Reviewerly/exHarmony huggingface dataset:
The Fundamentals of Cryptocurrencies

Cryptocurrencies lately are something that has been gaining notoriety as a way of creating value. This paper was a study of the due perception of Cryptocurrencies and their taxonomy and regulation and a hypothesis case of its implementation. In that regard, this study tries to separate Cryptocurrencies and Blockchains. It also analyzed the barriers to the implementation of a Central Bank Digital Currency that is in parity with a fiat currency, even though, there isn’t a real scenario where this was applied yet. It also evaluated the gains of a portfolio of assets with Cryptocurrencies, where it is shown that it is possible to have a good portfolio with considerable returns. As for the portfolio of Cryptocurrencies, it has a return of 257,62% and a risk of 0,5424 with the following proportions, 9,7% on DOGE, 1,8% on XRP, 34% on ETH, and 54,5% on BTC, considering the criteria of maximization of the return per increment of risk. In final regards, a suggestion of a Cryptocurrency that can solve nowadays problems in different industries, such as security and entertainment, as well as trying to answer the “Fundamentals of Cryptocurrencies”.
Impact of emerging technology on recent criminal scenario

Today the world is fully acknowledged by the digital Technology with a constant dependency of staying connected with everything. In this era of world and after the world experiencing covid pandemic, a new dimension of use of digital/ cyber technology and digitalization in all area including home to office work in day-to-day life emerged with broad spectrum. Technology has given a significant contribution in societal change with digital world in all aspects such as the medical instruments used in surgical treatment of various cardiac, pulmonary diseases orthopedic as well as carcinogenic diseases; cellular connectivity with accuracy &amp;consistency and timeless feasibility of data transfer and money transactions from one hand to another hand. With advancement of technology on a daily basis a significant impact can be seen on the society. This also creates a major impact on the current criminal scenario as well as it has imposed new challenges in the criminal justice system. The present research article is based on the recent case study encountered by the experts in forensic science which will be discuss in detail with their own experiences during presentation.
The effect of the processing and measurement operators on the expressive power of quantum models

There is an increasing interest in Quantum Machine Learning (QML) models, how they work and for which applications they could be useful. There have been many different proposals on how classical data can be encoded and what circuit ansätze and measurement operators should be used to process the encoded data and measure the output state of an ansatz. The choice of the aforementioned operators plays a determinant role in the expressive power of the QML model. In this work we investigate how certain changes in the circuit structure change this expressivity. We introduce both numerical and analytical tools to explore the effect that these operators have in the overall performance of the QML model. These tools are based on previous work on the teacher-student scheme, the partial Fourier series and the averaged operator size. We focus our analysis on simple QML models with two and three qubits and observe that increasing the number of parameterized and entangling gates leads to a more expressive model for certain circuit structures. Also, on which qubit the measurement is performed affects the type of functions that QML models could learn. This work sketches the determinant role that the processing and measurement operators have on the expressive power of simple quantum circuits.
A Bayesian Model for Inferring Total Solar Irradiance From Proxies and Direct Observations: Application to the ACRIM Gap

Abstract Differences among total solar irradiance (TSI) estimates are most pronounced during the so‐called “ACRIM Gap” of 1989–1991, when available satellite‐based observations disagree in trend and no observations exist from satellites with on‐board calibration. Different approaches to bias‐correcting noisy satellite‐based observations lead to discrepancies of up to 0.7 W/m 2 in the change in TSI during the Gap. Using a Bayesian hierarchical model for TSI (BTSI), we jointly infer TSI during the ACRIM Gap from satellite‐based observations and proxies of solar activity. In addition, BTSI yields estimates of noise and drift in satellite‐based observations and calibration for proxy records. We find that TSI across the ACRIM Gap changes by only 0.01 W/m 2 , with a 95% confidence interval of [−0.07, 0.09] W/m 2 . Our results are consistent with the PMOD CPMDF and Community Consensus TSI reconstructions and inconsistent with the 0.7 W/m 2 trend reported in the ACRIM composite reconstruction. Constraints on the trend across the ACRIM Gap are primarily obtained through constraints on the drift in the Nimbus‐7 satellite that are afforded by overlapping satellite and proxy observations.
Is the pathway length of sediments relevant to assess the background value in stream sediment geochemical exploration?

