Reviewerly/exHarmony huggingface dataset:
Pedestrian Recognition and Obstacle Avoidance for Autonomous Vehicles Using Raspberry Pi

The aim of this paper is twofold: firstly, to use ultrasonic sensors to detect obstacles and secondly to present a comparison of machine learning and deep learning algorithms for pedestrian recognition in an autonomous vehicle. A mobility scooter was modified to be fully autonomous using Raspberry Pi 3 as a controller. Pedestrians were initially simulated by card board boxes and further replaced by a pedestrian. A mobility scooter was disassembled and connected to Raspberry Pi 3 with ultrasonic sensors and a camera. Two computer vision algorithms of histogram of oriented gradients (HOG) descriptors and Haar-classifiers were trained and tested for pedestrian recognition and compared to deep learning using the single shot detection method. The ultrasonic sensors were tested for time delay for obstacle avoidance and were found to be reliable at ranges between 100 cm and 500 cm at small angles from the acoustic axis, and at delay periods over two seconds. HOG descriptor was found to be a superior algorithm for detecting pedestrians compared to Haar-classifier with an accuracy of around 83%, whereas, deep learning outperformed both with an accuracy of around 88%. The work presented here will enable further tests on the autonomous vehicle to collect meaningful data for management of vehicular cloud.
Modelling and Measurement of Folk Guitar: Truss Rod and Strings in Numerical Analysis of Tone

The study makes an attempt to model a complete vibrating guitar including its non-linear features, specifically the tension-compression of truss rod and tension of strings.The purpose of such a model is to examine the influence of design parameters on tone.Most experimental studies are flawed by uncertainties introduced by materials and assembly of an instrument.Since numerical modelling of instruments allows for deterministic control over design parameters, a detailed numerical model of folk guitar was analysed and an experimental study was performed in order to simulate the excitation and measurement of guitar vibration.The virtual guitar was set up like a real guitar in a series of geometrically non-linear analyses.Balancing of strings and truss rod tension resulted in a realistic initial state of deformation, which affected the subsequent spectral analyses carried out after dynamic simulations.Design parameters of the guitar were freely manipulated without introducing unwanted uncertainties typical for experimental studies.The study highlights the importance of acoustic medium in numerical models.
Resource-constrained Multi-agent Markov Decision Processes

Intelligent autonomous agents, designed to automate and simplify many aspects of our society, will increasingly be required to also interact with other agents autonomously. Where agents interact, they are likely to encounter resource constraints. For example, agents managing household appliances to optimize electricity usage might need to share the limited capacity of the distribution grid. This thesis describes research into new algorithms for optimizing the behavior of agents operating in constrained environments, when these agents have significant uncertainty about the effects of their actions on their state. Such systems are effectively modeled in a framework of constrained multi-agent Markov decision processes (MDPs). A single-agent MDP model captures the uncertainty in the outcome of the actions chosen by a specific agent. It does so by providing a probabilistic model of state transitions, describing the likelihood of arriving in a future state, conditional on the current state and action. Agents collect different rewards or penalties depending on the current state and chosen action, informing their objective of maximizing their expected reward. To include constraints, resource consumption functions are added to the actions, and the agents' (shared) objective is modified with a condition restricting their (cumulative) resource consumption. We propose novel algorithms to advance the state of the art in three challenging settings: computing static preallocations off-line, computing dynamic (re)allocations on-line, and optimally learning model dynamics through safe reinforcement learning under the constraints. Taken together, these algorithms show how agents can coordinate their actions under uncertainty and shared resource constraints in a broad range of conditions. Furthermore, the proposed solutions are complementary: static preallocations can be used as back-up strategy for when a communication disruption prevents the use of dynamic allocations.
Heikin-Ashi Transformation and Vix Index data for Stock Market Index Prediction and It's Effects

This paper performs a comprehensive analysis of Vix Index data with Heikin Ashi Transformation of stock index Neural Network Learning. It has been demonstrated that Heikin Ashi Transformation can improve the learning effect of Neural Network and the effect can also be filter out if volume weights are also considered. This paper introduces another improvement beside using volume-weighted data. Instead volatility index is used as an input and its effect for neural network learning process is analyzed.
Common-path interferometry with tilt carrier for surface measurement of complex optics

