Reviewerly/exHarmony huggingface dataset:
Erato: Automatizing Poetry Evaluation

We present Erato, a framework designed to facilitate the automated evaluation of poetry, including that generated by poetry generation systems. Our framework employs a diverse set of features, and we offer a brief overview of Erato’s capabilities and its potential for expansion. Using Erato, we compare and contrast human-authored poetry with automatically-generated poetry, demonstrating its effectiveness in identifying key differences. Our implementation code and software are freely available under the GNU GPLv3 license.
Scalable Interactive Keyword Query Interface over Knowledge Graph

Abstract goes here. Existing keyword query systems over knowledge graph can produce interesting results and are easy to use. However, they cannot handle the ambiguities that have matches in the knowledge graph, namely, multiple interpretations may be correct so that they cannot determine which interpretation is what the user expects. And they cannot scale to handle the knowledge graphs with more than billions of triples or thousands of types/predicates. On the one hand, we construct an interactive interface in which the above ambiguities will resort to the user. To enhance the user experience, we formalize the interaction problem and then propose an algorithm to find a best scheme of interaction (i.e., a verifying sequence with lowest interaction times and candidates) based on the dependency relations between mappings. On the other hand, we propose a new schema graph, i.e., type-predicate graph, which has good scalability while containing complete information for building query graph. No matter how large the knowledge graph is, the size of type-predicate graph is always very small because its size depends on the number of types and predicates whose number are far less than that of triples in knowledge graph. Finally, we have demonstrated our contributions with several well-directed experiments over real datasets (DBpedia and Yago).
Review of "Writing in the Clouds: Inventing and Composing in Internetworked Writing Spaces by John Logie," Logie, J. (2021). Writing in the clouds: Inventing and composing in internetworked writing spaces. Parlor Press.

In the wake of the controversy surrounding the new AI chatbot application, ChatGPT, I wonder how Logie would seek to include this new technology in his work. I ponder this because, throughout the book, Logie presents compelling evidence for why the concepts of invention, composition, and internetworked writing should be embraced and not feared. While some denounce the application and take to social media to disparage the possible negative impact on students, creativity, and composition, ChatGPT, I believe Logie would argue, would be a powerful tool we can implement to become "composers." He believes that through cloud computing services we are now more apt to collaborate, use, remix, and create rhetorical modes that extend far beyond the formulaic argument, therefore we are composers. So, Logie applies the idea of a composer as someone who is a "prosumer" (Toffler). This composer is media literate and transforms traditional rhetorical canons into multimodal compositions such as memes, Google Docs, and digital collages. However, his overarching argument is that internetworked writing tools have democratized writing through that same offering of innovative outlets. His book is arranged in a way that walks the reader through this argument.
Differential Evolution Image Contrast Enhancement Using Clustering

Differential Evolution (DE) is a population-based totally metaheuristic search algorithm that optimizes problems via iteratively enhancing candidate solutions primarily based on an evolutionary technique. The current study aims to show-case the capability and success of finding a globally optimal solution in order to enhance contrast and detail in color images. This paper introduces a new technique for contrast enhancement of an image based on clustering using Differential Evolution Algorithm. DE's role is to optimize the turnaround function's metrics by optimizing the outcome fitness standard. Experimental results were compared with the firefly image contrast enhancement technique and concluded that pro-posed method is an improvement over preceding technique.
The Importance of Data Encryption in Data Security

In today's digital agе, thе prolifеration of data-drivеn tеchnologiеs has rеvolutionizеd thе way information is еxchangеd and storеd. Amidst this transformation, еnsuring thе sеcurity and confidеntiality of sеnsitivе data has bеcomе paramount. Encryption, a fundamеntal tеchniquе in thе rеalm of cybеrsеcurity, plays a pivotal rolе in safеguarding digital information from unauthorizеd accеss and potеntial brеachеs. This rеviеw papеr mеticulously еxaminеs thе multifacеtеd importancе of еncryption in data sеcurity. Through an еxtеnsivе analysis of contеmporary rеsеarch studiеs, industry practicеs, and rеal-world applications, thе papеr еxplorеs thе undеrlying principlеs of еncryption algorithms, thеir еvolution, and thеir rеlеvancе in divеrsе sеctors, ranging from hеalthcarе and financе to govеrnmеnt and е-commеrcе. Thе rеviеw dеlvеs into thе challеngеs and opportunitiеs posеd by еncryption tеchnologiеs, еlucidating thе balancе bеtwееn sеcurity and usability. Furthеrmorе, it scrutinizеs еmеrging trеnds, such as quantum еncryption and homomorphic еncryption, which arе poisеd to shapе thе futurе landscapе of data sеcurity . By synthesizing a wealth of knowledge, this paper not only provides a comprehensive understanding of encryption's significance but also offers valuable insights for researchers, practitioners, and policymakers aiming to fortify the digital infrastructure against evolving cyber threats
Generation of keyboard handwriting during user authentication on mobile devices

