Reviewerly/exHarmony huggingface dataset:
An improved reptile search algorithm based on mathematical optimization accelerator and elementary functions

The reptile search algorithm (RSA) is a dynamic and effective meta-heuristic algorithm inspired by the behavior of crocodiles in nature and the way of hunting prey. Unlike other crawler search algorithms, it uses four novel mechanisms to update the location of the solutions, such as walking at high or on the belly, and hunting in a coordinated or cooperative manner. In this algorithm, the total number of iterations is divided into four intervals, and different position-updating strategies are used to make the algorithm easily fall into the local optimum. Therefore, an improved reptile search algorithm based on a mathematical optimization accelerator (MOA) and elementary functions is proposed to improve its search efficiency and make it not easily fall into local optimum. MOA was used to realize the switching of RSA’s four searching modes by introducing random perturbations of six elementary functions (sine function, cosine function, tangent function, arccosine function, hyperbolic secant function and hyperbolic cosecant function), four mechanisms are distinguished by random number instead of the original RSA algorithm’s inherent four mechanisms by iteration number, which increases the randomness of the algorithm and avoids falling into local optimum. The random perturbations generated by elementary functions are added to the variation trend of parameter MOA to improve the optimization accuracy of the algorithm. To verify the effectiveness of the proposed algorithm, 30 benchmark functions in CEC2017 were used for carrying out simulation experiments, and the optimization performance was compared with BAT, PSO, ChOA, MRA and SSA. Finally, two practical engineering design problems are optimized. Simulation results show that the proposed sechRSA has strong global optimization ability.
Treat robots as humans? Perspective choice in human-human and human-robot spatial language interaction

Spatial language interaction is critical for human-robot interactions. However, previous findings are inconsistent in people’s perspective choices for robots and humans. In two experiments, participants viewed complex spatial layouts and verbally instructed an imagined human or robot addressee to find the target location or route. In both tasks, participants only tended to describe from humans‘ but not robots’ perspective, at the cost of taking more time to generate instructions for humans than robots. The results showed that spatial language interaction is not only a spatial but also a social task, and people do not regard robots as human-like social partners.
A human-centred design strategy for self-training healthcare service among visually impaired persons

Challenges faced by healthcare services for visually impaired persons are becoming increasingly complex. There is a lack of interventions for people in the transitional stage of blindness (the process of vision loss). The present study described a human-centred design approach to help visually impaired persons to better adapt to the transitional stage. Fifteen visually impaired persons and their relatives were interviewed to understand user behaviors and need in the empathize phase. After discussing with stakeholders, we defined user personas and problem statements. In the ideate phase, a healthcare service, including a self-training space and a health management app, was developed to help visually impaired persons to train their auditory, tactile, and directional walking abilities in different environments through sensory compensation. The results showed a high usability score, which revealed how the human-centred design approach could help visually impaired persons to train themselves during the transitional stage effectively.
A comparative study of the mineral elements in different medicinal plant parts of <i>Daphne altaica</i> Pall.

Daphne altaica Pall. is an important plant in traditional Chinese medicine. Mineral elements play an important role in the formation of active ingredients in medicinal plants. In this study, 14 mineral elements were quantitatively determined by inductively coupled plasma emission spectrometry using the roots, stems and leaves of D. altaica. The distribution variance of mineral elements between different organs were analyzed by statistical analysis methods such as single factor analysis of variance (ANOVA), correlation analysis (CA), principal component analysis (PCA) and cluster heat map. The results show that the mineral element contents in corresponding parts of D. altaica exhibited significant variation, with the order from highest to lowest being K > Ca > Mg > Fe > Al > Mn > Na > Zn > Sr > Cr > Ni > Cu > Pb > Mo. Among them, the highest content of mineral elements are K, Ca, Mg, Mn, Cu and Mo in leaves, and the stems contains more Zn, Sr, Cr and Ni, while the content of mineral elements Fe, Al, Na and Pb in roots is much higher than the other two organs. The ANOVA revealed significant variations in the mineral element content across different regions of D. altaica (p < 0.05). Based on single factor analysis of variance, different parts of D. altaica had different fingerprint of mineral elements, and the concentration of the same mineral elements in different parts had significant difference (p < 0.05). The principal component analysis (PCA) model showed that the cumulative variance contribution rate of first two principal components was 99.10%, in which Mn, Mg and Ca have higher load values, which is consistent with the results of bi-plot and cluster analysis. These results indicated that there were great differences in absorption and utilization of mineral nutrient elements between roots, stems and leaves of D. altaica, and there was significant correlation among mineral elements, indicating that there was a complex synergistic and antagonistic effect among the elements. At the same time, the discriminant analysis of the characteristic elements provided a scientific basis for the quality control of D. altaica.
Target-Oriented Investigation of Online Abusive Attacks: A Dataset and Analysis

