Reviewerly/exHarmony huggingface dataset:
Interacting with Smart Virtual Assistants for Individuals with Dysarthria: A Comparative Study on Usability and User Preferences

Authors:
raw_author_name: Aisha Jaddoh
raw_affiliation_string: Computer Science and Engineering Department, Yanbu Industrial College, Yanbu 46411, Saudi Arabia; School of Computer Science and Informatics, Cardiff University, Cardiff CF24 4AG, UK
raw_author_name: Fernando Loizides
raw_affiliation_string: School of Computer Science and Informatics, Cardiff University, Cardiff CF24 4AG, UK
raw_author_name: Omer Rana
raw_affiliation_string: School of Computer Science and Informatics, Cardiff University, Cardiff CF24 4AG, UK

Abstract: This study explores the effectiveness and user experience of different interaction methods used by individuals with dysarthria when engaging with Smart Virtual Assistants (SVAs). It focuses on three primary modalities: direct speech commands through Alexa, non-verbal voice cues via the Daria system, and eye gaze control. The objective is to assess the usability, workload, and user preferences associated with each method, catering to the varying communication capabilities of individuals with dysarthria. While Alexa and Daria facilitate voice-based interactions, eye gaze control offers an alternative for those unable to use voice commands, including users with severe dysarthria. This comparative approach aims to determine how the usability of each interaction method varies, conducted with eight participants with dysarthria. The results indicated that non-verbal voice interactions, particularly with the Daria system, were favored because of their lower workload and ease of use. The eye gaze technology, while viable, presented challenges in terms of the higher workload and usability. These findings highlight the necessity of diversifying interaction methods with SVAs to accommodate the unique needs of individuals with dysarthria.
Exploring the role of blockchain technology in modern high-value food supply chains: global trends and future research directions

Authors:
raw_author_name: Mariantonietta Fiore
raw_affiliation_string: Department of Economics, University of Foggia, Foggia, Italy
raw_author_name: Antonino Galati
raw_affiliation_string: Department of Agricultural, Food and Forest Sciences, University of Palermo, Palermo, Italy

Abstract: Abstract Trust, safety, and quality are among the most important factors in the agri-food supply chains. Traceability is a powerful tool to ensure them, but implementing a transparent and effective system is a complex operation. As a result, innovative systems, like blockchain, could be introduced. Although research on its impacts in the agri-food is recent, the literature appears fragmented. The objective is to investigate the studied aspects of the blockchain adoption in agri-food, with the purpose of retrieving meaningful considerations about the current state of the art about strategic high-value supply chains, such as wine and olive oil, particularly subjected to fraudulent behaviors. A productivity measurement was applied to retrieve the evolution of the number of documents through the years, the most productive countries, the sources, the research areas, and the most significant papers in terms of number of citations received. To understand the research trends, a co-occurrence analysis was employed. Results show that most of the existing studies focus on the role of blockchain in the resolution of some critical issues as food safety and frauds. While wine is currently an emerging sector in which this approach can be implemented, olive oil still needs more attention. In both cases, blockchain could potentially help to support the profitability and sustainability of the production. The research underlines the importance of focusing on the environmental and social dimension of the blockchain phenomenon and the use of technology to improve the efficiency of agri-food chains and reduce waste and resource use.
Hybrid structural graph attention network for POI recommendation

Authors:
raw_author_name: Jinkai Zhang
raw_affiliation_string: School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
raw_author_name: Wenming Ma
raw_affiliation_string: School of Computer and Control Engineering, Yantai University, Yantai, 264005, China

Abstract: In the era of big data, information overload poses a challenge, complicating user decision-making. Recommendation systems aim to assist in this process. In recent years, research on point-of-interest (POI) recommendations has been gaining momentum with some studies pointing to issues that need to be resolved. Previous studies often used heterogeneous graphs to learn across different entity types, overlooking same-type entity relationships. Certain studies solely extract raw node features from a single source, thus disregarding information diversity, whereas others employ inappropriate methods that fail to preserve the inherent characteristics of the relevant information in the design of raw inputs. The integration of multiple sources of information can introduce a certain amount of noise into the data; however, the approaches used in related research may not be effective in handling this situation. To address these issues, we propose a hybrid structural graph attention network (HS-GAT) for POI recommendation. In this approach, multisource data are first preprocessed and relevant raw features are initialized. Subsequently, heterogeneous graphs are built for user-POI-POI attributes and POI-user-user attributes. These heterogeneous graphs are aggregated using a dual-attention mechanism, to create embedding matrices for users and POIs, which are then used to construct user-user and POI-POI homogeneous graphs. These graph structures are then combined with user and POI embeddings obtained from heterogeneous graphs and fed into a graph attention network (GAT) , which yields the final embedding representations for users and POIs. Finally, recommendations for POIs are made in the form of inner products. A comprehensive performance evaluation of HS-GAT on the Yelp, Boston, Chicago and London datasets demonstrated that the proposed approach outperforms other state-of-the-art methods.
Adaptive Ant Colony Optimization with Sub-Population and Fuzzy Logic for 3D Laser Scanning Path Planning

