Reviewerly/exHarmony huggingface dataset:
Enhancement of Mammographic Images Using Histogram-Based Techniques for Their Classification Using CNN

In the world, one in eight women will develop breast cancer. Men can also develop it, but less frequently. This condition starts with uncontrolled cell division brought on by a change in the genes that regulate cell division and growth, which leads to the development of a nodule or tumour. These tumours can be either benign, which poses no health risk, or malignant, also known as cancerous, which puts patients' lives in jeopardy and has the potential to spread. The most common way to diagnose this problem is via mammograms. This kind of examination enables the detection of abnormalities in breast tissue, such as masses and microcalcifications, which are thought to be indicators of the presence of disease. This study aims to determine how histogram-based image enhancement methods affect the classification of mammograms into five groups: benign calcifications, benign masses, malignant calcifications, malignant masses, and healthy tissue, as determined by a CAD system of automatic mammography classification using convolutional neural networks. Both Contrast-limited Adaptive Histogram Equalization (CAHE) and Histogram Intensity Windowing (HIW) will be used (CLAHE). By improving the contrast between the image's background, fibrous tissue, dense tissue, and sick tissue, which includes microcalcifications and masses, the mammography histogram is modified using these procedures. In order to help neural networks, learn, the contrast has been increased to make it easier to distinguish between various types of tissue. The proportion of correctly classified images could rise with this technique. Using Deep Convolutional Neural Networks, a model was developed that allows classifying different types of lesions. The model achieved an accuracy of 62%, based on mini-MIAS data. The final goal of the project is the creation of an update algorithm that will be incorporated into the CAD system and will enhance the automatic identification and categorization of microcalcifications and masses. As a result, it would be possible to increase the possibility of early disease identification, which is important because early discovery increases the likelihood of a cure to almost 100%.
ICO as Crypto-Assets Manufacturing within a Smart City

The digitalization of the economy provokes the rethinking of manufacturing processes. Despite numerous publications related to Industry 4.0 as a manufacturing approach, the production of fully digital and crypto-asset products was poorly researched. Besides having a supplementary role, crypto-assets may form an entire smart city product. The authors assess the manufacturing of smart city products, fully or partially formed by crypto-assets. The initial issuance of the crypto assets was usually addressed as an Initial Coin Offer, or through the process of increasing the issuer’s capital. The authors assess the Initial Coin Offer, and address it, like manufacturing to produce products for sale. The authors classify all milestones related to the crypto-assets’ issuance, distribution, and revaluation, and assign incomes and expenses to each milestone. Additionally, the ICO-based production costs and revenues were classified according to crypto-asset types, as defined by European Economic Area legislative acts.
Developing Trusted IoT Healthcare Information-Based AI and Blockchain

The Internet of Things (IoT) has grown more pervasive in recent years. It makes it possible to describe the physical world in detail and interact with it in several different ways. Consequently, IoT has the potential to be involved in many different applications, including healthcare, supply chain, logistics, and the automotive sector. IoT-based smart healthcare systems have significantly increased the value of organizations that rely heavily on IoT infrastructures and solutions. In fact, with the recent COVID-19 pandemic, IoT played an important role in combating diseases. However, IoT devices are tiny, with limited capabilities. Therefore, IoT systems lack encryption, insufficient privacy protection, and subject to many attacks. Accordingly, IoT healthcare systems are extremely vulnerable to several security flaws that might result in more accurate, quick, and precise diagnoses. On the other hand, blockchain technology has been proven to be effective in many critical applications. Blockchain technology combined with IoT can greatly improve the healthcare industry’s efficiency, security, and transparency while opening new commercial choices. This paper is an extension of the current effort in the IoT smart healthcare systems. It has three main contributions, as follows: (1) it proposes a smart unsupervised medical clinic without medical staff interventions. It tries to provide safe and fast services confronting the pandemic without exposing medical staff to danger. (2) It proposes a deep learning algorithm for COVID-19 detection-based X-ray images; it utilizes the transfer learning (ResNet152) model. (3) The paper also presents a novel blockchain-based pharmaceutical system. The proposed algorithms and systems have proven to be effective and secure enough to be used in the healthcare environment.
Heterogeneous Feature Fusion Module Based on CNN and Transformer for Multiview Stereo Reconstruction

For decades, a vital area of computer vision research has been multiview stereo (MVS), which creates 3D models of a scene using photographs. This study presents an effective MVS network for 3D reconstruction utilizing multiview pictures. Alternative learning-based reconstruction techniques work well, because CNNs (convolutional neural network) can extract only the image’s local features; however, they contain many artifacts. Herein, a transformer and CNN are used to extract the global and local features of the image, respectively. Additionally, hierarchical aggregation and heterogeneous interaction modules were used to improve these features. They are based on the transformer and can extract dense features with 3D consistency and global context that are necessary to provide accurate matching for MVS.
Fine-Grained Sentiment-Controlled Text Generation Approach Based on Pre-Trained Language Model

