Reviewerly/exHarmony huggingface dataset:
A Novel Twitter Sentimental Analysis Approach Using Naive Bayes Classification

The world is mutating into a better place due to the innovations happening around the globe. Since people spend much time regularly on social media to express their opinions, social networks are the primary sources of information regarding people's opinions and feelings on various topics. Twitter is a micro blogging and social networking site that allows users to post brief status updates of up to 140 characters in length. It is a rapidly growing service. This project resolves the issue of sentiment analysis on Twitter. Sentiment analysis is a form of natural language processing used to monitor public opinion on a specific product or subject. Sentiment analysis, also known as opinion mining, entails creating a framework to capture and analyze product opinions expressed in blog posts, comments, reviews, or tweets. The objective of this report is to provide an illustration of this fascinating problem as well as a model for performing sentiment analysis on Twitter tweets using the Naïve Bayes classification algorithm.
Breast Mass Classification Using Convolutional Neural Network

Cancer in breast is the most prevalent disease discovered in women’s breast cells, and it can also lead to death. Early detection and diagnosis help to reduce this mortality rate. Different Artificial Intelligent (AI) techniques such Machine Learning (ML), Deep Learning (DL) is being used in the medical industry to predict breast cancer. Different breast cancer images like mammogram, ultrasound and biopsy, etc. are used for analysis. This work aims to provide deep Convolutional Neural Network (CNN) architecture to analyze breast cancer through mammogram images. Nowadays, due to great performance deep CNN is widely used. In this paper, we presented CNN model with five layers of convolutional, five layers of max pooling, four layers of dropout and two layers of fully connected. CNN is developed using breast image dataset DDSM which is publicly available. Proposed CNN model has achieved 89.46% accuracy for breast mass classification as benign and malignant.
IPL Analysis and Match Prediction

A comprehensive analysis of the complete IPL dataset and visualization of different highlights necessary for IPL assessment is performed. Many machine learning (classification) algorithms have been used to compare and predict the winner of the match. Every game has its own requirements; similarly, the T-20 game also has its own which were not satisfied by current models. By using Python, the intricacy of data analysis is reduced as it shows the analysis results using visual portrayals. The dataset is loaded, and pre-processing is done trailed by feature selection. Four machine learning (classification) algorithms such as decision tree, K-nearest neighbour, SVM, and random forest are applied, and the outcomes are compared. The best of the four classification techniques is then applied to anticipate the winner of the match and visualize the results as graphs.
Nonlinear Bayesian filtering via holonomic gradient method with quasi moment generating function

Summary A symbolic‐numeric method is proposed for addressing the Bayesian filtering problems of a class of discrete‐time nonlinear stochastic systems. We first approximate the posterior probability density function to be Gaussian. The update law of the mean and variance is formulated as the evaluation of several integrals depending on certain parameters. Unlike existing methods, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF), this formulation considers the nonlinearity of system dynamics exactly. To evaluate the integrals efficiently, we introduce an integral transform motivated by the moment generating function (MGF), which we call a quasi MGF. Furthermore, the quasi MGF is compatible with the Fourier transform of differential operators. We utilize this compatibility to decrease the number of computations of Gröbner bases in the noncommutative rings of differential operators, which reduces the offline computational time. A numerical example is presented to show the efficiency of the proposed method compared to that of other existing methods such as the EKF, UKF, and PF.
The impact of COVID-19 on the financial performance of the listed firms in Palestine

The study aims to investigate the impact of the COVID-19 pandemic on the financial performance of Palestinian firms.The study uses secondary financial data from 15 firms that belong to the industrial, investment, and services sectors.The measurement of financial performance consists of calculating many financial ratios: current ratio, cash ratio, and average collection period as liquidity measures; debt to equity ratio as an indicator of leverage or solvency; net profit margin as an indicator of profitability.Using Wilcoxon Signed Rank Test and financial ratio analysis, the study compares ratios between the years 2018 and 2019 (pre the outbreak of COVID-19) and the years 2020 and 2021 (during the outbreak of COVID-19).The results indicate a significant decline in liquidity in the industrial and investment sectors and an improvement in liquidity in the service sector.The results also show a significant increase in debt in service sectors, while this ratio remains the same in other sectors.However, the profitability ratio does not show any significant difference before and during the outbreak of COVID-19.Finally, this study provides valuable evidence to regulators and decision-makers about the most affected sectors due to the COVID-19 pandemic in Palestinian listed firms.
Entity relation extraction in the medical domain: based on data augmentation

