Reviewerly/exHarmony huggingface dataset:
A secure and privacy‐preserved delegate‐based blockchain and federated learning for 6G networks

Summary The 6G networks are envisioned to provide Artificial Intelligence (AI) for distributed devices through deploying machine learning on base stations. However, due to the concern of data privacy, devices are not willing to transmit their raw data to base stations (BSs) for machine learning training. Federated learning (FL) is a promising paradigm that can enable distributed machine learning while protecting data privacy by collaboratively training AI models without sharing raw data. But the traditional FL requires a central node to complete global model update which makes the traditional FL facing single point attack. Further, the traditional FL cannot allow the distributed model sharing among untrusted devices which results in low training efficiency on devices. Blockchain is a promising approach to address the above issues since it can establish a secure decentralized and transaction sharing environment for untrusted devices. In this paper, we integrate blockchain into FL to build a distributed model training and sharing platform, where distributed mobile devices execute local model training and base stations maintain blockchain‐based model sharing. Moreover, in order to accelerate the establishment of blockchain, we design a lightweight consensus strategy based on a delegate committee. To select high‐quality nodes to form delegate committee, we design a deep reinforcement learning (DRL)‐based selection algorithm. Numerical results demonstrate that the proposed framework can achieve a higher precision compared with the traditional FL, and the proposed DRL selection algorithm can reduce consensus latency compared with the benchmarks.
Retrieving Twitter argumentation with corpus queries and discourse analysis

We propose a corpus linguistic approach for retrieving argumentation from tweets about Brexit. We analyse two corpora, one from before the referendum in 2016 and one from early 2019. Our approach is based on the manual development of morphosyntactic corpus queries (in CQP syntax) that target specific argumentation patterns. For the present corpora we have developed 130 queries targeting 34 logical formulae. The paper showcases the query development and presents quantitative and qualitative results on how Brexit-related arguments have changed on Twitter from 2016 to 2019.
Artificial Intelligence and Data Analytics-Based Emotional Intelligence

With the advent of technology, we have seen drastic changes in the way of life. Right from automation to improved security systems, technology got us covered. Machines deployed with intelligence stand the potential to drive cars, perform medical procedures, control traffic, and possibly everything that we can think of. Several large and small-scale organizations are adopting AI technologies to amplify their growth and surpass prior business results. Emotional AI can also help businesses to solve sensitive issues at the workplace. The global outlook is that AI imbibed into automation or robotics is going to revolutionize the markets and workforces. As per market estimates, over the next five years, the affective computing market will witness new heights surging to a growth rate of 43.0% CAGR in terms of revenue, with the global market reaching to US$ 1710 million by 2024 up from US$ 200 million in 2019. In the forecast period from 2019-2024, development around touchless AI models are expected to grow in the affective computing market between 2017 and 2025.
Serials Spoken Here

This column reports on sessions from the virtual Electronic Resources and Libraries 2022 conference. Four reports are about data in academic libraries including: text and data mining licensing and discovery, acquisition processes for datasets, EBSCO’s data visualization product, Panorama, and their development partners, and case studies using collections data science to demonstrate library value. Two reports cover working cross-departmental collaborations, one to better manage evidence-based acquisition and demand-driven acquisition ebook programs, and the other about overall library reorganization. The other reports summarize conference presentations on problematic language in subject headings and discovery tools, tracking perpetual access rights in license agreements, implementation of cyclical renewal assessments, and a pilot to test whole ebook interlibrary loan.
An efficient procedure for online/offline ID‐based signature using extended chaotic maps and group ring

Abstract In this article, we utilize the notions of extended chaotic maps and group ring to propose an efficient procedure for online/offline identity based (ID‐based) signature. The main purpose of engaging chaotic maps in our procedure is to reduce its computational complexity while maintaining the desired security and the purpose of including algebraic structure of group ring is to shorten the signature. We show that under chosen message attack, our signature procedure is secure under unforgeability of ID‐based short signature. Most of the available online/offline signature procedures allow one time use of the offline preadministered information (offline stockpiling), but the proposed procedure grants multitime usage of the offline stockpiling. Therefore, the offline preadministered information can be reused by the signer in polynomial time. We show that our signature procedure is efficient, fast and provides signature of the small size. Finally, we compare our procedure with the several existing schemes in the literature and discuss its advantages.
Schoolchildren's Family Values Formation in Russian National Cinematograph Works: Media Education Aspect