The sample catchment basin approach (SCBA) is one of the methods mainly used to assess the expected chemical composition of a stream sediment sample considering the geolithological variability of the catchment basin upstream of its collection point. In the original SCBA, the catchment surface considered for the purpose is limited to the area between two subsequent collected samples; in the true sample catchment basin approach (TSCBA), the catchment area of each collected sample is limited upstream only by the actual watershed. In both approaches, the dilution is generally assessed in relation to the extension of the single geolithological units falling within the sample catchment area used. However, it makes sense to think that when multiple geolithological units are outcropping upstream of a sampling location, the sediments originating from the ones closer to the collection point (which have traveled a shorter distance) could have a major influence on the geochemical composition of the samples. It is plausible that the degree of dilution occurring during the transport from the source to a given point could also be a function of the distance traveled by the sediments along the fluvial pattern. This also holds in the case of mineralization occurring within the catchment: the farther a mineralization is from a sampling point, the more its geochemical signal should be diluted at the sampling site. In the present study, using the TSCBA, a modification of the existing algorithm is proposed to include both the extension of the geological units and their position relative to the catchment outlet (represented by the sampling point). The territory around Feshark village (Isfahan, central Iran), where stream sediment and rock samples have been collected and analyzed, has been used as the study area. The geochemical background values (corresponding to the expected geochemical values following dilution) for each stream sediment sample were determined using the modified algorithm. The results were compared with those obtained by applying the original equation to the same dataset. The results demonstrated that the new equation could more accurately estimate background (expected) concentrations at the basin outlet, showing a better performance of the new method compared to the original one.
A data association algorithm for the robust confidence ellipsoid filter

In this paper, we propose an algorithm to deal with the data association problem of the robust confidence ellipsoid filter. First, at every time instant, for each target, we need to get a validation gate based on the confidence ellipsoid of the previous time instant. Herein, the validation gate is the minimum volume ellipsoid which guarantees that the true measurement of the target at the current time instant must be contained within it. To obtain the validation gate, an optimization problem is derived from the uncertain dynamic system and it is further converted into a semidefinite programming problem. Then, we make use of the measurements in the validation gate (candidate measurements) and the confidence ellipsoid of the previous time instant to gain the confidence ellipsoid of the current time instant. Similar to the process of obtaining the validation gate, the confidence ellipsoid is obtained by solving a semidefinite programming problem. The ellipsoid has the minimum volume and simultaneously, ensures that the true state vector is contained in it. Next, we propose a strategy to deal with the case where there is no measurement in the validation gate (missing measurement). Last, the numerical examples show the verifications of the proposed algorithm.
Statistical self-similarity of detrital zircon U-Pb ages reveals sedimentary provenance: A case study from the Chinese Loess Plateau

A major concern of the geoscience community is often where sediments originally come from — this is a material's provenance. Tracing provenance allows us to determine how a material moved from its source to its sink, and thereby to evaluate the tectonic and paleoclimatic history. Detrital zircon UPb geochronology has long been used for this purpose. However, current approaches to measuring (dis)similarity in zircon age distributions may lead to erroneous geologic interpretations. Here we propose an Age-Number fractal model to characterize zircon age densities and perform intersample comparisons using multidimensional scaling. The advantage of the new method is that compared to such traditional methods as Kolmogorov-Smirnov test, it accentuates holistic rather than partial differences between two age distributions and is less-affected by data biases related to grain-size sorting and uneven sample sizes. We then use this model to unravel the debatable provenance of the Chinese Loess Plateau (CLP) by comparing the zircon ages of Quaternary loess on the CLP with that of loose sediments from potential source regions. The model divides the CLP into four distinct provenance zones, receiving materials primarily from the northeastern Tibetan Plateau, upper Yellow River, and/or Mu Us Desert. Our observations corroborate that the aeolian (East Asian monsoon and Westerlies) and fluvial (Yellow River) processes play a joint role in the dust deposition. A new pattern of spatial heterogeneity in the loess provenance is established and can be interpreted as a combined effect of atmospheric circulation patterns, availability of riverine sediments, and such factors as aridification, implying a more dynamically-evolved CLP than previous thought. Our results will aid future development and interpretation of the dust-based paleoclimatic proxies.
Reasons for Community Language Learning in Ukrainian Complementary Schools in Italy

The increasing multiculturalism and multilingualism of Italian society has not had no real impact on Italian state language policy and planning: community languages are not taught in Italian state schools, neither to students with a migrant background, nor to native students. Given this absence of provision in the state system, the learning of community languages is primarily achieved through complementary education. Drawing on qualitative data from interviews with school organisers and teachers, this paper discusses the reasons for Ukrainian language learning in two Ukrainian complementary schools in Italy.
A Comparison between EFL Writing Errors in Computer-Based and Paper-Based Assignments