Objectives . This article discusses a new way of generating keyboard handwriting using a touch keyboard for authentication in currently existing mobile systems. Methods . Due to the insufficient reliability of single password authentication, the proposal is to use it in combination with characteristics which correspond to handwriting on mobile devices. This article demonstrates the possibility of using individual user characteristics in the formulation of keyboard handwriting on devices with touch keyboards. The type of keyboard used affects the characteristics of keyboard handwriting, so this aspect can be used to improve password authentication reliability. The authentication process in the information environment can be supplemented with data on the nature of the impact on a touch keyboard. The use of the built-in 3D Touch function is also of interest. This is available when working on mobile devices and appliances equipped with a touch keyboard. The paper demonstrates that the use of one parameter only is insufficient for accurate authentication. The study proposes a method of determining an acceptable error range for both the touch force and the intermediate interval during authentication. For this purpose, the Laplace function which formulates the interval of each characteristic depending on the required probability of user recognition is used. Results . Touch force and the intermediate interval are sufficient to obtain the necessary characteristics, in order to formulate a refined user portrait depending on the user’s keyboard handwriting. Experimental statistics are given separately for an average sample of three different users depending on touch force. They also provide the results of authentication when using both standard deviations of pressing and the intervals when using the touch keyboard for the iOSXcode platform. Conclusions . The conclusion relates to the possibility of user authentication by keyboard handwriting, formulated on the basis of both the touch force on the keyboard symbols and intervals between pressing. Using the values of the sample mean and standard deviations allows authentication according to the required recognition probability.
Structural Features of Intellectual Mobility of Students of Higher Educational Institutions

The article is devoted to the study of structural features of intellectual mobility of students of higher educational institutions. The article reveals the results of the expert assessment, which allowed us to identify four structural components of students' intellectual mobility: a need-motivational structural component, an intellectual-creative structural component, a social structural component, as well as a personal structural component
ON METRICS FOR INFORMATION VALUE QUANTIFICATION

In this paper, we aggregate, analyze, and exemplify several metrics for the information value. The metrics vary by absolute value, normalizations, or full probabilistic quantification. The normalization of the information value encompasses the division by (1) the expected and maximized utility of the base scenario (usually without additional information), (2) by the system performance, or (3) by the information acquirement costs. The latter can be aligned to the return over investment ratio to support and facilitate SHM system investment decision. With a case study on malevolent attacks to a generic iconic bridge, we exemplify the information value metrics and point to the application ranges and the implications of full distribution considerations.
Asset Allocation Method Based on Sentiment Signals and Causal Information using Multi-asset Classes

In this study, we demonstrate the usefulness of financial text for asset allocation with multi-asset classes, including stocks and bonds, by creating polarity indexes for several types of financial news through natural language processing. We performed clustering using a change-point detection algorithm on the created polarity indexes. We also constructed a multi-asset portfolio using an optimization algorithm and rebalanced it based on the detected change points. The results show that the proposed asset allocation method performed better than the comparison method, suggesting that polarity indexes can be useful in constructing asset allocation methods with multi-asset classes.
A Systematic Study about the Vulnerabilities and Security Enhancement in Block Chain Technology

In this paper we undertake a methodical analysis of the Block Chain system's vulnerabilities and examine security upgrade strategies that could suggest future directions for investigation into Block Chain technology and its applications. Smart contracts are self-executing programs that store cryptocurrency valued at billions of SEK on second-generation block chains like Ethereum. Once they are put into place, they cannot be changed. Although smart contracts are typically thought to be secure objects, a new class of vulnerabilities that are more likely to be ethical aspects of programming than software coding flaws are revealed by a methodical examination of technology and source code.
Label-defect tolerance ability of deep learning inversion networks and its applications

