Reviewerly/exHarmony huggingface dataset:
Fixed degree of decentralization DPoS consensus mechanism in blockchain based on adjacency vote and the average fuzziness of vague value

The Delegated Proof of Stake (DPoS) consensus mechanism is the fastest, most effective, most decentralized, and most flexible consensus model. The DPoS consensus uses the power of stakeholders to agree to vote in resolving consensus issues in a fair and democratic manner. However, in a traditional DPoS consensus mechanism, the degree of decentralization is undefined, and the number of agent nodes is fixed in advance. In this paper, a new fixed degree of decentralization for the DPoS consensus mechanism is proposed based on the adjacency vote and the average fuzziness of vague value. Moreover, the degree of decentralization is defined and discussed. As nodes are dynamically added and withdrawn, the total number of nodes in the blockchain changes. The proposed new consensus mechanism can automatically determine the number of agent nodes to be selected, based on the degree of decentralization and the total number of nodes in the blockchain. The adjacency vote and the average fuzziness of vague value are used to select agent nodes, and this method improves the fairness and security of the blockchain. We analyze five agent nodes, selected from 15 nodes, using the balloting distribution results. The feasibility and effectiveness of our new consensus mechanism are confirmed based on two test examples.
Property Identity and Relevant Conditionals

In ‘Properties, Propositions, and Conditionals’ Field [2021] advances further on our understanding of the logic and meaning of naive theories – theories that maintain, in the face of paradox, basic assumptions about properties and propositions. His work follows in a tradition going back over 40 years now, of using Kripke fixed-point model constructions to show how naive schemas can be (Post) consistent, as long as one embeds in a non-classical logic. A main issue in all this research is the question of finding a suitable conditional for the naive schemas, an implication connective that is well-behaved enough to be useful but weak enough to maintain coherence when there are paradoxes in the surrounding aether. Field takes several cues from the exemplary work of Ross Brady. Brady's work is predominantly concerned with models for naive theories in relevant logic. Field's considered view is that ‘relevant conditionals are just the wrong tool for naive theories’ [ibid.: 140]. In this note, I reply that a relevant conditional is an indispensable tool for at least one very important job: giving identity conditions for properties.
Lightweight URL-based phishing detection using natural language processing transformers for mobile devices

Hackers are increasingly launching phishing attacks via SMS and social media. Games and dating apps introduce yet another attack vector. However, current deep learning-based phishing detection applications are not applicable to mobile devices due to the computational burden. We propose a lightweight phishing detection algorithm that distinguishes phishing from legitimate websites solely from URLs to be used in mobile devices. As a baseline performance, we apply Artificial Neural Networks (ANNs) to URL-based and HTML-based website features. A model search results in 15 ANN models with accuracies >96%, comparable to state-of-the-art approaches. Next, we test the performance of deep ANNs on URL-based features only; however, all models perform poorly with the highest accuracy of 86.2%, indicating that URL-based features alone are not adequate to detect phishing websites even with deep ANNs. Since language transformers learn to represent context-dependent text sequences, we hypothesize that they will be able to learn directly from the text in URLs to distinguish between legitimate and malicious websites. We apply two state-of-the-art deep transformers (BERT and ELECTRA) for phishing detection. Testing custom and standard vocabularies, we find that pre-trained transformers available for immediate use (with fine-tuning) outperform the model trained with the custom URL-based vocabulary. Using pre-trained transformers to predict phishing websites from only URLs has four advantages: 1) requires little training time (~8 minutes), 2) is more easily updatable than feature-based approaches because no pre-processing of URLs is required, 3) is safer to use because phishing websites can be predicted without physically visiting the malicious sites and 4) is easily deployable for real-time detection and is applicable to run on mobile devices.
A Deep Learning Approach to Ancient Egyptian Hieroglyphs Classification

Nowadays, advances in Artificial Intelligence (AI), especially in machine and deep learning, present new opportunities to build tools that support the work of specialists in areas apparently far from the information technology field. One example of such areas is that of ancient Egyptian hieroglyphic writing. In this study, we explore the ability of different convolutional neural networks (CNNs) to classify pictures of ancient Egyptian hieroglyphs coming from two different datasets of images. Three well-known CNN architectures (ResNet-50, Inception-v3 and Xception) were taken into consideration and trained on the available images. The paradigm of transfer learning was tested as well. In addition, modifying the architecture of one of the previous networks, we developed a specifically dedicated CNN, named Glyphnet, tailoring its complexity to our classification task. Performance comparison tests were performed and Glyphnet showed the best performances in respect to the other CNNs. In conclusion, this work shows how the ancient Egyptian hieroglyphs identification task can be supported by the deep learning paradigm, laying the foundation for information tools supporting automatic documents recognition, classification and, most importantly, the language translation task.
Do all facial emojis communicate emotion? The impact of facial emojis on perceived sender emotion and text processing

