Reviewerly/exHarmony huggingface dataset:
Cryptocurrency puzzles: a comprehensive review and re-introduction

Purpose This study reviews existing cryptocurrency research to provide answers to three puzzles in the literature. First, is cryptocurrency more like gold (i.e., a commodity) or should it be classified as a new financial asset? Second, can we apply our knowledge of the traditional capital market to the emerging cryptocurrency market? Third, what might be the future of cryptocurrency? Design/methodology/approach Bibliometric analysis is used to assess 2,098 finance-related cryptocurrency publications from the Web of Science (WoS) Core Collection database from January 2009 to April 2022. Three key research streams are identified, namely, (1) cryptocurrency features, (2) behaviour of the cryptocurrency market and (3) blockchain implications. Findings First, cryptocurrency should be viewed and regulated as a new asset class rather than a currency or a new commodity. While it can provide diversification benefits to the portfolio, cryptocurrency cannot work as a safe haven asset. Second, crypto markets are typically inefficient. Asset bubbles exist and are exacerbated by behavioural finance factors. Third, cryptocurrency demonstrates increasing potential as a medium of exchange and store of value. Originality/value Extant review papers primarily study one or two particular research topics, overlooking the interaction between topics. The few existing systematic literature reviews in this area typically have a narrow focus on trend identification. This study is the first study to provide a comprehensive review of all financial-related studies on cryptocurrency, synthesising the research findings from 2,098 publications to answer three cryptocurrency puzzles.
Exploring the Lived Experience of Behavior Change Technologies: Towards an Existential Model of Behavior Change for HCI

The majority of behavior change and persuasive technologies are exclusively addressed to modify a specific behavior. However, the focus on behavior may cloud the “existential aspects” of the process of change. To explore the lived and meaning-laden experience of behavior change, we interviewed 23 individuals who have used behavior change technology in their everyday life. The study findings highlight that behavior change is tied to meanings that point to existential matters, relates to a nexus of life circumstances, and unfolds over long periods of time. By contrast, the technology used by the participants appears mostly to focus on the present target behavior, ignoring its links to the participants’ life “context” and “time,” also providing scarce help for sense-making. Based on these findings, we surface a preliminary “existential model of behavior change,” identify several barriers that may prevent the modification of behavior and propose some design suggestions to overcome them.
Predicting hate intensity of twitter conversation threads

Tweets are the most concise form of communication in online social media. Wherein a single tweet has the potential to make or break the discourse of the conversation. Online hate speech is more accessible than ever, and stifling its propagation is of utmost importance for social media companies and users for congenial communication. Most of the research has focused on classifying an individual tweet regardless of the tweet thread/context leading up to that point. One of the classical approaches to curb hate speech is to adopt a reactive strategy after the hateful content has been published. This strategy results in neglecting subtle posts that do not show the potential to instigate hate speech on their own but may portend in the subsequent discussion ensuing in the post’s replies. In this paper, we propose DRAGNET++, which aims to predict the intensity of hatred that a tweet can bring in through its reply chain in the future. Our model uses the semantic and propagating structure of the tweet threads to maximize the contextual information leading up to and the fall of hate intensity at each subsequent tweet. We explore three publicly available Twitter datasets — Anti-Racism contains the reply tweets of a collection of social media discourse on racist remarks during US political and COVID-19 background; Anti-Social presents a dataset of 40 million tweets amidst the COVID-19 pandemic on anti-social behaviors with custom annotations; and Anti-Asian presents Twitter datasets collated based on anti-Asian behaviors during COVID-19 pandemic. All the curated datasets consist of structural graph information of the Tweet threads. We show that DRAGNET++ outperforms all the state-of-the-art baselines significantly. It beats the best baseline by an 11% margin on the Person correlation coefficient and a decrease of 25% on RMSE for the Anti-Racism dataset with a similar performance on the other two datasets.
Federated Continuous Learning Based on Stacked Broad Learning System Assisted by Digital Twin Networks: An Incremental Learning Approach for Intrusion Detection in UAV Networks

