Reviewerly/exHarmony huggingface dataset:
On the pseudorandom properties of $ k $-ary Sidel'nikov sequences

<p style='text-indent:20px;'>In 2002 Mauduit and Sárközy started to study finite sequences of <inline-formula><tex-math id="M2">\begin{document}$ k $\end{document}</tex-math></inline-formula> symbols <p style='text-indent:20px;'><disp-formula> <label/> <tex-math id="FE1"> \begin{document}$ E_{N} = \left(e_{1},e_{2},\cdots,e_{N}\right)\in \mathcal{A}^{N}, $\end{document} </tex-math></disp-formula> <p style='text-indent:20px;'>where <inline-formula><tex-math id="M3">\begin{document}$ \mathcal{A} = \left\{a_{1},a_{2},\cdots,a_{k}\right\}(k\in \mathbb{N},k\geq 2) $\end{document}</tex-math></inline-formula> is a finite set of <inline-formula><tex-math id="M4">\begin{document}$ k $\end{document}</tex-math></inline-formula> symbols. Later many pseudorandom sequences of <inline-formula><tex-math id="M5">\begin{document}$ k $\end{document}</tex-math></inline-formula> symbols have been given and studied by using number theoretic methods. In this paper we study the pseudorandom properties of the <inline-formula><tex-math id="M6">\begin{document}$ k $\end{document}</tex-math></inline-formula>-ary Sidel'nikov sequences with length <inline-formula><tex-math id="M7">\begin{document}$ q-1 $\end{document}</tex-math></inline-formula> by using the estimates for certain character sums with exponential function, where <inline-formula><tex-math id="M8">\begin{document}$ q $\end{document}</tex-math></inline-formula> is a prime power. Our results show that Sidel'nikov sequences enjoy good well-distribution measure and correlation measure. Furthermore, we prove that the set of size <inline-formula><tex-math id="M9">\begin{document}$ \phi(q-1) $\end{document}</tex-math></inline-formula> of <inline-formula><tex-math id="M10">\begin{document}$ k $\end{document}</tex-math></inline-formula>-ary Sidel'nikov sequences is collision free and possesses the strict avalanche effect property provided that <inline-formula><tex-math id="M11">\begin{document}$ k = o(q^{\frac{1}{4}}) $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M12">\begin{document}$ \phi $\end{document}</tex-math></inline-formula> denotes Euler's totient function.
LEAP Submission for the Third DIHARD Diarization Challenge

The LEAP submission for DIHARD-III challenge is described in this paper. The proposed system is composed of a speech bandwidth classifier, and diarization systems fine-tuned for narrowband and wideband speech separately. We use an end-to-end speaker diarization system for the narrowband conversational telephone speech recordings. For the wideband multi-speaker recordings, we use a neural embedding based clustering approach, similar to the baseline system. The embeddings are extracted from a time-delay neural network (called x-vectors) followed by the graph based path integral clustering (PIC) approach. The LEAP system showed 24% and 18% relative improvements for Track-1 and Track-2 respectively over the baseline system provided by the organizers. This paper describes the challenge submission, the post-evaluation analysis and improvements observed on the DIHARD-III dataset.
Zonotopic Kalman Filter-based Interval Estimation for Discrete-Time Linear Systems with Unknown Inputs

This letter proposes an unknown input zonotopic Kalman filter-based interval observer for discrete-time linear time-invariant systems. In such contexts, a change of coordinates decoupling the state and the unknown inputs is often used. Here, the dynamics are rewritten into a discrete-time linear time-invariant descriptor system by augmenting the state vector with the unknown inputs. A zonotopic outer approximation of the feasible state set is then obtained with a prediction-correction strategy using the information from the system dynamics, known inputs and outputs. Bounds for both the state and unknown inputs are obtained from this zonotopic set. The efficiency of the proposed interval observer is assessed with numerical simulations.
Artificially Intelligent Self-Driven Car with Obstacle Avoidance

This article is About Artificially Intelligent Self-Driven Car with Obstacle Avoidance. The aim of the Project is to design and create an automated self-Driven car that can also be controlled by desktop application via internet. The car can be controlled from any PC as long as it is connected to an internet connection. A camera will also be used to observe the motion of the car. The user will just set the destination by just clicking on the map, the car will drive itself and will reach to its destination without any human involvement.
Approaches to Text Simplification: Can Computer Technologies Outdo a Human Mind?

