Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning in Blockchain-Enabled IIoT Networks

Blockchain-Enabled Industrial Internet of Things (IIoT) networks are a new norm in global developments. This paper discusses Blockchain-Enabled IIoT network challenges and the role of Reinforcement Learning techniques in offering solutions for those challenges. This paper provides (i) fundamental frameworks of Blockchain and IIoT networks, (ii) a sketch of RL techniques, e.g., Q-Learning, Multi-armed Bandit Learning, and Actor-Critic Learning, (iii) demonstrates Blockchain-Enabled IIoT networks concerning IIoT challenges, and (iv) Blockchain realizable solutions that improve IIoT. This study finds that Blockchain improves security, data integrity, and autonomy in IIoT networks. Moreover, it reduces the cost of instrument IIoT networks and builds trust among users. A blockchain-enabled most crucial solution materializes that Smart contracts facilitate transactions between parties. However, the Blockchain-Enabled IIoT network nevertheless faces diverse issues. Most importantly, the system needs to be optimized regarding forking events, energy, time to finality, transaction throughput, link security, and average Blocktime. Consequently, RL techniques such as Q-Learning plausibly provide solutions for these issues and improve the performance and efficiency of networks.
Numerical Simulation and Mathematical Modelling of Machine Learning Based Improved Crypto Currency Forecasting System

The crypto currency market is a rapidly growing trading and investment industry that has attracted merchants, investors, and entrepreneurs on a global scale never seen before in this century. By providing comparison studies and insights from the pricing data of crypto currency marketplaces, it will aid in documenting the behavior and habits of such a lucratively demanding and rapidly growing sector. The bitcoin market is at one of its all-time highs in 2021. The introduction of new exchanges has made crypto currencies more accessible to the general public, enhancing their attractiveness. This, together with the launch of several genuine crypto initiatives by some of the founders, has resulted in a surge in crypto currency users and interest. Virtual currencies are growing in popularity, with corporations like Microsoft, Dell, and Tesla all accepting them. As the number of individuals using decentralised digital currencies grows, As new currencies gain popularity, it's more important than ever to properly educate the public about them so that they understand what they possess and how their money is being invested. The algorithm predicts that the high, low, and open prices will all be close to the same value on the same day. The coefficients indicate that the high price has the biggest influence on the closing price. Contrary to popular assumption, “volume” does not add to the data on the closing price provided by the other factors. We also observed that the “marketcap” argument offers no more information. This is odd because market cap is often determined by the price of a product. After removing the two previously mentioned factors, we found no discernible difference in adjusted R2. It's worth mentioning that the model was built using data from the same time period. At the same moment as the closing price, the variables we obtained are made public. We are hopeful that the model will be able to forecast the closing price based on the existing high and low values for a given day.
Challenges for Post-Pandemic Virtual Education in Latin America: A Comparative Analysis of the Emergency Remote Higher Education Process in Chile, Mexico, and Ecuador

The COVID-19 pandemic forced Higher Education institutions all around the world to revise their praxis and update tools and numerous procedures. This study offers a comparative analysis of three Latin American students’ and instructors’ perspectives on a selection of both the pedagogical and emotional aspects of higher educational life that were affected. The report is about the participants’ perception of others’ empathy, their evaluation of the organization of teaching and learning and of collaborative learning experiences, their appreciation of the quality of learning assessment practices, and, eventually, their perception of learning. An exploratory study was carried out, based on survey research in Likert-scale form, responded to by 2742 students and 926 instructors. Significant differences were found among subsamples, with Chilean students and instructors having less favorable views in all dimensions compared with their Mexican and Ecuadorian counterparts; also, differences were found with respect to educational levels, discipline areas, and participants’ gender. This study contributes with a double-sided view of both protagonists’ perspectives (students’ and instructors’) to reinforce the importance of instructional design, instructor-student bond, meaningful and realistic assessments that allow for the application of knowledge, and opportunities for feedback in post-pandemic virtual education.
Postdigital Cityscapes

Technologies are interwoven into all aspects of postdigital cityscapes, from governance and education to culture and social interaction. The contemporary metropolis is circuited by data-traffic-systems, artificial intelligence (AI), surveillance architectures, geolocational apps, and metrics. These infrastructures are seemingly transparent, despite expansive cloud storage utilities, vast data centres, and stacks of networks. Difficulties in conceiving of the postdigital city stem from this presumed transparency as well as the evasive language of techno-positivism which distracts from the precarities, instabilities, and systemic inequities of postdigital urban architectures, arrangements, and imaginaries.
Adaptive Integration of Categorical and Multi-relational Ontologies with EHR Data for Medical Concept Embedding