Facial emojis can express a variety of positive and negative emotions, and are commonly used in digital, written communication. However, little is known about how emojis impact text processing and how different emoji-text combinations give rise to a sender's mental state. In this study, we investigated how facial emojis with positive valence (= happy emojis) and facial emojis with negative valence (= upset emojis) embedded in emotionally ambiguous/neutral text affect the perceived mental state of the sender using ratings (Experiment 1) and the processing of the text messages using Event-Related Potentials (Experiment 2). We predicted that (1) the same text message with happy and upset emojis would convey different sender mental states, and (2) emoji valence would affect the processing of subsequent text in valence-specific ways. Our Experiment 1 results showed that while texts with upset emojis convey specific sender mental states, texts with happy emojis convey positive emotion more generally, with no further differentiation between emojis. In ERPs (Experiment 2), we found that emojis affect subsequent text processing at N400, and emoji valence affects processing downstream at the second word. We concluded that all facial-emojis impact text processing, but happy and upset emojis carry differential social-emotional salience and impact text processing differently when content becomes available.
Towards 5G: Joint Optimization of Video Segment Caching, Transcoding and Resource Allocation for Adaptive Video Streaming in a Multi-Access Edge Computing Network

Caching and transcoding at multi-access edge computing (MEC) server and wireless resource allocation in eNodeB interact with each other and together determine the quality of experience (QoE) of dynamic adaptive streaming over HTTP (DASH) clients. However, the relationship among the three factors has not been explored, which has led to limited improvement in clients’ QoE. Therefore, we propose a joint optimization framework of video segment caching and transcoding in MEC servers and resource allocation to improve the QoE of DASH clients. Based on the established framework, we develop an MEC caching management mechanism that consists of the MEC caching partition, video segment deletion, and MEC caching space transfer. Then, a joint optimization algorithm that combines the video segment caching and transcoding in the MEC server and resource allocation is proposed. In the algorithm, the clients’ channel state and the playback status and cooperation among MEC servers are employed to estimate the client's priority, video segment representation switch and continuous playback time. Considering the above four factors, we develop a utility function model of clients’ QoE. Then, we formulate a mixed-integer nonlinear programming mathematical model to maximize the total utility of DASH clients, where the video segment caching and transcoding strategy and resource allocation strategy are jointly optimized. To solve this problem, we propose a low-complexity heuristic algorithm that decomposes the original problem into multiple subproblems. The simulation results show that our proposed algorithms efficiently improve client's throughput, received video quality and hit ratio of video segments while decreasing the playback rebuffering time, video segment representation switch and system backhaul traffic.
Evidential instance selection for K-nearest neighbor classification of big data

Many instance selection algorithms have been introduced to reduce the high storage requirements and computation complexity of K-nearest neighbor (K-NN) classification rules. However, the information provided by the neighbors of one instance was still not completely utilized in many studies. The information is usually in the form of a quantitative metric for determining whether an instance can be selected. Thus, many instances may have the same quality, which confuses the selection results. In addition, the proposed metrics are simply added without deeper fusion and the information loss has further negative effects. To address these issues, we propose a new instance selection algorithm for K-NN rules in the evidence theory framework called evidential instance selection (EIS). The basic idea is that all neighbors of every instance first provide distinct items of evidence regarding the estimated value of the label (called the estimation label) for each instance. After fusing the items of evidence and computing the conflicts among them, instances with higher conflict are considered more likely to be near the class boundaries. Finally, the selection of boundary instances is formalized as solving an optimal problem, where the objective function considers both the reduction rate and classification accuracy. When dealing with big data sets, EIS is enhanced as a distributed and parallel version called EIS-AS by applying Apache Spark to alleviate the computational bottleneck. We tested EIS and EIS-AS with 30 small data sets and six big data sets, respectively, which contained up to 11 million instances. The experimental results showed that EIS performed well at simplifying the raw training data and EIS-AS could cope with big data sets in an appropriate manner.
CHA: Categorical Hierarchy-based Attention for Next POI Recommendation