Authors:
raw_author_name: Junfang Song
raw_affiliation_string: College of Information Engineering, Xizang Minzu University, No. 6, East Section of Wenhui Road, Weicheng District, Xianyang 712082, China
raw_author_name: Yuanyuan Pu
raw_affiliation_string: College of Information Engineering, Xizang Minzu University, No. 6, East Section of Wenhui Road, Weicheng District, Xianyang 712082, China
raw_author_name: Xiaoyu Xu
raw_affiliation_string: College of Information Engineering, Xizang Minzu University, No. 6, East Section of Wenhui Road, Weicheng District, Xianyang 712082, China

Abstract: For the precise measurement of complex surfaces, determining the position, direction, and path of a laser sensor probe is crucial before obtaining exact measurements. Accurate surface measurement hinges on modifying the overtures of a laser sensor and planning the scan path of the point laser displacement sensor probe to optimize the alignment of its measurement velocity and accuracy. This manuscript proposes a 3D surface laser scanning path planning technique that utilizes adaptive ant colony optimization with sub-population and fuzzy logic (SFACO), which involves the consideration of the measurement point layout, probe attitude, and path planning. Firstly, this study is based on a four-coordinate measuring machine paired with a point laser displacement sensor probe. The laser scanning four-coordinate measuring instrument is used to establish a coordinate system, and the relationship between them is transformed. The readings of each axis of the object being measured under the normal measuring attitude are then reversed through the coordinate system transformation, thus resulting in the optimal measuring attitude. The nominal distance matrix, which demonstrates the significance of the optimal measuring attitude, is then created based on the readings of all the points to be measured. Subsequently, a fuzzy ACO algorithm that integrates multiple swarm adaptive and dynamic domain structures is suggested to enhance the algorithm’s performance by refining and utilizing multiple swarm adaptive and fuzzy operators. The efficacy of the algorithm is verified through experiments with 13 popular TSP benchmark datasets, thereby demonstrating the complexity of the SFACO approach. Ultimately, the path planning problem of surface 3D laser scanning measurement is addressed by employing the proposed SFACO algorithm in conjunction with a nominal distance matrix.
Face masks and fake masks: the effect of real and superimposed masks on face matching with super-recognisers, typical observers, and algorithms

Authors:
raw_author_name: Kay L. Ritchie
raw_affiliation_string: School of Psychology, University of Lincoln, Brayford Pool, Lincoln, LN6 7TS, UK
raw_author_name: Daniel J. Carragher
raw_affiliation_string: Psychology, Faculty of Natural Sciences, University of Stirling, Stirling, UK
raw_author_name: Josh P. Davis
raw_affiliation_string: School of Human Sciences, Institute of Lifecourse Development, University of Greenwich, London, UK
raw_author_name: Eilidh Noyes
raw_affiliation_string: School of Human and Health Sciences, University of Huddersfield, Huddersfield, UK
raw_author_name: Katie L. H. Gray
raw_affiliation_string: School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK
raw_author_name: Peter J. B. Hancock
raw_affiliation_string: Psychology, Faculty of Natural Sciences, University of Stirling, Stirling, UK

Abstract: Abstract Mask wearing has been required in various settings since the outbreak of COVID-19, and research has shown that identity judgements are difficult for faces wearing masks. To date, however, the majority of experiments on face identification with masked faces tested humans and computer algorithms using images with superimposed masks rather than images of people wearing real face coverings. In three experiments we test humans (control participants and super-recognisers) and algorithms with images showing different types of face coverings. In all experiments we tested matching concealed or unconcealed faces to an unconcealed reference image, and we found a consistent decrease in face matching accuracy with masked compared to unconcealed faces. In Experiment 1, typical human observers were most accurate at face matching with unconcealed images, and poorer for three different types of superimposed mask conditions. In Experiment 2, we tested both typical observers and super-recognisers with superimposed and real face masks, and found that performance was poorer for real compared to superimposed masks. The same pattern was observed in Experiment 3 with algorithms. Our results highlight the importance of testing both humans and algorithms with real face masks, as using only superimposed masks may underestimate their detrimental effect on face identification.
Discrete-Time Quantum Walks Community Detection in Multi-Domain Networks

