Reviewerly/exHarmony huggingface dataset:
Answer Generation through Unified Memory over Multiple Passages

Machine reading comprehension methods that generate answers by referring to multiple passages for a question have gained much attention in AI and NLP communities. The current methods, however, do not investigate the relationships among multiple passages in the answer generation process, even though topics correlated among the passages may be answer candidates. Our method, called neural answer Generation through Unified Memories over Multiple Passages (GUM-MP), solves this problem as follows. First, it determines which tokens in the passages are matched to the question. In particular, it investigates matches between tokens in positive passages, which are assigned to the question, and those in negative passages, which are not related to the question. Next, it determines which tokens in the passage are matched to other passages assigned to the same question and at the same time it investigates the topics in which they are matched. Finally, it encodes the token sequences with the above two matching results into unified memories in the passage encoders and learns the answer sequence by using an encoder-decoder with a multiple-pointer-generator mechanism. As a result, GUM-MP can generate answers by pointing to important tokens present across passages. Evaluations indicate that GUM-MP generates much more accurate results than the current models do.
Comparison of Adaptive Fuzzy EKF and Adaptive Fuzzy UKF for State Estimation of UAVs Using Sensor Fusion

Development of an Adaptive Fuzzy Extended Kalman Filter (AFEKF) and an Adaptive Fuzzy Unscented Kalman Filter (AFUKF) for the state estimation of unmanned aerial vehicles (UAVs) were presented in this paper based on real flight data of a fixed wing airplane. The Adaptive Neuro Fuzzy extension helps to estimate the values of the EKF's and UKF's Rk covariance matrix at each sampling instant when measurement update step is carried out. The ANFIS monitors the EKF's and UKF's performances attempt to eliminate the gap between theoretical and real innovation sequences' covariance. The investigations show that AFUKF can provide better performance in accuracy and less error than the AFEKF in case of real flight data for maneuvering fixed wing UAVs.
Feynman parameter integration through differential equations

We present a new method for numerically computing generic multi-loop Feynman integrals. The method relies on an iterative application of Feynman's trick for combining two propagators. Each application of Feynman's trick introduces a simplified Feynman integral topology which depends on a Feynman parameter that should be integrated over. For each integral family, we set up a system of differential equations which we solve in terms of a piecewise collection of generalized series expansions in the Feynman parameter. These generalized series expansions can be efficiently integrated term by term, and segment by segment. This approach leads to a fully algorithmic method for computing Feynman integrals from differential equations, which does not require the manual determination of boundary conditions. Furthermore, the most complicated topology that appears in the method often has less master integrals than the original one. We illustrate the strength of our method with a five-point two-loop integral family.
Data Redaction from Pre-trained GANs

Large pre-trained generative models are known to occasionally output undesirable samples, which undermines their trustworthiness. The common way to mitigate this is to re-train them differently from scratch using different data or different regularization -- which uses a lot of computational resources and does not always fully address the problem. In this work, we take a different, more compute-friendly approach and investigate how to post-edit a model after training so that it ''redacts'', or refrains from outputting certain kinds of samples. We show that redaction is a fundamentally different task from data deletion, and data deletion may not always lead to redaction. We then consider Generative Adversarial Networks (GANs), and provide three different algorithms for data redaction that differ on how the samples to be redacted are described. Extensive evaluations on real-world image datasets show that our algorithms out-perform data deletion baselines, and are capable of redacting data while retaining high generation quality at a fraction of the cost of full re-training.
Research on a BSDiff-Based System-Level On-Orbit Maintenance Technology of Spacecraft

To resolve the problem that the traditional on-orbit maintenance technologies will uplink a large number of telecommands, a BSDiff-based system-level on-orbit maintenance technology is proposed in this paper. By applying the BSDiff, the new file will be replaced by the patch file for transmitting to the spacecraft by the uplink channel. After the patch file is received by the spacecraft, the BSPatch algorithm will be applied to combines the old file stored in the storage and the patch file. And finally, the new file will be generated and then stored in the memory. Due to the volume of the patch file generated by this method is reduced by more than 80% compared to the new file, the number of telecommands is effectively decreased by the proposed technology, and it also has better portability and scalability.
Features analysis of internet traffic classification using interpretable machine learning models

Internet traffic classification is a fundamental task for network services and management. There are good machine learning models to identify the class of traffic. However, finding the most discriminating features to have efficient models remains essential. In this paper, we use interpretable machine learning algorithms such as decision tree, random forest and eXtreme gradient boosting (XGBoost) to find the most discriminating features for internet traffic classification. The dataset used contains 377,526 traffics. Each traffic is described by 248 features. From these features, we propose a 12-feature model with an accuracy of up to 99.76%. We tested it on another dataset with 19626 flows and obtained 98.40% of accuracy. This shows the efficiency and stability of our model. Also, we identify a set of 14 important features for internet traffic classification, including two that are crucial: port number (server) and minimum segment size (client to server).
Gray Augmentation Exploration with All-Modality Center-Triplet Loss for Visible-Infrared Person Re-Identification

