Reviewerly/exHarmony huggingface dataset:
Bitcoin mempool growth and trading volumes: Integrated approach based on QROF Multi-SWARA and aggregation operators

Investors are looking for objects in which they invest their funds successfully, evaluating the effectiveness of alternative markets and their instruments. Historically, cash flow indicators most effectively reflected the mood of the masses in relation to any financial asset, both in the short-and long-term. This article examines in detail the queue of already completed, but not confirmed transactions in the bitcoin network. The mempool is able to timely display the growth in the number of transactions awaiting confirmation, which makes it a leading indicator of future cash flows that could affect the trading volumes and market prices of bitcoin. This study evaluates bitcoin mempool priorities and two different analyses have been conducted for this purpose. Firstly, the mempool periods are examined through a statistical analysis. Secondly, the performance determinants of mempool are assessed with q-ROF Multi-SWARA. In addition to q-ROF sets, weights are computed with IFS and PFS. Demonstrated here is that the results of all fuzzy sets are identical. This outcome explains the reliability of the findings and they indicate that a transaction is the most important determinant of the bitcoin mempool. It emerged that the adjusted mempool data (+16.7%) for 7-day and 30-day moving averages was able, with a time lag of 24–48 h, to indicate significant volatility of future bitcoin trading volumes (+1.6%) on average. The obtained values confirm the empirical conclusion reached here that the mempool growth leads to cash flow growth. An increase in future cash flows results in a substantial rise in future trading volumes. The key takeaway from the analysis is that mempool is able to effectively predict future increases in trading volumes based on the prior cash flow growth projected into mempool growth. However, as a price indicator, mempool does show mixed results with mostly uncertainty in the direction of price movement.
Research in computing-intensive simulations for nature-oriented civil-engineering and related scientific fields, using machine learning and big data: an overview of open problems

Abstract This article presents a taxonomy and represents a repository of open problems in computing for numerically and logically intensive problems in a number of disciplines that have to synergize for the best performance of simulation-based feasibility studies on nature-oriented engineering in general and civil engineering in particular. Topics include but are not limited to: Nature-based construction, genomics supporting nature-based construction, earthquake engineering, and other types of geophysical disaster prevention activities, as well as the studies of processes and materials of interest for the above. In all these fields, problems are discussed that generate huge amounts of Big Data and are characterized with mathematically highly complex Iterative Algorithms. In the domain of applications, it has been stressed that problems could be made less computationally demanding if the number of computing iterations is made smaller (with the help of Artificial Intelligence or Conditional Algorithms), or if each computing iteration is made shorter in time (with the help of Data Filtration and Data Quantization). In the domain of computing, it has been stressed that computing could be made more powerful if the implementation technology is changed (Si, GaAs, etc.…), or if the computing paradigm is changed (Control Flow, Data Flow, etc.…).
Safe Reinforcement Learning via Shielding under Partial Observability

Safe exploration is a common problem in reinforcement learning (RL) that aims to prevent agents from making disastrous decisions while exploring their environment. A family of approaches to this problem assume domain knowledge in the form of a (partial) model of this environment to decide upon the safety of an action. A so-called shield forces the RL agent to select only safe actions. However, for adoption in various applications, one must look beyond enforcing safety and also ensure the applicability of RL with good performance. We extend the applicability of shields via tight integration with state-of-the-art deep RL, and provide an extensive, empirical study in challenging, sparse-reward environments under partial observability. We show that a carefully integrated shield ensures safety and can improve the convergence rate and final performance of RL agents. We furthermore show that a shield can be used to bootstrap state-of-the-art RL agents: they remain safe after initial learning in a shielded setting, allowing us to disable a potentially too conservative shield eventually.
Benign and Malignant Breast Tumor Classification in Ultrasound and Mammography Images via Fusion of Deep Learning and Handcraft Features