The edge of the Internet of Things (IoT), which consists of unmanned aerial vehicles (UAVs), is vulnerable to network intrusion because software and wireless connections are used extensively in the IoT. Designing an efficient intrusion detection system (IDS) model is imperative. However, when creating IDS models with distributed data collected by UAVs, it is necessary to take precautions to protect the data’s security and privacy. Furthermore, most of the IDS models are focused on one-time learning but not on continuous learning. To this end, we propose a federated continuous learning framework with a stacked broad learning system (FCL-SBLS) based on the digital twin network (DTN), which can learn and train the IDS model on new data quickly and continuously. In order to improve the efficiency and quality of the IDS model when training and aggregation, we employ an asynchronous federated learning (FL) architecture, and a deep deterministic policy gradient (DDPG)-based UAV selection scheme assisted by DTN is proposed to help the global IDS model aggregation. The presented algorithm is validated using the CIC-IDS2017 data set, and the simulation results reveal that our algorithm achieves higher efficiency and accuracy than the existing FL scheme.
A Game Theory-Based Incentive Mechanism for Collaborative Security of Federated Learning in Energy Blockchain Environment

With the digital transformation of the energy industry, energy blockchain is playing an important role in application areas such as energy data sharing and distributed power trading. In this process, the use of energy data is a top priority. Federated learning (FL) can enable the analysis and computation of energy data while protecting their privacy. However, traditional FL relies on a central server and parties involved are not fully trusted. In energy blockchain environment, FL also faces data poisoning attacks launched by energy departments, besides, the supervisory committee carrying out checking models can launch deception attacks. Therefore, we propose a game theory-based incentive mechanism for collaborative security of FL in energy blockchain environment, which can discourage nodes from taking malicious behaviors in iterative training of FL. First, we propose a FL model in energy blockchain environment, which can protect privacy and achieve collaborative security. Considering that game theory can be used to analyze the strategies of participants, we build a game model with energy departments and supervisory committee as players and design our incentive mechanism based on game theory, which is implemented by smart contracts. Even if the accuracy of model checking algorithm is low, malicious behaviors in FL can be reduced by using our incentive mechanism. In particular, we prove that our mechanism can lead game model to a Nash equilibrium (NE) that achieve collaborative security. Security analysis and experimental evaluation show that our incentive mechanism is feasible in energy blockchain with robustness, reliability and low complexity.
Utilizing Artificial Intelligence Techniques to Improve the Performance of Wireless Nodes

Advances in wireless systems have encouraged the growth and improvement of tiny, low-cost, efficient, and multi-tasking smart sensors. Wireless sensor networks are employed for sensing, collecting data and information, analysing it, and sending it to the main center to be considered quickly. During the performance, a wide range of issues appear, and the existence of some restrictions on the movement of sensors, reliable data collection mechanisms and transferring them properly and securely to the main center, and issues in the sensor network topology. In fact, all traditional methods need more ability to deal with and solve these situations. In this regard, the authors decided to highlight the importance and role of artificial intelligence technologies in designing wireless sensor networks by proposing a hybrid model that helps to construct a more practical sensor network and solve all the situations it faces. These techniques significantly improve network performance, extend battery life, develop smart nodes, and improve network functionality.
Bit Flipping Key Encapsulation for the Post-Quantum Era

It is a matter of time before quantum computers will break the cryptosystems like RSA and ECC underpinning today’s internet protocols. As Post-Quantum Cryptography (PQC) is a low-cost approach compared to others like quantum key distribution, the National Institute of Standards and Technology (NIST) has recently reviewed and analyzed numerous approaches to PQC. As a PQC candidate, Bit Flipping Key Encapsulation (BIKE) is expected to be standardized as a general-purpose Key Encapsulation Mechanism (KEM) by NIST. However, it lacks a comprehensive review of BIKE associated with technical analysis. This paper aims to present an in-depth review and analysis of the BIKE scheme with respect to relevant attacks. We provide a comprehensive review of the original McEliece (ME) scheme and present a detailed discussion on its practical challenges. Furthermore, we provide an in-depth study on the challenges of ME and BIKE cryptosystems in achieving the Indistinguishability under Chosen-Ciphertext Attack (IND-CCA) security. We provide an analysis of these cryptosystems and their security against several attacks before pointing out the research gaps for strengthening BIKE.
Augmented Reality for Building Authorities: A Use Case Study in Austria

The widespread usage of BIM and technological advances in augmented reality (AR) paves the way for Construction 4.0. This digital transformation has reached the construction industry and requires consideration of all life cycle phases. The approval phase has been little researched to date. In Vienna, the analogous process takes up to 18 months. This long duration inhibits progress in the building industry. One reason for this is objections from parties who are nonexperts and have difficulty understanding the project solely from its 2D plans. Therefore, the City of Vienna wants to redesign the building authority processes through the use of BIM and AR in the BRISE-Vienna research project. This article examines possible applications for public authorities. AR can be utilised at the interface between authorities and stakeholders and is intended to accelerate processes through better communication. For the City of Vienna, 12 AR use cases were developed from process analyses and expert interviews. In addition to the conception of the AR use cases for public authorities, a study was carried out regarding their benefits and feasibility. The use cases have the potential to increase the understanding of the project for experts and nonexperts. The best-rated AR use cases (plan checking and hearing during the permission process) will be further developed in the research project. The AR use cases should help to significantly accelerate processes in Vienna and, thus, serve as a basis for other cities and countries.
A Multi-Subsampling Self-Attention Network for Unmanned Aerial Vehicle-to-Ground Automatic Modulation Recognition System