Visible-Infrared Person Re-identification (VI-ReID) is a challenging pedestrian retrieval task due to the huge modality discrepancy and appearance discrepancy. To address this tough task, this letter proposes a novel gray augmentation exploration (GAE) method to increase the diversity of training data and seek the best ratio of gray augmentation for learning a more focused model. Additionally, we also propose a strong all-modality center-triplet (AMCT) loss to push the features extracted from the same pedestrian more compact but those from different persons more separate. Experiments conducted on the public dataset SYSU-MM01 demonstrate the superiority of the proposed method in the VI-ReID task.
THE EFFECTIVENESS OF TEACHING MATHEMATICS TO STUDENTS OF HUMANITIES

The article provides an analysis of the conceptual foundations of the methodology for teaching mathematics to students of the humanities; the possibility of applying the theoretical foundations and methods of mathematics in the social and humanitarian sphere. The issues of mathematical training of students of humanitarian specialties of universities, improvement of the methodological system of teaching mathematical disciplines on the basis of a learning model with the use of optimization methods are considered. A system for testing optimization methods of management for the humanities was designed.
Implementasi algoritma genetika dalam mengestimasi kepadatan populasi jackrabbit dan coyote

This article studies about the parameter estimation using genetic algorithm for a Lotka-Volterra prey-predator model. The secondary data consist of the density of jackrabbit as prey and coyote as predator in Southwest Presscott–Arizona are used. As results, the Mean Absolute Percentage Error (MAPE) are computed to compare the results of parameter estimation and the real data. We have shown that MAPE for jackrabbit and coyote respectively given by 7.75424% and 7.95283%. This results show that the parameter estimation with genetic algorithm using Lotka-Volterra model is passably. Furthermore, some numerical simulations are portrayed to show each population density for the next 100 years.
Predicting Damage to a Building During Earthquake

Abstract Earthquake is one of the most hazardous natural calamities. It occurs due to sudden shaking of ground which is caused by movement of seismic waves. The tectonic plates are always slowly moving, but they get stuck at their edges due to friction. When the stress on the edge overcomes the friction, there is an earthquake that releases energy in waves that travel through the earth's crust and cause the shaking that we feel. The problem statement of this project is determining the degree of damage that is done to buildings, thus when an earthquake occur we can help identify safe and unsafe buildings, thus avoiding death and injuries resulting from aftershocks. Leveraging the power of machine learning is one viable option that can potentially prevent massive loss of lives while simultaneously making rescue efforts easy and efficient. During this study, earthquake prediction was performed, by training different Machine Learning models on dataset collected from hackerearth website. During this research, machine learning algorithms like Random Forest, Neural Network and XGBoost Mechanism are separately applied and accuracies in the training and testing datasets were compared to pick out the best model.
Virtual Reality: History, Application and Future

Abstract: Virtual Reality has become very popular in last few years. The interest in Virtual Reality is growing rapidly. Very few people know about the VR there various uses and its problems related to it. In this study of VR it includes history, types of VR, and applications in new technologies in science work and entertainment fields. Components of VR &amp; issues related to VR are discussed. Positive and negative influence of virtual reality on life of average people. And lastly, the future of the Virtual Reality.
Simple and Effective Multi-sentence TTS with Expressive and Coherent Prosody

Generating expressive and contextually appropriate prosody remains a challenge for modern text-to-speech (TTS) systems. This is particularly evident for long, multi-sentence inputs. In this paper, we examine simple extensions to a Transformer-based FastSpeech-like system, with the goal of improving prosody for multi-sentence TTS. We find that long context, powerful text features, and training on multi-speaker data all improve prosody. More interestingly, they result in synergies. Long context disambiguates prosody, improves coherence, and plays to the strengths of Transformers. Fine-tuning word-level features from a powerful language model, such as BERT, appears to profit from more training data, readily available in a multi-speaker setting. We look into objective metrics on pausing and pacing and perform thorough subjective evaluations for speech naturalness. Our main system, which incorporates all the extensions, achieves consistently strong results, including statistically significant improvements in speech naturalness over all its competitors.
Crime and punishment: Re-reading Agatha Christie’s novel murder on the orient express

