Reviewerly/exHarmony huggingface dataset:
Adaptive Obstacle Avoidance Control Based on First-order Low-pass Filter for Cleaning Robots

Cleaning robots play an increasingly important role in ship cleaning. This paper presents an adaptive obstacle avoidance control algorithm based on first-order low-pass filter (LPF) for cleaning robots. Firstly, this paper designs a nonlinear sliding mode observer to estimate the slip parameters online because the tracked cleaning robots are easy to slip and it is hard to measure the slip parameters, and uses a first-order LPF to attenuate the chattering effects of the sign function. Secondly, for track slipping, we design an adaptive sliding mode control algorithm based on Lyapunov method. Finally, we design an obstacle avoidance algorithm based on potential function. The simulation results show that the observer can estimate the slip parameters accurately, the control algorithm can assure the robot track the expect trajectory and accomplish the function of obstacle avoidance.
An Overview of the Festo Semantic Platform

We present an overview of the Festo Semantic Platform that is in productive usage at the automation company Festo to derive value adding information from product data. First we describe the current state of the platform. At its core OWL and SWRL reasoning is applied to infer compatibility relationships between products such as electric axes, mounting kits, motors and controllers. Further we outline an ongoing effort to establish a more well-defined data processing pipeline to gain more flexibility when integrating new knowledge domains. Finally, we discuss future directions, especially the role of the platform in skill based product search.
Action localization and classification in long-distance surveillance

Suspicious human behaviors can be defined by the user, and in long distance imaging it may include bending the body during walking or crawling, in contrast to regular walking for instance. State-of-the-art methods using convolutional neural networks (CNNs) dealt in general with “clean” signals, in which the object of interest is relatively close to the camera, and therefore fairly clear and easily distinguished from the surrounding environment. This makes it easier to capture detailed information regarding the object and its action. However, in relatively long distance imaging (few kilometers and above) additional difficulties occur which affect the performances of these tasks, since the captured videos are likely to be degraded by the atmospheric path that cause blur and spatiotemporal-varying distortions. Both of these degradation types may reduce the ability for action recognition. These effects become more significant for longer imaging distances and smaller sizes of the objects of interest in the image. The images of objects in imaging through long distance are usually relatively small, and hence, the range of actions that can be resolved is more limited, particularly under strong atmospheric effects. In this study, we perform action localization by first applying optical flow unique processing, and also using a variant of SSD (Single Shot MultiBox Detector) to regress and classify detection boxes in each video frame potentially containing an action of interest.
Innovations in Statistical Analysis and Genetic Algorithms

We proposed new techniques in statistics and in genetic algorithms. We used biological principles to innovate new approaches in genetic algorithms that yielded improved solutions to optimization problems, finding improved best known results for multiple instances. These mimicked patterns in the natural world, including female choice of mates, as well as alpha male social structures. We also highlight inconsistencies between biological processes and their genetic algorithm counter-parts. Two other innovations in methodology and statistics include our development of the sequential correlated Bonferroni test which controls for false positive results that occur from running multiple statistical tests. It incorporates the correlation between significant p-values, thereby resulting in a less conservative filter. We also developed the statistical underpinnings of a new approach for estimating transition points (in species or any other defined population) between stages. Transition from one stage to the next is a natural part of life, yet it can be difficult to estimate, particularly in cases where only a few transitions occur in every measurement period. We confirmed the validity and applicability of this new approach demonstrating low standard errors and robust output.
Research on FAGP Alarm System Based on Multi-Temperature Sensor

In this paper, the temperature sensor alarm system based on FPGA is analyzed and studied in detail. Firstly, the hardware circuits and functions of the modules such as FPGA chip, power supply circuit, display module and configuration parameters are analyzed, and the software of the temperature sensor alarm system based on the chip of FPGA is compiled. Then, the FGPA chip is selected to realize the fuzzy controller, which reduces the programming difficulty and shortens the design cycle. Finally, a temperature sensor alarm system is developed, which can realize real-time display and alarm function of building in sensitive areas. The experimental results show that the combination of the FPGA chip and the intelligent temperature sensor DS18B20 enable the temperature alarm system to be intelligent, convenient, extensible with low cost. At the same time, the speed of real-time system control is accelerated, the implementation is simple and the simulation effect is excellent.
Deep learning for behaviour recognition in surveillance applications