Next Point-of-interest (POI) recommendation is a key task in improving location-related customer experiences and business operations, but yet remains challenging due to the substantial diversity of human activities and the sparsity of the check-in records available. To address these challenges, we proposed to explore the category hierarchy knowledge graph of POIs via an attention mechanism to learn the robust representations of POIs even when there is insufficient data. We also proposed a spatial-temporal decay LSTM and a Discrete Fourier Series-based periodic attention to better facilitate the capturing of the personalized behavior pattern. Extensive experiments on two commonly adopted real-world location-based social networks (LBSNs) datasets proved that the inclusion of the aforementioned modules helps to boost the performance of next and next new POI recommendation tasks significantly. Specifically, our model in general outperforms other state-of-the-art methods by a large margin.
Preserving Symmetries for Variational Quantum Eigensolvers in the Presence of Noise

One of the most promising applications of noisy intermediate-scale quantum computers is the simulation of molecular Hamiltonians using the variational quantum eigensolver. We show that encoding symmetries of the simulated Hamiltonian in the VQE ansatz reduces both classical and quantum resources compared to other, widely available ansatze. Through simulations of the H$_2$ molecule, we verify that these improvements persist in the presence of noise. This simulation is performed with IBM software using noise models from real devices. We also demonstrate how these techniques can be used to find molecular excited states of various symmetries using a noisy processor. We use error mitigation techniques to further improve the quality of our results.
A Conceptual Model to Investigate the Role of Mobile Game Applications in Education during the COVID-19 Pandemic

During the COVID-19 pandemic, educational mobile games may play a significant role to facilitate students’ learning. Several studies have indicated that these games using mobile phones may improve students’ learning motivation and effectiveness when they are equipped with appropriate learning strategies. However, investigating the impact of learning strategies in students’ utilization of educational mobile games has received little scholarly attention during the COVID-19 pandemic. Hence, this research proposed two learning games scenarios to fill this gap. In the first scenario, students were offered an educational mobile game with a learning strategy called ‘scaffolding strategy’; while in the second scenario, the same game was offered without the strategy. To achieve this objective, an experimental design with a research model was developed to examine the role of scaffolding learning strategy in students’ use of educational mobile games. In this experimental study, 43 students from two classes participated in the two learning scenarios. The results indicate that educational mobile gaming with the scaffolding learning strategy significantly influenced students’ utilization of the mobile game. In addition, the adoption of the learning strategy significantly affected students’ perceived enjoyment, perceived usefulness, perceived ease of use, and behavioural intention to use, compared with the same game without the learning strategy. The results also indicate that the introduction of the scaffolding learning strategy into the educational mobile game will increase students’ learning effectiveness and motivation.
Digital competence in the training of pre-service teachers: Perceptions of students in the degrees of early childhood education and primary education

(2021). Digital competence in the training of pre-service teachers: Perceptions of students in the degrees of early childhood education and primary education. Journal of Digital Learning in Teacher Education: Vol. 37, No. 4, pp. 262-278.
Design and Implementation of Blind Modulation Classification for Asynchronous MIMO-OFDM System

In this paper, we design and implement a tree-based blind modulation classification algorithm for asynchronous multiple-input multiple-output and orthogonal frequency division multiplexing (MIMO-OFDM) systems. It can classify many of the linearly modulated signals, such as binary phase-shift keying (BPSK), quadrature PSK (QPSK), offset QPSK, minimum shift keying, and 16-quadrature amplitude modulation. The proposed classifier works in the presence of unknown frequency, timing, and phase offsets and with no prior knowledge of channel state information. Classification is performed in three steps. In the first step, pre-processing is done on the received signal to nullify the effect of timing offset. In the second step, key features are extracted by calculating higher-order cumulants of the frequency-domain signal. In the third step, thresholds are determined by using the likelihood ratio test. A closed-form theoretical derivation for the probability of correct classification is obtained. The Monte Carlo simulations are conducted to compare the performance of the proposed algorithm with the existing algorithms. Finally, the proposed algorithm is validated through radio frequency testbed measurements over an indoor propagation environment.
IoT Big Data provenance scheme using blockchain on Hadoop ecosystem