Agatha Christie was one of the most prominent writers during the Golden Age of Detective Fiction.She was at the height of her success in the 1920s and 30s and was often called the "Queen of Mystery" by her fans and lovers.She is even more important in the historical context since she started writing at a time when not many female authors were writing.Her contemporaries and predecessors mostly included male writers.When she started writing, Sherlock Holmes was at the pinnacle of stardom and it was a challenge for Christie to create a detective that would be different than Sherlock and yet appeal to the readers.Credit goes to Christie for taking up this challenge and making a
Blockchain Difficulties and Valuable Open Doors: A Study

Abstract: Blockchain One of the hottest technologies in the market but what exactly the blockchain is . – A blockchain is a digital transaction of records that is organized in pieces of information called blocks. These blocks connect with each other through a cryptographic validation known as a hashing function. These blocks when are connected together form a structure of solid chain - a blockchain. As of now, the blockchain technology has touched many of the fields like financial services, Internet of things , Digital identities, social media etc However, as all things in the world have pros and cons blockchain is also having some cons which are not been addressed till date and since it is an emerging technology , there are some areas in which blockchain technology still needs to be discovered and how these can be used in those fields to have an outlast benefits of the technology. In this paper , our aim is to give the overall picture of the blockchain , how it works what are the challenges of the technology and what are the opportunities that can be explored in the blockchain field.
The Metamodern Trends in the Contemporary Opera Productions. Towards New Expressiveness

This paper focuses on the director's theater as an important stage in the evolution of the opera genre and at the same time an ambiguous cultural phenomenon. Due to the rapid growth of interest in opera in the last quarter of the twentieth century, the concept of director's opera, which owes its formation and flourishing to the postmodern condition, begins to spread in the West. At present the term director's opera is actively used in Russian art criticism. As a result, it was found that the majority of researchers of musical theater in their works associate this concept with the provocative nature of works and too speculative stage interpretations of operas, as well as apparent liberty in the treatment of classical heritage, leading to the destruction of the opera canon. Simultaneously, with the theoretical reflection of the phenomenon of director' opera, as a result of the major crises of the turn of the 20th and 21st centuries, as well as fatigue from the corrosive, destructing irony of postmodernism, the concept of metamodernism appears. According to the theorists of metamodernism, the new cultural paradigm aims to recode modern culture through art, and music was the first to embrace metamodernism that later on penetrated and conquered various cultural spheres. Defending its social significance and vitality, opera theater, following other art forms, changes its aesthetics, demonstrating trends associated with metamodernism, for instance allows for unexpected collaborations that welcome very different people to shine their otherness. As a result of the study of literature and empirical materials, the author concludes that the outlined intensions, which appeared in the period 2016–2018, can be considered as significant examples of the penetration of metamodern trends in contemporary opera theater. As the examples, the writer considers production of Verdi's opera La Traviata by Wilson and Arthur Honegger’s oratorio Joan of Arc at the Stake by Castellucci.
Hand Gesture Recognizer with Alert Message Display and Vocalizer for People with Disabilities Using Deep Learning

Abstract: Communication is the basic medium by which we can interact and understand each other but unfortunately this process can prove to be difficult for the people with disabilities. This project is a small contribution to address this issue and help them communicate better. It uses the concept of neural networks in order to help the people with disabilities communicate better with just their hand gestures. The hand gesture recognizer and actuator system acts as an interface between computer and human using hand gestures. This project is a combination of gesture identification, classification and it displays the message pertaining to the gesture while simultaneously reading the message out loud. The user has to perform a particular gesture, the webcam captures this and identifies and recognizes it against a set of known gestures and displays and vocalizes the gesture. This is a very useful hands-free approach. There could be an emergency situation for them and in order to inform people around them, this system is an effective tool. The system has a set of samples to be trained against and then the trained model is tested against a set of test data.
Prevalence and speciation of malaria parasites among patients attending Ahoada and Amassoma Communities’ general hospitals in Rivers and Bayelsa State, Nigeria

Malaria is one of the deadly diseases recognized globally in children and adults especially in Nigeria and some African countries. This study was designed to determine Prevalence and Speciation of malaria parasites from patients attending Ahoada and Amassoma Communities’ General Hospitals in Rivers and Bayelsa States respectively. A total of two hundred (200) venous blood collected from Amassoma [101(40 males, 61 Female) and Ahoada [99; 46male, 53 females) into sterile Ethylene Diamine Tetra Acetic Acid (EDTA) bottles from patients attending the above mentioned General Hospitals were immediately transferred to Medical Microbiology Laboratory, Department of Medical Laboratory Science for analysis using Microscopy by staining, Rapid Diagnostic Tests kits (SD Bioline kits; Pf/pan and Pf specific kit) and molecular analysis by Polymerase Chain Reaction (PCR) technique. Twenty (20) positive samples were subjected to Polymerase Chain Reaction Technique for Speciation. Findings showed 141(70.5%) subjects positive to malaria parasites comprised of Children 25(12.5%) between age of 6 and 17 years, adult female 63(31.5%) and male 53(26.5%) between the age of 18 and 90. Also noted were subjects with age ranges of 6-10, 21-25, 51-55 and 86-90 years had the highest percentage prevalence of 100%, followed by 41-45 years with a prevalence rate of 83.3% and the least prevalence was 33.3% from 56-60 years. Observations showed that out of 20 positive samples subjected to molecular diagnosis for Speciation, 17(85%) were Plasmodium falciparum, thus revealing their predominance of 90%. In conclusion, Plasmodium falciparum was the predominant parasite among Plasmodia species responsible for malaria infection in both Rivers and Bayelsa States. Female and children are vulnerable to malaria infection as revealed in this study. Therefore, the government is encouraged to take women and children’s health into consideration by conducting malaria test for them weekly to enable prompt treatment where applicable.
Computing the Parameterized Burrows--Wheeler Transform Online

