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In this paper, the implementation of artificial neural networks (multilayer perceptron [MLP] and radial base functions [RBF]) and the upgraded Markov chain model have been studied and performed to identify the human behavior patterns during rock, paper, and scissors game. The main motivation of this research is the design and construction of an intelligent robot with the ability to defeat a human opponent. MATLAB software has been used to implement intelligent algorithms. After implementing the algorithms, their effectiveness in detecting human behavior pattern has been investigated. To ensure the ideal performance of the implemented model, each player played with the desired algorithms in three different stages. The results showed that the percentage of winning computer with MLP and RBF neural networks and upgraded Markov model, on average in men and women is 59%, 76.66%, and 75%, respectively. Obtained results clearly indicate a very good performance of the RBF neural network and the upgraded Markov model in the mental modeling of the human opponent in the game of rock, paper, and scissors. In the end, the designed game has been employed in both hardware and software which include the Zana intelligent robot and a digital version with a graphical user interface design on the stand. To the best knowledge of the authors, the precision of novel presented method for determining human behavior patterns was the highest precision among all of the previous studies.

Examination is a vital role to measure the capabilities of students in their learning. Hence, generating question paper in an effective way is a decisive job for educators in educational institution. Using traditional method, it is monotonous and time consuming. Today, Autonomous Examination Paper (AEP) is used to produce exam paper. Many researchers have proposed effective AEPs to be used by educators. This paper aims to investigate about AEP development and to construct AEP in UiTM Cawangan Perlis. As a result, Ad-Hoc Question Paper Application (AQPA) has been developed using Fisher-Yates algorithm to generate questions for exam paper in the university. Evaluation based on Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) reveal that lecturers in the university manage to interact with AQPA and willing to use it as a tool to minimize their workload. However, more improvement must be done on AQPA to be an effective AEP. To conclude, AEP brings significance to educators and can be improved with the latest technology.

Paper waste in the mockups design with regular, irregular, and amorphous patterns is a critical problem in digital printing presses. Paper waste reduction directly impacts production costs, generating business and environmental benefits. This problem can be mapped to the two-dimensional irregular bin-packing problem. In this paper, an iterated local search algorithm using a novel neighborhood structure to detect overlaps between amorphous shapes is introduced. This algorithm is used to solve the paper waste problem, modeled as one 2D irregular bin-packing problem. The experimental results show that this approach works efficiently and effectively to detect and correct the overlaps between regular, irregular, and amorphous figures.

Imperfect Channel State Information at the Transmitter (CSIT) is inevitable in modern wireless communication networks, and results in severe multi-user interference in multi-antenna Broadcast Channel (BC). While the capacity of the multi-antenna (Gaussian) BC with perfect CSIT is known and achieved by Dirty Paper Coding (DPC), the capacity and the capacity-achieving strategy of the multi-antenna BC with imperfect CSIT remain unknown. Conventional approaches therefore rely on applying communication strategies designed for perfect CSIT to the imperfect CSIT setting. In this work, we break this conventional routine and make two major contributions. First, we show that linearly precoded Rate-Splitting (RS), relying on the split of messages into common and private parts and linear precoding at the transmitter, and successive interference cancellation at the receivers, can achieve larger rate regions than DPC in multi-antenna BC with partial CSIT. Second, we propose a novel achievable scheme, denoted as Dirty Paper Coded Rate-Splitting (DPCRS), that relies on RS to split the user messages into common and private parts, and DPC to encode the private parts. We show that the rate region achieved by DPCRS in Multiple-Input Single-Output (MISO) BC with partial CSIT is enlarged beyond that of conventional DPC and that of linearly precoded RS. Gaining benefits from the capability of RS to partially decode the interference and partially treat interference as noise, DPCRS is less sensitive to CSIT inaccuracies, networks loads and user deployments compared with DPC and other existing transmission strategies. The proposed DPCRS acts as a new benchmark and the best-known achievable strategy for multi-antenna BC with partial CSIT., accepted by IEEE transactions on communications for publication

