Reviewerly/exHarmony huggingface dataset:
Cross-lingual Text Reuse Detection at Document Level for English-Urdu Language Pair

In recent years, the problem of Cross-Lingual Text Reuse Detection (CLTRD) has gained the interest of the research community due to the availability of large digital repositories and automatic Machine Translation (MT) systems. These systems are readily available and openly accessible, which makes it easier to reuse text across languages but hard to detect. In previous studies, different corpora and methods have been developed for CLTRD at the sentence/passage level for the English-Urdu language pair. However, there is a lack of large standard corpora and methods for CLTRD for the English-Urdu language pair at the document level. To overcome this limitation, the significant contribution of this study is the development of a large benchmark cross-lingual (English-Urdu) text reuse corpus, called the TREU (Text Reuse for English-Urdu) corpus. It contains English to Urdu real cases of text reuse at the document level. The corpus is manually labelled into three categories (Wholly Derived = 672, Partially Derived = 888, and Non Derived = 697) with the source text in English and the derived text in the Urdu language. Another contribution of this study is the evaluation of the TREU corpus using a diversified range of methods to show its usefulness and how it can be utilized in the development of automatic methods for measuring cross-lingual (English-Urdu) text reuse at the document level. The best evaluation results, for both binary ( F 1 = 0.78) and ternary ( F 1 = 0.66) classification tasks, are obtained using a combination of all Translation plus Mono-lingual Analysis (T+MA) based methods. The TREU corpus is publicly available to promote CLTRD research in an under-resourced language, i.e., Urdu.
Self-supervision for Tabular Data by Learning to Predict Additive Homoskedastic Gaussian Noise as Pretext

The lack of scalability of data annotation translates to the need to decrease dependency on labels. Self-supervision offers a solution with data training themselves. However, it has received relatively less attention on tabular data, data that drive a large proportion of business and application domains. This work, which we name the Statistical Self-Supervisor (SSS), proposes a method for self-supervision on tabular data by defining a continuous perturbation as pretext. It enables a neural network to learn representations by learning to predict the level of additive isotropic Gaussian noise added to inputs. The choice of the pretext transformation is motivated by intrinsic characteristics of a neural network fundamentally performing linear fits under the widely adopted assumption of Gaussianity in its fitting error and the preservation of locality of a data example on the data manifold in the presence of small random perturbations. The transform condenses information in the generated representations, making them better employable for further task-specific prediction as evidenced by performance improvement of the downstream classifier. To evaluate the persistence of performance under low-annotation settings, SSS is evaluated against different levels of label availability to the downstream classifier (1% to 100%) and benchmarked against self- and semi-supervised methods. At the most label-constrained, 1% setting, we report a maximum increase of at least 2.5% against the next-best semi-supervised competing method. We report an increase of more than 1.5% against self-supervised state of the art. Ablation studies also reveal that increasing label availability from 0% to 1% results in a maximum increase of up to 50% on either of the five performance metrics and up to 15% thereafter, indicating diminishing returns in additional annotation.
The Teacher in the Digital World - New Roles, Representations, Perceptions

The fast development of the Internet and digital technologies has led to many fundamental transformations in the sphere of education and has challenged the traditional pillars of the brick-and-mortar school as an institution. In light of these processes, the present study aims to reveal the teacher’s current position by studying their new roles, representations, and how they are perceived. The following insights are the result of case studies of 6 popular Bulgarian educational online platforms and Internet sites in their development for the period between 2019 and 2022 and include observations on the processes that go parallel in the Bulgarian educational institution. The research methods used in the present study include observation of online educational practices, content analysis of educational and media materials, as well as 45 in-depth semi-structured interviews with 8 creators of educational platforms, 12 teachers, 15 students, and 10 parents. The article aims to open a discussion on what the new roles of teachers in the Digital World are and how they may respond to society's new expectations. The first part of it analyses the change in the position of the teacher – from a central authority in the classroom to a guide who leads the learners through their way to knowledge. The second one regards the new need for teachers today to compete for their audience’s attention. The last two parts raise a discussion about the balance between technological and human experience. They discuss the need for teachers to obtain deeper knowledge about the impact of technology on different spheres of the human world and underline the irreplaceable role of the teacher as a human being.
Neuro-Psychological Approaches for Artificial Intelligence

