Reviewerly/exHarmony huggingface dataset:
Study of the Relevance of Polynomial Order in Takagi-Sugeno Fuzzy Inference Systems Applied in Diagnosis Problems

Fuzzy Logic has been implemented successfully for different kind of problems. However, there is an opportunity for these methods to be improved in the realm of classifications problems. The present paper is focused in a specific application of classification problems, the diagnosis systems, this problem consists in training an intelligent system to learn the relationship between symptoms and diagnosis, this kind of problems are usually based in powerful non-linear methods for example Modular Neural-Networks or complex hybrids models, however, in this paper are applied the Type-1 Takagi Sugeno Fuzzy Systems (TSK) but analyzing the improvement of their performance by increasing the order of the Sugeno polynomial. The conventional Takagi-Sugeno Fuzzy Systems are based in the aggregation of first-order polynomial but it is interesting to observe the effect of increase the order of this polynomial, the TSK Fuzzy Diagnosis Systems are evaluated by their accuracy obtained in ten benchmark dataset of the UCI Dataset Repository, for different kind of diseases and different difficult levels.
Wheelchair Controlled by Eye Movement Using Raspberry Pi for ALS Patients

The mobility of people who have suffered a degenerative disease or an accident is partially or totally reduced, which limits their locomotive independence. Therefore, this paper presents a proposal that facilitates the mobility of people suffering from moderate levels of amyotrophic lateral sclerosis (ALS). A control system has been adapted to an electric wheelchair to provide it with a certain degree of intelligence. The acquisition of multimedia data is done with a small camera adapted to a glasses frame that the person must use. For eye patterns tracking, a recognition system is performed using the LabVIEW software environment. The control system that regulates the movement of the wheelchair was designed on the Raspberry Pi embedded board as a low-cost proposal. Experimental tests and user surveys validate the correct operation of this device.
A Hardware Inference Accelerator for Temporal Convolutional Networks

Traditionally, Recurrent Neural Networks (RNNs) are used for time-series prediction. However, recent results have shown that Temporal Convolutional Networks (TCNs) outperform RNNs in terms of accuracy and training time. By using dilated convolutions TCNs are able to capture long term temporal dependencies from time series. The use of existing Convolutional Neural Networks (CNNs) inference accelerators for the computation of dilated convolutions significantly decreases the throughput and causes computation overhead. In order to take into account the one-dimensionality of the convolutions and presence of dilation, this paper proposes a dedicated hardware inference accelerator for TCNs. We use this accelerator to run an adaptation of WaveNet for anomaly detection in an ECG time series dataset. Our results show that it can achieve 6.3 · 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−4</sup> DRAM access/MAC, 0.043 on-chip SRAM access/MAC and 8 ms/inference. This lower number of on-chip and off-chip memory accesses significantly decreases the overall power consumption and increases the throughput.
Research on Image Super-resolution Reconstruction Method Based on Improved SRCNN

Abstract Existing image convolutional neural network based on image super-resolution algorithm has a problem of image texture blurred to improve. In this paper, we first analyze the factors of reconstructed image quality, then use the parametric rectified linear unit (PRLU) to solve the problem of over-compression in the original network. And combining the existing network models and image processing algorithms adjusts the parameters of the neural network, The network model of 9-1-5 in Super-Resolution Convolutional Neural Network (SRCNN) is improved to a three-layer model of 5-3-5. The number of convolution cores in the first and second layers is adjusted to 32, and the magnification is increased by four times from three times. Finally, simulation experiments are carried out on DIV2K dataset in NTIRE2017. Experimental results show that the proposed algorithm achieves good super-resolution results, and the subjective visual effect and objective evaluation indices are both improved obviously. The image super-resolution technology has great application demand in the fields of video surveillance, virtual scene restoration and so on. There is still a great research prospect for the image super-resolution technology.
Analytical Study and Empirical Validations on the Impact of Scale Factor Parameter of Differential Evolution Algorithm

Differential Evolution (DE) is a popular optimization algorithm in the repository of Evolutionary Algorithm (EAs). The DE algorithm is known for its simple algorithmic structure, which has minimal number (only three) of control parameters. A propitious avenue for enhancement of DE's performance is making it a self-adaptive algorithm. There exist many algorithms for self-adapting one or more of DE parameters. The self-adaptiveness of any parameter needs critical analysis on the impact of that parameter. This paper analyzes and presents the impact of the parameter - mutation scale factor (F) of DE. Including empirical evidences for understanding the effect of F on the nature of convergence of DE at solving a problem is the novelty of this paper. The experiment includes implementing a set of benchmark functions, with diversified features, using different variants of DE, in order to critically analyze the role of F.
Smoother Soft-NMS for Overlapping Object Detection in X-Ray Images

