Reviewerly/exHarmony huggingface dataset:
A Hybrid Framework Using Natural Language Processing and Collaborative Filtering for Performance Efficient Feedback Mining and Recommendation

Product development insights may be found through user reviews on App stores, product forums, and social media. This feedback is often regarded as the “voice of the users”. This feedback has been subject to a lot of recent research, intending to create systems that can automatically extract, filter, analyze, and report the concerned feedback data in near real time. As per our survey results, often this user feedbacks do not reach the concerned organization promptly due to the volume, veracity, and velocity of feedback from multiple channels. In this rese arch work, we propose using sentiment analysis and social media mining an automatic engine which can be used for better product recommendation and automatic routing of relevant feedback to the product development teams. Our proposed solution is scheduled to run at regular intervals pulling dynamic reviews in an optimized manner with a lesser time complexity and higher efficiency. The reviews are collated from distributed platforms followed by building a domain classification engine on the principles of TF-IDF and Supervised Classifier. This system is used to classify the reviews of the respective enterprises. A sentiment analysis system is built using combined Rule-Based Mining and Supervised Learning Models which makes use of polarity to classify if the feedback is positive or negative. If the polarity is negative, the feedback gets routed to the concerned enterprise for immediate action and if the polarity is positive, it is passed to a user-based collaborative filtering engine which acts as a recommendation system.
The Online Behaviour of the Algerian Abusers in Social Media Networks

Connecting to social media networks becomes a daily task for the majority of people around the world, and the amount of shared information is growing exponentially. Thus, controlling the way in which people communicate is necessary, in order to protect them from disorientation, conflicts, aggressions, etc. In this paper, we conduct a statistical study on the cyber-bullying and the abusive content in social media (i.e. Facebook), where we try to spot the online behaviour of the abusers in the Algerian community. More specifically, we have involved 200 Facebook users from different regions among 600 to carry out this study. The aim of this investigation is to aid automatic systems of abuse detection to take decision by incorporating the online activity. Abuse detection systems require a large amount of data to perform better on such kind of texts (i.e. unstructured and informal texts), and this is due to the lack of standard orthography, where there are various Algerian dialects and languages spoken.
Text Style Transfer: A Comprehensive Study on Methodologies and Evaluation

Text Style Transfer (TST) rewords a sentence from one style (e.g., polite) to another (e.g., impolite) while conserving the meaning and content. This domain has attracted the attention of many researchers as it makes natural language generation (NLG) tasks more user-oriented. TST finds its applications widely in industry such as conversational bots and writing assistance tools. With the success of deep learning, a plethora of research works on style transfer based on machine learning have been proposed, developed, and tested. This systematic review presents the past work on TST clustered into categories based on machine learning and deep learning algorithms. It briefly explains the various subtasks within TST and assembles its publicly available datasets. It also summarizes the automatic and manual evaluation practices used for style transfer tasks and finally, sheds some light on current challenges and points towards promising future directions for research in the TST domain.
Comparative Analysis of LDA Algorithm for Low Resource Indian Languages with Its Translated English Documents

Nowadays, social media acts as an information medium that generates a huge amount of data. Irrespective of the age groups, many people across the globe use social media to share their thoughts and emotions in the form of tweets, comments, etc. This information can be used as a source of data for research. This generates a humongous amount of data not only in English but also in many other languages. In this paper, we have focused our research on native Indian languages such as Kannada, Tamil, and Telugu. Latent Dirichlet Allocation (LDA) is an algorithm that can process a large set of text data to produce topic modeling, clustering words, and topics in a document. We have analyzed the performance of the LDA method for native language. Dataset in Kannada and Tamil works better with respect to coherence and English translated dataset of Telugu is Optimal when compared with original Telugu data. With respect to perplexity, LDA works better for native language dataset.
A Comparative Study of Loss Functions for Deep Neural Networks in Time Series Analysis

Currently, deep neural networks are widely used for analyzing temporal data. These networks can adapt their architecture to specific needs and deliver good performance. Researchers and developers frequently update their architecture to meet the requirements, but this process can be quite time-consuming. A crucial aspect of DNN architecture is the loss function, which plays a crucial role in calculating gradients. Most research and applications in time series analysis use the mean squared error (MSE) loss function. In this paper, we aim to explore existing loss functions to address the challenge of selecting the appropriate loss function for DNNs. We conduct experiments on time series datasets to evaluate the impact of different loss functions on DNN model performance. Our findings indicate that the Huber loss function outperforms other loss functions in time series analysis. Additionally, we discuss the potential for custom loss functions as future work, beyond the limitations of existing methods.
Rugby Ball Detection, Tracking and Future Trajectory Prediction Algorithm