Narrowly specialized information is addressed to a limited circle of professionals though it provokes interest among people without specialized education. This gives rise to a need for the popularization of scientific information. This process is carried out through simplified texts as a kind of secondary texts that are directly aimed at the addressee. Age, language proficiency and background knowledge are the main features which are usually taken into consideration by the author of the secondary text who makes changes in the text composition, as well as in its pragmatics, semantics and syntax. This article analyses traditional approaches to text simplification, computer simplification and summarization. The authors compare human-authored simplification of literary texts with the newest trends in computer simplification to promote further development of machine simplification tools. It has been found that the samples of simplified scientific texts seem to be more natural than the samples of simplified literary texts since technical background knowledge can be processed with machine tools. The authors have come to the conclusion that literary and technical texts should imply different approaches for adaptation and simplification. In addition, personal readers’ experience plays a great part in finding the implications in literary texts. In this respect it might be reasonable to create separate engines for simplifying and adapting texts from diverse spheres of knowledge. Keywords Text Simplification; Natural Language Processing (NLP); Pragmatic Adaptation; Professional Communication; Literary Texts
Principles of the Interaction of Time and Space

Time-space of opposing properties interact to create everything. Proposition 1 : Energy is the combination of time-space Proposition 2 : Time is positive ( + ), space is negative ( - ) 1. Time has dynamic divergence property ( positive ) and space has static convergence property ( negative ). These are unique properties of time-space. Main phenomena of convergence is gravity and particles, and anti-gravity and waves are the divergence phenomena. 2. Mutual combination of time and space which are opposite elements, and the mutual exclusion of time-time, space-space which are the same elements is their interrelationship properties. Main combining phenomena are energy formation, movement, electromagnetic attraction, and exclusion phenomena are time delay, length contraction, electromagnetic repulsion. 3. Unique and interrelationship properties coexist or combine with opposite properties, but offset each other. Gravity and anti-gravity coexist, and positive and negative charges are combined, but they all offset and balance each other. 4. Cosmos formation process is Chaos → time-space → light and darkness → basic particles → atoms and molecules → stars and galaxies 5. Everything is time-space. Energy ( mass, charge ), movement, and field ( gravity field, electromagnetic field, quantum field ) are all time-space.
A New Simple Technique to Bootstrap Various Lattice Zero-Knowledge Proofs to QROM Secure NIZKs.

Many of the recent advanced lattice-based \(\varSigma \)-/public-coin honest verifier (HVZK) interactive protocols based on the techniques developed by Lyubashevsky (Asiacrypt’09, Eurocrypt’12) can be transformed into a non-interactive zero-knowledge (NIZK) proof in the random oracle model (ROM) using the Fiat-Shamir transform. Unfortunately, although they are known to be secure in the classical ROM, existing proof techniques are incapable of proving them secure in the quantum ROM (QROM). Alternatively, while we could instead rely on the Unruh transform (Eurocrypt’15), the resulting QROM secure NIZK will incur a large overhead compared to the underlying interactive protocol.
Solar Forecasting Performance using Artificial Intelligence Algorithms

Renewable energy sources, especially Solar Energy, are to play a larger role in Hybrid Generation in the upcoming and future energy and utility requirements. The importance of renewable energies has been growing at a fast pace, both because of the need to solve problems related to environmental issues and as a way of helping the increasingly difficult management of electricity grids. Solar power forecasting can solve number of the equality issues, in the concern of accurate forecasts of solar output from design and equipments. The techniques of Artificial Intelligence have already shown their effectiveness in tasks of high complexity, namely, Regression, Classification, and Forecasting. Also in the field of Renewable energies these tools can be extremely useful, in particular in the prediction of Solar Irradiance.we developed two algorithms in Python of prediction of Solar Irradiance based on two methods of Artificial Intelligence, which are the Artificial Neural Networks and the K-Nearest Neighbors Method. In the forecasting process, the models are trained with subsets of the one-year Solar Irradiance register in the city of Lisbon and then the next hour’s forecast is carried out. In order to understand the best method to perform predictions of solar irradiance among those studied, a comparative study between the models was carried out, taking into consideration the prediction errors and the simulation times of both models in the simulations made in different situations.
A REVIEW: FEDERATED LEARNING FOR HEALTH CARE APPLICATION