Representation learning has been applied to Electronic Health Records (EHR) for medical concept embedding and the downstream predictive analytics tasks with promising results. Medical ontologies can also be integrated to guide the learning so the embedding space can better align with existing medical knowledge. Yet, properly carrying out the integration is non-trivial. Medical concepts that are similar according to a medical ontology may not be necessarily close in the embedding space learned from the EHR data, as medical ontologies organize medical concepts for their own specific objectives. Any integration methodology without considering the underlying inconsistency will result in sub-optimal medical concept embedding and, in turn, degrade the performance of the downstream tasks. In this article, we propose a novel representation learning framework called ADORE ( AD aptive O ntological RE presentations) that allows the medical ontologies to adapt their structures for more robust integrating with the EHR data. ADORE first learns multiple embeddings for each category in the ontology via an attention mechanism. At the same time, it supports an adaptive integration of categorical and multi-relational ontologies in the embedding space using a category-aware graph attention network. We evaluate the performance of ADORE on a number of predictive analytics tasks using two EHR datasets. Our experimental results show that the medical concept embeddings obtained by ADORE can outperform the state-of-the-art methods for all the tasks. More importantly, it can result in clinically meaningful sub-categorization of the existing ontological categories and yield attention values that can further enhance the model interpretability.
Controlling a Dynamic System Through Reinforcement Learning

Control theory deals with the problem of finding a control law for a given dynamic system on which one can act by means of a command. The goal is then to bring the system from a given initial state to a certain final state, possibly respecting certain criteria. In this paper, we are interested in the problems of optimal control of dynamical systems, seen under the prism of reinforcement learning (RL). RL is an area of machine learning that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences. We compare the Q-learning, a model-free reinforcement algorithm, to the feedback controller Linear Quadratic Regulator on the OpenAI Gym Cart-Pole system, a toy example which is quite famous in both the control engineering and RL community.
DdERT: Research on Named Entity Recognition for Mine Hoist Using a Chinese BERT Model

This study aims to solve the problem of named entity recognition of complex mechanical equipment faults, especially the problems of many professional terms, long sentences, fuzzy entity boundaries, entity nesting, and abbreviation ambiguity, in mine hoist fault text. Therefore, this study proposes a named entity recognition method based on domain dictionary embedding. The method first uses the fault domain knowledge of the mine hoist to construct a domain-specialized dictionary and generate a word vector of characteristic words. Secondly, the BERT pre-trained language model is used to obtain dynamic word vectors, and a dictionary adapter is loaded to obtain contextual domain lexical features to improve recognition accuracy. Finally, the conditional random field (CRF) is the model classifier to output the annotation sequence with the highest score. The experimental results show that this model achieves better than several baseline models and effectively improves the accuracy of fault named entity identification for mine hoists. The innovation of this study is the combination of domain dictionary embedding and a BERT pre-trained language model, which improves the accuracy and robustness of named entity recognition. Therefore, the results of this study have essential research significance for improving the accuracy of fault named entity identification of mine hoists and the construction of fault knowledge maps.
Few-Shot Object Detection with Local Feature Enhancement and Feature Interrelation

Few-shot object detection (FSOD) aims at designing models that can accurately detect targets of novel classes in a scarce data regime. Existing research has improved detection performance with meta-learning-based models. However, existing methods continue to exhibit certain imperfections: (1) Only the interacting global features of query and support images lead to ignoring local critical features in the imprecise localization of objects from new categories. (2) Convolutional neural networks (CNNs) encounter difficulty in learning diverse pose features from exceedingly limited labeled samples of unseen classes. (3) Local context information is not fully utilized in a global attention mechanism, which means the attention modules need to be improved. As a result, the detection performance of novel-class objects is compromised. To overcome these challenges, a few-shot object detection network is proposed with a local feature enhancement module and an intrinsic feature transformation module. In this paper, a local feature enhancement module (LFEM) is designed to raise the importance of intrinsic features of the novel-class samples. In addition, an Intrinsic Feature Transform Module (IFTM) is explored to enhance the feature representation of novel-class samples, which enriches the feature space of novel classes. Finally, a more effective cross-attention module, called Global Cross-Attention Network (GCAN), which fully aggregates local and global context information between query and support images, is proposed in this paper. The crucial features of novel-class objects are extracted effectively by our model before the feature fusion between query images and support images. Our proposed method increases, on average, the detection performance by 0.93 (nAP) in comparison with previous models on the PASCAL VOC FSOD benchmark dataset. Extensive experiments demonstrate the effectiveness of our modules under various experimental settings.
Generative Image Steganography based on Guidance Feature Distribution