Parameterized strings are a generalization of strings in that their characters are drawn from two different alphabets, where one is considered to be the alphabet of static characters and the other to be the alphabet of parameter characters. Two parameterized strings are a parameterized match if there is a bijection over all characters such that the bijection transforms one string to the other while keeping the static characters (i.e., it behaves as the identity on the static alphabet). Ganguly et al. [SODA 2017] proposed the parameterized Burrows--Wheeler transform (pBWT) as a variant of the Burrows--Wheeler transform for space-efficient parameterized pattern matching. In this paper, we propose an algorithm for computing the pBWT online by reading the characters of a given input string one-by-one from right to left. Our algorithm works in $O(|\Pi| \log n / \log \log n)$ amortized time for each input character, where $n$ and $\Pi$ denote the size of the input string and the alphabet of the parameter characters, respectively.
Efficient verification of Affleck-Kennedy-Lieb-Tasaki states

Affleck-Kennedy-Lieb-Tasaki (AKLT) states are an important class of many-body quantum states that are useful in quantum information processing, including measurement-based quantum computation in particular. Here we propose a general approach for constructing efficient verification protocols for AKLT states on arbitrary graphs with local spin measurements. Our verification protocols build on bond verification protocols and matching covers (including edge coloring) of the underlying graphs, which have a simple geometric and graphic picture. We also provide rigorous performance guarantee that is required for practical applications. With our approach, most AKLT states of wide interest, including those defined on 1D and 2D lattices, can be verified with a constant sample cost, which is independent of the system size and is dramatically more efficient than all previous approaches. As an illustration, we construct concrete verification protocols for AKLT states on various lattices and on arbitrary graphs up to five vertices.
Abstract morphing using the Hausdorff distance and Voronoi diagrams

This paper introduces two new abstract morphs for two $2$-dimensional shapes. The intermediate shapes gradually reduce the Hausdorff distance to the goal shape and increase the Hausdorff distance to the initial shape. The morphs are conceptually simple and apply to shapes with multiple components and/or holes. We prove some basic properties relating to continuity, containment, and area. Then we give an experimental analysis that includes the two new morphs and a recently introduced abstract morph that is also based on the Hausdorff distance (Van Kreveld et al. Between shapes, using the Hausdorff distance. Computational Geometry 100:101817, 2022). We show results on the area and perimeter development throughout the morph, and also the number of components and holes. A visual comparison shows that one of the new morphs appears most attractive.
Human Activity Recognition using Machine Learning

Abstract: Nowadays, activity recognition is one of the most popular uses of machine learning algorithms. It's utilized in biomedical engineering, game production, and producing better metrics for sports training, among other things. Data from sensors linked to a person may be used to build supervised machine learning models that predict the activity that the person is doing. We will use data from the UCI Machine Learning Repository in this work. It contains data from the phone's accelerometer, gyroscope, and other sensors, which is used to build supervised prediction models using machine learning techniques like as SVM, Random Forest. This may be used to forecast the person's kind of movement, which is separated into six categories: walking, walking upstairs, walking downstairs, sitting, standing, and lying. We'll use a confusion matrix to compare the accuracy of different models.
Design of a service-learning model for citizen training

The purpose of the research was to present a proposal for the design of a service-learning model for citizenship training in the area of Personal Development and Citizenship. The research methodology is basic, descriptive, non-experimental design, transversal, and propositional. The survey technique was used with a 35-question questionnaire instrument. We worked with a population of 144 students. The results obtained show that 61% of the students have knowledge of the use of service-learning at the middle level and 60% at the intermediate level for the citizenship training variable. To overcome this problem, a series of strategies have been proposed. It was concluded that the proposal of the model will allow to empower and stimulate the participation of school adolescents by integrating experiential learning and curricular content.&#x0D; Keywords: Learning, service, citizenship training, active participation, community service.