Advances in writing technologies have fostered the gradual move from Paper-Based (PB) to Computer-Based (CB) writing assessment in foreign language (FL) education. The trend was further boosted by the outburst of the Covid-19 pandemic, which led to drastic changes in teaching and assessment practices. In higher education (HE) contexts, writing assessment in English as a foreign language (EFL) is more and more often managed by means of online learning platforms, where students create and submit written assignments on the computer. This paper examines a sample of 100 EFL exam essays completed by 100 Italian university students. Half of the essays were computer-typed on Moodle from home during the Covid-19 lockdown, whereas the other half were handwritten on paper during a face-to-face exam session after the Covid-19 emergency. The study presented herein compares the amount and types of formal errors in the CB and PB written assignments respectively. The results of the comparison may be useful to suggest hypotheses on the impact that both writing modality and environment can have on the quality of EFL writing in assessment contexts.
The Effect of COVID-19 Transmission on Cryptocurrencies

In recent years, Bitcoin and other cryptocurrencies like Ethereum and Dogecoin have emerged as important asset classes in general, and diversification and hedging instruments in particular. The recent COVID-19 pandemic has provided the chance to examine and assess cryptocurrencies’ behavior during extremely stressful times. The methodology of this study is based on an estimate using the ARDL model from 22 January 2020 to 12 March 2021, allowing us to analyze the long-term and short-term relationship between cryptocurrencies and COVID-19. Our results demonstrate that there is cointegration between the chosen cryptocurrencies in the market and COVID-19. The results indicate that Bitcoin, ETH, and DOGE prices were affected by COVID-19, which means that the pandemic seriously affected the three cryptocurrency prices.
Identification of Children's Personality Using Iterative Dichotomizer 3 (ID3) Algorithm

The purpose of this study was to determine the steps of implementing a system to identify the personality of an early childhood and to determine the effect of an early childhood personality identification system on increasing a child's potential to determine their interests and talents. In developing an expert system, the authors use the development method with steps including data collection, analysis, system design, due diligence, program evaluation and implementation. Based on calculations using the ID3 algorithm using the Weka application and manual calculations, it can be seen whether a personality identification is introverted or extroverted. The design of the data classification process is executed to produce a decision tree. From the research based on the research hypothesis, it can be concluded that from the experiments that have been carried out to predict a child's personality using the interactive dichotomous algorithm (ID3) method, predictions can be made to find out a child's personality.
Web-based Product Marketing Information System for MSMEs Tanggulangin Wallet and Bag Manufacturers

The existence of MSMEs has an influence on improving people's welfare which can provide many business opportunities for the community. However, MSMEs currently have weaknesses in marketing their products, moreover they are still carrying out a manual marketing strategy using the method of making brochures, pamphlets and promoting their products in several stores. At the present time it is not easy to do marketing, the lack of the role of internet technology which can hinder MSMEs in terms of marketing their products. So that the business can develop and get maximum results, of course the role of internet technology is also very important for MSMEs in developing their business products. One way is to build an information system with PHP and MySQL programming as database storage. The end result of this research is the advantage of an information system that is useful for the long term with the aim of facilitating online marketing and sales to be able to expand market reach.
HoaKV: High-Performance KV Store Based on the Hot-Awareness in Mixed Workloads

Key–value (KV) stores based on the LSM-tree have become the mainstream of contemporary store engines, but there are problems with high write and read amplification. Moreover, the real-world workload has a high data skew, and the existing KV store lacks hot-awareness, leading to its unreliable and poor performance on the highly skewed real-world workload. In this paper, we propose HoaKV, which unifies the key design ideas of hot issues, KV separation, and hybrid indexing technology in a system. Specifically, HoaKV uses the heat differentiation in KV pairs to manage the hot data and the cold data and conducts real-time dynamic adjustment data classification management. It also uses partial KV separation technology to manage differential KV pairs for large and small KV pairs in the cold data. In addition, HoaKV uses hybrid indexing technology to index the hot data and the cold data, respectively, to improve the performance of reading, writing, and scanning at the same time. In the mixed read and write workloads experments show that HoaKV performs significantly better than several state-of-the-art KV store technologies such as LevelDB, RocksDB, PebblesDB, and WiscKey.
An Underwater Dense Small Object Detection Model Based on YOLOv5-CFDSDSE

Underwater target detection is a key technology in the process of exploring and developing the ocean. Because underwater targets are often very dense, mutually occluded, and affected by light, the detection objects are often unclear, and so, underwater target detection technology faces unique challenges. In order to improve the performance of underwater target detection, this paper proposed a new target detection model YOLOv5-FCDSDSE based on YOLOv5s. In this model, the CFnet (efficient fusion of C3 and FasterNet structure) structure was used to optimize the network structure of the YOLOv5, which improved the model’s accuracy while reducing the number of parameters. Then, Dyhead technology was adopted to achieve better scale perception, space perception, and task perception. In addition, the small object detection (SD) layer was added to combine feature information from different scales effectively, retain more detailed information, and improve the detection ability of small objects. Finally, the attention mechanism squeeze and excitation (SE) was introduced to enhance the feature extraction ability of the model. This paper used the self-made underwater small object dataset URPC_UODD for comparison and ablation experiments. The experimental results showed that the accuracy of the model proposed in this paper was better than the original YOLOv5s and other baseline models in the underwater dense small object detection task, and the number of parameters was also reduced compared to YOLOv5s. Therefore, YOLOv5-FCDSDSE was an innovative solution for underwater target detection tasks.
A Study on Online Health Community Users’ Information Demands Based on the BERT-LDA Model