Breast cancer is a disease that affects women in different countries around the world. The real cause of breast cancer is particularly challenging to determine, and early detection of the disease is necessary for reducing the death rate, due to the high risks associated with breast cancer. Treatment in the early period can increase the life expectancy and quality of life for women. CAD (Computer Aided Diagnostic) systems can perform the diagnosis of the benign and malignant lesions of breast cancer using technologies and tools based on image processing, helping specialist doctors to obtain a more precise point of view with fewer processes when making their diagnosis by giving a second opinion. This study presents a novel CAD system for automated breast cancer diagnosis. The proposed method consists of different stages. In the preprocessing stage, an image is segmented, and a mask of a lesion is obtained; during the next stage, the extraction of the deep learning features is performed by a CNN—specifically, DenseNet 201. Additionally, handcrafted features (Histogram of Oriented Gradients (HOG)-based, ULBP-based, perimeter area, area, eccentricity, and circularity) are obtained from an image. The designed hybrid system uses CNN architecture for extracting deep learning features, along with traditional methods which perform several handcraft features, following the medical properties of the disease with the purpose of later fusion via proposed statistical criteria. During the fusion stage, where deep learning and handcrafted features are analyzed, the genetic algorithms as well as mutual information selection algorithm, followed by several classifiers (XGBoost, AdaBoost, Multilayer perceptron (MLP)) based on stochastic measures, are applied to choose the most sensible information group among the features. In the experimental validation of two modalities of the CAD design, which performed two types of medical studies—mammography (MG) and ultrasound (US)—the databases mini-DDSM (Digital Database for Screening Mammography) and BUSI (Breast Ultrasound Images Dataset) were used. Novel CAD systems were evaluated and compared with recent state-of-the-art systems, demonstrating better performance in commonly used criteria, obtaining ACC of 97.6%, PRE of 98%, Recall of 98%, F1-Score of 98%, and IBA of 95% for the abovementioned datasets.
Blind Image Quality Assessment: A Fuzzy Neural Network for Opinion Score Distribution Prediction

Image quality assessment (IQA) has always been a popular research topic. There have been many methods proposed for predicting image quality, also known as the mean opinion score (MOS). However, it is worth noting that different people may assign different opinion scores to the same image. Image quality described by all subjective opinion scores can express rich subjective information about the image, such as diversity and uncertainty, which cannot be accurately described by a single MOS. Therefore, this paper proposes a fuzzy neural network to predict the opinion score distribution (OSD) of image quality. The fuzzy neural network includes three sub-networks: a feature extraction network, a feature fuzzification network, and a fuzzy learning network. First, a novel network is designed to extract image features. The extracted features are then fuzzified by fuzzy theory to model the epistemic uncertainty in the feature extraction process. Finally, the OSD of image quality is predicted using the fuzzy learning network by learning the mapping from fuzzy features to fuzzy uncertainty when rating image quality. In addition, to train the proposed fuzzy neural network, we employ a new loss function based on the quantile and the cumulative density function. We experimentally validate the feasibility and superiority of the proposed method in two aspects. On the one hand, we demonstrate the performance of the proposed method in predicting the OSD of image quality on the SJTU IQSD and KonIQ-10K databases. On the other hand, we also prove the feasibility of the proposed method in predicting the MOS of image quality on several popular IQA databases, including CSIQ, TID2013, LIVE MD, and LIVE Challenge.
An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry

Deep Neural Networks (DNNs) are being adopted as components in software systems. Creating and specializing DNNs from scratch has grown increasingly difficult as state-of-the-art architectures grow more complex. Following the path of traditional software engineering, machine learning engineers have begun to reuse large-scale pre-trained models (PTMs) and fine-tune these models for downstream tasks. Prior works have studied reuse practices for traditional software packages to guide software engineers towards better package maintenance and dependency management. We lack a similar foundation of knowledge to guide behaviors in pre-trained model ecosystems. In this work, we present the first empirical investigation of PTM reuse. We interviewed 12 practitioners from the most popular PTM ecosystem, Hugging Face, to learn the practices and challenges of PTM reuse. From this data, we model the decision-making process for PTM reuse. Based on the identified practices, we describe useful attributes for model reuse, including provenance, reproducibility, and portability. Three challenges for PTM reuse are missing attributes, discrepancies between claimed and actual performance, and model risks. We substantiate these identified challenges with systematic measurements in the Hugging Face ecosystem. Our work informs future directions on optimizing deep learning ecosystems by automated measuring useful attributes and potential attacks, and envision future research on infrastructure and standardization for model registries.
Verifi-Chain: A Credentials Verifier Using Blockchain and IPFS

Submitting fake certificates is a common problem in Southeast Asia, which prevents qualified candidates from getting the jobs they deserve. When applying for a job, students must provide academic credentials as proof of their qualifications, acquired both inside and outside the classroom. Verifying academic documents before hiring is crucial to prevent fraud. Employing blockchain technology has the potential to address this issue. Blockchain provides an electronic certificate that is tamperproof and non-repudiable, making it difficult for students to manipulate their academic credentials. This paper presents a prototype for an academic credential verification model that leverages the security features of blockchain and InterPlanetary File System (IPFS). Certificates are temporarily stored in a database before being transferred to IPFS, where a unique hash code is generated using a hashing algorithm. This hash code serves as the certificate’s unique identity and is stored in the blockchain nodes. Companies can verify an applicant’s credentials by searching for the applicant and accessing their already verified certificates. Utilizing IPFS as a middleman storage platform lowers the expenses of directly storing massive data on the blockchain. To sum it up, the proposed solution would make the process of certificate verification more efficient, secure, and cost-effective. It would save time and resources that would otherwise be used to manually verify certificates.
Understanding the risks associated with wallets, depository services, trading, lending, and borrowing in the crypto space