Entity relation extraction is an important task in the construction of professional knowledge graphs in the medical field. Research on entity relation extraction for academic books in the medical field has revealed that there is a great difference in the number of different entity relations, which has led to the formation of a typical unbalanced data set that is difficult to recognize but has certain research value.In this article, we propose a new entity relation extraction method based on data augmentation. According to the distribution of individual entity relation classes in the data set, the probability of whether a text is augmented during training was calculated. In text-oriented data augmentation, different augmentation methods perform differently in different language environments. The reinforcement of learning determines which data augmentation method to use in the current language environment. This strategy was applied to the entity relation extraction of the medical professional book, Pharmacopoeia of the People's Republic of China, and different data augmentation methods (i.e., no data augmentation, traditional data augmentation, and reinforcement learning-based data augmentation) were compared under the same neural network model.The deep-learning model using data augmentation was better than the model without data augmentation, as data augmentation significantly improved the evaluation indicators of the relation classes with low data volumes in the unbalanced data set and slightly improved the evaluation indicators of the relation classes with sufficient features and large data volumes. Additionally, the deep-learning model using reinforcement learning-based data augmentation was superior to the deep-learning model using traditional data augmentation. We found that after the application of reinforcement learning-based data augmentation, the evaluation indicators of the multiple relation classes were much better than those to which reinforcement learning-based data augmentation had not been applied.For unbalanced data sets, data augmentation can effectively improve the ability of the deep-learning model to obtain data features, and reinforcement learning-based data augmentation can further enhance this ability. Our experiments confirmed the superiority of reinforcement learning-based data augmentation.
Use of Virtual and Augmented Reality to Digitize and Optimize Turbomachinery Assembly Operation

Abstract This paper describes an innovative digital approach to optimize turbomachinery manufacturing processes. By adopting virtual and augmented reality technology it is possible to build a digital assembly sequence and make it available to the shop-floor. These digital technologies are changing the way operators interact with 3D digital content and by access to the work instructions having all the info they need in their hands. Using the engineering 3D CAD structure, the design is validated through simulation in 1:1 scale immersive environment applying virtual reality features such as gravity, kinematics, part collision, surface sliding, parts flexibility. Then manufacturing routing is built defining a 3D Bill of Process and relevant assembly sequence operation by operation, including assembling tooling, work areas and ergonomics validation. Finally, this digital content flows to the shopfloor where the operator can access the digital work instructions through unique interface using virtual or augmented technology and can interact with component 3D model visualizing metadata and BOM codes. Virtual and Augmented Reality have been successfully applied to produce an innovative aeroderivative gas turbine prototype. Having the opportunity to validate the design in a very detailed way and to define a pre-validated assembly sequence, the assembly process has been speeded up ensuring the on-time delivery and the scraps and rework reduced. The team has been quickly able to re-structure the sequence whenever it was needed to make sure to not stop the production. More than 8000 components have been organized and supplied to the operators exactly per sequence just in time being able to complete the first-time assembly in 8 weeks, as per schedule. The possibility to immediately locate the 3D virtual component using both VR and AR interfaces has enabled fast accessibility check, possible errors detection and improved quality. Component codes embedded in the 3D model have allowed an immediate identification of the real parts needed for the assembly while navigating the digital structure ensuring the product completeness. This digital approach has also changed the way of training the operators (10+) by reproducing complex operation sequence in a consequence free virtual environment improving safety of our operators and products. By focusing on the importance of 3D native data (attributes and product manufacturing information), the new approach of leveraging virtual and augmented reality applications will make possible to access information whenever and wherever needed, empowering the workforce, and democratizing the knowledge. This new approach is also enabling remote solutions, playing a key role in the energy transition challenge and being a game changer for Oil &amp; Gas and Energy industries.
Software Systems Implementation and Domain-Specific Architectures towards Graph Analytics

Graph analytics, which mainly includes graph processing, graph mining, and graph learning, has become increasingly important in several domains, including social network analysis, bioinformatics, and machine learning. However, graph analytics applications suffer from poor locality, limited bandwidth, and low parallelism owing to the irregular sparse structure, explosive growth, and dependencies of graph data. To address those challenges, several programming models, execution modes, and messaging strategies are proposed to improve the utilization of traditional hardware and performance. In recent years, novel computing and memory devices have emerged, e.g., HMCs, HBM, and ReRAM, providing massive bandwidth and parallelism resources, making it possible to address bottlenecks in graph applications. To facilitate understanding of the graph analytics domain, our study summarizes and categorizes current software systems implementation and domain-specific architectures. Finally, we discuss the future challenges of graph analytics.
A channel sampling strategy for measurement of mineral modal and chemical composition of drill cores: application to lower oceanic crustal rocks from IODP Expedition 345 to the Hess Deep rift