Machine learning, a branch of artificial intelligence, is increasingly used in health research, including nursing and maternal outcomes research. Machine learning algorithms are complex and involve statistics and terminology that are not common in health research. The purpose of this methods paper is to describe three machine learning algorithms in detail and provide an example of their use in maternal outcomes research. The three algorithms, classification and regression trees, least absolute shrinkage and selection operator, and random forest, may be used to understand risk groups, select variables for a model, and rank variables’ contribution to an outcome, respectively. While machine learning has plenty to contribute to health research, it also has some drawbacks, and these are discussed as well. In order to provide an example of the different algorithms’ function, they were used on a completed cross-sectional study examining the association of oxytocin total dose exposure with primary cesarean section. The results of the algorithms are compared to what was done or found using more traditional methods.

Temporary blockchain forks are part of the regular consensus process in permissionless blockchains such as Bitcoin. As forks can be caused by numerous factors such as latency and miner behavior, their analysis provides insights into these factors, which are otherwise unknown. In this paper we provide an empirical analysis of the announcement and propagation of blocks that led to forks of the Bitcoin blockchain. By analyzing the time differences in the publication of competing blocks, we show that the block propagation delay between miners can be of similar order as the block propagation delay of the average Bitcoin peer. Furthermore, we show that the probability of a block to become part of the main chain increases roughly linearly in the time the block has been published before the competing block. Additionally, we show that the observed frequency of short block intervals between two consecutive blocks mined by the same miner after a fork is conspicuously large. While selfish mining can be a cause for this observation, other causes are also possible. Finally, we show that not only the time difference of the publication of competing blocks but also their propagation speeds vary greatly.

The introduction of new therapeutic agents in chronic lymphocytic leukemia (CLL) and follicular lymphoma (FL), including the new kinase inhibitor idelalisib, has changed the therapeutic landscape of these diseases. However, the use of idelalisib is associated with a peculiar profile of side effects, which require an optimization of the current approach to prophylaxis and supportive treatment. Moving from the recognition that the abovementioned issue represents an unmet need in CLL and FL, a multidisciplinary panel of experts was convened to produce a consensus document aiming to provide practical recommendations for the management of the side effects during idelalisib therapy for CLL and FL. The present publication represents a consensus document from a series of meetings held during 2017. The Panel generated clinical key questions using the criterion of clinical relevance through a Delphi process and explored 4 domains, ie, diarrhea/colitis, transaminitis, pneumonitis, and infectious complications. Using the consensus method, the Panel was able to shape recommendations which may assist hematologist to minimize adverse events and guarantee adherence to treatment in patients with CLL and FL candidate to receive idelalisib.

This article centers on the specific algorithm, devised to help students select the theme of the qualifying paper. The article also reviews the contents of the customs affairs specialists qualifying paper. The method of selecting the theme of the qualifying paper using the mind-map is offered. The Mind-map offers a convenient scheme for a student to select his or her own research advisor, work experience placement, object and subject of the qualifying paper. This article represents an analysis of example of student's qualifying paper on currency control theme choice, lays out the contents of theoretical and practical parts of the qualifying paper named “Institutional analysis of currency control on the territory of the Chelyabinsk region”. Methods of currency control scientific analysis with the aid of statistical methods and graphic techniques are offered.

International audience; As part of the Historic Photographic Paper Classification Challenge, a multitude of approaches to quantifying paper texture similarity have been developed. These approaches have yielded encouraging results when applied to very controlled datasets containing photomicrographs of familiar specimens. In this paper, we report on the k-nearest neighbors classification performance of two multiscale analysis-based texture similarity approaches when applied to a much larger reference collection of silver gelatin photographic papers. The clusters for this data set were derived from a visual sorting experiment conducted by art conservators and paper experts later extended through crowd-sourcing. The results show that these texture similarity approaches, when combined with a simple k-nearest neighbors classification algorithm, yield workable performances with accuracy of up to 69%. We discuss this outcome in the context of available data and the cross-validation procedure used, then provide suggestions for improvement.