One of the major challenges in deep learning inversion is the insufficient number of well logs, which leads to a scarcity of labels that adequately capture the vertical lithology patterns and lateral variations within geological formations. Forward modeling after well log interpolation is one commonly used approach to augment the labels. However, it is crucial to ascertain the rationale behind such interpolation and determine the optimal interpolation method to enhance inversion accuracy. Through model testing, we confirm that Deep Learning (DL) networks have the ability to tolerate labels’ defects, which we name as the Label-Defect Tolerance Ability (LDTA). This understanding can be used to guide how to construct a large number of effective labels from a limited amount of well data. In a real data test, we have increased about 4% of the accuracy by our label augmentation method.
Assistive Path Control System for Blind and Deaf people with Visual and Depth perception Challenge

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Untangling the Role of Data Processing in Enhancing Recommender Systems Performance

Recommender systems, pivotal in guiding user choices in numerous online platforms, rely heavily on the integrity and quality of the underlying data. This study delves into the transformative potential of data preprocessing on the effectiveness of such systems, using the Sephora cosmetics dataset as a case study. By juxtaposing results from both Collaborative Filtering (CF) and Content-Based Filtering (CBF) approaches—with and without preprocessing—we unveil the marked improvements in prediction accuracy post-preprocessing. Our findings accentuate that meticulous data preparation isn't merely a precursor but a fundamental aspect of optimizing recommender systems. Through this exploration, we aim to highlight best practices and underscore the inextricable link between data hygiene and the success of recommendation algorithms.
Movie Recommendation System Using Content Based Filtering

The movie recommendation system plays a crucial role in assisting movie enthusiasts in finding movies that match their interests, saving them from the overwhelming task of sifting through countless options. In this paper, we present a content-grounded movie recommendation system that leverages an attribute-based approach to offer personalized movie suggestions to users. The proposed method focuses on attributes such as cast, keywords, crew, and genres of movies to predict users' preferences accurately. Through extensive evaluation, our content-grounded recommendation system demonstrated significant improvements in performance compared to conventional methods. The precision and recall scores increased by an average of 20% and 25%, respectively, resulting in more accurate and relevant movie recommendations for users. The philosophy behind our approach lies in the belief that content-based methods can overcome some limitations of collaborative filtering, especially when dealing with new or niche movies with limited user ratings. By considering the specific attributes of movies and matching them to users preferences, our system can provide more tailored recommendations, enhancing user satisfaction and engagement. Overall, our content-based movie recommendation system showcases the potential of attribute-based approaches to deliver efficient and personalized recommendations. By reducing the burden on users to find suitable movies, we aim to enrich their movie-watching experience and foster their passion for cinema.
Indistinguishability obfuscation

At least since the initial public proposal of public-key cryptography based on computational hardness conjectures (Diffie and Hellman, 1976), cryptographers have contemplated the possibility of a “one-way compiler” that translates computer programs into “incomprehensible” but equivalent forms. And yet, the search for such a “one-way compiler” remained elusive for decades. We examine a formalization of this concept with the notion of indistinguishability obfuscation ($i\mathcal{O}$). Roughly speaking, $i\mathcal{O}$ requires that the compiled versions of any two equivalent programs (with the same size and running time) be indistinguishable to any efficient adversary. Finally, we show how to construct $i\mathcal{O}$ in such a way that we can prove the security of our $i\mathcal{O}$ scheme based on well-studied computational hardness conjectures in cryptography.
Simulation-based search

Planning is one of the oldest and most important problems in artificial intelligence. Sim­u­lation-based search algorithms, such as AlphaZero, have achieved superhuman performance in chess and Go and are used widely in real-world applications of planning. In this paper we provide a unified framework for simulation-based search. Algorithms in this framework interleave operators for policy evaluation (better estimating the value function of the current policy) and policy improvement (using the value function to form a better policy). These operators are applied to states and actions that are sampled in sequential trajectories, and that may branch recursively into other sampled trajectories. The value function and policy may also be represented by a function approximator. Our framework includes a broad family of search algorithms that includes Monte-Carlo tree search, sparse sampling, nested Monte-Carlo search, classification-based policy iteration, and AlphaZero.
Differential privacy: getting more for less