In artificial intelligence (AI) research, neuro-symbolic learning, which is the combining of neuroscience and symbolic representation methods based on artificial neural networks, has a long and illustrious history. In recent years, significant progress has been achieved in the fields of neuroscience and artificial intelligence. Neuropsychology has provided substantial inspiration for new types of algorithms and architectures, independent of and complementing the mathematical and logic-based methods and notions that have dominated traditional AI approaches. In this chapter, the authors provide an organized overview of current trends by discussing and organizing recent research from major conferences and publications and presenting them in an analytical order. The purpose of this chapter is to provide a handy starting point for further research on the general topic of neuro-psychological approaches for the further advancement of AI techniques.
Generating visual representations for zero-shot learning via adversarial learning and variational autoencoders

Computer vision tasks rely heavily on a huge amount of training data for classification, but in everyday situations, it is impossible to assemble a large amount of training data. Zero-shot learning (ZSL) is a promising domain for the applications in which we have no labeled data available for novel classes. It aims to recognize those unseen classes, by transferring semantic information from seen to unseen classes. In this paper, we propose a generative approach for generalized ZSL that combines the strength of Conditional Variational Autoencoder (CVAE) and Conditional Generative Adversarial Network (CGAN). The key to our approach is synthesizing visual features by including a Regressor that works on cycle-consistency loss, which will constrain the whole generative process. For experimental purposes, four challenging data sets, i.e. CUB, AWA1, AWA2 and SUN, are used in both conventional and generalized settings. Our proposed approach achieves significantly better results on these standard datasets in both settings.
Outlier detection in a repeated measure design

Abstract This paper considered the problem of outlier detection in a repeated measure design (RMD) using the Estimate distance and the Liu and Weng's residual methods. The comparative performance of the two methods in identifying outlier in a RMD was evaluated through empirical and simulation studies. For the empirical study, two types of outliers (outliers I and II) were randomly introduced into the real‐life dataset from physiological research at a time. The results obtained revealed that the Liu and Weng's Residual and Estimate Distance tests correctly detected the outlier I randomly introduced into all the subjects at a time. However, the Liu and Weng's Residual test was only able to correctly detect the outlier II in subjects 1, 4 and 6 while the Estimate distance test could not detect the outlier II in any of the subject. In the simulation study, random samples of size 105, 120, 135, and 150 with a corresponding number of subjects ( k = 7, 8, 9, 10), respectively, were generated from a multivariate normal distribution. Two types of outliers (outliers I and II) were randomly introduced into the simulated datasets. The results of the simulation study indicated that the Liu and Weng's residual and Estimate distance methods correctly detected the outlier I randomly introduced into the simulated data for all the sample sizes considered. However, the Liu and Weng's Residual test outperformed the Estimate distance test in detecting the outlier II. Thus, the Liu and Weng's Residual proved to be more powerful than the Estimate Distance test in identifying outlier in a RMD.
Scoping Review of Literature on the Application of Blockchain in Finance

The use of blockchain in the banking sector has drawn more attention globally. There are a lot of advantages and drawbacks to implementing blockchain in the financial sector, according to academics. These papers will be examined in this scoping review in order to determine the knowledge gaps in the subject area. To find pertinent publications for the study, a thorough manual search was done in the electronic Science Direct database. Articles were manually evaluated and vetted for eligibility by reading the abstract. Data were taken from the appropriate studies in order to synthesize, collect, rate the quality, and create a narrative summary of the results. A total of 100 articles (both review articles and research articles) were included in our final analysis. The results of the current study point to areas that need further investigation, such as the general public's ignorance about blockchain technology and its potential outside of the banking sector.
Marketing Trends in the Cryptocurrency Industry

This chapter examines the marketing trends and strategies used in the cryptocurrency industry. A qualitative research design was used, with case studies of five cryptocurrency firms as the primary data source. Secondary data sources such as websites, social media accounts, articles, and blogs were also analyzed. The study found that content marketing, social media marketing, and influencer marketing are the industry's most commonly used marketing strategies. Community building and brand reputation were also found to be essential for the success of companies operating in the cryptocurrency industry. However, the study's limitations including the limited sample size, reliance on secondary data, the potential for researcher bias, and lack of triangulation should be considered when interpreting the findings. The insights gained from this study can inform the development of marketing strategies in the cryptocurrency industry and guide future research in this area.
The Impact of Cryptocurrencies on the Financial Market