To facilitate better interpretation of dielectric response measurements—thereby directing numerical evidence for condition assessments of oil-paper-insulated equipment in high-voltage alternating current (HVAC) transmission systems—a novel approach is presented to estimate the parameters in the extended Debye model (EDM) using wideband frequency domain spectroscopy (FDS). A syncretic algorithm that integrates a genetic algorithm (GA) and the Levenberg-Marquardt (L-M) algorithm is introduced in the present study to parameterize EDM using the FDS measurements of a real-life 126 kV oil-impregnated paper (OIP) bushing under different controlled temperatures. As for the uncertainty of the EDM structure due to variable branch quantity, Akaike’s information criterion (AIC) is employed to determine the model orders. For verification, comparative analysis of FDS reconstruction and results of FDS transformation to polarization–depolarization current (PDC)/return voltage measurement (RVM) are presented. The comparison demonstrates good agreement between the measured and reconstructed spectroscopies of complex capacitance and tan δ over the full tested frequency band (10−4 Hz–103 Hz) with goodness of fit over 0.99. Deviations between the tested and modelled PDC/RVM from FDS are then discussed. Compared with the previous studies to parameterize the model using time domain dielectric responses, the proposed method solves the problematic matching between EDM and FDS especially in a wide frequency band, and therefore assures a basis for quantitative insulation condition assessment of OIP-insulated apparatus in energy systems.

DNA computer based algorithm is developed and evaluated for recyclable waste paper segregation.The concepts of replication and massive parallelism operations are used.The matching stage consists of Copy, Extract, Detect and Merge or Union operations.Gel electrophoresis operation is used to identify the candidate paper object grade.Success rate are 92% for WP, 90% for ONP and 93% for OCC with template size 5i?5. This article explores the application of DNA computing in recyclable waste paper sorting. The primary challenge in paper recycling is to obtain raw materials with the highest purity. In recycling, waste papers are segregated according to their various grades, and these are subjected to different recycling processes. Highly sorted paper streams facilitate high quality end products, while saving on processing chemicals and energy. In the industry, different sensors are used in paper sorting systems, namely, ultrasonic, lignin, gloss, stiffness, infra-red, mid-infra red, and color sensors. Different mechanical and optical paper sorting systems have been developed based on the different sensors. However, due to inadequate throughput and some major drawbacks related to mechanical paper sorting systems, the popularity of optical paper sorting systems has increased. The automated paper sorting systems offer significant advantages over the manual systems in terms of human fatigue, throughput, speed, and accuracy. This research has two objectives: (1) to use a web camera as an image sensor for the vision system in lieu of different sensors; and (2) to develop a new DNA computing algorithm based on the theme of template matching techniques for segregating recyclable waste papers according to paper grades. Using the concepts of replication and massive parallelism operations, the DNA computing algorithm can efficiently reduce the computational time of the template matching method. This is the main strength of the DNA computing algorithm in actual inspections. The algorithm is implemented by using a silicon-based computer to verify the success rate in paper grade identification.

The feeding liquid chemicals system at the pulp and paper enterprise was chosen as a research object. The research selected the key points for collecting information on technological parameters that affect the technological process. An algorithm has been developed, taking into account the specificity of the sensors and measuring instruments used, the controls and the programmable logic controllers (PLC). The subsystem has been integrated into the enterprise automated dispatch control system. В качестве исследуемого технологического объекта была выбрана система подачи жидких химикатов на целлюлозно-бумажном предприятии. В ходе исследования были выбраны ключевые моменты для сбора информации о технологических параметрах, которые влияют на технологический процесс. Разработан алгоритм с учетом специфики используемых датчиков и измерительных приборов, органов управления и программируемых логических контроллеров (ПЛК). Разработанная подсистема интегрирована в автоматизированную систему диспетчерского управления предприятия.

This paper studies the second-order coding rates for memoryless channels with a state sequence known non-causally at the encoder. In the case of finite alphabets, an achievability result is obtained using constant-composition random coding, and by using a small fraction of the block to transmit the empirical distribution of the state sequence. For error probabilities less than 0.5, it is shown that the second-order rate improves on an existing one based on independent and identically distributed random coding. In the Gaussian case (dirty paper coding) with an almost-sure power constraint, an achievability result is obtained using random coding over the surface of a sphere, and using a small fraction of the block to transmit a quantized description of the state power. It is shown that the second-order asymptotics are identical to the single-user Gaussian channel of the same input power without a state.