Abstract The diversity and sheer increase in the number of connected Internet of Things (IoT) devices have brought significant concerns associated with storing and protecting a large volume of IoT data. Storage volume requirements and computational costs are continuously rising in the conventional cloud-centric IoT structures. Besides, dependencies of the centralized server solution impose significant trust issues and make it vulnerable to security risks. In this paper, a layer-based distributed data storage design and implementation of a blockchain-enabled large-scale IoT system are proposed. It has been developed to mitigate the above-mentioned challenges by using the Hyperledger Fabric (HLF) platform for distributed ledger solutions. The need for a centralized server and a third-party auditor was eliminated by leveraging HLF peers performing transaction verifications and records audits in a big data system with the help of blockchain technology. The HLF blockchain facilitates storing the lightweight verification tags on the blockchain ledger. In contrast, the actual metadata are stored in the off-chain big data system to reduce the communication overheads and enhance data integrity. Additionally, a prototype has been implemented on embedded hardware showing the feasibility of deploying the proposed solution in IoT edge computing and big data ecosystems. Finally, experiments have been conducted to evaluate the performance of the proposed scheme in terms of its throughput, latency, communication, and computation costs. The obtained results have indicated the feasibility of the proposed solution to retrieve and store the provenance of large-scale IoT data within the Big Data ecosystem using the HLF blockchain. The experimental results show the throughput of about 600 transactions, 500 ms average response time, about 2–3% of the CPU consumption at the peer process and approximately 10–20% at the client node. The minimum latency remained below 1 s however, there is an increase in the maximum latency when the sending rate reached around 200 transactions per second (TPS).
Model-free control of dynamical systems with deep reservoir computing

Abstract We propose and demonstrate a nonlinear control method that can be applied to unknown, complex systems where the controller is based on a type of artificial neural network known as a reservoir computer. In contrast to many modern neural-network-based control techniques, which are robust to system uncertainties but require a model nonetheless, our technique requires no prior knowledge of the system and is thus model-free. Further, our approach does not require an initial system identification step, resulting in a relatively simple and efficient learning process. Reservoir computers are well-suited to the control problem because they require small training data sets and remarkably low training times. By iteratively training and adding layers of reservoir computers to the controller, a precise and efficient control law is identified quickly. With examples on both numerical and high-speed experimental systems, we demonstrate that our approach is capable of controlling highly complex dynamical systems that display deterministic chaos to nontrivial target trajectories.
Development of Virtual Reality Automotive Lab for Training in Engineering Students

A Virtual Reality application was developed to be used as an immersive virtual learning strategy for Oculus Rift S Virtual Reality glasses and through Leap Motion Controller™ infrared sensors, focused on students of the Automotive Systems Engineering academic program, as a practical teaching-learning tool in the context of Education 4.0 and the pandemic caused by COVID-19 that has kept schools closed since March 2020. The technological pillars of Industry 4.0 were used to profile students so that they can meet the demands of their professional performance at the industrial level. Virtual Reality (VR) plays a very important role for the production-engineering sector in areas such as design and autonomous cars, as well as in training and driving courses. The VR application provides the student with a more immersive and interactive experience, supported by 3D models of both the main parts that make up the four-stroke combustion engine and the mechanical workshop scenario; it allows the student to manipulate the main parts of the four-stroke combustion engine through the Oculus Rift S controls and the Leap Motion Controller™ infrared sensors, and relate them to the operation of the engine, through the animation of its operation and the additional information shown for each part that makes it up in the application.
GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning

Federated Learning (FL) bridges the gap between collaborative machine learning and preserving data privacy. To sustain the long-term operation of an FL ecosystem, it is important to attract high-quality data owners with appropriate incentive schemes. As an important building block of such incentive schemes, it is essential to fairly evaluate participants’ contribution to the performance of the final FL model without exposing their private data. Shapley Value (SV)–based techniques have been widely adopted to provide a fair evaluation of FL participant contributions. However, existing approaches incur significant computation costs, making them difficult to apply in practice. In this article, we propose the Guided Truncation Gradient Shapley (GTG-Shapley) approach to address this challenge. It reconstructs FL models from gradient updates for SV calculation instead of repeatedly training with different combinations of FL participants. In addition, we design a guided Monte Carlo sampling approach combined with within-round and between-round truncation to further reduce the number of model reconstructions and evaluations required. This is accomplished through extensive experiments under diverse realistic data distribution settings. The results demonstrate that GTG-Shapley can closely approximate actual Shapley values while significantly increasing computational efficiency compared with the state-of-the-art, especially under non-i.i.d. settings.