. In response, fully homomorphic encryption (FHE) was developed to address this issue by enabling arbitrary computation on encrypted data without decryption. However, the high computational costs of homomorphic evaluations restrict the practical application of FHE schemes. To tackle these computational and memory challenges, a variety of optimization approaches and acceleration efforts are actively being pursued. This paper introduces the KeySwitch module, a highly efficient and extensively pipelined hardware architecture designed to accelerate the costly key switching operation in homomorphic computations. Built on top of an area-efficient number-theoretic transform design, the KeySwitch module exploited the inherent parallelism of key switching operation and incorporated three main optimizations: fine-grained pipelining, on-chip resource usage, and high-throughput implementation. An evaluation on the Xilinx U250 FPGA platform demonstrated a 1.6× improvement in data throughput compared to previous work with more efficient hardware resource utilization. This work contributes to the development of advanced hardware accelerators for privacy-preserving computations and promoting the adoption of FHE in practical applications with enhanced efficiency.
Shape-selective processing in deep networks: integrating the evidence on perceptual integration

Understanding how deep neural networks resemble or differ from human vision becomes increasingly important with their widespread use in Computer Vision and as models in Neuroscience. A key aspect of human vision is shape: we decompose the visual world into distinct objects, use cues to infer their 3D geometries, and can group several object parts into a coherent whole. Do deep networks use the shape of objects similarly when they classify images? Research on this question has yielded conflicting results, with some studies showing evidence for shape selectivity in deep networks, while others demonstrated clear deficiencies. We argue that these conflicts arise from differences in experimental methods: whether studies use custom images in which only some features are available, images in which different features compete, image pairs that vary along different feature dimensions, or large sets of images to assess how representations vary overall. Each method offers a different, partial view of shape processing. After comparing their advantages and pitfalls, we propose two hypotheses that can reconcile previous results. Firstly, deep networks are sensitive to local, but not global shape. Secondly, the higher layers of deep networks discard some of the shape information that the lower layers are sensitive to. We test these hypotheses by comparing network representations for natural images and silhouettes in which local or global shape is degraded. The results support both hypotheses, but for different networks. Purely feed-forward convolutional networks are unable to integrate shape globally. In contrast, networks with residual or recurrent connections show a weak selectivity for global shape. This motivates further research into recurrent architectures for perceptual integration.
Central bank digital currencies: policy implications

This paper lists fifteen key policy implications resulting from a decision to introduce retail and/or wholesale central bank digital currencies (CBDCs). It makes the distinction between ‘medium of exchange’ and the ‘exchange mechanism’. The former is one of the functions of money. The latter is a function of the technological approach in establishing the unit of account, store of value, and the payment protocol. Payments, transfers and settlement, are explored in respect of (i) wholesale CBDCs, (ii) retail CBDCs, (iii) digital payment platforms and, (iv) stablecoins. Each require distinct policy frameworks, and scholarly opinion on these are very diverse. Most academics agree on the privacy concerns related to account based digital money. The main areas of disagreement, however, are over which institutions/entities should be allowed to issue digital money, how such issuance should be supervised, and how decentralised digital tokens should be addressed from a policy perspective.
Think-4-EmCoDe framework: Highlighting key qualities in embodied co-design techniques for children

Embodied, co-design and children-specific methods have been gaining traction in Interaction Design for Children, and their potential and benefits are well-established. Yet methods at the intersection of all three areas are scarcer. In addition, design researchers lack tools for the analysis and adaptation of existing methods, and the creation of new ones to suit the particularities of the design project at hand. Here, we present the Think-4-EmCoDe framework for the design and analysis of embodied co-design methods and techniques for children, which foregrounds key qualities in embodied interaction design research: (1) embodied awareness; (2) reflective imaginary; (3) emergence; (4) embodied memory; (5) situated relationality; (6) contingency; (7) playful engagement; (8) play practice; (9) developmental scaffolds; and (10) social dialogue. The framework has been developed drawing from several theories, methods, and frameworks in the areas of co-design and embodied design approaches. This framework allows designers to focus on both general design qualities for embodied experiences and the specific child perspective. We illustrate the use of the framework through the analysis of three design techniques and discuss its potential as an inspirational tool to understand, adapt and refine embodied co-design techniques for children.
A Unified Learning Approach for Malicious Domain Name Detection

The DNS firewall plays an important role in network security. It is based on a list of known malicious domain names, and, based on these lists, the firewall blocks communication with these domain names. However, DNS firewalls can only block known malicious domain names, excluding communication with unknown malicious domain names. Prior research has found that machine learning techniques are effective for detecting unknown malicious domain names. However, those methods have limited capabilities to learn from both textual and numerical data. To solve this issue, we present a novel unified learning approach that uses both numerical and textual features of the domain name to classify whether a domain name pair is malicious or not. The experiments were conducted on a benchmark domain names dataset consisting of 90,000 domain names. The experimental results show that the proposed approach performs significantly better than the six comparative methods in terms of accuracy, precision, recall, and F1-Score.
Invoking ‘Empathy for the Planet’ through Participatory Ecological Storytelling: From Human-Centered to Planet-Centered Design