This article describes how with the advent of computer-based technology, there is movement from manual to automated systems for different aspects of the education system. Testing is an essential part of teaching process that helps academics in classifying the level of students and evaluating the outcomes of their teaching process. The testing process requires a large amount of attention and professionalism. Automated Test Paper Generation is a system applying the shuffling algorithm in designing different sets of questions without repetition and duplication. It helps the faculty in developing and designing exams with a particular level of difficulty required in evaluating the students by using the utility-based agent. The system includes a knowledge base of many questions' types that are linked to a test engine where the faculty can specify the type and the difficulty level of the exam and then the system will assemble the exam and produce the output as electronic or paper-based. Questions will be picked randomly from the knowledge database. This automated system provides cost saving and time efficient solutions.

The aim of this concept paper is the description of a new tool to support institutions in the implementation of targeted countermeasures, based on quantitative and multi-scale elements, for the fight and prevention of emergencies, such as the current COVID-19 pandemic. The tool is a cloud-based centralized system, a multi-user platform that relies on artificial intelligence (AI) algorithms for the processing of heterogeneous data, which can produce as an output the level of risk. The model includes a specific neural network which is first trained to learn the correlations between selected inputs, related to the case of interest: environmental variables (chemical&ndash, physical, such as meteorological), human activity (such as traffic and crowding), level of pollution (in particular the concentration of particulate matter) and epidemiological variables related to the evolution of the contagion. The tool realized in the first phase of the project will serve later both as a decision support system (DSS) with predictive capacity, when fed by the actual measured data, and as a simulation bench performing the tuning of certain input values, to identify which of them led to a decrease in the degree of risk. In this way, we aimed to design different scenarios to compare different restrictive strategies and the actual expected benefits, to adopt measures sized to the actual needs, adapted to the specific areas of analysis and useful for safeguarding human health, and we compared the economic and social impacts of the choices. Although ours is a concept paper, some preliminary analyses have been shown, and two different case studies are presented, whose results have highlighted a correlation between NO2, mobility and COVID-19 data. However, given the complexity of the virus diffusion mechanism, linked to air pollutants but also to many other factors, these preliminary studies confirmed the need, on the one hand, to carry out more in-depth analyses, and on the other, to use AI algorithms to capture the hidden relationships among the huge amounts of data to process.

A summary of contributions made by significant papers from the first 25 years of the Field-Programmable Logic and Applications conference (FPL) is presented. The 27 papers chosen represent those which have most strongly influenced theory and practice in the field.

This document contains supplemental material for the paper [2] . The notations in this document are the same as in [2] . In particular, we first present here the proof of Theorem 1 in [2] . This theorem expresses the locally most powerful unbiased (LMPU) test, which is a general method for local detection, in the presence of known nuisance parameters. Second, we present here the Matlab code of the LMPU and the generalized LMPU for the special case of detection of a small deviation in the frequency of sinusoidal signals, which arises in various signal processing applications.

Hyperproperties generalize trace properties by expressing relations between multiple computations. Hyperpropertes include policies from information-flow security, like observational determinism or non-interference, and many other system properties including promptness and knowledge. In this paper, we give an overview on the model checking problem for temporal hyperlogics. Our starting point is the model checking algorithm for HyperLTL, a reduction to Buchi automata emptiness. This basic construction can be extended with propositional quantification, resulting in an algorithm for HyperQPTL. It can also be extended with branching time, resulting in an algorithm for HyperCTL*. However, it is not possible to have both extensions at the same time: the model checking problem of HyperQCTL* is undecidable. An attractive compromise is offered by MPL[E], i.e., monadic path logic extended with the equal-level predicate. The expressiveness of MPL[E] falls strictly between that of HyperCTL* and HyperQCTL*. MPL[E] subsumes both HyperCTL* and HyperKCTL*, the extension of HyperCTL* with the knowledge operator. We show that the model checking problem for MPL[E] is still decidable.