Abstract. We report a new sampling strategy for collecting representative samples of drill core. By splitting the core with a diamond saw into working and archive halves, the saw cuttings constitute a “channel” sample, the best subsample from which to obtain an average mineralogical and geochemical composition of a core. We apply this procedure to sampling core of the lower oceanic crust in the Hess Deep obtained during Expedition 345 of the Integrated Ocean Drilling Program (now International Ocean Discovery Program). Our results show that particles produced by sawing range from sand to clay sizes. Sand- and silt-sized cuttings can be sampled with a spatula, whereas clay-sized particles remained in suspension after 12 h and could be collected only by settling, aided by centrifuge. X-ray diffraction (XRD) analysis and Rietveld refinement show that phyllosilicates were fractionated into the clay-sized fraction. Thus, collection of both the sedimented fraction and the clay-sized suspended fraction (commonly &gt; 15 wt % of the total) is necessary to capture the whole sample. The strong positive correlation between the recovered sample mass (in grams) and length of core cut demonstrates that this sampling protocol was uniform and systematic, with almost 1.4 g sediment produced per centimeter of core cut. We show that major-element concentrations of our channel samples compare favorably with the compositions of billet-sized samples analyzed aboard the JOIDES Resolution, but the results show that individual billet analyses are rarely representative of the whole core recovered. A final test of the validity of our methods comes from the strong positive correlation between the loss on ignition (LOI) values of our channel samples and the H2O contents calculated from the modal mineralogy obtained by X-ray diffraction and Rietveld refinement. This sampling procedure shows that grain-sized fractionation modifies both mineralogical and chemical compositions; nevertheless, this channel sampling method is a reliable method of obtaining representative samples of bulk cores. With the ever-increasing precision offered by modern analytical instrumentation, this sampling protocol allows the accuracy of the analytical results to keep pace.
"Dom zmarłych” z Chodlika, gm. Karczmiska, woj. lubelskie. przyczynek do studiów nad grobami typu alt käbelich

The article presents the results of archaeological research into an Alt Käbelich-type grave discovered in Chodlik, Karczmiska county, Lublin province. The pit contained layer burials of five individuals. The bone material included also horse remains. Other archaeological material contained fragments of clay utensils as well as pieces of metal and bone artefacts destroyed in fire. On the basis of 3D documentation, at attempt has been made to reconstruct the object by means of digital technology. The Chodlik discovery is a ontribution to the research into the occurrence of the Alt Käbelich type of graves and the concept of the so-called “house of the dead” in the Western Slavic Dominion.
An Efficient Redirection of Positional Interactions in Mixed/ Augmented Reality

With the rise in remote work culture and increased computing capabilities of head-mounted displays (HMDs), more immersive, collaborative experiences are desired in remote–local mixed/augmented reality (MR/AR). Photorealistic full-body avatar representations of users in remote workspace interactions have shown to have increased social presence, nonverbal behavior, and engagement. However, a direct mapping of the body pose angles from local to the remote workspace will, in most cases, result in positional errors during human–object interaction, caused by the dissimilarity between remote and local workspaces. Hence, the interaction must be retargeted, but it should be retargeted in such a way that the original intent of the body pose should be preserved. However, these two objectives sometimes contradict each other. As a result, a multi-objective optimization (MO) problem can be formulated where the primary objective is to minimize positional errors and the secondary objective is to preserve the original interaction body pose. The current state-of-the-art solution uses an evolutionary computation-based inverse kinematic (IK) approach to solve the MO problem where the weights between the objectives must be set by the user based on trial and error, leading to a suboptimal solution. In this paper, we present a new dynamic weight allocation approach to this problem, where a user has the flexibility to set a chosen minimum error tolerance, and the weights will be distributed between the objectives based on a dynamic allocation algorithm. We have used a two-pronged approach to test the adaptability and robustness of this mechanism: (i) on motion-captured human animations of varying levels of speeds, error tolerances, redirections and (ii) we conducted an experiment involving 12 human participants and recorded, redirected their actions performed during a book-shelving task in AR. Compared to the static weighting, the dynamic weighted mechanism showed a net ([Formula: see text] objective) decrease in error ranging from 20.5% to 34.42% across varying animation speeds and a decrease in error ranging from 11.44% to 36.2% for the recorded human actions during the AR task, demonstrating its robustness and better pose preservation across interactions.
Development and evaluation of cloud-based virtual reality for design evaluation: multicriteria comparative analysis