As a contactless security technology, X-ray security inspection machine is widely used in the detection of dangerous object in all kinds of densely populated public places to ensure the safety. Unlike a natural image, various objects overlapping with each other can be observed in an X-ray image for its perspectivity. It brings us a challenge that the traditional NMS (Non-maximum suppression) algorithm will suppress the less significant objects. In this paper, we propose a Smoother Soft NMS based on the difference in aspect ratios and areas of different object bounding boxes to improve the accuracy of overlapping object detection. We also propose a special data augmentation method to simulate the generation of complex samples of overlapping objects. On our dataset, we boost the mean Average Precision of ResNet-101 FPN from 89.44% to 96.67% and Cascade R-CNN from 96.43% to 97.21%. Detector trained by Smoother Soft NMS has a significant improvement in overlapping cases.
Design of a vertical search engine for synchrotron data: a big data approach using Hadoop ecosystem

A synchrotron as an experimental physics facility can provide the opportunity of a multi-disciplinary research and collaboration between scientists in various fields of study such as physics, chemistry, etc. During the construction and operation of such facility valuable data regarding the design of the facility, instruments and conducted experiments are published and stored. It takes researchers a long time going through different results from generalized search engines to find their needed scientific information so that the design of a domain specific search engine can help researchers to find their desired information with greater precision. It also provides the opportunity to use the crawled data to create a knowledgebase and also to generate different datasets required by the researchers. There have been several other vertical search engines that are designed for scientific data search such as medical information. In this paper we propose the design of such search engine on top of the Apache Hadoop framework. Usage of Hadoop ecosystem provides the necessary features such as scalability, fault tolerance and availability. It also abstracts the complexities of search engine design by using different open source tools as building blocks, among them Apache Nutch for the crawling block and Apache Solr for indexing and query processing. Our primary results obtained by implementing the proposed method in single node mode, the index of over a hundred thousand pages was created with the average fetch interval of 30 days having 28 segments and approximately 570 MB size. The performance factors such as the usage of available bandwidth and system load were logged using Linux’s sysstat package.
An Improved Fuzzy Clustering Segmentation Algorithm Based on Animal Behavior Global Optimization

The bio-inspired optimization algorithms play vital role in many research domains and this work analyzes animal behavior optimization algorithm. Medical image segmentation helps the physicians for disease diagnosis and treatment planning. This work incorporates ABO algorithm for cluster centroid selection in Fuzzy C-means clustering segmentation algorithm. The Animal Behavior Optimization (ABO) algorithm was developed based on the group behavior and was validated on 13 benchmark functions. The dominant nature of an animal species decides the fitness function value and each solution in problem space depicts the animal position. The ABO algorithm was coupled with the classical FCM for the analysis of region of interest in abdomen CT and brain MR datasets. The results were found to be efficient when compared with the FCM coupled with artificial bee colony (ABC), firefly, and cuckoo optimization algorithms. The promising results generated by ABC makes it an efficient one for real-world problems.
A Novel Approach to Modeling Enterprise Services Leveraging Object Cloning and Multilevel Classification

Object-oriented modeling is concerned with capturing common properties of objects. The dominant thinking in this realm is to classify objects that share certain properties into what is called a class, which in turn enables us to instantiate additional objects. Deep modeling takes a step further by introducing the notion of clabject that might be instantiated multiple times until its instantiation potency runs out. This initiative has gained a lot of momentum of late, primarily due to the inadequacy of the classical mechanics of two-level object instantiation. There exists a less familiar way of reasoning in object-orientation that takes its root from the prototype theory. We believe that they co-exist as two sides of the same coin. Unfortunately, prototype-based modeling still stays on the sidelines in the mainstream of conceptual modeling and related areas (e.g., enterprise modeling). In this paper, we argue that the two methods actually complement each other. We propose a hybrid modeling suite that allows for both instantiation and cloning in enterprise modeling. We formally state that a clabject not only features the so-called potency (i.e., for how many levels this clabject might further be classified) but also carries the notion of characteristics (i.e., the extent to which this clabject resembles those being represented). We demonstrate our novel ways of modeling for capturing business processes in a service-oriented enterprise architecture.
Exploring Convolutional Neural Network for Multi-spectral Face Recognition

Multi-spectral face recognition has procured noteworthy consideration over a most recent times because of its potential capacity to acquire spatial and spectral information over the electromagnetic range, which cannot be obtained using traditional visible imaging techniques. With the advances in deep learning, Convolutional Neural Network (CNN) based approach has become an essential method in the field of face recognition. In this work, we present two face recognition techniques using face image at nine unique spectra ranging from Visible (VIS) to Near-Infra-Red (NIR) range of the electromagnetic spectrum. This paper is based on the application of using CNN as feature extractor along with Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) as a classifier on the images of nine different spectra ranging from 530 nm–1000 nm. The obtained performance evaluation results show highest $$Rank-1$$ recognition rate of 84.52% using CNN-KNN, demonstrating the significance of using CNN extracted features for improved accuracy.
Improving Expertise in Local Law Enforcement: Utilizing Virtual Environments to Assess Officer Performance and Standardize Training Procedures