We propose a novel common-path non-null interference system for the surface measurement of complex optics. Because of the common-path structure, systematic errors are mostly eliminated and the complexity of system calibration is reduced for the proposed setup. However, optical design of common-path non-null interference systems is of great difficulty because many off-axis beams are used to compensate for the local gradient of the measured piece. Different from the classical common-path interferometers, which pay more attention to the beam quality of the on-axis field of view (FOV), the proposed system requires better quality for both on-axis and off-axis outgoing beams. In the proposed setup, the lens groups with wide FOVs afford wave aberrations better than 0.1λ for the on-axis and off-axis FOVs. Simultaneously, the off-axis beams are prevented from generating pseudo reference beams by optimizing the parameters of the lenses and the aperture. In addition, multiple tilted test beams are generated by a point-source generator based on a fiber array, which is more versatile than a lens-array-type point-source generator. Further, a universal measurement system with high accuracy and time savings is formed, as evidenced by the measurement results of a parabolic mirror and different types of cylindrical mirrors.
A Fuzzy Classifier for Data Streams with Infinitely Delayed Labels

In data stream learning, classification is a prominent task which aims to predict the class labels of incoming examples. However, in classification, most of the approaches from literature make assumptions that limit the usefulness of the methods in real scenarios such as the supposition that the label of an example will be available right after its prediction, i.e., there is no time delay to acquiring actual labels. It is a very optimistic assumption, since labeling the entire data stream is usually not feasible. Some recent approaches overcome this limitation, considering unsupervised learning methods to deal with delayed labels. Also, some proposals explore concepts of fuzzy set theory to add more flexibility to the learning process, although restricted to data streams with no delayed labels. In this paper, we propose a fuzzy classifier for data streams with infinitely delayed labels called FuzzMiC. Our algorithm generates a model based on fuzzy micro-clusters that provides flexible class boundaries and allows the classification of evolving data streams. Experiments show that our approach is promising in dealing with incremental changes.
A Qualitative Comparison of NoSQL Data Stores

Due to the proliferation of big data with large volume, velocity, complexity, and distribution among remote servers, it became obvious that traditional relational databases are unsuitable for meeting the requirements of such data.This led to the emergence of a novel technology among organizations and business enterprises; NoSQL datastores.Today such datastores have become popular alternatives to traditional relational databases, since their schema-less data models can manipulate and handle a huge amount of structured, semistructured and unstructured data, with high speed and immense distribution.Those data stores are of four basic types, and numerous instances have been developed under each type.This implies the need to understand the differences among them and how to select the most suitable one for any given data.Unfortunately, research efforts in the literature either consider differences from a theoretical point of view (without real use cases), or address performance issues such as speed and storage, which is insufficient to give researchers deep insight into the mapping of a given data structure to a given NoSQL datastore type.Hence, this paper provides a qualitative comparison among three popular datastores of different types (Redis, Neo4j, and MongoDB) using a real use case of each type, translated to the others.It thus highlights the inherent differences among them, and hence what data structures each of them suits most.
Quantum walks in quasi-periodic arrays of waveguides

In this paper, we present results of numerical investigation of quantum walks (QWs) in different type arrays of single-mode waveguides: (i) regular arrays of identical waveguides (AIW), (ii) periodic arrays of two different waveguides (PAW), (iii) quasi-periodic arrays of waveguides with Fibonacci sequences (FAW), (iv) quasi-periodic arrays of waveguides with Thue-Morse sequences (TMAW). Our simulations show in contrast with randomly disordered systems, localized QWs in quasi-periodic arrays of waveguides FAWs and TMAWs are predictable and controllable due to the deterministic disorder nature of the quasi-periodic systems.
User-aware conversational agents

Conversational agents are becoming increasingly popular. These systems present an extremely rich and challenging research space for addressing many aspects of user awareness and adaptation, such as user profiles, contexts, personalities, emotions, social dynamics, conversational styles, etc. Adaptive interfaces are of long-standing interest for the HCI community. Meanwhile, new machine learning approaches are introduced in the current generation of conversational agents, such as deep learning, reinforcement learning, and active learning. It is imperative to consider how various aspects of user-awareness should be handled by these new techniques. The goal of this workshop is to bring together researchers in HCI, user modeling, and the AI and NLP communities from both industry and academia, who are interested in advancing the state-of-the-art on the topic of user-aware conversational agents. Through a focused and open exchange of ideas and discussions, we will work to identify central research topics in user-aware conversational agents and develop a strong interdisciplinary foundation to address them.
IWEARUP.COM USER INTERFACE ANALYSIS

IWEARUP.COM is a website that is an e-commerce based. It contains information about buying, selling, distributing, and marketing fashion products. A business website is an example of using design as a marketing tool. Display of charming website with design is an attraction. However, a good website design must be able to display information clearly. Especially how to make the interface possible as it is not confused with the information displayed. Poor interfaces affect the users productivity or experience in visiting a website. This is a visual hierarchy which is the most important principles behind every website design. With an instrumental case study of the approach to produce conclusions that can be applied in designing e-commerce-based website. The goal is to make the website design in line with the content that will increase the website design and increase knowledge about the visual hierarchy of web design and its relation to the user interface.
RETRACTED ARTICLE: Image processing algorithm of Hartmann method aberration automatic measurement system with tensor product model