The problem of family, family relations and basic family values formation is very essential nowadays.The difficulties and problems of the modern families lie in unreadiness and unwillingness of young people to take the responsibility for the closest people, inability to cope with even slight obstacles in life, provoke a great number of divorces and don't promote strengthening and saving of the best family traditions characteristic for Russian family relations culture.In the article media educational abilities of schoolchildren's family values formation in works of Russian cinematograph are displayed.The problem is viewed in the context of modern students' audience preparation (future teachers, psychologists, youth work organizers) for professional activity with schoolchildren in conditions of media clubs' activity, work in media studios, school media centres and so on.The basic directions of media educational activity, aimed at modern scholars' family values formation are based on the complex approach to the analysis of audiovisual media text, including sociocultural context study, basic plot lines characteristics, main and optional characters' family relations models investigation, composition of comparative characteristics of family happiness principles, manifested in films of different years, study and analysis of professional materials of media critics and media educationalists, devoted to this or that work of cinematograph and so on.In the process of feature films analysis, the main goals of media education are viewed, consisting in self-directed selection, analytical evaluation and media text interpretation realization, and also audiovisual space creative expansion with the purpose of modern schoolchildren's horizon, creative abilities, critical thinking development.
Improving Context-Aware Synthesis and Placement of Object Instances

A vast amount of research has been conducted about deep learning and its applications in Computer Vision (CV). However, the application to project an object instance onto a real image or video in a semantically coherent manner, such that the projected object is indistinguishable from a real object, is only in its infancy. In our research, we aim to evaluate a generative model which is able to generate and place an object instance onto an image in a semantically coherent manner using a where and a what module; both of these employ Generative Adversarial Networks (GANs). Furthermore, we improve the shape generation by adding a classifier before the training data is used. Finally, we intend to increase the training stability by using an alternative training methodology and adjusting the Jenson-Shannon divergence to the Wasserstein distance. The implication of this work is the improved stability of an existing generative model, which inserts instances onto an image. Furthermore, we were also able to improve its performance.
Autonomous Navigation Using Model-Based Reinforcement Learning

Autonomous driving does not yet have an industry-standard approach. One of the currently promising approaches is reinforcement learning. A novel model-based deep reinforcement learning algorithm, called MuZero, is able to perform well in observation spaces with a higher complexity than its predecessors. As a step towards autonomous driving, this paper employs MuZero for racing on unseen race tracks based on LIDAR observations. Furthermore, we propose a modification to the algorithm to support a continuous action space. We compare our continuous version of MuZero with its original, discrete variant since autonomous driving is inherently a continuous control problem. We also compare our results with a current benchmark reinforcement learning algorithm: Proximal Policy Optimization (PPO). A solution is proposed and verified to progressively generate race tracks which results in the MuZero agent being able to achieve high rewards on race tracks that it has never seen. Also, the performance of the continuous variant of MuZero is compared to PPO and discrete MuZero. Results show that both PPO and discrete MuZero achieve similar peak performance, while the latter does this with a much higher data-efficiency. Furthermore, we show that continuous MuZero is able to improve its policy, but stagnates at a lower peak performance.
A Real-time Volume Control System for Electric Guitars Based on Fuzzy Inference

Recently, musicians are writing songs at home instead of using the specialized recording equipment available in record studios. Also, high-quality recording requires that the volume should be close to the maximum level and the noise ratio be reduced when audio is input to the audio interface. In this paper, we propose a real-time volume control system for electric guitars based on fuzzy inference. Experimental results show that the proposed system can realize low volume and high-quality home recording by dynamically changing the volume of input audio based on fuzzy inference.
Usage of Non-Fungible Tokens in Property Registration: A Case Study for Albania

In this day and age, technology has become an inseparable part of human kind. One branch of technology is blockchain. What makes blockchain a great innovation is the ability to store transactions in a reliable, transparent, immutable and decentralized way. The latest derivates of this technology are non-fungible tokens (NFT). Non-fungible tokens are cryptographically unique, non-divisible and not replaceable assets and they represent a lot of future implementation possibilities. In the post-communist era in Albania, there have been many issues related with property registration. A lot of conflicts regarding the ownership of land between different subjects have yet to be resolved even though 30 years have passed by. This paper explores the idea of using non-fungible tokens in property registration as a way to solve the ongoing problems in this area. A theoretical approach analyzing the advantages and disadvantages of implementing this technology will be provided.
Implementation of an Animal Information Presentation System Using Mixed Reality Technology

In this study, we implemented an animal information presentation system using a mixed reality (MR) headset. We adopted the MR system with an MR headset instead of the augmented reality (AR) system with mobile terminals because it is intended for zoo usage. However, there is a concern that the use of AR systems may narrow the field of vision, which is caused by concentration on viewing information on mobile devices and a close physical distance between users in some cases. Considering the social situation caused by the coronavirus, it is crucial to avoid creating a situation with a close physical distance. This allows a user to browse animal information while ensuring a certain physical distance. Therefore, using the system of an MR headset secures a field of view in real space and achieves a hands-free operation.
Development of a Virtual Object-Sharing System Among Multiple Users in a Mixed Reality Space