In sustainable design and innovation, appreciation of the Planet as an equal stakeholder with humans and businesses continues to rise. Yet a consistent challenge arises in that people have difficulties relating to the nonhuman and interpret the world in terms of human values and experiences. We need more practical tools to stimulate a connection, especially in its affective dimension, to the Planet and to include nonhuman stakeholders in sustainability developments. To anchor Planetary understanding and considerations, we investigate the role of participatory storytelling to stimulate a reappraisal of the needs of nonhuman stakeholders through empathy building. To posit this, we defined empathy for the Planet as a holistic relationship with human and nonhuman stakeholders. We facilitated workshops where design students, design professionals, and business stakeholders could co-create environmental stories using human and nonhuman character personas. We analyzed the personas, stories, and participants’ feedback on the process experience and impact and observed that story creators experienced empathy for the Planet through projecting and blending their own emotions and intents onto the characters. We discuss, therefore, how ecological story co-creation can be a tool for self-reflection, collective sense-making, and the inclusion of the voice of Planetary stakeholders relevant for sustainable design and to drive sustainability engagement in general. This research confirms the role of stories and imagination in creating a bridge to the natural world through new, human and nonhuman, perspectives.
F3-Net: Multiview Scene Matching for Drone-Based Geo-Localization

Scene matching involves establishing a mapping relationship between heterogeneous images, which is crucial for drone visual geo-localization. However, it poses a significant challenge for multi-view images such as those captured by drones and satellites. To address this issue, this paper proposes an end-to-end geo-localization framework named F3-Net for calculating the similarity of multi-source and multi-view images. The key contributions of F3-Net are as follows: 1) The Split and Fusion (SF) module is designed to fully exploit the features through the global self-attention mechanism. 2) To improve the multi-view semantic features, a Target Feature Enhancement (TFE) module is introduced, based on the principle of invariance target semantic consistency. 3) After multi-view feature learning, a Feature Alignment and Unity (FAU) module with Earth Mover distance is used to calculate the similarity of non-aligned features. F3-Net fully exploits the multi-source image feature correspondence and multi-view image semantic consistency. Different from the traditional siamese network, the features of multi-view images are regarded as probability distribution, so F3-Net can quantify and eliminate the feature differences of multi-view images in the learning process. Experiments show that F3-Net can effectively overcome multi-view changes and achieve high accuracy on University-1652 dataset.
B-APFDQN: A UAV Path Planning Algorithm Based on Deep Q-Network and Artificial Potential Field

Deep Q-network (DQN) is one of the standard methods to solve the Unmanned Aerial Vehicle(UAV) path planning problem.However, the way agent deepens its cognition of the environment through frequent random trial-and-error leads to slow convergence.This paper proposes an optimized DQN with Artificial Potential Field (APF) as prior knowledge called B-APFDQN for path planning.Replacing the traditional neural network which has only one Q-value output with a multi-output neural network to promote the training process in combination with APF.Furthermore, a SA-ε-greedy algorithm that can automatically adjust the stochastic exploration frequency with steps and successes is proposed in order to prevent the agent from falling into local optimum.We remove the nodes that do not affect the path connectivity and apply the B-spline algorithm to make the path shorter and smoother.Simulation experiments show that the proposed B-APFDQN algorithm performs better than the classical DQN, has a strong ability to avoid falling into local optimum, and the obtained paths are smooth and shorter, which proves the effectiveness of B-APFDQN in the UAV path planning problem.
Safe Policy Improvement for POMDPs via Finite-State Controllers

We study safe policy improvement (SPI) for partially observable Markov decision processes (POMDPs). SPI is an offline reinforcement learning (RL) problem that assumes access to (1) historical data about an environment, and (2) the so-called behavior policy that previously generated this data by interacting with the environment. SPI methods neither require access to a model nor the environment itself, and aim to reliably improve upon the behavior policy in an offline manner. Existing methods make the strong assumption that the environment is fully observable. In our novel approach to the SPI problem for POMDPs, we assume that a finite-state controller (FSC) represents the behavior policy and that finite memory is sufficient to derive optimal policies. This assumption allows us to map the POMDP to a finite-state fully observable MDP, the history MDP. We estimate this MDP by combining the historical data and the memory of the FSC, and compute an improved policy using an off-the-shelf SPI algorithm. The underlying SPI method constrains the policy space according to the available data, such that the newly computed policy only differs from the behavior policy when sufficient data is available. We show that this new policy, converted into a new FSC for the (unknown) POMDP, outperforms the behavior policy with high probability. Experimental results on several well-established benchmarks show the applicability of the approach, even in cases where finite memory is not sufficient.
The Emergence of Robotics in Tourism and Hospitality: A Bibliometric Analysis from 2017 to 2023

We present an analysis of the emergence of robotic technology researches in tourism and hospitality from 2017 to 2023. Bibliometric analysis was used to analyze 47 articles published from 2017 to 2023 based on source, document type, journal name, publisher name, topic trends, and author collaborations. We use the combination of Robotics + Tourism + Hospitality as our main keywords in searching related article titles, abstracts, and keywords to get metadata, where the Scopus database is the main source. The tools used in this bibliometric analysis are Harzing's Publish or Perish for importing source data from Scopus database. Furthermore, we employ VoS Viewer for data visualization and further used for citation and metrics analysis. The results indicate that most related articles to are published in scientific journals, compared to other publication outlets e.g., book chapter, conference proceedings and book. Related articles published in scientific journals have received the highest citations compared to others. Based on network and overlay visualization, the most dominant terms (often appearing) are "AI", "services robots", "human robot interaction", "autonomy".