The crypto space offers numerous opportunities for users to grow their wealth through trading, lending, and borrowing activities. However, these opportunities come with inherent risks that need to be carefully managed to protect your assets and maximize returns. By understanding the risks associated with wallets and depository services, trading, lending, and borrowing, users can make informed decisions and enjoy the benefits of the rapidly evolving world of cryptocurrencies. This review paper analyses 43 papers for the period of 2019–2023 and proposes recommendations for policy makers. The results confirm that international regulators expect national authorities to implement a regulatory framework for digital assets comparable to those that already exist for traditional finance. For national authorities, this means having and using the powers, tools and resources to regulate and oversee a growing market. Authorities should cooperate and coordinate with each other, at the national and international levels, to encourage consistency and knowledge sharing. Market operators (exchanges), service providers, exchanges and wallets, create effective risk management structures, as well as reliable mechanisms for collecting, storing, protecting and reporting data.
Dr ChatGPT tell me what I want to hear: How different prompts impact health answer correctness

This paper investigates the significant impact different prompts have on the behaviour of ChatGPT when used for health information seeking. As people more and more depend on generative large language models (LLMs) like ChatGPT, it is critical to understand model behaviour under different conditions, especially for domains where incorrect answers can have serious consequences such as health. Using the TREC Misinformation dataset, we empirically evaluate ChatGPT to show not just its effectiveness but reveal that knowledge passed in the prompt can bias the model to the detriment of answer correctness. We show this occurs both for retrieve-then-generate pipelines and based on how a user phrases their question as well as the question type. This work has important implications for the development of more robust and transparent question-answering systems based on generative large language models. Prompts, raw result files and manual analysis are made publicly available at https://github.com/ielab/drchatgpt-health_prompting.
Equality-Friendly Well-Founded Semantics and Applications to Description Logics

We tackle the problem of defining a well-founded semantics for Datalog rules with existentially quantified variables in their heads and negations in their bodies. In particular, we provide a well-founded semantics (WFS) for the recent Datalog+/- family of ontology languages, which covers several important description logics (DLs). To do so, we generalize Datalog+/- by non-stratified nonmonotonic negation in rule bodies, and we define a WFS for this generalization via guarded fixed-point logic. We refer to this approach as equality-friendly WFS, since it has the advantage that it does not make the unique name assumption (UNA); this brings it close to OWL and its profiles as well as typical DLs, which also do not make the UNA. We prove that for guarded Datalog+/- with negation under the equality-friendly WFS, conjunctive query answering is decidable, and we provide precise complexity results for this problem. From these results, we obtain precise definitions of the standard WFS extensions of EL and of members of the DL-Lite family, as well as corresponding complexity results for query answering.
Social Media Is NOT that Bad! The Lexical Quality of Social Media

There is a strong correlation between spelling errors and web text content quality. Using our lexical quality measure, based in a small corpus of spelling errors, we present an estimation of the lexical quality of the main Social Media sites. This paper presents an updated and complete analysis of the lexical quality of Social Media written in English and Spanish, including how lexical quality changes in time.
A Longitudinal Study of Students in an Introductory Cybersecurity Course