The emergence of cryptocurrencies has posed challenges to governments in managing the money supply. For years, governments have exercised monetary policy by controlling the supply of national currencies. However, cryptocurrencies are decentralized, meaning governments have little or no power to control them. This chapter provides a literature review on the impact of cryptocurrencies on (1) the national monetary policy, (2) the international monetary system, and (3) the role of cryptocurrency within the banking system. Research reveals that governments could consider developing their own cryptocurrency to maintain power and influence the money supply. Alternatively, they can use the legal framework to enable or disable cryptocurrency as legal tender within their jurisdictions. Due to its global nature, cryptocurrency can be used as an international payment method and become an integrated part of the FOREX market. Lastly, as cryptocurrency continues to gain popularity worldwide, regulations on crypto exchanges and issuers will be needed to avoid price bubbles.
Trending Technologies in the Cryptocurrency Market

Cryptocurrencies that are virtual and dematerialized are online and entirely digital currencies. Cryptocurrency has been the subject of many studies from different aspects; however, it is still a new area of investment for businesses as there are positive trends in the crypto space. Trending technologies, on the other hand, are making significant changes in all industries, and the cryptocurrency market is no exception. The employment of trending technologies can facilitate the crypto market with pattern recognition and secure transactions. This chapter aims to analyze the application of trending technologies in the crypto market and the benefits they can add to traders and brokers.
The New Architecture of Smart Contracts and Its Impact on Performance, Vulnerability, Pollution, and Energy Saving

The main purpose of the research is to provide a solution that can resolve the well-known issues in the current smart contract concept and fully change the architecture used in smart contracts and blockchain. For this purpose, the author lists the issues, analyzes them, and provides the solution explaining how it will change the situation and what kind of positive impact it will have not only on the performance of smart contracts and their extensibility but also the impact on pollution and saving of energy.
People in Cryptocurrency

Cryptocurrency has transformed the global economic system. It offers a novel and creative form of trade that has rapidly transformed the financial markets and the world of cash. A person's best allocation is determined by combining their priorities for risk reduction and upside opportunity with bitcoin's predicted diversity and return qualities. However, people have a poor tolerance for blockchain-based cryptocurrencies, owing to the rise of online fraud and the lack of a legal framework. In addition, there is a widespread misperception about its use on various platforms, which has left a void in the literature addressing this topic. This chapter will discuss the role and views of cryptocurrency users.
Meaning differences between English clippings and their source words: A corpus-based study

Abstract This paper uses corpus data and methods of distributional semantics in order to study English clippings such as dorm (&lt; dormitory ), memo (&lt; memorandum ), or quake (&lt; earthquake ). We investigate whether systematic meaning differences between clippings and their source words can be detected. The analysis is based on a sample of 50 English clippings. Each of the clippings is represented by a concordance of 100 examples in context that were gathered from the Corpus of Contemporary American English. We compare clippings and their source words both at the aggregate level and in terms of comparisons between individual clippings and their source words. The data show that clippings tend to be used in contexts that represent involved text production, which aligns with the idea that clipped words signal familiarity with their referents. It is further observed that individual clippings and their source words partly diverge in their distributional profiles, reflecting both overlap and differences with regard to their meanings. We interpret these findings against the theoretical background of Construction Grammar and specifically the Principle of No Synonymy.
Optimized deep knowledge-based no-reference image quality index for denoised MRI images

The quality of the Magnetic Resonance Imaging (MRI) image influences the disease diagnosis and consequent treatment. However, noise distortion severely impacts these images and tends to interfere with diagnosis during the data acquisition/transmission. This contribution proposes a novel No-reference Image Quality Index (NIQI) method for the intelligent estimation of MRI images and to evaluate its efficacy compared to well-established approaches. A novel Optimized Deep Knowledge-based NIQI (ODK-NIQI) method is developed and tested rigorously. The ODK-NIQI method combines shuffle shepherd optimization and improved deep mish-activated ConvNet approach. The implementation of the projected approach is conducted in MATLAB software. The results demonstrate that the proposed method achieves the best performance and the highest consistency of objectives for both the noisy and denoised MRI brain images investigated. Additionally, the proposed method shows significant improvement over the traditional NIQI techniques using standard performance metrics comprising the Spearman's Rank Correlation Coefficient (SROCC), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Pearson Linear Correlation Coefficient (PLCC). Overall, the proposed ODK-based NIQI strategy performs well in denoising MRI images.
Financial Market Innovation, Blockchain, and Sustainability

