Reviewerly/exHarmony huggingface dataset:
On the asymptotic rate of convergence of Stochastic Newton algorithms and their Weighted Averaged versions

Most machine learning methods can be regarded as the minimization of an unavailable risk function. To optimize the latter, with samples provided in a streaming fashion, we define general (weighted averaged) stochastic Newton algorithms, for which a theoretical analysis of their asymptotic efficiency is conducted. The corresponding implementations are shown not to require the inversion of a Hessian estimate at each iteration under a quite flexible framework that covers the case of linear, logistic or softmax regressions to name a few. Numerical experiments on simulated and real data give the empirical evidence of the pertinence of the proposed methods, which outperform popular competitors particularly in case of bad initializations.
Smart Groups: A system to orchestrate collaboration in hybrid learning environments. A simulation study

COVID-19 has brought new hybrid learning environments with some students in the classroom and some others online, synchronously, due to the needs of social distancing. These new hybrid learning environments pose new challenges, for example for group collaboration. This paper presents Smart Groups, a system aimed at helping teachers to orchestrate collaboration in hybrid learning environments and assesses its usability and usefulness through a simulation study. Smart Groups identifies the students that are in the classroom and online, automates the creation of groups (recommending collaborative learning flow patterns to teachers and considering the previous work done by students), supports the communication among students and the use of additional tools and resources for collaboration, and helps maintain the safety distance among the students who are in the classroom. The usability of Smart Groups has been assessed through a mock-up by 60 users (41 students and 19 teachers) with the system usability scale (SUS) obtaining good results (mean = 75.47, standard deviation = 14.95, median = 76.25). A subgroup (10 teachers out of 19) carried out follow-up interviews using the technology acceptance model (TAM) and highlighted the usefulness of Smart Groups to orchestrate collaboration in hybrid learning environments. Implications for practice or policy: Smart Groups supports teachers in the orchestration of groups in hybrid learning environments. Smart Groups facilitates group coordination and communication among students. Smart Groups helps maintain the safety distance.
A programmable qudit-based quantum processor

Controlling and programming quantum devices to process quantum information by the unit of qudit shows great potential to enhance the capabilities of qubit-based quantum technologies. Here, we report a programmable qudit-based quantum processor in silicon-photonic integrated circuits and demonstrate its enhancement of quantum computational parallelism. The processor monolithically integrates all the key functionalities and capabilities of initialisation, manipulation, and measurement of the two-ququart states and multi-value quantum-controlled logic gates with high-level fidelities. We implemented the basic quantum Fourier transform algorithms to benchmark the enhancement of quantum parallelism using qudits, allowing the implementations of more than one million high-fidelity preparations, operations and projections of qudit states in the processor. Our work shows an integrated quantum technology for qudit-based quantum computing with enhanced capacity, accuracy, and efficiency.
A Comparison of Techniques for Class Imbalance in Deep Learning Classification of Breast Cancer

&lt;p&gt;Tools based on deep learning models have been created in recent years to aid radiologists in the diagnosis of breast cancer from mammograms. However, the datasets used to train these models may suffer from class imbalance, i.e., there are often fewer malignant samples than benign or healthy, which can bias the model towards the healthy class. In this study, we systematically evaluate several popular techniques to deal with this class imbalance, namely, class weighting, over-sampling, and under-sampling, as well as a synthetic lesion generation approach to increase the number of malignant samples. These techniques are applied when training on three diverse Full-Field Digital Mammography datasets, and tested on in-distribution and out-of-distribution samples. The experiments show that a greater imbalance is associated with a greater bias towards the majority class, which can be counteracted by any of the standard class imbalance techniques. On the other hand, these methods provide no benefit to model performance with respect to Area Under the Curve of the Recall Operating Characteristic (AUC-ROC), and indeed under-sampling leads to a reduction of 0.066 in AUC in the case of a 19:1 benign to malignant imbalance. Our synthetic lesion methodology leads to better performance in most cases, with increases of up to 0.07 in AUC on out-of-distribution test sets over the next best experiment.&lt;/p&gt;
Pedestrian Behavior Recognition via a Smart Graph-based Optimization