Despite a body of research revolving around online abusive language, aiming at different objectives such as detection, diffusion prediction, and mitigation, existing research has seldom looked at factors motivating this behaviour. To further research in this direction, we investigate the motivations behind online abuse by looking at the characteristics of the targets of such abuse, i.e. is the abuse more prominent for specific characteristics of the targets? To enable target-oriented research into online abuse, we introduce the Online Abusive Attacks (OAA) dataset, the first benchmark dataset providing a holistic view of online abusive attacks, including social media profile data and metadata for both targets and perpetrators, in addition to context. The dataset contains 2.3K Twitter accounts, 5M tweets, and 106.9K categorised conversations. Further, we conduct an in-depth statistical analysis of online abuse centred around the targets’ characteristics. We identify two types of abusive attacks: those motivated by characteristics of the targets (identity-based attacks) and others (behavioural attacks). We find that online abusive attacks are predominantly motivated by the targets’ identities (97%), behavioural attacks accounting for a much smaller proportion (3%). Abuse is also more likely to target users who are popular and have a verified status. Interestingly, an analysis of the user bios shows no clear indication that keywords used in the bios are likely to trigger abuse. Additionally, we also look at the frequency with which perpetrators perform online abusive attacks. Our analysis shows a large number of infrequent perpetrators, with only a few recurrent perpetrators. Findings from our study have important implications for the development of abusive language detection models that incorporate an awareness of the targets to improve their potential for prediction.
Bayesian Experimental Design for Efficient Sensor Placement in Two-Dimensional Electromagnetic Imaging

Careful sensor placement is crucial in electromagnetic imaging experiments as it significantly impacts the quality and accuracy of the measurements. This study examines the placement of a network of sensors to advance the Bayesian learning with the aim of achieving a minimal level of uncertainty in a qualitative imaging regime. The quality of the measured data, associated with a network of sensors, is assessed by computing the expected Kullback-Leibler divergence between the prior and the posterior distributions, wherein the Laplace approximation is invoked to reduce the associated computational cost. The numerical experiment is carried out to evaluate various sensor placement scenarios to identify the network geometry that can enhance the quality of inversion.
Physical Realization of Measurement Based Quantum Computation

Quantum computers, leveraging the principles of quantum mechanics, hold the potential to surpass classical computers in numerous applications, with implications across various domains. Besides the well-known gate model, Measurement-based Quantum Computation (MBQC) is another promising computational approach to achieve universal quantum computation. In MBQC, large ensembles of qubits are prepared in a highly entangled cluster state, forming the basis for executing quantum computations through sequential measurements. Cluster states are realized using both continuous variables (CV) and discrete variables (DV) techniques. In the CV-based methods, Frequency Domain Multiplexing (FDM), Time Domain Multiplexing (TDM), Spatial Domain Multiplexing (SDM), and hybrid schemes are employed. This paper thoroughly discusses and compares these approaches, elucidating their strengths and limitations. Additionally, the generation of photonic cluster states in DV is explored and some recent results are reported. Some recent state-of-the-art advancements in photonic and superconducting qubits entanglement, which can potentially serve as cluster states, are also presented. Finally, we highlight the approach that exhibits the most promising characteristics for achieving efficient cluster state realization in the context of MBQC.
Orchestrator for ensuring interdependency between safety and cybersecurity in railway control systems

Nowadays, more and more cyber-attacks are documented in transportation systems and this impacts human and environment. Thus, it is important to consider the integration of safety and cybersecurity in both industry and academia. This brings many advantages such as virtualization technologies, but it also creates new vulnerabilities. However, a safety orchestrator that is able to address a variety of safety anomalies that come from safety-related properties and to avoid conflicts with the functionalities of a cybersecurity orchestrator is not found in the literature. To reply to this need, in this paper we propose a new safety architecture for the railway systems. The core of this architecture is STELAC, the first safety orchestrator. This orchestrator covers reliability, availability, maintainability, and integrity-related anomalies as they are the properties which interact and influence safety according to RAMS. This orchestrator is also able to prevent functionality violations between safety and cybersecurity, in order to handle their interdependency. According to the results of the implementation, it is shown that STELAC ensures safety affected by the rest of the aforementioned properties and has little impact on decision-making time, which makes it an effective methodology for using the NFV architecture in railway systems safely and securely.
Preface