Abstract Nowadays, the society has entered the digital information age, and the information contained in the image is far more than the sum of the information contained in other media. In the Internet industry, image processing technology can be used to quickly find the required picture information. Other applications include disaster prevention, industrial automation production lines, semiconductor, electronics, tobacco, and food industries. After the meter glyph spot image is collected, there are several spots in the image, and the corresponding pixel values are stored in memory. In order to process images, they should be distinguished and marked so that the spot has definite eigenvalues. To this end, this paper proposes an image processing method. Firstly, an image denoising method combining self-snake model and P-M model is introduced. Secondly, the recursive HOSVD dimensionality reduction algorithm based on tensor product model is used to further process the image. The center of the Hartmann aperture image is solved by the centroid of all the spots, and the center overlap algorithm for determining the centroid distance of the aperture image by the symmetry of the centroid of the spot centroid can reduce the number of calculations. The experimental results show that this method can effectively identify and process the spot of the image and greatly reduce the time complexity and computational complexity of the algorithm.
Bayesian Learning of Conditional Kernel Mean Embeddings for Automatic Likelihood-Free Inference

In likelihood-free settings where likelihood evaluations are intractable, approximate Bayesian computation (ABC) addresses the formidable inference task to discover plausible parameters of simulation programs that explain the observations. However, they demand large quantities of simulation calls. Critically, hyperparameters that determine measures of simulation discrepancy crucially balance inference accuracy and sample efficiency, yet are difficult to tune. In this paper, we present kernel embedding likelihood-free inference (KELFI), a holistic framework that automatically learns model hyperparameters to improve inference accuracy given limited simulation budget. By leveraging likelihood smoothness with conditional mean embeddings, we nonparametrically approximate likelihoods and posteriors as surrogate densities and sample from closed-form posterior mean embeddings, whose hyperparameters are learned under its approximate marginal likelihood. Our modular framework demonstrates improved accuracy and efficiency on challenging inference problems in ecology.
Biclique cryptanalysis on Midori block cipher

Biclique cryptanalysis is a typical key-recovery attack can significantly decrease computation and data complexity by utilising the main idea of meet-in-the-middle attack and the principle of biclique structure. In this paper, we first demonstrate the block cipher Midori64/128 and present a general method of biclique attack. Next, we describe the steps in constructing a biclique and attacking a full-round Midori block cipher. By investigating a simple key schedule, we present a full-round biclique cryptanalysis of Midori. Then, we construct a five-round biclique structure of four-dimensional and a four-round biclique structure for Midori64, with data complexity of 236 and 216 and computational complexity of 2126.45 and 2126.69, respectively. Moreover, we use a four-round biclique structure to attack Midori128 with data complexity of 272 and computational complexity of 2127.01. These findings are superior to current results.
Pembelajaran Arthropoda Menggunakan Booklet Sebagai Sumber Belajar Mandiri Siswa Kelas X SMA/MA

Abstract. This Research and Development (R and D) concerns on learning media development that can be used as an alternative learning source of booklet on the subject of animal world sub chapter arthropoda. The R and D’s model used Analyze, Design, Develop, Implement, Evaluate (ADDIE). Validity test obtained from two expert validators and Practicality test obtained from the using of media by teacher and students. The Develop stage of this study conducted by validating e-magazine for further be implemented on SMAN 5 Jeneponto, Class of X MIA3. The number of subjects comprised of 35 students. Data collected from interview, observation and questionnaire. The result of validity test showed the average of media feasibility percentage is 85%. While the subject matter feasibility gained 86%. Both of these result showed very decent category which was meant that the media is feasible to be used. The average of teacher and students respond gained 91% and 89,2 % respectively toward positive respond. From the point of feasibility which had been evaluated by validity and practical test, this study can be conclude that booklet is valid and practice learning media to be implemented in learning process. Keywords: booklet, interactive, validity, practicality, arthropoda
Non-negative Radial Basis Function Neural Network in Polynomial Feature Space