Without modification, generative steganography is more secure than modification-based steganography. However, existing generative steganography methods still have the limitations, such as low embedding capacity and poor quality. To solve these issues, a synthesis-based generative steganographic model is proposed in this paper. In the image synthesis task, guidance features are utilized to synthesize images with specific styles and attributes. Due to the consistency of the guidance features before and after image synthesis, the features can be used as cover for steganography. The proposed model adopts the mean and standard deviation to quantify the distribution of guidance features, enabling the secret hiding within different trends of the feature distribution. By controlling the statistical dispersion of the embedded guidance features through the mean and standard deviation, the original feature distribution is preserved, and the synthesized image maintains good generation quality. The space of guidance features contains styles and attribute descriptions of various images, and offering a large space for information hiding. According to the experimental results, compared with existing steganography, the proposed steganographic model achieves better quality and hidden capacity with strong robustness.
Co-Training-Teaching: A Robust Semi-Supervised Framework for Review-Aware Rating Regression

Review-aware Rating Regression (RaRR) suffers the severe challenge of extreme data sparsity as the multi-modality interactions of ratings accompanied by reviews are costly to obtain. Although some studies of semi-supervised rating regression are proposed to mitigate the impact of sparse data, they bear the risk of learning from noisy pseudo-labeled data. In this article, we propose a simple yet effective paradigm, called co-training-teaching ( CoT 2 ), for integrating the merits of both co-training and co-teaching toward robust semi-supervised RaRR. CoT 2 employs two predictors trained with different feature sets of textual reviews, each of which functions as both “labeler” and “validator.” Specifically, one predictor (labeler) first labels unlabeled data for its peer predictor (validator); after that, the validator samples reliable instances from the noisy pseudo-labeled data it received and sends them back to the labeler for updating. By exchanging and validating pseudo-labeled instances, the two predictors are reinforced by each other in an iterative learning process. The final prediction is made by averaging the outputs of both the refined predictors. Extensive experiments show that our CoT 2 considerably outperforms the state-of-the-art recommendation techniques in the RaRR task, especially when the training data is severely insufficient.
The effect of inequality on poverty and severity of poverty in sub‐Saharan Africa: The role of financial development institutions

Abstract The present study investigates the incidence of financial institutions' dynamics of depth and access in the effect of income inequality on poverty and the severity of poverty in 42 sub‐Saharan African countries from 1980 to 2019. The Gini index is used to measure income inequality while poverty is measured as the poverty headcount ratio, and the severity of poverty is generated as the square of the poverty gap index. An interactive quantile regression approach is used as an empirical strategy. Income inequality unconditionally increases poverty dynamics while the financial institutions' depth and access mitigate the adverse effects of income inequality on poverty dynamics. Financial institutions' policy thresholds or minimum financial institution levels needed to completely dampen the adverse effects of income inequality on poverty dynamics are provided. The findings are contingent on existing levels of poverty, poverty measurement, and proxies for financial institutions. Policy implications are discussed. Related Articles Abdulai, Abdul‐Gafaru, Justice Nyigmah Bawole, and Emmanuel Kojo Sakyi. 2018. “Rethinking Persistent Poverty in Northern Ghana: The Primacy of Policy and Politics over Geography.” Politics &amp; Policy 46(2): 233–62. https://doi.org/10.1111/polp.12250 . Adegboye, Alex, Kofo Adegboye, Uwalomwa Uwuigbe, Stephen Ojeka, and Eyitemi Fasanu. 2023. “Taxation, Democracy, and Inequality in Sub‐Saharan Africa: Relevant Linkages for Sustainable Development Goals.” Politics &amp; Policy 51(4): 696–722. https://doi.org/10.1111/polp.12547 . Nchofoung, Tii, Simplice Asongu, Vanessa Tchamyou, and Ofeh Edoh. 2022. “Gender, Political Inclusion, and Democracy in Africa: Some Empirical Evidence.” Politics &amp; Policy 51(1): 137–55. https://doi.org/10.1111/polp.12505 .
Complex quantum network models from spin clusters

Abstract In the emerging quantum internet, complex network topology could lead to efficient quantum communication and robustness against failures. However, there are concerns about complexity in quantum communication networks, such as potentially limited end-to-end transmission capacity. These challenges call for model systems in which the impact of complex topology on quantum communication protocols can be explored. Here, we present a theoretical model for complex quantum communication networks on a lattice of spins, wherein entangled spin clusters in interacting quantum spin systems serve as communication links between appropriately selected regions of spins. Specifically, we show that ground state Greenberger-Horne-Zeilinger clusters of the two-dimensional random transverse-field Ising model can be used as communication links between regions of spins. Further, the resulting quantum networks can have complexity comparable to that of the classical internet. Our work provides a generative model for further studies towards determining the network characteristics of the emerging quantum internet.
Bitcoin equilibrium dynamics: a long term approach