Sentiment-controlled text generation aims to generate texts according to the given sentiment. However, most of the existing studies focus only on the document- or sentence-level sentiment control, leaving a gap for finer-grained control over the content of generated results. Fine-grained control allows a generated review to express different opinions toward multiple aspects. Some previous works attempted to generate reviews conditioned on aspect-level sentiments, but they usually suffer from low adaptability and the lack of an annotated dataset. To alleviate these problems, we propose a novel pre-trained extended generative model that can dynamically refer to the prompt sentiment, together with an auxiliary classifier that extracts the fine-grained sentiments from the unannotated sentences, thus we conducted training on both annotated and unannotated datasets. We also propose a query-hint mechanism to further guide the generation process toward the aspect-level sentiments at every time step. Experimental results from real-world datasets demonstrated that our model has excellent adaptability in generating aspect-level sentiment-controllable review texts with high sentiment coverage and stable quality since, on both datasets, our model steadily outperforms other baseline models in the metrics of BLEU-4, METETOR, and ROUGE-L etc. The limitation of this work is that we only focus on fine-grained sentiments that are explicitly expressed. Moreover, the implicitly expressed fine-grained sentiment-controllable text generation will be an important puzzle for future work.
NewSQL Databases Assessment: CockroachDB, MariaDB Xpand, and VoltDB

Background: Relational databases have been a prevalent technology for decades, using SQL (Structured Query Language) to manage data. However, the emergence of new technologies, such as the web and the cloud, has brought the requirement to handle more complex data. NewSQL is the latest technology that incorporates the ability to scale and ensures the availability of NoSQL (Not Only SQL) without losing the ACID properties (Atomicity, Consistency, Isolation, Durability) associated with relational databases. Methods: We evaluated CockroachDB, MariaDB Xpand, and VoltDB with OSSpal methodology and experimentally using the Star Schema Benchmark (SSB). The scalability and performance capabilities of each database were assessed. Results: Applying the OSSpal methodology, the results showed that MariaDB Xpand outperformed CockroachDB and VoltDB. On the other hand, we concluded that with Star Schema Benchmark, CockroachDB had better scalability, while VoltDB had a faster query execution time. Conclusions: CockroachDB and VoltDB are the best performing databases in terms of scalability and performance.
Training Autoencoders Using Relative Entropy Constraints

Autoencoders are widely used for dimensionality reduction and feature extraction. The backpropagation algorithm for training the parameters of the autoencoder model suffers from problems such as slow convergence. Therefore, researchers propose forward propagation algorithms. However, the existing forward propagation algorithms do not consider the characteristics of the data itself. This paper proposes an autoencoder forward training algorithm based on relative entropy constraints, called relative entropy autoencoder (REAE). When solving the feature map parameters, REAE imposes different constraints on the average activation value of the hidden layer outputs obtained by the feature map for different data sets. In the experimental section, different forward propagation algorithms are compared by applying the features extracted from the autoencoder to an image classification task. The experimental results on three image classification datasets show that the classification performance of the classification model constructed by REAE is better than that of the classification model constructed by other forward propagation algorithms.
Detection of Low Frequency Seismicity at Mt. Vesuvius Based on Coherence and Statistical Moments of Seismic Signals

Mt. Vesuvius is a high-hazard active volcano surrounded by a densely populated area. Since human activities generate high levels of seismic noise, recognizing low-amplitude seismic events in the signals recorded by the local seismic monitoring network operating at Vesuvius is very difficult. Here, we describe an automatic procedure applied to continuous data with the aim of finding low-amplitude–low-frequency events hidden in the recorded signals. The methodology is based on the computation of two spectral parameters, central frequency Ω and shape factor ẟ, at selected sites, and the coherence of the seismic signal among different sites. The proposed procedure is applied to 28 months of recordings from 2019 to 2021, tuning the search parameters in order to find low-frequency signals similar to those occasionally observed in the past at the same volcano. The results allowed us to identify 80 seismic events that have the spectral features of low-frequency earthquakes or tremor. Among these, 12 events characterized by sufficiently high signal-to-noise ratio have been classified as deep low-frequency earthquakes, most of which are not reported in the catalog. The remaining events (more than 60) are characterized by similar spectral features but with an extremely low amplitude that prevents any reliable location of the source and definitive classification. The results of this work demonstrate that the low-frequency endogenous activity at Mt. Vesuvius volcano is more frequent that previously thought.
Harnessing virtual reality for management training: a longitudinal study