Innovative technology and improvements in intelligent machinery, transport networks, emergency mechanisms, and educational facilities define the modern world. It is difficult to comprehend the settings, do crowd monitoring, and observe people. In this research, we present an effective method for understanding crowd data across a sustainable framework and for identifying pedestrian anomalous and normal behavior. Preprocessing is conducted initially, followed by person detection and contour separation. Then, the features are extracted using a force flow matrix and deep flow, and the graph mining algorithm is used to optimize the feature space. Finally, we utilized CNN to distinguish between regular and irregular pedestrian behavior. For the ADOC and UMN collections, the suggested technique has an acceptable performance percentage of 83.30 and 83.83 percent, correspondingly. In conclusion, the acquired results are shown to be more effective than other approaches described in the scientific literature.
Rendahnya Literasi Remaja di Indonesia: Masalah dan Solusi

&lt;p&gt;The ease of access to social media owned by teenagers is considered to be able to make teenagers spend most of their time without clear goals. On the other hand, youth are part of a digital community that participates in the progress of their country. This phenomenon is the background of this study. The goal of this research was to find a solution to the youth's low literacy rate. The method of this study is a literature review. The research findings show that in order to realize literate youth, collaboration and commitment from three important elements are needed, namely family, government, and society. The conclusion from this study is that the solution to low adolescent literacy is to realize literate youth through efforts to build high motivation for individual youth to think, read, and write, which can be built through the role of family elements. The next element is that the government, as a regulator, can play a role in issuing policies that support Indonesian families to improve literacy. The author considers that the various benefits received by adolescents through social media should be accompanied by restrictions on access to their use. Limiting access to social media can be outlined in a clear government policy. The next element is the community, which can play a role in educating and raising awareness regarding climate change and strengthening literacy for young people in Indonesia through a healthy internet campaign for young people. Communities can also build youth literacy communities and obtain assistance from the government, both at the central government and local government levels.&lt;/p&gt;
Oracles in Decentralized Finance: Attack Costs, Profits and Mitigation Measures

Decentralized finance (DeFi) is by far the most popular application of blockchain technology. Despite the wide acceptance of new financial instruments and services, there are still many unexplored areas in the field. We dedicate this research to the understanding of one of the most crucial limitations of decentralized finance—oracles. DeFi protocols, as well as other blockchain applications, function in a closed environment and regularly need to fetch real-world information (e.g., assets’ prices)—the tool used for this purpose is called an oracle. We review the existing oracle types in DeFi applications and focus our research on the least explored one: when another protocol, typically a decentralized exchange, serves as a price oracle. After explaining the mechanisms behind the decentralized exchanges, we introduce an algorithmic model that allows one to safely design a decentralized oracle and adjust crucial parameters. We believe that understanding and implementing the logic presented in the model can help to reduce the chances of price manipulations attacks, which are the most frequent incident types in DeFi.
Satellite‐air‐ground integrated multi‐source earth observation and machine learning processing brain for tailings reservoir monitoring and rapid emergency response

Abstract Tailings reservoirs are an inevitable part of the production process of metal mines and are typical of post‐mining and post‐industrial lands. Tailings reservoirs are often located in mountainous areas so earth observation and remote sensing is a necessary monitoring means. However, existing observation are generally based on satellite, air or ground platforms, Combined satellite‐air‐ground observation is rare, and the coordination ability of processing methods to cope with it is insufficient. Therefore, the response to sudden major environmental and disaster events is slow and lagging. In this study, inspired by the human brain, a satellite‐air‐ground integrated multi‐source earth observation and machine learning processing system was built up for tailings reservoir stereoscopic monitoring and rapid emergency response (SAGTR). Firstly, the brain‐inspired human visual multimodal data acquisition by satellite‐air‐ground integration platform has been established to break through the traditional single‐source and single‐mode data approach and involves satellite remote sensing images, UAV hyperspectral images, and ground parameters. Moreover, it is unique among current tailings reservoir monitoring systems. In general, our proposed SAGTR framework has promise for improving the comprehensive monitoring and emergency response to tailings reservoir failures.
PMHE: a wearable medical sensor assisted framework for health care based on blockchain and privacy computing