This paper presents a custom object detection and tracking algorithm for position estimation and trajectory prediction of a moving rugby ball. The approach of the algorithm is to combine the accuracy of object detection provided by the custom trained YOLOv5 model and the speed of the KCF tracker to perform a linear trajectory prediction of the ball. Kalman filter is used to ensure the optimal estimation of the current position and increase the accuracy of prediction for the future trajectory. Multi-threading is implemented to concurrently detect and track the ball in consecutive frames, resulting a computationally efficient approach.
Deep Learning-Based Semantic Segmentation of Blood Cells from Microscopic Images

Morphological analysis and differential cell counting are important in characterizing many diseases including malaria and leukaemia. The basic building block involved is cell segmentation and is a challenging but beneficial task in cytopathology. Microscopy being the gold standard for cell analysis, approaches have been discovered from traditional image processing operations to deep learning techniques for cell segmentation from microscopy images. In the last few years, classification networks were extended for image segmentation using the pixel-based classification method, known as semantic segmentation. Convolutional neural networks exhibited good performance in image segmentation. However, the networks suffered some limitations due to fully connected layers and pooling layers that restricted the size of images to be given as input and resulted in the loss of spatial context. In this research, experiments were carried out with two popular CNN architectures UNet and SegNet, traditionally used for semantic segmentation of natural images. By identifying the capacity of these networks for cell segmentation on natural images, we have experimented on a custom-built RBC, WBC and platelet cell segmentation dataset based on ALL-IDB. We critically evaluate the performance of both architectures with an intuitive explanation of their difference in performance. The UNet outperformed SegNet that too with limited labelled training data giving a promising Dice score of 0.97. With the experiments and analysis conducted in this work, we propose that the UNet is a very good choice for cell segmentation in cytopathology applications.
A Variational Bayes Approach to Factor Analysis

Factor analysis models are useful dimensionality-reduction techniques for the covariance of observed data. A Bayesian approach to inference for these models offers several benefits over the frequentist counterparts, including regularized estimates and inclusion of subjective prior information. However, implementation of Bayesian FA is routinely based on Markov Chain Monte Carlo (MCMC) techniques that are computationally expensive and often do not scale in high dimensions. To improve scalability, we propose a Variational Bayes algorithm that approximates the posterior distribution at a fraction of the MCMC computational cost. We compare the performance of variational Bayes and MCMC based algorithms in a simulation study and show that even in low dimensions variational Bayes is much faster than MCMC while still producing highly accurate point estimates.
Lingualyzer: A computational linguistic tool for multilingual and multidimensional text analysis

Abstract Most natural language models and tools are restricted to one language, typically English. For researchers in the behavioral sciences investigating languages other than English, and for those researchers who would like to make cross-linguistic comparisons, hardly any computational linguistic tools exist, particularly none for those researchers who lack deep computational linguistic knowledge or programming skills. Yet, for interdisciplinary researchers in a variety of fields, ranging from psycholinguistics, social psychology, cognitive psychology, education, to literary studies, there certainly is a need for such a cross-linguistic tool. In the current paper, we present Lingualyzer ( https://lingualyzer.com ), an easily accessible tool that analyzes text at three different text levels (sentence, paragraph, document), which includes 351 multidimensional linguistic measures that are available in 41 different languages. This paper gives an overview of Lingualyzer, categorizes its hundreds of measures, demonstrates how it distinguishes itself from other text quantification tools, explains how it can be used, and provides validations. Lingualyzer is freely accessible for scientific purposes using an intuitive and easy-to-use interface.
Resisting Adversarial Manipulation in Collaborative Filtering: Techniques and Performance Analysis

Adversarial attacks pose a substantial threat to the reliability and trustworthiness of collaborative filtering-based recommendation systems. This article delves into the exploration of various defense techniques against adversarial manipulation in collaborative filtering, focusing specifically on item-based recommenders. We investigate the vulnerabilities that adversarial attacks exploit within collaborative filtering models and propose defensive strategies to bolster the resilience of these systems. Through a series of extensive experiments and rigorous performance analysis, we evaluate the effectiveness of the proposed defense techniques in mitigating adversarial threats. Our primary goal is to fortify the security and integrity of collaborative filtering-based item recommendations; ensuring users receive trustworthy and accurate suggestions despite potential adversarial challenges.
The Million Patient Labset: A large, de-identified dataset of longitudinal clinical laboratory results from one million patients