Purpose Virtual reality (VR) technologies have been gaining popularity in training and development in many fields to promote embodied training. However, its adoption in management has been slow and rigorous empirical research to understand its impact on learning and retention is scarce. Thus, this paper aims to examine the benefits of VR technologies for management training. Design/methodology/approach Through a longitudinal experiment comparing VR platforms and a traditional video platform, this study examines two as yet unexplored benefits of VR technologies vis-à-vis management training – the cognitive outcome and affective reaction of the training experience over time. Findings This study finds that, for cognitive outcomes, immediate gains are similar across video and VR platforms, but subsequent knowledge retention is significantly higher for VR platforms. In terms of affective reaction, VR platforms generate significantly more enjoyment, which carries over to two weeks later, and is partially associated with higher knowledge retention. Practical implications This study has implications for management and human resource trainers and system designers interested in integrating VR for training and development purposes. Originality/value This study makes a unique contribution by unpacking the long-term benefits of an embodied training system, as well as identify a possible link between cognitive outcomes and affective reaction.
A laser source driver in 0.18 <i>μ</i>m SiGe BiCMOS technology for high speed quantum key distribution

Quantum key distribution (QKD) has rapidly developed recently. The repetition frequency of the QKD system increases from tens or hundreds of MHz to GHz. A laser diode (LD) operating in gain-switched mode is widely used as a weak coherent light source in a QKD system. We present an LD driver circuit fabricated in 0.18 μm SiGe heterojunction bipolar transistor bipolar complementary metal oxide semiconductor technology. The circuit can operate at frequencies up to 2.5 GHz, satisfying the requirements of high-speed laser drives in practical QKD systems. The output current of the driver circuit can reach 100 mA when driving an LD whose input equivalent resistance is ∼21 Ω. The extinction ratio of the 1550-nm distributed feedback LD light source driven by our driver circuit reaches 23 dB at the operating frequency of 2.5 GHz, meeting the requirements of QKD systems. This circuit will be used in miniaturized QKD systems.
Consolidating cybersecurity in Europe: A case study on job profiles assessment

To address the issue of educating and training new experts in cybersecurity, it is crucial to identify the specific educational needs of the various professions that exist in the field. We measure these needs by analysing six cybersecurity-related job profiles—each with its own specific skill requirements—that have been assessed by academic and industrial organisations from the cybersecurity community in 14 European countries. We find that it is possible to identify a series of "transversal" skills relevant to all job profiles, and thus of utmost importance in the cybersecurity curricula. However, we also observe that academic and industrial priorities differ substantially, and that skills related to the area of Human security do not rank particularly high, possibly exposing the difficulty of integrating such concepts in traditional education.
Recent advances in domain-driven data mining

Data mining research has been significantly motivated by and benefited from real-world applications in novel domains. This special issue was proposed and edited to draw attention to domain-driven data mining and disseminate research in foundations, frameworks, and applications for data-driven and actionable knowledge discovery. Along with this special issue, we also organized a related workshop to continue the previous efforts on promoting advances in domain-driven data mining. This editorial report will first summarize the selected papers in the special issue, then discuss various industrial trends in the context of the selected papers, and finally document the keynote talks presented by the workshop. Although many scholars have made prominent contributions with the theme of domain-driven data mining, there are still various new research problems and challenges calling for more research investigations in the future. We hope this special issue is helpful for scholars working along this critically important line of research.
Blockchain for achieving accountable outsourcing computations in edge computing

Edge Computing as a paradigm, provides services of outsourcing computations to a large number of end users. Since edge nodes are trustless, the integration of sampling-based replication calculation and the blockchain is used to verify the correctness of computation results in a trustless environment. However, the blockchain with the nature of decentralization, is confronted with some problems of high resource consumption, such that the verification with computational overhead cannot be directly deployed on the blockchain. Thus, we propose an accountable verification scheme based on an off-chain block. The off-chain block meets some requirements of Edge Computing, i.e., reduced latency of services, and edge nodes with heterogeneous resources. The off-chain block tries to address two challenges for reliable outsourcing computations: (i) how to generate the block efficiently and securely, and (ii) how to achieve accountable verification. In detail, the block is based on a Directed Acyclic Graph, in which the transactions of computation results and verification reports are updated in full decentralization. The hash of the block is recorded on the blockchain. Moreover, the integration of off-chain verification and on-chain arbitration provides reliable verification. A trust evaluation model achieves accountability for edge nodes. Besides, we conducted the security analysis based on some performance properties. Finally, the Raspberry Pis are leveraged to simulate lightweight edge nodes to prove the scalability of our outsourcing computations. A consortium blockchain with groups is also implemented to reveal the efficiency of blockchain updates of the proposed scheme.
Digital resources and the reading habits of university students in Nigeria