The key to the success of differential privacy, now the gold standard for privacy-preserving data analysis, is the ability to quantify and reason about cumulative privacy loss over many differentially private interactions. When upper bounds on privacy loss are loose, the deployment of the algorithms is by definition conservative. Under high levels of composition, much potential utility is lost. We survey two general approaches to getting more utility: privacy amplification methods, which are algorithmic, and definitional methods, which admit a wider class of algorithms and lead to tighter analyses of existing algorithms.
Revisiting the Role and Impact of Information Technology Capability

This study is a conceptual replication of Bharadwaj (2000) investigating the impact of IT capability on a firm’s financial performance. The original study demonstrated that firms with superior IT capability will deliver superior financial performance manifested by higher profit ratios and lower cost ratios. However, conflicting findings emerged regarding the relationship between IT capability and a firm’s financial performance (Chae et al., 2014). Furthermore, in the past decade, advancements in statistical analysis, such as panel data modeling techniques that were not used in these past studies have provided more robust analytical techniques to observe patterns over time. Hence, incorporating the conceptual foundations as in prior studies on IT capability (e.g., Bharadwaj, 2000; Santhanam &amp; Hartono, 2003), we re-investigate the impact of IT capability on a firm’s financial performance but do so by using a large longitudinal dataset and leveraging the power of panel data analysis. Our findings are consistent with the results of Bharadwaj (2000), Santhanam and Hartono (2003), and Choi and George (2016), suggesting that IT capability has a significant positive impact on financial performance. Our results do not support the findings of Chae et al. (2014), who found no association between IT capability and financial performance. We discuss the implications of our findings for the continued use of the theoretical framework of IT capability derived from the resource-based view.
A mathematical perspective of machine learning

What lies at the heart of modern neural network-based machine learning is the ability to approximate very high dimensional functions with good accuracy. This opens up two major avenues of research. The first is to develop machine learning-based algorithms for scientific problems that suffer from the curse of dimensionality. The second is to build a theoretical framework that helps us to form a better foundation for machine learning. For the latter, the most important questions that need to be addressed include: Why do neural network models work so well in high dimension? Why does their performance depend so sensitively on the choice of the hyperparameters? Can we develop more robust and equally accurate new machine learning models and algorithms? In this article, we review some of the major progresses made in these directions.
On mathematical modeling in image reconstruction and beyond

Imaging has been playing a vital role in the development of natural sciences. Advances in sensory, information, and computer technologies have further extended the scope of influence of imaging, making digital images an essential component of our daily lives. Image reconstruction is one of the most fundamental problems in imaging. For the past three decades, we have witnessed phenomenal developments of mathematical models and algorithms in image reconstruction. In this paper, we will first review some progress of the two prevailing mathematical approaches, i.e., the wavelet frame-based and PDE-based approaches, for image reconstruction. We shall discuss the connections between the two approaches and the implications and impact of the connections. Furthermore, we will review how the studies of the links between the two approaches lead us to a mathematical understanding of deep convolutional neural networks, which has led to further developments in modeling and algorithmic design in deep learning and new applications of machine learning in scientific computing.
Mean estimation in high dimension

In this note we discuss the statistical problem of estimating the mean of a random vector based on independent, identically distributed data. This classical problem has recently attracted a lot of attention both in mathematical statistics and in theoretical computer science and numerous intricacies have been revealed. We discuss some of the recent advances, focusing on high-dimensional aspects.
Stochastic gradient descent: where optimization meets machine learning

Stochastic gradient descent (SGD) is the de facto optimization algorithm for training neural networks in modern machine learning, thanks to its unique scalability to problem sizes where the data points, the number of data points, and the number of free parameters to optimize are on the scale of billions. On the one hand, many of the mathematical foundations for stochastic gradient descent were developed decades before the advent of modern deep learning, from stochastic approximation to the randomized Kaczmarz algorithm for solving linear systems. On the other hand, the omnipresence of stochastic gradient descent in modern machine learning and the resulting importance of optimizing performance of SGD in practical settings have motivated new algorithmic designs and mathematical breakthroughs. In this note, we recall some history and state-of-the-art convergence theory for SGD which is most useful in modern applications where it is used. We discuss recent breakthroughs in adaptive gradient variants of stochastic gradient descent, which go a long way towards addressing one of the weakest points of SGD: its sensitivity and reliance on hyperparameters, most notably, the choice of step-sizes.