Extract This volume collects seventeen papers of mine dealing with speech acts and related issues of a pragmatic character, covering a range of thirty-six years from 1984 to 2020. The idea and the decision to submit the collection to Oxford University Press were inspired by Rae Langton, to whom I am extremely grateful. I am also grateful for advice and support on this project to Claudia Bianchi, Laura Caponetto, Bianca Cepollaro, and Paolo Labinaz. It was no easy matter to review my life through its academic products but, in the end, the main lines, motivations, and outcomes of my approach to speech as action have now emerged with much greater clarity than before from the numerous separate papers scattered across the years and around various sites of publication. The papers are ordered by date of composition. I have deliberately excluded from the selection papers of a merely introductory nature, review articles, papers addressed to an Italian readership analysing samples from texts or conversations in Italian, and works written in collaboration with other authors. Sixteen of the papers selected, already published in English, were subject to some editing (especially concerning language, style, and footnotes), while one paper was translated into English from Italian. Any new footnotes or additions to the original ones are in square brackets. All references have been gathered in one list at the end of the volume.
Full round impossible differentials for Feistel ciphers

In this paper a family of l round balanced Feistel ciphers with non-bijective functions F is being considered. For any such algorithm, the existence of impossible differentials for an arbitrary number of rounds l is proved. Construction method and lower bound of the number of such impossible differentials is obtained. The GRANULE cipher belongs to the family under consideration, for which a new approach for finding impossible differentials is proposed. Its superiority in comparison with other previously known approaches is shown both in terms of the number of impossible differentials found and in terms of the number of rounds. Experimental confirmation of the theoretical bound of the number of impossible differentials has been obtained.
Sensor-based implicit authentication through learning user physiological and behavioral characteristics

Nowadays, a large amount of users’privacy data is stored in mobile intelligent devices, and authentication systems have become an important defense to protect that data. The traditional explicit authentication, however, is under significant threat from a great deal of side channel attacks, such as those based on computer vision and touch screen attacks. Users’authentication keys are facing a high risk of leakage, whereas implicit authentication is one of the effective methods to resist various side channel attacks. Nevertheless, existing user implicit authentication approaches still have weaknesses in security and usability, which increase users’authentication burdens and pose potential safety issues. To address this problem, our paper proposes a user implicit authentication approach based on physiological and behavioral characteristics, using the most commonly performed actions in daily life to pre-authenticate users. In our method, multiple sensors are utilized to extract physiological and behavioral characteristics of users, and the sensor data is fused by aligning the extracted characteristic data and using the Kalman filter to reduce its noise. Users’authentication results can be determined by comparing the similarities between their reference and authentication samples using the siamese neural network. The experimental results show that our approach improves the performance of implicit authentication while ensuring its security and usability.
ETHICS-2023 Session E4 - Tutorial: AI Safety, governance, and alignment tutorial

The field of AI safety, governance, and alignment (SGA) is concerned with questions about how to integrate AI with human values. Topics in this area deal with (1) the transformative capabilities of AI within the global innovation helix; (2) the difficulty of controlling AI, esp. with regard to designing & setting goals, and interpreting & explaining AI behavior; and (3) the difficulty of governing AI, esp. with regard to establishing policies that ensure we develop beneficial AI and implementing practices across the broad domains of the global innovation helix to prevent catastrophic outcomes for humanity. With this in mind, one of the greatest tools that we have for obviating any risk to humanity is to educate technologists so that they can implement strategies during the design and testing phases of AI, prior to release.
Lightweight Certificateless Encryption Supporting Equality Test without Bilinear Pairing

Certificateless cryptography enjoys the advantages of simplified certificate management and no key escrow problem. Equality test allows one to judge whether two ciphertexts are generated from two same plaintexts or not without decrypting them. Both of thest two techniques rely heavily on the cryptographic tool, namely the bilinear pairing. However, the bilinear pairing requires more computational costs and is normally not suitable for lightweight devices. In this paper, we study how to construct certificateless encryption scheme that supports equality test without resorting to bilinear pairing. Our scheme combines the building blocks of pairing-free certificateless encryption and ciphertext equality test technique.
Ethical issues of community-driven blockchain systems