The study examined digital resources (DRs) and the reading habits of university students in Nigeria. This study aims to establish the relationship between DRs and the reading habits of university students in Nigeria. The descriptive survey design was adopted for the study. The population of the study comprised all university students in Nigeria. An online questionnaire was used to collect data. The questionnaire was validated by two experts in research methodology and educational technology. 412 university students who responded to the questionnaire were used as the sample for the study. The sample size is justified by Fox et al. (2007) and Meyer (1979), who recommended 384 samples from an infinite population range. Cronbach’s alpha was used to establish the reliability of the instrument, which yielded 0.76. Data were analyzed with frequency counts and simple percentages, and statistical product and service solutions version 23 was used to generate the mean and standard deviation while Pearson’s product-moment correlation coefficient was used to test the hypotheses at the 0.05 level of significance. The findings established that the majority of the university students used DRs daily and the Internet resources were mostly used by the students. The students had positive perceptions of the impacts of DR usage on their reading habits. Lastly, the test of the hypothesis showed that there is a significant relationship between DRs and the reading habits of university students in Nigeria. Based on the findings, the researchers recommended that the university management should continue to provide the Internet services and computer systems so that students can continue to enjoy more access to DRs to enhance their learning.
Planning and analysis of safety-optimal lunar sun-synchronous spatiotemporal routes

Lunar sun-synchronous roving is an affordable and promising manner of acquiring continuous power supply and warmth in robotic polar exploration. Sun-synchronous route planning is challenging to meet the spatiotemporal constraints of lunar topography and time-varying illumination. Moreover, in pursuit of spaceflight feasibility and safety, the safety-optimal planning method adapting to spatiotemporal maps remains to be addressed. This paper takes the first step to investigate this issue, in which the distribution of roving speed is considered a critical factor. Three-dimensional lunar spatiotemporal maps are constructed based on topography and illumination analysis. A spatiotemporal planning algorithm named MSST-A* is proposed, which takes a multi-speed node expansion mechanism, thus supporting speed distribution optimization. Afterward, route evaluation functions involving motion, energy, and integrated risk, together with the optimal heuristic function, are designed for safety-optimal planning. Two comparative tests are conducted, and results indicate that the proposed approach is practical and superior in terms of motion safety and energy reliability. Besides, an integrated-risk-minimal route circumnavigating the Shackleton Crater at the south pole in three lunar days is solved, demonstrating the existence of long-term lunar sun-synchronous routes with good engineering significance.
Attack graph analysis: An explanatory guide

Attack graph analysis is a model-based approach for network-security analysis. It analyzes a directed graph called an attack graph. Usually, each node in it corresponds to a malicious event caused by attackers, and the edges correspond to the causal relations between events. We can obtain an attack graph from the network topology, its configuration, and the distribution of vulnerabilities. An attack graph gives us various information relevant to network security. Also, there are several relevant algorithms to find desirable security controls applicable to the network. Over twenty years of research have made much progress in this field. However, it comprises a breadth of definitions and discussions, and it is difficult for people new to this field to comprehend the key ideas. This article aims to briefly introduce this method to prospective researchers by summarizing their progress by selecting and reviewing foundational studies. We elaborate on the essential concepts, such as exploit dependency, AND/OR graph, monotonicity, and cycle handling.
Analyzing the Classification Results for Korean Hatespeech and Bias Detection Models in Malicious Comment Dataset

With the development of Internet communication technology, opinions on various issues can be freely expressed on the Internet. However, some people have abused their freedom of expression, causing psychological harm by writing comments expressing their hatred towards others. In order to address this problem, research on automatic detection of malicious comments using machine learning models has been actively conducted. In this study, we constructed the detection models for hate speech and bias to classify KOCO (KOrean hate COmments) dataset using popular language classification models such as logistic regression with term frequency-inverse document frequency, KoBERT, KoELECTRA, KcELECTRA and KoGPT2 models. Through the experiments, we demonstrated that sentence length, reflection of context information, and mis-labeled data highly affected the classification performance of most models. As a result, we presented considerations for automatic detection of malicious comments and directions for constructing the comment dataset to improve the detection models in future research.
Educational Integration of the Metaverse Environment in the Context of Web 3.0 Technologies

It can be said that artificial intelligence and Metaverse are the most popular and current developments in computer and communication technologies. What these technologies are, how they basically work, their definition, studies and researches, new research topics, historical development, possible contributions to life and the world of education are examined in this chapter. In addition, the contributions of similar technologies in the past to educational environments and learning processes have been evaluated.