The word 'blockchain' was introduced years ago, but the meaning has changed over time. Initially it was developed only as the tool for bitcoin cryptocurrency transactions. Developed countries like US and China show the positive relationship between blockchain and financial market innovation. In the present era of initialization where people are more concerned about the safety and security of transaction, blockchain is seen as a solution in the financial market sustainability. The Indian government in association with RBI has planned to launch virtual currency for the development of the Indian financial market and economy. This study reveals the relationship between blockchain and financial market innovation in the Indian context and the factors that impact the significance and relationship. The purpose of the study is to estimate the conceptual framework of blockchain in the field of financial market innovation and sustainability. Thus, this study tries to reveal the key factors of blockchain technology that impact the innovation in the field of financial markets.
Detecting High-Resolution Adversarial Images with Few-Shot Deep Learning

Deep learning models have enabled significant performance improvements to remote sensing image processing. Usually, a large number of training samples is required for detection models. In this study, a dynamic simulation training strategy is designed to generate samples in real time during training. The few adversarial examples are not only directly involved in the training but are also used to fit the distribution model of adversarial noise, helping the real-time generated samples to be similar to adversarial examples. The noise of the training samples is randomly generated according to the distribution model, and the random variation of training inputs reduces the overfitting phenomenon. To enhance the detectability of adversarial noise, the input model is no longer a normalized image but a JPEG error image. Experiments show that with the proposed dynamic simulation training strategy, common classification models such as ResNet and DenseNet can effectively detect adversarial examples.
Path Loss Prediction Accuracy Based On Random Forest Algorithm in Palembang City Area

Path loss is a mechanism where the signal from the transmitting antenna to the receiver in a wireless network is attenuated during transmission across a medium due to external field conditions. In the telecommunication design, precise and efficient calculations are required. Random forest, as a machine learning-based path loss prediction model, is used in this study. Machine learning-based path loss prediction, random forest, has a low level of complexity and a high level of predictability. The data was collected using the drive test method at the Trans Musi busway area on the 4G network in Palembang, South Sumatra, Indonesia. The data ratio comprised 20% of the testing set and the rest of the training set. As a result, it was obtained that the prediction accuracy of 9.24% of mean absolute percentage error (MAPE) and root mean square error (RMSE) was 13.6 decibels (dB). Using hyperparameter tuning for random forest results in optimizing the model used, resulting in accuracy prediction for 8.00% of MAPE and RMSE was 11.8 dB, which is better than the previous results.
Amharic Language Image Captions Generation Using Hybridized Attention-Based Deep Neural Networks

This study aims to develop a hybridized deep learning model for generating semantically meaningful image captions in Amharic Language. Image captioning is a task that combines both computer vision and natural language processing (NLP) domains. However, existing studies in the English language primarily focus on visual features to generate captions, resulting in a gap between visual and textual features and inadequate semantic representation. To address this challenge, this study proposes a hybridized attention-based deep neural network (DNN) model. The model consists of an Inception-v3 convolutional neural network (CNN) encoder to extract image features, a visual attention mechanism to capture significant features, and a bidirectional gated recurrent unit (Bi-GRU) with attention decoder to generate the image captions. The model was trained on the Flickr8k and BNATURE datasets with English captions, which were translated into Amharic Language with the help of Google Translator and Amharic Language experts. The evaluation of the model showed improvement in its performance, with a 1G-BLEU score of 60.6, a 2G-BLEU score of 50.1, a 3G-BLEU score of 43.7, and a 4G-BLEU score of 38.8. Generally, this study highlights the effectiveness of the hybrid approach in generating Amharic Language image captions with better semantic meaning.
Deep Cross-Network Alignment with Anchor Node Pair Diverse Local Structure

Network alignment (NA) offers a comprehensive way to build associations between different networks by identifying shared nodes. While the majority of current NA methods rely on the topological consistency assumption, which posits that shared nodes across different networks typically have similar local structures or neighbors, we argue that anchor nodes, which play a pivotal role in NA, face a more challenging scenario that is often overlooked. In this paper, we conduct extensive statistical analysis across networks to investigate the connection status of labeled anchor node pairs and categorize them into four situations. Based on our analysis, we propose an end-to-end network alignment framework that uses node representations as a distribution rather than a point vector to better handle the structural diversity of networks. To mitigate the influence of specific nodes, we introduce a mask mechanism during the representation learning process. In addition, we utilize meta-learning to generalize the learned information on labeled anchor node pairs to other node pairs. Finally, we perform comprehensive experiments on both real-world and synthetic datasets to confirm the efficacy of our proposed method. The experimental results demonstrate that the proposed model outperforms the state-of-the-art methods significantly.