Machine learning has recently made great progress in object classification, and especially object classification in images. In some cases, machine learning has even shown better ability than the human. There is a great potential to partially, or fully, automate sensor data analysis in surveillance systems. The automation could greatly facilitate the human operator’s work in finding critical information. After the success of object classification in images, the next step is to try to achieve similar progress in behaviour recognition using similar approaches. The paper includes a brief state-of-the-art for automatic behaviour recognition, and especially behaviours related to surveillance. The focus is on approaches based on deep learning. The paper presents an experiment with a specific deep learning algorithm, namely the long short-term memory (LSTM). For behaviours in surveillance applications, actual training data are rare. One approach for overcoming this is to use a deep learning algorithm that is pre-trained for a nearby application, and then transferred to the current application. Another is to expand the amount of training data using simulations. A difficulty with simulations is to create data that have similar characteristics as real sensor data, that is, include relevant noise and uncertainties. The paper briefly discusses the case of simulated data for the development of a deep learning method.
A Global Extraction Method of High Repeatability on Discretized Scale-Space Representations

This paper presents a novel method to extract local features, which instead of calculating local extrema computes global maxima in a discretized scale-space representation. To avoid interpolating scales on few data points and to achieve perfect rotation invariance, two essential techniques, increasing the width of kernels in pixel and utilizing disk-shaped convolution templates, are adopted in this method. Since the size of a convolution template is finite and finite templates can introduce computational error into convolution, we sufficiently discuss this problem and work out an upper bound of the computational error. The upper bound is utilized in the method to ensure that all features obtained are computed under a given tolerance. Besides, the technique of relative threshold to determine features is adopted to reinforce the robustness for the scene of changing illumination. Simulations show that this new method attains high performance of repeatability in various situations including scale change, rotation, blur, JPEG compression, illumination change, and even viewpoint change.
A Study on Internet of Things with Blockchain Technology

With the beginning of Industry 4.0 revolution smart homes, smart cities and smart automation environment, the Internet of Things arose as an impactful technology. Due to this technology millions of devices will be connected to one another and generates massive digital data exchange mechanism. However, the devices used in this are vulnerable to hacks. In this paper we present a study of IoT as a technology. Moreover, this paper even discusses about Blockchain which is the technology used for generating Bitcoins, to resolve the existing security challenges in IoT.
Development and Application of Integrated English Online Teaching System Based on Web

To improve the development and deployment efficiency of the system, a new online English intelligent learning system is designed by using the combination of Java and AI language Prolog. The system is established with Struts+Spring+Hibernate(SSH) lightweight and Java EE framework. The degree of coincidence among the modules in the system is greatly reduced, which facilitates the expansion of future functions. Firstly, the main business processes are modeled and analyzed. Then the detailed architecture of the system is proposed, which includes the design of administrator management module, teacher teaching activity module, student learning activity module and main operation interface. The specific functional modules of the major business roles are designed separately, and the related database tables of each functional module are designed. Finally, the interface design and test of the developed system are performed, which proves that the scheme basically achieves the design objectives, and the function modules have good performance, also meeting the requirement of College English learning and management.
Fuzzy Hot Spot Identification for Big Data: An Initial Approach

Hot spot identification problems are present across a wide range of areas, such as transportation, health care and energy. Hot spots are locations where a certain type of event occurs with high frequency. A recent big data approach is capable of identifying hot spots in a dynamic manner, through the processing of large volumes of sensor data arriving as a stream. However, the method may produce imprecise results due to its crisp interpretation of hot spot locations and reliance on a fixed hot spot radius value. This paper presents an initial approach to addressing this shortcoming through incorporating the concept of fuzzy hot spots into the process. Experimental results on large real-world transportation datasets demonstrate the improved way in which this approach handles uncertainty in the definition of hot spots, and highlight promising future research areas for further application of fuzzy systems to the hot spot identification problem.
A hypocrite called Rameau?: Thoughts on the similarities and differences in the treatment of enharmonic in Rameau's theories on harmony vs. his practice in his harpsichord composition