Abstract Laboratory testing is integral to the diagnosis and treatment of disease. Advances in electronic medical records and computation have initiated an era of ‘big data’ for laboratory medicine. However, most health data continues to exist in isolated and inaccessible information silos, limiting public utility. To address this gap, we have developed a large, de-identified, longitudinal dataset of laboratory results from over one million patients, called the Million Patient Labset (MPL), which is free and open to the public. The MPL was produced from the combination of two large healthcare systems with IRB and information security approvals. It includes 1,112,550 patients from across 1,920 healthcare sites in two states from 2017 through 2021, with 4,404 different clinical assays represented. Sites included primary care clinics, secondary care facilities for dialysis, infusions, rehabilitation, and other subspecialties, and tertiary centers for major surgery, trauma care, cancer care, and other complex medical services. Assays having fewer than 100 results were excluded. Limited patient demographic and encounter metadata are also included. Protected health information was de-identified in accordance with Health Insurance Portability and Accountability Act of 1996 (HIPAA) safe harbor standards. Service dates and times were altered to obscure absolute timestamps, while approximately preserving interval lengths between test results of the same patient, and general age and time of testing averages across patients. Time of day, day of week, and seasonality were approximately preserved. Data were validated through randomly selected comparisons against the original patient records, prior to destroying re-identification keys. In conclusion, we have produced a large de-identified open-access dataset of clinical laboratory results from over one million real patients. We hope this resource will be valuable for researchers exploring human clinical variation, medical artificial intelligence, reference interval optimization, and other data-driven studies.
Intellectual Property in the Days of AI-Language Software

The quick development of AI technology has completely changed how information is produced and shared, especially in the area of language production. There has been a paradigm change in how intellectual property (IP) laws are understood and used. This abstract explores the many opportunities and problems that AI language software presents for intellectual property rights. This paper highlights the changing conceptions of authorship and ownership while examining the effects of AI-generated material on copyright, trademark, and patent laws. It also examines the moral implications of content produced by AI, focusing on questions of transparency, responsibility, and attribution. The paper also examines the innovative idea of AI-generated ideas and how inventorship and patent eligibility could be redefined.The paper explores the developing body of legal precedents and international regulatory frameworks pertaining to AI-generated material, evaluating how well they protect the rights of artists and innovators. In order to identify trends in IP protection for works created by AI, it examines previous legal decisions and legislative changes. Additionally, this abstract considers the need for flexible legal frameworks that strike a balance between promoting innovation and upholding artists' rights in the digital era. It suggests some tactics for improving IP enforcement and protection systems in the face of quickly developing AI technology. This abstract offer a thorough analysis of the intricate interactions that arise between AI language software and intellectual property, highlighting the necessity for a progressive legal system that can successfully negotiate this ever-changing terrain. Through tackling these obstacles, we can promote creativity while guaranteeing the ongoing acknowledgement and defence of intellectual property rights in the AI era.
Multi-Task Faces (Mtf) Data Set: A Legally and Ethically Compliant Collection of Face Images for Various Classification Tasks

Human facial data hold vast potential for a variety of tasks. However, privacy regulations such as the EU General Data Protection Regulation have motivated the removal or limited the publication of facial data sets from the internet due to inadequate data collection methods. While synthetic data sets have been proposed, they do not faithfully represent the real data distribution. Additionally, most available data sets are task-specific. To address these issues, we present the Multi-Task Faces (MTF) data set, a meticulously curated collection of face images labeled for multi-classification tasks including face, race, gender, and age classification-- that strictly follows ethical collection rules and copyright regulations. In this paper, we detail the data collection and processing procedures, and evaluate data quality across five DL models. The reported results constitute a baseline for further research. The MTF data set is accessible on Github.
Generative AI and User-Generated Content: Evidence from Online Reviews

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ANÁLISE DO SOMBREAMENTO PARCIAL DE UM SISTEMA FOTOVOLTAICO URBANO

Urban photovoltaic (PV) systems generally have many surrounding elements, such as neighboring buildings and vegetation, which can cause partial shading in the PV installation. In general, the effects of this type of shading are not studied in detail. In this context, the aim of this work was to carry out a detailed analysis of the effects of partial shading in a PV system installed in Porto Alegre. The power and voltage of the PV system over the course of a day were analyzed using shadow photos taken on site and shadow prediction from a SketchUp model. I-V curves were also simulated using measured solar radiation and temperature data. The individual voltages of the PV modules were analyzed over another day. Over the course of a day, power reductions were verified during the afternoon (daytime period with shading), which were related to voltage increases in the modules. As soon as the shadow hit a sub-module, the maximum power point (MPP) would shift to a lower voltage, and the inverter maximum power point tracker (MPPT) would increase the voltage to a maximum and then reduce to try to find this new point. After 3 pm, the MPP voltage was out of the MPPT operating range, and the system was no longer polarized at the MPP. The analysis of individual voltages confirmed this behavior. Each time a sub-module was shaded, everyone's voltage increased and then decreased. Then the shaded sub-module was reverse-biased so that the bypass diode would carry electrical current. Shading losses could be reduced by using an inverter operating at lower voltages or micro inverters in this case study. The shading effects discussed in detail can contribute to a better understanding of the behavior of other partial shading cases.
Do Entrepreneurs Use Cryptocurrency to Hedge Against Their Business Risk?