In recent years, mixed reality (MR) technology has been attracting much attention. A user can superimpose virtual objects on a real space using an MR device. In this study, we developed a virtual object-sharing system. This system can share virtual objects superimposed on real space among multiple users. In previous studies, the virtual space-sharing system we developed required large-scale virtual reality equipment and virtual space construction in advance. The virtual object-sharing system developed in this study can easily place virtual objects in real space using MR technology. Furthermore, the virtual objects can be shared among multiple users.
An Intelligent Interface for Human-Computer Interaction in Legal Domain

Technological evolution and advances in the field of artificial intelligence have brought about considerable transformations in every area of our lives, also changing our various needs. In particular, the ever-increasing development and use of messaging applications have enabled the growth of services closer to users, as they can be seen as excellent means of advertising, sales and customer service. This is precisely why business models have changed drastically, moving towards new technologies such as ChatBots. The messaging applications nowadays are used daily while ensuring that they can keep up with the pace of this increasingly hectic and demanding world thanks to their 24/7 availability, low costs and customised real-time services. This paper aims to provide a general description and design principle of a ChatBot, designed and developed for the CREA2 (Conflict Resolution with Equitative Algorithms) platform, which includes the management and automatic resolution of disputes concerning the division of assets, trying to avoid costs and bureaucracy.
Uncertainty quantification for operators in online reinforcement learning

In online reinforcement learning, operators predict the return by weighting the successors’ estimated value. However, due to the lack of uncertainty quantification, weights assigned by operators are affected by the potentially biased estimations. As a result, the partial order of estimated values is ineffective. To increase the probability of outputting an optimal partial order, this paper introduces the hedonistic expected value (HEV), an upper bound of the return’s expectation to quantify the uncertainty. Notably, for compatibility reasons, some complex operators are rewritten as the weighted-sum forms. Based on the weighted-sum form of the operator, the variant Q-learning, namely uncertainty quantification based Q-learning is proposed in this paper. In the proposed algorithm, the weights assigned by HEV of the successors are compatible with the existing operators. The prediction of the return is not only the sum over the weights succeeding the operator but also over the weights following HEV through re-weighting. The greediness of the re-weighted operator is unchanged, and the contraction mapping indicates the convergence can be maintained. We demonstrate that the proposed algorithm with HEV performs favorably in practice.
Human-Centered Protocols for Secure Data Management in Distributed Systems

In this paper a human-centered protocol will be presented for efficient and secure information management in distributed systems. Such techniques will be used in creation new security applications oriented on application of personal features in security solutions. Human-centered information management allow to facilitate data distribution in cloud infrastructure and distributed systems. Such techniques also allow to increase security of data management procedures.
A Transportation Routing Method Based on A$$^{*}$$ Algorithm and Hill Climbing for Swarm Robots in WLAN Environment

The Vehicle Routing Problem (VRP) is an optimization problem that satisfies various constraints and minimizes the total route cost by multiple vehicles. In this paper, we propose a transportation routing method based on $$A^{*}$$ algorithm and Hill Climbing (HC) considering WLAN connected Swarm Robots (WSRs). In addition, we compare the simulation results of the proposed method for the initial solution with/without the clustering k-means method considering the normal and uniform distributions of loads. The proposed method optimizes the transportation sequence and can decide the transportation route of each WSR considering WLAN connectivity.
Generating Personalized Phishing Emails for Social Engineering Training Based on Neural Language Models

To prevent phishing attacks, social engineering training is a practical way by reinforcing the concepts of being aware of phishing emails. However, existing social engineering training relies on manual planning and artificially design, which suffers from scale and cost concerns. In this paper, we explore the idea of using natural language generation techniques for automatic social engineering training phishing mail generation. We present AI-Phishing, a novel phishing mail generation to facilitate personalized social engineering training planning. Users can utilize AI-Phishing to generate personalized phishing emails according to a given title and keywords.
Using Deep Learning to Predict Final HER2 Status in Invasive Breast Cancers That are Equivocal (2+) by Immunohistochemistry