In this paper, we investigate the deep learning applications of radio automatic modulation recognition (AMR) applications in unmanned aerial vehicle (UAV)-to-ground AMR systems. The integration of deep learning in a UAV-aided signal processing terminal can recognize the modulation mode without the provision of parameters. However, the layers used in current models have a small data processing range, and their low noise resistance is another disadvantage. Most importantly, large numbers of parameters and high amounts of computation will burden terminals in the system. We propose a multi-subsampling self-attention (MSSA) network for UAV-to-ground AMR systems, for which we devise a residual dilated module containing ordinary and dilated convolution to expand the data processing range, followed by a self-attention module to improve the classification, even in the presence of noise interference. We subsample the signals to reduce the number of parameters and amount of calculation. We also propose three model sizes, namely large, medium, and small, and the smaller the model, the more suitable it will be for UAV-to-ground AMR systems. We conduct ablation experiments with state-of-the-art and baseline models on the common AMR and radio machine learning (RML) 2018.01a datasets. The proposed method achieves the highest accuracy of 97.00% at a 30 dB signal-to-noise ratio (SNR). The weight file of the small MSSA is only 642 KB.
Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes

Over the decades, a tremendous increase has been witnessed in the production of documents available in digital form. The increased production of documents has gained so much momentum that their rate of production jumps two-fold every five years. These articles are searched over the internet via search engines, digital libraries, and citation indexes. However, the retrieval of relevant research papers for user queries is still a pipedream. This is because scientific documents are not indexed based on some subject classification hierarchies. Hence, the classification of these documents becomes a challenging task for the researchers. Classification of the documents can be two-fold: one way is to assign a single label to each document and the other is to assign multi-labels to each document based on its belonging domains. Classification of the documents can be performed by using either the available metadata or the whole content of the documents. While performing classification, there are many challenges which may belong to the dataset, feature selection technique, preprocessing methodology, and which classification model is suitable for the classification of the documents. This paper highlights the issues for single-label and multi-label classification by using either metadata or content of the documents and why metadata-based approaches are better than content-based approaches in terms of feasibility.
Quantum walks on random lattices: Diffusion, localization, and the absence of parametric quantum speedup

Discrete-time quantum walks, quantum generalizations of classical random walks, provide a framework for quantum information processing, quantum algorithms, and quantum simulation of condensed-matter systems. The key property of quantum walks, which lies at the heart of their quantum information applications, is the possibility for a parametric quantum speedup in propagation compared to classical random walks. In this work we study propagation of quantum walks on percolation-generated two-dimensional random lattices. In large-scale simulations of topological and trivial split-step walks, we identify distinct prediffusive and diffusive behaviors at different timescales. Importantly, we show that even arbitrarily weak concentrations of randomly removed lattice sites give rise to a complete breakdown of the superdiffusive quantum speedup, reducing the motion to ordinary diffusion. By increasing the randomness, quantum walks eventually stop spreading due to Anderson localization. Near the localization threshold, we find that the quantum walks become subdiffusive. The fragility of quantum speedup implies dramatic limitations for quantum information applications of quantum walks on random geometries and graphs.
Deep reinforcement learning for a multi-objective operation in a nuclear power plant

Nuclear power plant (NPP) operations with multiple objectives and devices are still performed manually by operators despite the potential for human error. These operations could be automated to reduce the burden on operators; however, classical approaches may not be suitable for these multi-objective tasks. An alternative approach is deep reinforcement learning (DRL), which has been successful in automating various complex tasks and has been applied in automation of certain operations in NPPs. But despite the recent progress, previous studies using DRL for NPP operations have limitations to handle complex multi-objective operations with multiple devices efficiently. This study proposes a novel DRL-based approach that addresses these limitations by employing a continuous action space and straightforward binary rewards supported by the adoption of a soft actor-critic and hindsight experience replay. The feasibility of the proposed approach was evaluated for controlling the pressure and volume of the reactor coolant while heating the coolant during NPP startup. The results show that the proposed approach can train the agent with a proper strategy for effectively achieving multiple objectives through the control of multiple devices. Moreover, hands-on testing results demonstrate that the trained agent is capable of handling untrained objectives, such as cooldown, with substantial success.
An automatic data quality approach to assess semantic data from cultural heritage institutions