This paper studies if entrepreneurs use cryptocurrency to hedge against business risk. Cryptocurrency is a newly an emerging asset class that distinct from other traditional asset classes and less been analyzed from the entrepreneur’s perspective. To implement the analyses, we address the endogeneity issue of being an entrepreneur using parents background and show that entrepreneurs prefer risk diversification hypotheses to hedge against their business risks.
Thermodynamic Perspectives on Computational Complexity: Exploring the P vs. NP Problem

The P vs. NP problem, central to computational theory, remains unresolved despite extensive efforts grounded mainly in mathematical logic and algorithmic theory. Going beyond traditional boundaries, this paper marries information theory, thermodynamics, and computational complexity, unveiling a panorama rich with interdisciplinary intersections. Central to our discourse is the concept of entropy, explored both in its information-theoretic nuance and thermodynamic interpretation and its potential relationship with computational 'hardness'. We introduce the mechanism of Entropy-Driven Annealing (EDA) as a means to elucidate the energy landscapes underlying computational problems. Through this lens, we add insights into the inherent nature of NP problems, postulating their possible distinctiveness from P problems based on thermodynamic costs. We also touch upon speculative thermodynamic avenues that might pave the way for polynomial-time solutions to NP challenges. Bridging these diverse domains is challenging, yet the confluence promises refreshing perspectives on quintessential computational dilemmas. While the P vs. NP problem remains unresolved, this interdisciplinary approach provides a fresh perspective on the underlying computational challenges.
Impact of Keywords in Product Title on Product Sales Conversion

In recent years, the e-commerce sector has emerged as an integral component of consumers' daily lives and an essential facet of the global retail industry. As evidenced by the fact that over 15% of total retail sales in the United States, amounting to $269.5 billion, occurred through e-commerce platforms in Q2 2023, the retail sector is witnessing a significant shift towards the digital realm. Given this transformation, retail businesses are presented with a compelling imperative to enhance every facet of their online operations. This study delves into the realm of product sales conversion within the Amazon e-commerce platform, specifically focusing on the impact of incorporating top search keywords in product titles as opposed to not including them. Employing a rigorous methodology, four A/B testing experiments were conducted, facilitated by the retailer's experimentation tools and reaching an extensive audience of 2.5 million unique customer accounts. The analysis of the experimental results, grounded in a Bayesian approach, coupled with a 95% confidence interval derived from posterior probability distributions, revealed a substantial and statistically significant enhancement in product sales conversion when top search keywords were integrated into product titles. These findings hold significant implications for brands seeking to optimize their product titles for greater sales conversion, offering valuable insights into the effective utilization of product titles as a tool for driving sales in the dynamic landscape of e-commerce.
FAIR Data Austria – Paving the Way for Enhanced Research Data Management and Collaboration

In an age dominated by digital information, the importance of accessible and well-managed research data cannot be overstated. The FAIR Data Austria project is at the forefront of pioneering initiatives that cultivate a culture of data sharing and collaboration, adhering to the principles of Findability, Accessibility, Interoperability and Reusability (FAIR). The project addresses the challenges associated with implementing FAIR principles – from technical complexity to cultural barriers – and takes a multi-faceted approach that includes capacity building initiatives, community engagement and the development of tools and infrastructure to facilitate FAIR research practices.
Interacting With Curves: How to Validly Test and Probe Interactions in the Real (Non-linear) World

Hypotheses involving interactions, where one variable modifies the association between another two, are very common. They are typically tested relying on models that assume effects are linear, e.g., with a regression like y=a+bx+cz+dx·z. In the real world, however, few effects are linear, invalidating inferences about interactions. For instance, in realistic situations, the false-positive rate can be 100% for detecting an interaction, and a probed interaction can reliably produce estimated effects of the wrong sign. This paper proposes a revised toolbox for studying interactions in a curvilinear-robust manner, giving correct answers 'even' when effects aren't linear. It's applicable to most study designs, and produces results that are analogous to those of current -often invalid- practices. The presentation combines statistical intuition, demonstrations with published results, and simulations.