Invasive breast carcinomas are routinely tested for HER2 using immunohistochemistry (IHC), with reflex in situ hybridization (ISH) for those scored as equivocal (2+). ISH testing is expensive, time-consuming, and not universally available. In this study, we trained a deep learning algorithm to directly predict HER2 gene amplification status from HER2 2+ IHC slides. Data included 115 consecutive cases of invasive breast carcinoma scored as 2+ by IHC that had follow-up HER2 ISH testing. An external validation data set was created from 36 HER2 IHC slides prepared at an outside institution. All internal IHC slides were digitized and divided into training (80%), and test (20%) sets with 5-fold cross-validation. Small patches (256×256 pixels) were randomly extracted and used to train convolutional neural networks with EfficientNet B0 architecture using a transfer learning approach. Predictions for slides in the test set were made on individual patches, and these predictions were aggregated to generate an overall prediction for each slide. This resulted in a receiver operating characteristic area under the curve of 0.83 with an overall accuracy of 79% (sensitivity=0.70, specificity=0.82). Analysis of external validation slides resulted in a receiver operating characteristic area under the curve of 0.79 with an overall accuracy of 81% (sensitivity=0.50, specificity=0.82). Although the sensitivity and specificity are not high enough to negate the need for reflexive ISH testing entirely, this approach may be useful for triaging cases more likely to be HER2 positive and initiating treatment planning in centers where HER2 ISH testing is not readily available.
Empirical comparison of spatial experience between photo-based IVE and real space

The photo-based immersive virtual environment (IVE) has become an increasingly popular method to deliver spatial experiences because it provides users with a high sense of presence and interactivity. However, there is still a lack of empirical evidence on the degree of realism of spatial experiences. Thus, the present study aims to investigate the reality of photo-based IVE spatial experience. We recruited 74 participants and conducted surveys to compare a photo-based IVE, conventional methods, and actual visit in terms of the spatial information and appraisal of the space. The results indicated that the users’ impression of the space in the photo-based IVE was similar to that in the real space because of a high sense of presence, but they less accurately recall the specific physical characteristics of the space owing to high cognitive load. The findings of the present study contribute to the understanding of spatial experiences via photo-based IVE.
Zeta revisited: A rejoinder

Abstract Zeta is a relatively recently developed tool for authorship attribution and computational stylistics (Craig and Kinney, 2009, Shakespeare, Computers, and the Mystery of Authorship. Cambridge: Cambridge University Press). In ‘The Interpretation of Zeta Test Results’ (2019), Perves Rizvi raised several objections to the method. I responded to and rejected those objections in ‘Zeta Revisited’ (Hoover, 2021a, Zeta revisited. Digital Scholarship in the Humanities, 1–20). Rizvi has renewed two of his main objections in a critique of my response in his ‘The Interpretation of Zeta Test Results: A Supplement’ (2022). First, he reiterates and expands his argument that the separation of base and counter segments in a Zeta analysis is a mechanical consequence of Zeta, rather than a significant and meaningful finding. Second, he again rejects the use of the bisector line to attribute texts in a Zeta analysis as unsound and adds new arguments that it leads to invalid attributions. This rejoinder provides further evidence and argument that both of Rizvi’s claims must be rejected.
$$\pi $$VAE: a stochastic process prior for Bayesian deep learning with MCMC

Stochastic processes provide a mathematically elegant way to model complex data. In theory, they provide flexible priors over function classes that can encode a wide range of interesting assumptions. However, in practice efficient inference by optimisation or marginalisation is difficult, a problem further exacerbated with big data and high dimensional input spaces. We propose a novel variational autoencoder (VAE) called the prior encoding variational autoencoder ( π VAE). π VAE is a new continuous stochastic process. We use π VAE to learn low dimensional embeddings of function classes by combining a trainable feature mapping with generative model using a VAE. We show that our framework can accurately learn expressive function classes such as Gaussian processes, but also properties of functions such as their integrals. For popular tasks, such as spatial interpolation, π VAE achieves state-of-the-art performance both in terms of accuracy and computational efficiency. Perhaps most usefully, we demonstrate an elegant and scalable means of performing fully Bayesian inference for stochastic processes within probabilistic programming languages such as Stan.
Lipreading Using Liquid State Machine with STDP-Tuning

Lipreading refers to the task of decoding the text content of a speaker based on visual information about the movement of the speaker’s lips. With the development of deep learning in recent years, lipreading has attracted extensive research. However, the deep learning method requires a lot of computing resources, which is not conducive to the migration of the system to edge devices. Inspired by the work of Spiking Neural Networks (SNNs) in recognizing human actions and gestures, we propose a lipreading system based on SNNs. Specifically, we construct the front-end feature extractor of the system using Liquid State Machine (LSM). On the other hand, a heuristic algorithm is used to select appropriate parameters for the classifier in the backend. On small-scale lipreading datasets, our recognition accuracy achieves good results. We claim that our network performs better in terms of accuracy and ratio of learned parameters compared to other networks, and has superior advantages in terms of network complexity and training cost. On the AVLetters dataset, our model achieves a 5% improvement in accuracy over traditional methods and a 90% reduction in parameters over the state-of-the-art.