Authors:
raw_author_name: Xiaoyang Liu
raw_affiliation_string: Chongqing University of Technology , School of Computer Science and Engineering, , N0.69 Hongguang Road, Banan District, Chongqing 400054 , China; Department of Computer Science and Technology, School of Computer Science and Engineering, Chongqing University of Technology, N0.69 Hongguang Road, Banan District, Chongqing 400054, China
raw_author_name: Giacomo Fiumara
raw_affiliation_string: Department of Mathematical, University of Messina , V.le F. Stagno D’Alcontres, 31, 98166 Messina , Italy
raw_author_name: Pasquale De Meo
raw_affiliation_string: Department of Ancient and Modern Civilizations, University of Messina, Physics and Earth Sciencesviale Ferdinando Stagno d’Alcontres , 98166 Messina , Italy

Abstract: Abstract The problem of detecting communities in real-world networks has been extensively studied in the past, but most of the existing approaches work on single-domain networks, i.e. they consider only one type of relationship between nodes. Single-domain networks may contain noisy edges and they may lack some important information. Thus, some authors have proposed to consider the multiple relationships that connect the nodes of a network, thus obtaining multi-domain networks. However, most community detection approaches are limited to multi-layer networks, i.e. networks generated from the superposition of several single-domain networks (called layers) that are regarded as independent of each other. In addition to being computationally expensive, multi-layer approaches might yield inaccurate results because they ignore potential dependencies between layers. This paper proposes a multi-domain discrete-time quantum walks (MDQW) model for multi-domain networks. First, the walking space of network nodes in multi-domain network is constructed. Second, the quantum permutation circuit of the coin state is designed based on the coded particle state. Then, using different coin states, the shift operator performs several quantum walks on the particles. Finally, the corresponding update rule is selected to move the node according to the measurement result of the quantum state. With continuous update iteration, the shift operator automatically optimizes the discovered community structure. We experimentally compared our MDQW method with four state-of-the-art competitors on five real datasets. We used the normalized mutual information (NMI) to compare clustering quality, and we report an increase in NMI of up to 3.51 of our MDQW method in comparison with the second-best performing competitor. The MDQW method is much faster than its competitors, allowing us to conclude that MDQW is a useful tool in the analysis of large real-life multi-domain networks. Finally, we illustrate the usefulness of our approach on two real-world case studies.
Combining Critical and Quantum Metrology

Authors:
raw_author_name: Helmut Ritsch
raw_affiliation_string: Institut für Theoretische Physik, Universität Innsbruck, A-6020 Innsbruck, Austria
raw_author_name: Karol Gietka
raw_affiliation_string: Institut für Theoretische Physik, Universität Innsbruck, A-6020 Innsbruck, Austria

Abstract: Critical metrology relies on the precise preparation of a system in its ground state near a quantum phase transition point where quantum correlations get very strong. Typically, this increases the quantum Fisher information with respect to changes in system parameters and thus improves the optimally possible measurement precision limited by the Cram\'er-Rao bound. Hence critical metrology involves encoding information about the unknown parameter in changes of the system's ground state. Conversely, in conventional metrology methods like Ramsey interferometry, the eigenstates of the system remain unchanged, and information about the unknown parameter is encoded in the relative phases that excited system states accumulate during their time evolution. Here we introduce an approach combining these two methodologies into a unified protocol applicable to closed and driven-dissipative systems. We show that the quantum Fisher information in this case exhibits an additional interference term originating from the interplay between eigenstate and relative phase changes. We provide analytical expressions for the quantum and classical Fisher information in such a setup, elucidating as well a straightforward measurement approach that nearly attains the maximum precision permissible under the Cram\'er-Rao bound. We showcase these results by focusing on the squeezing Hamiltonian, which characterizes the thermodynamic limit of Dicke and Lipkin-Meshkov-Glick Hamiltonians.
Physical Reservoir Computing Enabled by Solitary Waves and Biologically Inspired Nonlinear Transformation of Input Data

Authors:
raw_author_name: Ivan S. Maksymov
raw_affiliation_string: Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Bathurst, NSW 2795, Australia

Abstract: Reservoir computing (RC) systems can efficiently forecast chaotic time series using the nonlinear dynamical properties of an artificial neural network of random connections. The versatility of RC systems has motivated further research on both hardware counterparts of traditional RC algorithms and more-efficient RC-like schemes. Inspired by the nonlinear processes in a living biological brain and using solitary waves excited on the surface of a flowing liquid film, in this paper, we experimentally validated a physical RC system that substitutes the effect of randomness that underpins the operation of the traditional RC algorithm for a nonlinear transformation of input data. Carrying out all operations using a microcontroller with minimal computational power, we demonstrate that the so-designed RC system serves as a technically simple hardware counterpart to the ‘next-generation’ improvement of the traditional RC algorithm.
Entanglement and entropy in multipartite systems: a useful approach