The name of Jean-Philippe Rameau (1683-1764) inevitably appears in the historical considerations of musical theorists. Rameau is considered the father of modern harmony, as well as one of the pioneers of the modern understanding of tonality. At the same time, he was almost certainly the most prominent figure in French music of the first half of the 18th century. Along with the Couperin family, as a remarkably gifted composer for keyboard instruments, he is seen as the progenitor of the long tradition of French keyboard music, which can be traced all the way to our own time. Such an interesting and multi-talented figure provides a very welcome opportunity for musicologists to assess the relationship between the theoretical and the creative; the empirical and the evanescent; and perhaps even the conscious and the subconscious. Rameau the theorist obviously underwent a long evolution; however, this path was not always progressive and straightforward, but seems to have meandered and vacillated as he abandoned some of his ideas as the result of personal insecurity and (possibly) the harsh criticisms that mostly came from Jean-Jacques Rousseau (1712-1778). This led to obvious inconsistencies between the practice of Rameau the composer and the ideas of Rameau the theorist. This paper offers a chronological comparison between Rameau's postulates about tonality theories and its subsystems (diatonic, chromatic and - above all - enharmony) and his contemporaneous compositions for harpsichord, and will attempt to infer if and how much the composer adhered to his own postulates.
A Test Cases Generation Technique Based on an Adversarial Samples Generation Algorithm for Image Classification Deep Neural Networks

With widely applied in various fields, deep learning (DL) is becoming the key driving force in industry. Although it has achieved great success in artificial intelligence tasks, similar to traditional software, it has defects that, once it failed, unpredictable accidents and losses would be caused. In this paper, we propose a test cases generation technique based on an adversarial samples generation algorithm for image classification deep neural networks (DNNs), which can generate a large number of good test cases for the testing of DNNs, especially in case that test cases are insufficient. We briefly introduce our method, and implement the framework. We conduct experiments on some classic DNN models and datasets. We further evaluate the test set by using a coverage metric based on states of the DNN.
ANN-Based Model for Simple Grammatical Cases Teaching in Spanish Language

The Multilayer Perceptron (MLP) is one of the most powerful and popular network architectures, due to its ability to solve a large number of problems successfully. The objective of this work is to develop a model based on neural networks to solve simple grammar cases in the Spanish language, which in turn, allows the teaching of Artificial Neural Networks (ANN) in students. The presented model consists of 12 stages that allow to simulate simple grammatical cases. An illustrative example is presented with two programs that allow to simulate the grammatical case “Uppercase Identification” and “Infinitive Verbs” using MLP; each had a training scenario and a learning verification scenario.
Towards a model to optimized collect of vital signs through adaptive strategies

Wearable devices have emerged from the advancement of communication technology and miniaturization of electronic components. These devices monitor periodically the user's vital signs and generally have low battery autonomy. This article proposes ODIN, a model to optimized collect of vital signs based on adaptive strategies. The comparison with related works indicates that ODIN is the only proposal that presents an adaptive collect of vital signs, which enables the optimized generation of a historical contexts. A prototype was implemented and applied in an evaluation. The evaluation consists in a simulation with requests control in order to optimize the parameters of the collect allowed a 214% increase in battery life in adaptative scenario compared to a collect without adaptivity.
Hadoop as a Service On premise Vs over Cloud

Data processing and analysis has become difficult due the increase in data availability. Traditional methods are inefficient in handling it. So, the need is to analyze and process data in a parallel way. Haddoop solved the problem to some extent but it is difficult to be deployed and require upgraded hardware requirements. This paper proposed a framework for providing support of Hadoop as Service. An algorithm is implemented for data identification and lastl the proposed framework is compared with current system on certain parameters.
A Novel Self Organizing Feature Map for Uncertain Data

In real-world applications, sometimes there are uncertainties in the data set whether from the collection process or from the natural language. There are not many algorithms that can deal with this kind of data set. Therefore, in this paper, we develop a linguistic self-organizing feature map (LSOFM) that works with vectors of fuzzy numbers. The algorithm is an extension of the regular self-organizing feature map (SOFM). We found that the results from the LSOFM are similar to that from the SOFM. The results from the LSOFM can provide information that contains all the uncertainties from the input while the SOFM cannot.
Visual time-sensitive SAR target detection technology based on human brain mapping