In this work, we describe a new approach that uses deep neural networks (DNN) to obtain regularization parameters for solving inverse problems. We consider a supervised learning approach, where a network is trained to approximate the mapping from observation data to regularization parameters. Once the network is trained, regularization parameters for newly obtained data can be computed by efficient forward propagation of the DNN. We show that a wide variety of regularization functionals, forward models, and noise models may be considered. The network-obtained regularization parameters can be computed more efficiently and may even lead to more accurate solutions compared to existing regularization parameter selection methods. We emphasize that the key advantage of using DNNs for learning regularization parameters, compared to previous works on learning via optimal experimental design or empirical Bayes risk minimization, is greater generalizability. That is, rather than computing one set of parameters that is optimal with respect to one particular design objective, DNN-computed regularization parameters are tailored to the specific features or properties of the newly observed data. Thus, our approach may better handle cases where the observation is not a close representation of the training set. Furthermore, we avoid the need for expensive and challenging bilevel optimization methods as utilized in other existing training approaches. Numerical results demonstrate the potential of using DNNs to learn regularization parameters., 27 pages, 16 figures

Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because it’s unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate.

International Journal of Advances in Materials Science and Engineering (IJAMSE) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Materials Science and Engineering. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of Materials Science and Engineering. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on Materials Science and Engineering advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of Materials Science and Engineering

SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments., Comment: Article source data is available here: https://github.com/scipy/scipy-articles

Free-falling paper shapes exhibit rich, complex and varied behaviours that are extremely challenging to model analytically. Physical experimentation aids in system understanding, but is time-consuming, sensitive to initial conditions and reliant on subjective visual behavioural classification. In this study, robotics, computer vision and machine learning are used to autonomously fabricate, drop, analyse and classify the behaviours of hundreds of shapes. The system is validated by reproducing results for falling discs, which exhibit four falling styles: tumbling, chaotic, steady and periodic. A previously determined mapping from a non-dimensional parameter space to behaviour groups is shown to be consistent with these new experiments for tumbling and chaotic behaviours. However, steady or periodic behaviours are observed in previously unseen areas of the parameter space. More complex hexagon, square and cross shapes are investigated, showing that the non-dimensional parameter space generalizes to these shapes. The system highlights the potential of robotics for the investigation of complex physical systems, of which falling paper is one example, and provides a template for future investigation of such systems. The dynamics of paper shapes in free fall are still not fully understood, despite being discussed for more than 150 years. Collecting large amounts of data has the potential to give us new insights and a robotics system could generate and analyse data in large quantities.

From 1880 to 2005, the Eastman Kodak Company manufactured black-and-white fiber-based gelatin silver paper in a wide variety of weights, grades, and formats. Kodak manufacturer records and sample books include details about Kodak paper surface characteristics and are an invaluable resource for understanding photographic paper materials. Using the extensive number of Kodak data books, manuals, and manufacturing records spread out in the collections of three Rochester, New York-based institutions—George Eastman Museum, University of Rochester Special Collections, and Image Permanence Institute—I created a chronological guide to Kodak photographic paper surface characteristics. This guide is not an approximate identification guide for Kodak papers but rather a resource that can be used to fill in gaps and propose questions about Kodak manufacturing history that is no longer easily accessible. The guide aims to help researchers, photography archivists and historians, conservators, collection managers, or anyone else interested in Kodak history gain access to a better understanding of photographic paper produced by Kodak from 1930 to 1955. The process for creating the guide is described in this article and may be used as a starting point for future research while also illuminating the importance of documenting and providing access to technological and material details about photographic objects.

We investigate the performance of a watermarking system in which the encoder is forced to use a binning strategy based on antipodal binary-valued sequences. The use of antipodal binary random binning has several advantages, including the possibility of relying on simple and effective binary code constructions and the ease with which this kind of schemes can cope with amplitude scaling. By relying on a novel binning strategy, we derive a lower bound of the Gelfand-Pinsker capacity of the watermark channel when the encoder is forced to use an antipodal binary auxiliary random variable, showing that for low to moderate bit-rates, the bound coincides with Costa’s capacity. We exploit the properties of the new binning strategy, to develop a practical watermarking system and show that the new scheme outperforms previous constructions, exhibiting very good performance also in the presence of gain attack. Preliminary results on audio signals show that the new scheme retains its good performance also when used for the watermarking of real multimedia data.