This paper discusses the innovation and development of public-permissionless blockchain systems in the context of the triple helix model and its subsequent versions. We argue that the triple helix model fails to explain the bottom-up and community-driven innovation of this technology, which does not rely on any of the primary institutions presented in the helix models. The absence of these institutions affects the ethics of the innovation as it is left to the developers to make ethics-related design choices, which might clash with general ethical norms and values established and protected by the primary institutions. Ethical issues of public-permissionless systems are often characterized by being unaccountable and immutable, as there is no single data controller. Such design decisions can therefore have a long-term negative effect on society.
A Stackelberg Game Based Resource Trading Mechanism Design for Blockchain-enabled Industrial IoT

Resource-consuming blockchain consensus (such as Proof-of-Work) and ever-growing blockchain database inevitably hinder widespread implementation of blockchain in industrial Internet of Things (IIoT) with limited computation and storage resources. To address this issue, we employ the edge computing technology for blockchain-enabled IIoT network, wherein resource-limited lightweight nodes (LNs) offload computation-intensive consensus and block storage tasks to a resource-rich edge node (EN). We model the resource trading mechanism between the EN and LNs as a Stackelberg game, wherein the EN decides the unit prices of its computation and storage resources respectively, and the LNs decide the amount of purchased resources from the EN. Furthermore, we obtain the unique Nash equilibrium among the LNs, and analyze the Stackelberg equilibrium for the entire network. Finally, the simulation results demonstrate the optimal computation and storage resource prices are inversely correlated at the EN side.
Dynamic and Privacy-Preserving Cloud Auditing with Updatable Designated Verifier

Remote data possession checking (RDPC) provides an efficient way to check the integrity of files stored in cloud storage. The verifier of public authentication can be any cloud user, while the verifier of private authentication can only be the data owner. However, in most real cases, the data owner expects that only the designated verifier can perform the integrity check task, and the verifier cannot obtain any knowledge of the data. At the same time, when the contract expires and other situations occur, the data owner can efficiently update the designated verifier and avoid recalculating all the verification tags. Therefore, we propose a new designated verifier provable data possession (DVPDP) scheme that implements designated verifier auditing, efficient update of the designated verifier, privacy protection of data content, and dynamic data operations. Theoretical analysis and experimental results show that our scheme is feasible and effective in practical applications.
Image Encryption Algorithm Based on Chaotic System and Cellular Automata

In order to solve the problem of low efficiency and not safe enough in current image encryption schemes, a novel image encryption algorithm is proposed in this work to improve efficiency and security by employing chaotic system and Cellular Automata. Firstly, horizontal and vertical coordinates of plaintext image pixels are scrambled by the designed row and column scrambling algorithm simultaneously. Secondly, the scrambled image is diffused by the decisive key matrix composed of chaotic sequence regionally. Finally, the diffused image is regarded as the input of Cellular Automata, the CA evolution rule of input is judged according to the chaotic index value, the ciphertext image is obtained after the evolution. Experimental results and theoretical analysis show that the proposed algorithm in this paper has high security, good robustness and can meet the requirement of real-time encrypted communication.
3D Attention Network for Face Forgery Detection

With the rapid development of face forgery techniques, a large number of face synthesis videos are widely spread on the Internet, which threatens the security and trustworthiness of digital content online. It is necessary to develop face forgery detection methods. Many existing methods use only 2D CNNs to detect video frames. There are few 3D networks designed for face forgery detection. In this work, we propose to use 3D CNN for video-level face forgery detection and add a lightweight attention module to construct a 3D attention network. The network extracts both spatial and temporal features. The attention maps generated by the attention module focus on several forged regions of the fake face. To avoid the discrepancy of different regions affecting the detection results, a global attention pool is designed to replace the global average pool. The experiments implemented on FaceForensics++ show that our model achieves great accuracy and exceeds most existing methods. Cross-dataset evaluation implemented on Celeb-DF verifies that our model has strong transferability and generalization ability.
GANs Investigation for Multimodal Medical Data Interpretation : Basic Architectures and Overview

Medical imaging technologies have drastically changed the way healthcare professionals diagnose and treat patients. However, medical imaging datasets are limited in terms of size and diversity, which can lead to inaccurate diagnosis and treatment plans. To overcome these limitations, Generative Adversarial Networks (GANs) have been used to generate new medical images based on existing datasets. GANs are a type of deep learning artificial intelligence that can use existing datasets to create new synthetic images that look almost indistinguishable from real images. GANs can be used to generate medical images of organs, tissues, and diseases that are not present in existing datasets, providing healthcare professionals with more accurate and diverse datasets to diagnose and treat patients. GANs for medical image synthesis is a powerful tool that can revolutionize the healthcare industry. In this paper an investigation of the most used Gans architecture are presented. a state of the art of the latest works applying Gans are also discussed based on the adopted modality.