Purpose The study aims to introduce a cloud-based virtual reality (VR) approach and investigate its applicability and performance in aiding the remote design evaluation process by assessing the clients' convenience perception toward cloud-based VR-aided design evaluation (Cloud-based VR Approach) compared to 3D model-aided design evaluation (3D Model Approach) and rendering images-aided design evaluation (Image Approach). Design/methodology/approach A multicriteria comparative study was conducted with 26 university students using the “analytic hierarchy process” technique to compare the three approaches applied to home finishing material selection tasks based on the five “service convenience” dimensions, consisting of access convenience, decision convenience, transaction convenience, benefit convenience and post-benefit convenience. Findings The results indicated that the “Cloud-based VR Approach” was perceived to be more convenient than the “3D Model Approach” and the “Image Approach” based on the aspects of “decision convenience”, “transaction convenience”, “benefit convenience” and “post-benefit convenience”. The only aspect that the Cloud-based VR Approach was comparatively less convenient than the 3D Model Approach and Image Approach for the user was “access convenience”. Overall, the findings showed that the developed Cloud-based VR Approach had more potential than the conventional approaches in aiding the design evaluation process under ongoing social distancing measures requiring designers and clients to work remotely. Originality/value The disastrous impacts of the COVID-19 pandemic on logistical systems have resulted in massive disruptions to the construction industry worldwide. Various construction activities have been halted and most meetings moved online. Design evaluation conducted between clients and designers is one of the important activities affected by such an impact. Thus, this study presents the Cloud-based VR Approach as an innovative means to maintain essential ongoing activities and meeting of the current design evaluation approach with respect to the social distancing measures.
Gooey Gut Trail: Board Game Play to Understand Human-Microbial Interactions

Our gastrointestinal health is influenced by complex interactions between our gut bacteria and multiple external factors. A wider understanding of these concepts is vital to help make gut-friendly decisions in everyday life; however, its complexity can challenge public understanding if not approached systematically. Research suggests that board games can help to playfully navigate complex subjects. We present Gooey Gut Trail (GGT), a board game to help players understand the multifactorial interactions that influence and sustain gut microbial diversity. Through the embodied enactment of in-game activities, players learn how their habits surrounding diet, physical activity, emotions, and lifestyle influence the gut microbial population. A qualitative field study with 15 participants revealed important facets of our game design that increased participants' awareness, causing them to reflect upon their habits that influence gut health. Drawing upon the study insights, we present five design considerations to aid future playful explorations on nurturing human-microbial relationships.
A strategy creating high-resolution adversarial images against convolutional neural networks and a feasibility study on 10 CNNs

To perform image recognition, Convolutional Neural Networks (CNNs) assess any image by first resizing it to its input size. In particular, high-resolution images are scaled down, say to 224×244 for CNNs trained on ImageNet. So far, existing attacks, aiming at creating an adversarial image that a CNN would misclassify while a human would not notice any difference between the modified and unmodified images, proceed by creating adversarial noise in the 224×244 resized domain and not in the high-resolution domain. The complexity of directly attacking high-resolution images leads to challenges in terms of speed, adversity and visual quality, making these attacks infeasible in practice. We design an indirect attack strategy that lifts to the high-resolution domain any existing attack that works efficiently in the CNN's input size domain. Adversarial noise created via this method is of the same size as the original image. We apply this approach to 10 state-of-the-art CNNs trained on ImageNet, with an evolutionary algorithm-based attack. Our method succeeded in 900 out of 1000 trials to create such adversarial images, that CNNs classify with probability ≥0.55 in the adversarial category. Our indirect attack is the first effective method at creating adversarial images in the high-resolution domain.
Action2Score: An Embedding Approach to Score Player Action

Multiplayer Online Battle Arena (MOBA) is one of the most successful game genres. MOBA games such as League of Legends have competitive environments where players race for their rank. In most MOBA games, a player's rank is determined by the match result (win or lose). It seems natural because of the nature of team play, but in some sense, it is unfair because the players who put a lot of effort lose their rank just in case of loss and some players even get free-ride on teammates' efforts in case of a win. To reduce the side-effects of the team-based ranking system and evaluate a player's performance impartially, we propose a novel embedding model that converts a player's actions into quantitative scores based on the actions' respective contribution to the team's victory. Our model is built using a sequence-based deep learning model with a novel loss function working on the team match. We showed that our model can evaluate a player's individual performance fairly and analyze the contributions of the player's respective actions.