Data-driven machine learning (ML) has become a potential strategy for generating accurate, robust statistics models from medical data, gathered in huge amounts by modern healthcare systems, with the rapid development of computer software and hardware technologies. A large amount of healthcare data from clinicians, patients, insurance companies, the pharmaceutical industry, and others are openly accessible. This abundance of data and sophisticated access to it offered data science technology an unparalleled chance to obtain data-driven insights and improve the quality of healthcare provision.Existing medical data are not properly used by ML models, mainly because it is contained in data silos. However, ML will not be able to achieve its full potential and ultimately migrate from research to medical care systems without access to complete data. This study reviews federated learning (FL), especially in the health care systems. We summarise in particular the broad solutions to federated learning's statistical challenges, system impediments, and privacy issues and identify the consequences and potentials for health care systems.
"On Perception," special issue of Performance Research

‘On Perception’ raises questions about the perceptual strategies and effects of performance and their implications. The authors are motivated by the potential of performance to affect artists’ and audiences’ perceptual range and to re-sensitize us to connections between environment, human (inter)action and creative thinking. Drawing on varied theories of perception and its embodiment, the included studies derive from the fields of virtual reality design, landscape design, audio walks, theatre, dance, music, performance art, and literature. Topics highlighted include the ethics of proprioceptive manipulation, auditory and embodied attunement, (dis)embodied perception, psychedelic perception, sensory resistance, perceptual deterritorialization, discursive perceptual framing and the perceptual generation of communal Indigenous or Black identities. These topics are examined as effects of performance praxis and spectatorship, dramaturgical strategies or performative theories. Together, they explore, move through and reach beyond bodily and discursive constraints of perception to produce relational connections and enable imagination.
An Autonomous Driving Based on Deep Learning Image Recognition

Image recognition refers, in the context of machine vision, the software's ability to recognize images in objects, places, people, writing and activities. Computers can use camera vision technology and artificial intelligence software to recognize pictures. The purpose of this research is to provide the latest technology technique for images recognition for driver. During the driving they can use this technology to detect deferent object. In the years before 2010, researchers used the local picture characteristics that they had identified using hybrid machine learning methods to address problems relating to image identification and classification. Since the year 2010, however, several deep learning techniques for image recognition have been developed and put through their paces. When it comes to general object identification contests, techniques that employ deep learning to identify images surpass tactics that were utilized before deep learning was introduced by a significant margin. In addition to explaining how deep learning is used to address problems in photo identification, this article covers the latest deep learning imaging technologies available at the time of writing (at the time of writing). It is hard and time-consuming to detect the appropriate mapping function from many training data and instructor labels in a time-consuming and difficult field of image recognition technology. This article discusses how deep learning is being used in the area of image identification, as well as the most recent advancements in autonomous deep learning driving, as a result of these advances. The research aim is to provide the latest technology  with the use of deep learning for images recognition.
A robust approach for deep neural networks in presence of label noise: relabelling and filtering instances during training.

Deep learning has outperformed other machine learning algorithms in a variety of tasks, and as a result, it has become more and more popular and used. However, as other machine learning algorithms, deep learning, and convolutional neural networks (CNNs) in particular, perform worse when the data sets present label noise. Therefore, it is important to develop algorithms that help the training of deep networks and their generalization to noise-free test sets. In this paper, we propose a robust training strategy against label noise, called RAFNI, that can be used with any CNN. This algorithm filters and relabels instances of the training set based on the predictions and their probabilities made by the backbone neural network during the training process. That way, this algorithm improves the generalization ability of the CNN on its own. RAFNI consists of three mechanisms: two mechanisms that filter instances and one mechanism that relabels instances. In addition, it does not suppose that the noise rate is known nor does it need to be estimated. We evaluated our algorithm using different data sets of several sizes and characteristics. We also compared it with state-of-the-art models using the CIFAR10 and CIFAR100 benchmarks under different types and rates of label noise and found that RAFNI achieves better results in most cases.
A case study on initiating an international residential college program at a Korean university

This study investigates the initiation and operation of an International Residential College program at a university in Korea that aimed to enhance the students’ English language skills and to promote global awareness. The program is introduced and the participating students’ satisfaction was measured for each sub-program. The program consisted of an ‘English conversation’ course as curricular and three evening classes, cultural events, and free talking sessions as extra- currular activities. The participants were 35 sophomores. Those that resided in the dormitory had a foreigner roommate. The results of the students survey show that English conversation course satisfaction was 4.49; high due to the intensity of meeting 4 days a week throughout the semester in a small size class. The satisfaction rate for extra-curricular classes held in the evenings in the dormitory ranged from 4.17 to 4.4 proving that the particpants were overall content. The high satisfaction rate 4.35 shows that students were happy with their roommates. Also, the results of a needs analysis for future program are given. It is hoped that similar programs may be implemented to help university students gain cultural and linguistic competence without going overseas.
Modified Pixel Value Ordering-based Predictor for Reversible Data Hiding on Video