To detect targets from synthetic aperture radar (SAR) images, this study describes a visual time-sensitive SAR target detection technology based on human brain mapping. Expert brain central responding messages are introduced into the automatic analysis method of SAR images. A fusing detection model between brain central responding messages and features of SAR targets is built by expert on-line brain–machine combined modelling. The experimental results show that the technology paper proposed succeeds in automatically detecting and recognising targets of SAR images in actual combat environment, and owns better detection results and higher detection efficiency than DARPA's work. In conclusion, this study proposes an ideal man–machine fusion technology which is worthy of high research value and broad application prospect.
ARCHIMUSIC3D: Multimodal Playful Transformations between Music and Refined Urban Architectural Design

The commonly used 3D architectural design tools for urban environments fail to capture aspects of an urban design which are aesthetic as well as functional. This paper describes an innovative multimodal user interface through which an urban designer can work on the music transcription of a specific urban environment applying music compositional rules and filters in order to identify discordant entities, highlight imbalanced parts and make design corrections. The proposed platform offers sonification of an Urban Virtual Environment (UVE), simulating a real-world cityscape, offering visual interpretation and musically playful modification of its soundscape. The system presented offers: The ability to view and convert an urban street to music (ready to play) based on a specific grammar of converting architectural elements to musical elements; secondly, the ability to transform this music in order to `harmonize' it based on musical rules as if composing and playing music; and finally, the prospect of converting back the aesthetically and harmonically “corrected” musical piece to a newly refined street or urban design, visualized in 3D. The presented platform comprises of three scenes, which compile the three main parts of the system's multimodal interface; e.g., the 3D scene, the Digital Audio Workstation (DAW) scene and the TouchOSC mobile controller. The purpose of this paper is to assist architects and urban designers in 1) identifying urban dissonances, 2) refining their design using musical rules and 3) interactively presenting the output both visually and acoustically.
Autocorrelation Values of Generalized Cyclotomic Sequences with Period pn+1

Recently Edemskiy proposed a method for computing the linear complexity of generalized cyclotomic binary sequences of period p n + 1 , where p = d R + 1 is an odd prime, d , R are two non-negative integers, and n &gt; 0 is a positive integer. In this paper we determine the exact values of autocorrelation of these sequences of period p n + 1 ( n ≥ 0 ) with special subsets. The method is based on certain identities involving character sums. Our results on the autocorrelation values include those of Legendre sequences, prime-square sequences, and prime cube sequences.
On Quality Evaluation System and Methods of Pre-School Education in Engineering Colleges

As an important part of quality-oriented education, pre-school education in engineering colleges is playing an increasingly prominent role in modern education. However, the lack of evaluation system and methods seriously restricts the development of pre-school education. This paper establishes a comprehensive evaluation index system on the pre-school education quality in engineering colleges. Then, based on the analysis of various factors that affect the quality of pre-school education in engineering colleges, an evaluation model of education quality is constructed theoretically by combining the analytic hierarchy process (AHP) with the fuzzy comprehensive evaluation. This model can not only quickly evaluate the pre-school education quality in engineering colleges, but also find out the influencing factors. The result shows that this method is scientific and practical, and can play a role in the supervision of pre-school education quality in engineering colleges.
Semi-supervised adversarial training of a lightweight neural network for visual recognition

Deep learning is a widely utilized approach specifically for computer vision applications. Visual recognition is one of the applications utilizing deep learning. Several challenges limit the performance of visual recognition methods. One of the most important challenges is the insufficient number of labeled data in the datasets. To overcome this challenge, the recent studies propose sophisticated methods which require high computational resources, which may create another problem. That is, the implementation of such algorithms on mobile devices is quite challenging. Especially, these issues are encountered in surveillance systems that utilize the drones and/or CC-TVs. To solve these problems and obtain high accuracy, the network should be able to extract both representative and discriminative features from such a small amount of data. In this paper, we propose a generative adversarial semi-supervised training method for visual recognition. Experiments are performed to evaluate a lightweight deep convolutional neural network as a classifier network that is trained by the proposed method and a conditional/unconditional generator networks that are examined in adversarial training.
Adding temporal semantic to textual media objects with eye tracking technology

In general, continuous media objects can use their inner temporal information to create relationship with another media objects. However, the same is not possible for discrete media objects, such as texts. This work proposes an architecture to insert time into text semantics. The approach used in this work is based on the use of an eye tracker device to capture the user's reading position during the presentation of a text media object in a reader device. Thus we are synchronize media objects in a multimedia application according to the user reading pace. This paper presents an general architecture for the proposed approach and an implementation of that architecture focused on children books reading.