Abstract In recent years, cultural heritage institutions have been exploring the benefits of applying Linked Open Data to their catalogs and digital materials. Innovative and creative methods have emerged to publish and reuse digital contents to promote computational access, such as the concepts of Labs and Collections as Data . Data quality has become a requirement for researchers and training methods based on artificial intelligence and machine learning. This article explores how the quality of Linked Open Data made available by cultural heritage institutions can be automatically assessed. The results obtained can be useful for other institutions who wish to publish and assess their collections.
TCGNN: Packet-grained network traffic classification via Graph Neural Networks

Network traffic classification is the fundamental and vital function for network management, network security and so on. With the traffic scenarios becoming more and more complex, current commonly used practices, e.g., port-based and payload-based classification methods, can hardly work. Even though the new emerging resorts, i.e., machine learning or deep learning methods, have increased classification accuracy, the performance is still under improvement. To improve the classification accuracy and performance, we propose a novel Graph Neural Network (GNN) based Traffic Classification proposal named TCGNN considering the insight of observing packets from a graph aspect. TCGNN first transforms each network packet into an undirected graph. Then it adopts a two-layer graph convolutional network with three different aggregation strategies so as to learn the latent application representation from the packet-transformed graph. Finally, relying on GNN’s powerful ability in learning graph representation, TCGNN can identify unknown network packets with an extremely high accuracy rate. Extensive experiments on two real-world traffic classification datasets demonstrate the superior effectiveness of TCGNN over the existing packet-grained traffic classification methods.
An effective multimodal representation and fusion method for multimodal intent recognition

Intent recognition is a crucial task in natural language understanding. Current research mainly focuses on task-specific unimodal intent recognition. However, in real-world scenes, human intentions are complex and need to be judged by integrating information such as speech, tone, expression, and action. Therefore, this paper proposes an effective multimodal representation and fusion method (EMRFM) for intent recognition in real-world multimodal scenes. First, text, audio, and vision features are extracted based on pre-trained BERT, Wav2vec 2.0, and Faster R-CNN. Then, considering the complementarity and consistency among the modalities, the modality-shared and modality-specific encoders are constructed to learn shared and specific feature representations of the modalities. Finally, an adaptive multimodal fusion method based on an attention-based gated neural network is designed to eliminate noise features. Comprehensive experiments are conducted on the multimodal intent recognition MIntRec benchmark dataset. Our proposed model achieves higher accuracy, precision, recall, and F1-score than state-of-the-art multimodal learning methods. We also conduct multimodal sentiment recognition experiments on the CMU-MOSI dataset, and our model still outperforms state-of-the-art methods. In addition, the experiment demonstrates that the model’s multimodal representation well learned the modality’s shared and specific features. The multimodal fusion of the model achieves adaptive fusion and effectively reduces possible noise interference.
Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review

Breast cancer remains the leading cause of cancer-related deaths in women worldwide. Current screening regimens and clinical breast cancer risk assessment models use risk factors such as demographics and patient history to guide policy and assess risk. Applications of artificial intelligence methods (AI) such as deep learning (DL) and convolutional neural networks (CNNs) to evaluate individual patient information and imaging showed promise as personalized risk models. We reviewed the current literature for studies related to deep learning and convolutional neural networks with digital mammography for assessing breast cancer risk. We discussed the literature and examined the ongoing and future applications of deep learning techniques in breast cancer risk modeling.
Agile Software Requirements Engineering Challenges-Solutions—A Conceptual Framework from Systematic Literature Review

Agile software requirements engineering processes enable quick responses to reflect changes in the client’s software requirements. However, there are challenges associated with agile requirements engineering processes, which hinder fast, sustainable software development. Research addressing the challenges with available solutions is patchy, diverse and inclusive. In this study, we use a systematic literature review coupled with thematic classification and gap mapping analysis to examine extant solutions against challenges; the typologies/classifications of challenges faced with agile software development in general and specifically in requirements engineering and how the solutions address the challenges. Our study covers the period from 2009 to 2023. Scopus—the largest database for credible academic publications was searched. Using the exclusion criteria to filter the articles, a total of 78 valid papers were selected and reviewed. Following our investigation, we develop a framework that takes a three-dimensional view of agile requirements engineering solutions and suggest an orchestrated approach balancing the focus between the business context, project management and agile techniques. This study contributes to the theoretical frontier of agile software requirement engineering approaches and guidelines for practice.