Authors:
raw_author_name: J. A. Casas
raw_affiliation_string: Instituto de Física Teórica, IFT-UAM/CSIC, Universidad Autónoma de Madrid, Cantoblanco, 28049, Madrid, Spain

Abstract: Abstract Quantum entanglement and quantum entropy are crucial concepts in the study of multipartite quantum systems. In this work, we show how the notion of concurrence vector, re-expressed in a particularly useful form, provides new insights and computational tools for the analysis of both. In particular, using this approach for a general multipartite pure state, one can easily prove known relations in an easy way and to build up new relations between the concurrences associated with the different bipartitions. The approach is also useful to derive sufficient conditions for genuine entanglement in generic multipartite systems that are computable in polynomial time. From an entropy-of-entanglement perspective, the approach is powerful to prove properties of the Tsallis-2 entropy, such as the subadditivity, and to derive new ones, e.g., a modified version of the strong subadditivity which is always fulfilled; thanks to the purification theorem these results hold for any multipartite state, whether pure or mixed.
Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions

Authors:
raw_author_name: Wei Ni
raw_affiliation_string: Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sydney,NSW 2122, Australia
raw_author_name: Xin Wang
raw_affiliation_string: Key Laboratory for Information Science of Electromagnetic Waves (MoE), Department of Communication Science and Engineering, Fudan University, Shanghai 200433, China
raw_author_name: H. Vincent Poor
raw_affiliation_string: Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA

Abstract: The development of applications based on artificial intelligence and implemented over wireless networks is increasingly rapidly and is expected to grow dramatically in the future. The resulting demand for the aggregation of large amounts of data has caused serious communication bottlenecks in wireless networks and particularly at the network edge. Over-the-air federated learning (OTA-FL), leveraging the superposition feature of multi-access channels (MACs), enables users at the network edge to share spectrum resources and achieves efficient and low-latency global model aggregation. This paper provides a holistic review of progress in OTA-FL and points to potential future research directions. Specifically, we classify OTA-FL from the perspective of system settings, including single-antenna OTA-FL, multi-antenna OTA-FL, and OTA-FL with the aid of the emerging reconfigurable intelligent surface (RIS) technology, and the contributions of existing works in these areas are summarized. Moreover, we discuss the trust, security and privacy aspects of OTA-FL, and highlight concerns arising from security and privacy. Finally, challenges and potential research directions are discussed to promote the future development of OTA-FL in terms of improving system performance, reliability, and trustworthiness. Specifical challenges to be addressed include model distortion under channel fading, the ineffective OTA aggregation of local models trained on substantially unbalanced data, and the limited accessibility and verifiability of individual local models.
Fake news detection using recurrent neural network based on bidirectional LSTM and GloVe

Authors:
raw_author_name: Laith Abualigah
raw_affiliation_string: Computer Science Department, Al Al-Bayt University, Mafraq, 25113, Jordan
raw_author_name: Mohammad Sh. Daoud
raw_affiliation_string: College of Engineering, Al Ain University, 112612, Abu Dhabi, United Arab Emirates
raw_author_name: Maryam Altalhi
raw_affiliation_string: Department of Management Information Systems, College of Business Administration, Taif University, P.O. Box 11099, 21944, Taif, Saudi Arabia
raw_author_name: Hazem Migdady
raw_affiliation_string: CSMIS Department, Oman College of Management and Technology, 320, Barka, Oman

Abstract: In the world of technology, the electronic and technical development of the fields of communication and the internet has increased, which has caused a renaissance in the virtual world. This development has greatly impacted virtual communities for the ease and speed of communication and information transfer through social media platforms, making these platforms likable and easy to use. The social network faces major challenges due to its extensive use. As a result, many people have become involved in cybercrimes. There are accounts on the internet that are malicious. Platforms for social networking online, such as Facebook and Twitter, allow all users to freely generate and consume massive volumes of material regardless of their traits. While individuals and businesses utilize this information to gain a competitive edge, spam or phony users create important data. According to estimates, 1 in 200 posts on social media contain spam, and 1 in 21 tweets contain spam. The problem was centered around the accuracy of detecting false news and correcting it or preventing its dissemination before it spread in the network. A new method is given based on improving the false news detection system; the level of improvement was significant in the preprocessing stage where Glove is used, which is an unsupervised learning algorithm developed by researchers at Stanford University aiming to generate word embeddings by aggregating global word co-occurrence matrices from a given corpus. The basic idea behind the GloVe word embedding is to derive the relationship between the words from statistics. The proposed method contains deep learning algorithms of convolutional neural network (CNN), deep neural network (DNN), and long short-term memory (LSTM). The RNN with GloVe in the preprocessing stage using the Curpos fake news dataset to enhance the system, due to the sequential processes and classification, has the highest accuracy of 98.974%.