Abstract A Longitudinal Study of Students in an Introductory Cybersecurity CourseWhile some aspects of information assurance can be traced back to the earliest implementationsof cryptography, the ﬁeld of cybersecurity is relatively new, and thus, pedagogical “best practices”have not been adequately investigated. The tremendous growth within the ﬁeld over the past twodecades has resulted in a substantial number of organizations (both education and commercial)offering a wide variety of educational approaches in an attempt to meet the growing demand forgraduates and employees possessing skills in cybersecurity. This growth has been so rapid thatno one has taken the time to ask the question: are we doing this the right way? In order for usto identify and promote instructional best practices within cybersecurity courses, an instrumentcapable of measuring these values is needed. This paper contains the results of the initial phase inour development of such an instrument.This work is a longitudinal study of undergraduate and graduate students enrolled in an intro-ductory cybersecurity course. The purpose of the study is to identify course components andinstructional approaches that affect both students’ success in the classroom and the likelihood thatthey will continue to pursue cybersecurity both as a student and as part of their career. Given thevariation in the content being presented in such courses, we focus this effort on student charac-teristics that have been shown to lead towards student success in the classroom and student careerselection. These characteristics include: the self-efﬁcacy of students in relation to cybersecurityas well as student interest in further coursework, research and/or jobs that involve cybersecurityconcepts. [1, 2] Using a series of student interviews over the course of the semester, we are able toidentify and measure the changes in student self-efﬁcacy and interest that occur as the semester pro-gresses. Furthermore, we identify pedagogical practices which students found most useful throughthis semester-long investigation. The results from this study will be used to construct a Likert-scalesurvey that will allow cybersecurity educators to evaluate student outcomes consistently betweenvarious teaching approaches. This will allow for systematic, informed, pedagogical decisions tobe made in the cybersecurity classroom.References[1] A. Bandura, C. Barbaranelli, G. V. Caprara, and C. Pastorelli. Self-Efﬁcacy Beliefs as Shapers of Children’s Aspirations and Career Trajectories. Child Development, 72(1):187–206, 2001.[2] H. Fencl and K. Scheel. Engaging Students: An Examination of the Effects of Teaching Strategies on Self-Efﬁcacy and Course Climate in a Nonmajors Physics Course. Journal of College Science Teaching, 35(1):20, 2005. 1
The efficiency of Color Models layers at Color Images as Cover in text hiding

The color images are used widely as a cover in hiding of the information. Since the variety of applications of the color image there are several color models of color image. Most color models consist of three layers. The nature of the color mode of the cover plays a main role in determining the robustness and security of hiding algorithm.&#x0D; The objective of this paper tests the layers, components, of the color models of cover color images, to figure out which color layer of each color model is best (less affected) to use as a cover to hide information within each color model in the hiding process. The experiments concentrate on the hiding texts in two positions of each layer, 7th bits and 8th bits (LSB). Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) were used to measure the affected of hidden text in layers. The tests were done on nine colors models: RGB, HSV, HIS, HSL, HSB, YCbCr, La*b*, LUV and CMYK.&#x0D; The results show that some of color models have best layer to hide text such as YCbCr, HSI, CMYK, HSL and LUV . In other color models which have been tested, there is not a distinctive layer. The layer H is the worst because any hiding leaves a clear impact on the cover image.
Improving the capability of detecting joints and fractures in rock mass from roof bolt drilling data by using wavelet analysis

To optimise ground supporting and mitigate ground instability, a proper understanding of the ground conditions is critical. The concept of monitoring drilling parameters of a bolter for ground characterisation, which refers to identifying geological features included locations of joints and strengths of rock layers, has been studied in the past few decades. Several intelligent drilling units have been developed for joint detection but have limited capabilities. For instance, the existing systems fail to discriminate joints with the aperture of less than 3.175 mm and tend to generate false alarms. The objective of this research was to develop more efficient and sensitive detection programs for joint detection. To achieve this objective, a series of full-scale drilling tests with various simulated joint conditions have been conducted, and new detection programs have been proposed based on pattern recognition algorithms. Moreover, wavelet analysis has been applied to pre-process data to further promote detection programs. [Received: July 15, 2017; Accepted: November 18, 2017]
Geodesic Distances in Probabilistic Spaces for Patch-Based Ultrasound Image Processing

Many recent ultrasound image processing methods are based on patch comparison, such as filtering and segmentation. Identifying similar patches in noise-corrupted images is a key factor for the performance of these methods. While the Euclidean distance is ideal to handle the patch comparison under additive Gaussian noise, finding good measures to compare patches corrupted by multiplicative noise is still an open research. In this paper, we deduce several new geodesic distances, arising from parametric probabilistic spaces, and suggest them as similarity measures to process RF and log-compressed ultrasound images in patch-based methods. We provide practical examples using these measures in the fields of ultrasound image filtering and segmentation, with results that confirm the potential of the technique.
Extreme learning machine based selected harmonic elimination for single phase inverters

In this study, a new system for the elimination of selected harmonics in single phase inverters is proposed. Extreme learning machine is used for the selected harmonic elimination system. The nonlinear equation set representing the inverter output voltage for the specific values of the modulation index is solved off line using Newton-Raphson method. The corresponding switching angles are obtained. The extreme learning machine is trained and tested with these modulation index and switching angles. Modulation index is used as an input to the extreme learning machine and switching signals are generated. A conventional carrier-modulator comparison process is used to produce switching instants in real time. The signals are applied to a single phase inverter feeding single phase load. Simulations are made using Matlab/Simulink and verified by experimental results. It was shown that the selected harmonics are eliminated from the load current and voltage for any given modulation index.