Radial basis function neural network (RBFNN) is an effective nonlinear learning model, which has a strong nonlinear fitting capability. The hidden neurons and the weight play important roles in the neural network. In the existing researches, the hidden neurons are computed in the form of a rigorous linear combination. And the weight is hard to be solved. To address these issues, in this paper a novel neural network named non-negative radial basis function neural network (NRBFNN) is proposed. The main thought of non-negative matrix factorization (NMF) is to be used to train the parameters of RBFNN. According to the structure of the neural network, the label information of the samples is decomposed into the weight matrix and the mapped feature by activation functions in polynomial feature space. And the proposed method is able to obtain the weight matrix and the hidden neurons implied in the activation functions iteratively. Furthermore, the proposed NRBFNN is able to improve the representability of the hidden neurons and the iterative formulas of the weight can ensure the solvability and interpretability of it. ORL, Yale and Caltech 101 face databases are selected for evaluations. Experimental results show that the proposed algorithm outperforms several related algorithms.
Some optimal combinatorial batch codes with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e1524" altimg="si19.gif"><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>5</mml:mn></mml:math>

A combinatorial batch code was defined by Paterson, Stinson and Wei as a purely combinatorial description of batch codes, which were first introduced by Ishai, Kushilevitz, Ostrovsky and Sahai. It is a system consisting of m subsets of an n-element set such that any k distinct elements can be retrieved by reading at most one (or in general, t) elements from each subset and the number of total elements in m subsets is N. For given parameters n,k,m, the goal is to determine the minimum N, denoted by N(n,k,m). So far, for k≥5 and m+3≤n<mk−2, precise values of N(n,k,m) have not been established except for some special parameters. In this paper, we determine the N(n,5,m) for n≤⌊m24⌋+⌈m∕2⌉3+⌊m∕2⌋3, and give a bound on N(n,5,m) for ⌊m24⌋+⌈m∕2⌉3+⌊m∕2⌋3<n≤m3, which is tight for some m,n.
The use of Fourier‐domain analyses for unwrapping phase images of low SNR

Purpose We report a new postprocessing procedure that uses Fourier‐domain data analyses to improve the accuracy and reliability of phase unwrapping for MRI data of low SNR. Methods The developed method first identifies the Fourier‐domain energy peak locations corresponding to different image‐domain areas from which a robust measurement of image‐domain phase gradients can be obtained even for MRI data of low SNR. The phase‐gradient information measured from critical brain regions using the above‐mentioned Fourier‐domain analysis is then combined with the conventional temporal‐domain or spatial‐domain phase‐unwrapping procedure to remove phase wraps. The developed method was tested with MRI data obtained from 30 healthy adult volunteers, and its performance was quantitatively evaluated. Results The developed Fourier‐domain analysis could robustly quantify image‐domain phase gradients even for MRI data with low SNR (e.g., SNR 2). Experimental results show that the Fourier‐domain analyses could further reduce phase wrap artifact in data produced by the conventional temporal‐domain or spatial‐domain phase‐unwrapping procedures. Conclusion Our results demonstrate that the developed phase‐unwrapping method can reduce residual phase wraps resulting from conventional procedures in critical brain regions (e.g., near the air‐tissue interfaces) and should prove valuable for studies that require accurate measurements of MRI phase values, such as QSM, B 0 field mapping, and temperature mapping.
Bridge Critical State Search by Using Quantum Genetic Firefly Algorithm

When performing flutter analysis through the traditional methods, it is difficult to solve high-order strong nonlinear equations. For overcoming this difficulty, this paper establishes a double-parameter optimization model for searching the flutter critical wind speed and frequency. A new hybrid firefly algorithm called the quantum genetic firefly algorithm is presented to search the optimal solution to the optimization model. The proposed algorithm is the combination of the firefly algorithm and the quantum genetic algorithm. The results of the quantum genetic firefly algorithm are compared with the results shown by the firefly algorithm and quantum genetic algorithm. Numerical and experimental results of the proposed algorithm are competitive and in most cases are better than that of the firefly algorithm and quantum genetic algorithm.
Monitoring tailings flocculation performance using hyperspectral imagery

In‐line flocculation of oil sands tailings is a technique widely used in the oil sands industry to dewater fluid fine tailings. In this method, flocculants are added to the tailings to aggregate the fine particles and increase the settling rate and dewatering performance. Among the factors controlling the flocculation performance, the flocculant dose, rate and time of mixing play a crucial role. Consequently, the development of techniques that can monitor flocculation and provide feedback information to adjust flocculant dose and mixing conditions is of significant interest to the oil sands industry. This paper examines how hyperspectral imagery can be used to monitor tailings flocculation through post‐depositional spectral measurements and image analysis. The results show that hyperspectral imagery can detect under‐dosed and over‐sheared samples, but experiments over a wider range of flocculation conditions are required to validate the results and calibrate the method. This study represents the initial step in the development of a spectral method for monitoring and assessment of the flocculation process.