Abstract Nowadays, smart medical cloud platforms have become a new direction in the industry. However, because the medical system involves personal physiological data, user privacy in data transmission and processing is also easy to leak in the smart medical cloud platform. This paper proposed a medical data privacy preserving framework named PMHE based on blockchain and fully homomorphic encryption technology. The framework receives personal physiological data from wearable devices on the client side, and uses blockchain as data storage to ensure that the data cannot be tampered with or forged; Besides, it uses fully homomorphic encryption method to design disease prediction models implemented by smart contracts. In PMHE, data is encoded and encrypted on the client side, and encrypted data is uploaded to the cloud platform via the public Internet, preventing privacy leakage caused by channel eavesdropping; smart contracts run on the blockchain platform for disease prediction, and the operators participating in computing are encrypted user data too. So, privacy and security issues caused by platform data leakage are avoided. The client-to-cloud interaction protocol is also designed to overcome the defect that fully homomorphic encryption only supports addition and multiplication by submitting tuples on the client side, to ensure that the prediction model can perform complex computing. In addition, the design of the smart contract is introduced in detail, and the performance of the system is analyzed. Finally, experiments are conducted to verify the operating effect of the system, ensuring that user privacy is not leaked without affecting the accuracy of the model, and realizing a smart medical cloud platform in which data can be used but cannot be borrowed.
Review of Conservation of Resources Theory in Job Demands and Resources Model

This paper provides background information about the underpinning theory of the model of job demands-resources model (JD-R model), which is the conservation of resources theory. The Conservation of resources (COR) theory became highly popular among researchers. Conservation of resources (COR) theory postulates the link between job demands-resources, personal resources, organizational commitment, work engagement, turnover intentions, and job performance. This paper discusses COR theory, which is the main theory that underpins the present research. This paper reviews the assumptions and development of the theory and presents an overview of important findings obtained with the theory and its association with JD-R model. The paper concludes with an agenda for future research and a brief discussion of the practical application of the theory in JD-R model.
The Flash Loan Attack Analysis (FAA) Framework—A Case Study of the Warp Finance Exploitation

Decentralized finance (DeFi) has exploded in popularity with a billion-dollar market cap. While uncollateralized lending, known as a flash loan, emerged from DeFi, it has become a primary tool used by attackers to drain investment tokens from DeFi networks. The existing countermeasures seem practical, but no comprehensive quantitative analysis framework was available to test them. This paper proposes the Flash loan Attack Analysis (FAA) framework, which aids security practitioners in understanding the DeFi system’s effects on preventative methods when various factors change. The quantitative predictions can help security professionals in identifying hidden dangers and more efficiently adopting countermeasure strategies. The simulation predicts that the existing strategy, fair reserves, can fully protect the platform in a typical market environment; however, in a highly volatile market where the token price drops by 60% in a single hour, it will be broken, causing more than $8 million in damage.
The use of multi-sensor satellite imagery to analyze flood events and land cover changes using change detection and machine learning techniques in the Barito watershed

Indonesia is one of the countries in the world that is frequently affected by floods. Flood disasters can have various negative impacts and therefore need to be analyzed to determine prevention and mitigation measures. This study examined land cover change, flood detection, and flood distribution using multitemporal Sentinel-1 and Landsat-8 satellite imagery in the Barito watershed. A combination of change detection and the application of the Otsu algorithm was used to detect floodplains from Sentinel-1 imagery. Land use/land cover (LULC) changes are detected using a combination of change detection and machine learning in the form of a random forest algorithm. The overlay technique was used to analyze the distribution of floodplains. In this study, the floodplain in the study area was mapped to 109,623 ha. The change detection method detects a decrease in the areas of primary forest, secondary forest, fields, rice fields, shrubs and ponds, respectively, by 13,020 ha, 116,235 ha, 259 ha, 146,696 ha, 47,308 ha, and 9,601 ha. Settlements, bare land, plantations and water bodies increase by 14,879 ha, 64,830 ha, 218,916 ha, and 34,768 ha, respectively. Flooding was mainly found in the classes of rice fields, water bodies and primary forests.
Graph schema and best graph type to compare discrete groups: Bar, line, and pie

Different graph types may differ in their suitability to support group comparisons, due to the underlying graph schemas. This study examined whether graph schemas are based on perceptual features (i.e., each graph type, e.g., bar or line graph, has its own graph schema) or common invariant structures (i.e., graph types share common schemas). Furthermore, it was of interest which graph type (bar, line, or pie) is optimal for comparing discrete groups. A switching paradigm was used in three experiments. Two graph types were examined at a time (Experiment 1: bar vs. line, Experiment 2: bar vs. pie, Experiment 3: line vs. pie). On each trial, participants received a data graph presenting the data from three groups and were to determine the numerical difference of group A and group B displayed in the graph. We scrutinized whether switching the type of graph from one trial to the next prolonged RTs. The slowing of RTs in switch trials in comparison to trials with only one graph type can indicate to what extent the graph schemas differ. As switch costs were observed in all pairings of graph types, none of the different pairs of graph types tested seems to fully share a common schema. Interestingly, there was tentative evidence for differences in switch costs among different pairings of graph types. Smaller switch costs in Experiment 1 suggested that the graph schemas of bar and line graphs overlap more strongly than those of bar graphs and pie graphs or line graphs and pie graphs. This implies that results were not in line with completely distinct schemas for different graph types either. Taken together, the pattern of results is consistent with a hierarchical view according to which a graph schema consists of parts shared for different graphs and parts that are specific for each graph type. Apart from investigating graph schemas, the study provided evidence for performance differences among graph types. We found that bar graphs yielded the fastest group comparisons compared to line graphs and pie graphs, suggesting that they are the most suitable when used to compare discrete groups.
Case Study III: Tuning of Deep Neural Networks