As the economy and society develop and the standard of living improves, people's health awareness increases and the demand for health information grows. This study introduces an advanced BERT-LDA model to conduct topic-sentiment analysis within online health communities. It examines nine primary categories of user information requirements: causes, symptoms and manifestations, examination and diagnosis, treatment, self-management and regulation, impact, prevention, social life, and knowledge acquisition. By analyzing the distribution of positive and negative sentiments across each topic, the correlation between various health information demands and emotional expressions is investigated. The model established in this paper integrates BERT's semantic comprehension with LDA's topic modeling capabilities, enhancing the accuracy of topic identification and sentiment analysis while providing a more comprehensive evaluation of user information demands. This research furthers our understanding of users' emotional reactions and presents valuable insights for delivering personalized health information in online communities.
Optimization of Several Deep CNN Models for Waste Classification

With urbanization, population, and consumption on the rise, urban waste generation is steadily increasing. Consequently, waste management systems have become integral to city life, playing a critical role in resource efficiency and environmental protection. Inadequate waste management systems can adversely affect the environment, human health, and the economy. Accurate and rapid automatic waste classification poses a significant challenge in recycling. Deep learning models have achieved successful image classification in various fields recently. However, the optimal determination of many hyperparameters is crucial in these models. In this study, we developed a deep learning model that achieves the best classification performance by optimizing the depth, width, and other hyperparameters. Our six-layer Convolutional Neural Network (CNN) model with the lowest depth and width produced a successful result with an accuracy value of 89% and an F1 score of 88%. Moreover, several state-of-the-art CNN models such as VGG19, DenseNet169, ResNet101, Xception, InceptionV3, RegnetX008, RegnetY008, EfficientNetV2S trained with transfer learning and fine-tuning. Extensive experimental work has been done to find the optimal hyperparameters with GridSearch. Our most comprehensive DenseNet169 model, which we trained with fine-tuning, provided an accuracy value of 96.42% and an F1 score of 96%. These models can be successfully used in a variety of waste classification automation.
A Method for Correcting Endoscopic Images to Measure the Size of Defects on the Inner Surface of a Hole

A method is proposed for correcting endoscopic images to enable the measurement of inner surface defects in holes. The distortion is decomposed into circumferential and axial components based on imaging principles, and geometric constraints are added to simplify the correction model parameters to a central coordinate and nonlinear parameter, improving accuracy at the edge of endoscopic images. Experimental results demonstrate good universality with an average error rate of 5.35% in defect measurements, making it applicable for automatic and intelligent detection of hole parts and pipelines.
DialogCIN: Contextual Inference Networks for Emotional Dialogue Generation

In recent years, emotional dialogue generation garnered widespread attention and made significant progress in the English-speaking domain. However, research on emotional dialogue generation in Chinese still faces two critical issues: firstly, the lack of high-quality datasets with emotional characteristics makes it difficult for models to fully utilize emotional information for emotional intervention; secondly, there is a lack of effective neural network models for extracting and integrating inherent logical information in the context to fully understand dialogues. To address these issues, this paper presented a Chinese dialogue dataset called LifeDialog, which was annotated with sentiment features. Additionally, it proposed DialogCIN, a contextual inference network that aims to understand dialogues based on a cognitive perspective. Firstly, the proposed model acquired contextual representations at both the global and speaker levels. Secondly, different levels of contextual vectors were separately inputted into the understanding unit, which consists of multiple inference modules. These modules iteratively performed reasoning and retrieval to delve into the inherent logical information of the dialogue context. Subsequently, appropriate emotions were predicted for feedback. Finally, an emotion-aware decoder was employed to generate a response. Experimental results on our manually annotated dataset, LifeDialog, demonstrated that DialogCIN can effectively simulate human cognitive inference processes, enabling a better understanding of dialogue context and improving the quality of generated dialogues.
Antifragility and business model innovation in the age of digital transformation: strategies and ethical implications

This paper explores the relationship between antifragility and business model innovation in the context of digital transformation. Through a literature review and case studies of disrupted industries, we identify strategies for adopting an antifragile app