Communication security has become an issue where massive data exchanges take place. Hiding personal data in a particular file during transmission can be a possible way to protect those data. Nevertheless, some parameters, such as stego file quality and the data capacity, are still challenging. In this paper, a method that modified Pixel-based Pixel Value Ordering (PPVO) Predictor is proposed for reversible data hiding on video. PPVO can embed the payload into a pixel whose value equals the maximum or minimum context pixel. To increase the number of payloads embedded, we modify the PPVO method by using only both right and bottom pixels to develop the predictors. The embedding process is carried out if the considered pixel has the same value as that in the right or bottom. The experimental result shows that this method has a significant increase in the embedding capacity and quality. Nevertheless, the overall maximum capacity of each cover is less than some other methods, while its corresponding quality is still the highest.
A Tutorial on Learning Disentangled Representations in the Imaging Domain.

Disentangled representation learning has been proposed as an approach to learning general representations. This can be done in the absence of, or with limited, annotations. A good general representation can be readily fine-tuned for new target tasks using modest amounts of data, or even be used directly in unseen domains achieving remarkable performance in the corresponding task. This alleviation of the data and annotation requirements offers tantalising prospects for tractable and affordable applications in computer vision and healthcare. Finally, disentangled representations can offer model explainability and can help us understand the underlying causal relations of the factors of variation, increasing their suitability for real-world deployment. In this tutorial paper, we will offer an overview of the disentangled representation learning, its building blocks and criteria, and discuss applications in computer vision and medical imaging. We conclude our tutorial by presenting the identified opportunities for the integration of recent machine learning advances into disentanglement, as well as the remaining challenges.
Gestión educativa en tiempos de COVID-19 para la orientación profesional a estudiantes de escuelas pedagógicas en la especialidad primera infancia

This article addresses the strategy deployed at the Floro Perez Pedagogical School in Santiago de Cuba, as part of the educational management for the professional orientation of the students who are trained as future educators in the early childhood specialty, based on the conditions that generated the coping with COVID -19, taking into account the global dimension of the pandemic and the challenges it imposed on our daily lives. With the arrival of SARS-COV-2 in the country, all institutions implemented emerging measures to face its effects. At this point, it is necessary to reflect on the actions developed by the pedagogical school, the implemented work system and the role of the cadres and faculty in the comprehensive response to the different processes, to guarantee the continuity and completion of studies with high motivation, responsibility and love for the profession on the part of the students.
Algorithm for Automated Generation of a Training Sample for Solving the Problem of Determining Semantic Similarity between a Pair of Keywords using Machine Learning Methods

Currently, machine learning is an effective approach to solving many problems of information-analytical systems. To use such approaches, a training set of examples is required. Collecting a training dataset is usually a time-consuming process. Its implementation requires the participation of several experts in the subject area for which the training set is collected. Moreover, for some tasks, including the task of determining the semantic similarity of keyword pairs, it is difficult even to correctly draw up instructions for experts to adequately evaluate the test examples. The reason for such difficulties is that semantic similarity is a subjective value and strongly depends on the scope, context, person, and task. The article presents the results of research on the search for models, algorithms and software tools for the automated formation of objects of the training sample in the problem of determining the semantic similarity of a pair of words. In addition, models built on an automated training sample allow us to solve not only the problem of determining semantic similarity, but also an arbitrary problem of classifying edges of a graph. The methods used in this paper are based on graph theory algorithms.
Perancangan Websıte Rebrandıng Bersama Rıztour Terhadap Kulıner dı Batam

PT. Riztour Indo Wisata is a business engaged in tourism which was founded in 2014, located at Ruko Shangrila Garden Blok A No. 18, Sekupang. The obstacles faced by PT. Riztour Indo Wisata while running its business activities in the form of the absence of a culinary website system that functions as a promotional tool to increase tourists who want to travel to the city of Batam. The methodology applied in this research is applied research that aims to solve problems in the modern world. The method used in this activity is SDLC (System Development Life Cycle) method which has 5 stages, namely requirement analysis, design, development, testing, maintenance. The tools are hosting, domain, wordpress, and page builder from elementor. The output of this research is in the form of a culinary website system that contains information about restaurants, recipes, typical food of the city of Batam. With this system, tourists PT. Riztour Indo Wisata makes it easier and faster to find information about culinary in the city of Batam