Abstract A surrogate model based Hyperparameter Tuning (HPT) approach for Deep Learning (DL) is presented. This chapter demonstrates how the architecture-level parameters (hyperparameters) of Deep Neural Networks (DNNs) that were implemented in / can be optimized. The implementation of the tuning procedure is 100% accessible from R , the software environment for statistical computing. How the software packages (, , and ) can be combined in a very efficient and effective manner will be exemplified in this chapter. The hyperparameters of a standard DNN are tuned. The performances of the six Machine Learning (ML) methods discussed in this book are compared to the results from the DNN. This study provides valuable insights in the tunability of several methods, which is of great importance for the practitioner.
Leveraging Machine Learning and Semi-Structured Information to Identify Political Views from Social Media Posts

Social media platforms make a significant contribution to modeling and influencing people’s opinions and decisions, including political views and orientation. Analyzing social media content can reveal trends and key triggers that will influence society. This paper presents an exhaustive analysis of the performance generated by various implementations of the Naïve Bayes classifier, combined with a semi-structured information approach, to identify the political orientation of Twitter users, based on their posts. As research methodology, we aggregate in a semi-structured format a database of over 86,000 political posts from Democrat (right) and Republican (left) ideologies. Such an approach allows us to associate a Democrat or Republican label to each tweet, in order to create and train the model. The semi-structured input data are processed using several NLP techniques and then the model is trained to classify the political orientation based on semantic criteria and semi-structured information. This paper examines several variations of the Naïve Bayes classifier suite: Gaussian Naïve Bayes, Multinomial Naïve Bayes, Calibrated Naïve Bayes algorithms, and tracks a variety of performance indices and their graphical representations: Prediction Accuracy, Precision, Recall, Confusion Matrix, Brier Score Loss, etc. We obtained an accuracy of around 80–85% in identifying the political orientation of the users. This leads us to the conclusion that this type of application can be integrated into a more complex system and can help in determining political trends or election results.
Proposal of Decentralized P2P Service Model for Transfer between Blockchain-Based Heterogeneous Cryptocurrencies and CBDCs

This paper proposes a solution to the transfer problem between blockchain-based heterogeneous cryptocurrencies and CBDCs, with research derived from an analysis of the existing literature. Interoperability between heterogeneous blockchains has been an obstacle to service diversity and user convenience. Many types of cryptocurrencies are currently trading on the market, and many countries are researching and testing central bank digital currencies (CBDCs). In this paper, existing interoperability studies and solutions between heterogeneous blockchains and differences from the proposed service model are described. To enhance digital financial services and improve user convenience, transfer between heterogeneous cryptocurrencies, transfer between heterogeneous CBDCs, and transfer between cryptocurrency and CBDC should be required. This paper proposes an interoperable architecture between heterogeneous blockchains, and a decentralized peer-to-peer (P2P) service model based on the interoperable architecture for transferring between blockchain-based heterogeneous cryptocurrencies and CBDCs. Security threats to the proposed service model are identified and security requirements to prevent the identified security threats are specified. The mentioned security threats and security requirements should be considered when implementing the proposed service model.
Mobile Learning Tools to Support in Teaching Programming Logic and Design: A Systematic Literature Review

Learning programming logic remains an obstacle for students from different academic fields. Considered one of the essential disciplines in the field of Science and Technology, it is vital to investigate the new tools or techniques used in the teaching and learning of Programming Language. This work presents a systematic literature review (SLR) on approaches using Mobile Learning methodology and the process of learning programming in introductory courses, including mobile applications and their evaluation and validation. We consulted three digital libraries, considering articles published from 2011 to 2022 related to Mobile Learning and Programming Learning. As a result, we found twelve mobile tools for learning or teaching programming logic. Most are free and used in universities. In addition, these tools positively affect the learning process, engagement, motivation, and retention, providing a better understanding, and improving content transmission.