In the long run, Bitcoin transaction fees are the only source of revenue for miners. They compete broadly in two main ways: proof of work effort to win blocks; and transaction processing to gather fee rewards into the blocks they win. This paper contributes to existing literature by developing a dynamic model that separates these two functions, and explores implications for aggregate efficiency outcomes. Specifically, when set by free market forces (unrestricted by artificially imposed block size caps), what happens to overall transaction prices and quantities relative to total energy use? When is it worth Stackelberg-leading miners investing in efficiency-improving R&amp;amp;D? What effect does this have on overall efficiency over time? By explicitly separating specialised capital dedicated to SHA256 hashing (for proof of work) from transaction processing capital (for transaction collection and verification), this paper sheds light on these questions. One key conclusion is that miner innovation lowers energy use per transaction over time for elastic enough transaction demand schedules. The more competitors Bitcoin has (existing fiat and data services, and other new Blockchain-based systems), the stronger is this conclusion.
Artificial Intelligence Based Global Solar Radiation Prediction

Solar energy is one of the cleanest renewable energy sources and has no environmental impact. Solar radiation data is important to solar engineers, designers and architects which is also fundamental for efficiently determining irrigation water needs and potential yield of crops, among others. Solar energy is mainly used to meet the growing electricity demand and decline the amount of CO2 emission thus preserving fossil fuels and natural resources. The temperature and sunshine duration are measured by most of the meteorological services all over the world but global solar radiation measurements are limited due to the restricted number of solar radiation measuring stations and some of the data are missing. In order to estimate the solar radiation in the other areas where the meteorological stations are not established, the theoretical solar radiation estimation models proposed by various researchers have proved handy. One of the main important assignments is to recognize the site with high solar energy potential for renewable power generation. This assists in accomplishing the target of the Indian solar mission project by the year 2022. The present work aims at the prediction of solar radiation using artificial neural network models which are applied to four different locations across India. As validation, measured and estimated solar radiation data were analyzed in terms of the square of the correlation coefficient and RMSE. The outcomes of this study will play a vital role in the estimation of global solar radiation with less percentage of error.
EMOS: Enhanced moving object detection and classification via sensor fusion and noise filtering

Abstract Dynamic object detection is essential for ensuring safe and reliable autonomous driving. Recently, light detection and ranging (LiDAR)‐based object detection has been introduced and shown excellent performance on various benchmarks. Although LiDAR sensors have excellent accuracy in estimating distance, they lack texture or color information and have a lower resolution than conventional cameras. In addition, performance degradation occurs when a LiDAR‐based object detection model is applied to different driving environments or when sensors from different LiDAR manufacturers are utilized owing to the domain gap phenomenon. To address these issues, a sensor‐fusion‐based object detection and classification method is proposed. The proposed method operates in real time, making it suitable for integration into autonomous vehicles. It performs well on our custom dataset and on publicly available datasets, demonstrating its effectiveness in real‐world road environments. In addition, we will make available a novel three‐dimensional moving object detection dataset called ETRI 3D MOD.
Federated learning vulnerabilities, threats and defenses: A systematic review and future directions

Today, a broad range of items, ranging from smartphones to smart cars are connected together via the Internet, also known as the Internet of Things (IoT). The IoT is powered by Machine Learning (ML) to facilitate client services and applications. Traditionally, centralized ML techniques require the collection and processing of enormous data sets, which may not be feasible in the context of realistic IoT applications scenarios, due to the exponential increase in IoT devices. Federated learning (FL) is a new paradigm of ML training that relies on decentralized collaborative learning, between various clients, where data is located locally with each client. FL has brings about many advantages, such as helping preserve privacy where the client's local data is retained locally to train the model. While FL has emerged as an attractive and promising training solution to protect clients’ privacy, it needs additional exploration to specify its potential security implications, as these may preclude its routine adoption. Existing FL algorithms, security and privacy techniques exhibit new vulnerabilities, which could be exploit by attackers to compromise the FL model. Thus, it is critical to increase awareness of the potential consequences associated with novel threats to FL models. Prior research has examined various FL concepts, such as algorithms, attacks, privacy, vulnerabilities, etc. However, these concepts were not encompassed in a single survey. To the best of our knowledge, this is the first survey combining analysis of FL security and privacy techniques by highlighting sources of FL vulnerabilities, possible attacks and privacy techniques, and presenting FL basics including data distribution and aggregation algorithms. We then present state of the art studies in FL attack studies, and privacy techniques for protecting against these attacks. Finally, we conclude by identifying FL challenges and future research directions to address current limitations.