Training for non-federal law enforcement agencies is subject to little regulation—training academies and education requirements often vary between departments—resulting in new officers with varying abilities and on-job proficiencies. The role of expertise can be evaluated in law enforcement and assessed to determine best practices for ideal on-job performance, especially in incidents requiring officers to make potentially lethal, rapid critical decisions (e.g., a use-of-force scenario). Training in a virtual environment has successfully been utilized to maximize management decision-making performance and accelerate the training process. A supplementation of cognitive skills training to existing law enforcement training procedures is proposed along with a recommendation on utilizing virtual environments to enhance expert training and develop a safer community environment.
Multiple Norms and Boundary Constraint Enforced Image Deblurring via Efficient MCMC Algorithm

Image non-blind deblurring is still an ill-posed problem. Uncertainty in solutions occurs when singular vectors of forward model matrix spanning the noise subspace have rather small singular values. This letter proposes a new image deblurring algorithm, called MNBC-Gibbs (multiple norms and boundary constraint enforced Gibbs sampling). To be more specific, the quadratic and sparseness-inducing norms are combined to construct regularization term, and the objective function is gradually minimized without requirement of regularization parameter choice. In particular, we propose an efficient Markov chain Monte Carlo (MCMC) method equipped with closed-form solution, artifacts processing and non-negative constraint to approximate the posterior distribution and estimate uncertainty for the unknown. Satisfactory deblurring results with sharp edges can be generated while maintaining smoothness without raising extra noise. The quantitative evaluations on different blur kernels and comparison with state-of-the-art image deblurring methods demonstrate the superiority of the proposed method. In addition, we show that our method can effectively deal with real blurry images.
A Study on Various Database Models: Relational, Graph, and Hybrid Databases

Relational database is a popular database for storing various types of information. But due to the ever-increasing growth of data, it becomes hard to maintain and process the database. So, the graph model is becoming more and more popular since it can store and handle big data more efficiently compared to relational database. But both relational database and graph database have their own advantages and disadvantages. To overcome their limitations, they are combined to make a hybrid model. This paper discusses relational database, graph database, their advantages, their applications and also talks about hybrid model.
A "Labyrinth" toy for 6-7 Years Old Children as a Way of Intellectual Development of a Child

The article considers the problem of a child’s quick loss of interest in a toy and its solution by creating an intellectual toy based on the experience of scientific research in the field of labyrinthology. Passing puzzles in the form of a maze have a beneficial effect on the child’s intellect, develops spatial thinking, memory and teaches the child how to find solutions to problems at a subconscious level, also disciplines and develops concentration. For a hull development the art design method was applied. A prototype of the designed toy is a cryptex - a portable storage used to hide secret messages invented by Leonardo Da Vinci and implemented by Justin Kirk Nevinson. The labyrinth system is based on the fractal tree method. The fractal labyrinth has a hierarchical (grid) structure, which creates more chances for a quick passage of the labyrinth without obstacles, but since the goal of the toy is to develop the intelligence of the child, it was decided to complicate the structure of the labyrinth by adding dead-end branches and additional tracks. Entering a dead-end branch allows the child to find and analyze the optimal moves in the game.
System for Measuring Three-Dimensional Micro-Structure Based on Phase Shifting Fringe Projection

We have developed a telecentric three dimensional measurement system based on fringe projection techniques to measure the absolute phase corresponding to the three dimensional shape of micro structure profile, like micro bumps, pillars and solder bumps etc. Fringe projecting module with adjustable fringe density and projecting angles are designed to flexibly and accurately introduce the phase shifting algorithm. In tradition, all of the fringe projection techniques are used by two ways, which one is the tri-linear camera obtained three of fringe images by the separate line camera, and the other one is the three fringes image taken and processed through area camera one by one to measure three dimensional profiles. The advantage of the former can provide for high speed scanning phase measuring of an object, but it can't measure mirror like sample, like wafer bumps, because of the hardware limited. And the latter can measure mirror like sample but the technology need to be changed the phase module of the light source lead to the measurement speed to be slow. In this paper, we use the area camera to acquire three of fringe images with 120° phase shifting simultaneously by the pixels in each rows without changing the phase module of the light source are sufficient to reconstruct a 3D model. By using this technique, we can further improve the measuring speed of the structure light system, and the system will be more resistant to fast moving objects. The measurement accuracy for the height of the micro bumps range of 5-2000 μm is verified as less than 1 μm.
Incorporation of Neighborhood Concept in Enhancing SOM Based Multi-label Classification

The self-organizing map (SOM), which is a type of neural network, helps in the exploratory phase of data mining by projecting the input data into a lower-dimensional map consisting of a grid of neurons. In recent years, SOM has also been applied for classification of data points. The prominent utility of SOM based classification is evident from the use of no labeled data during training. In this paper, a self-organizing map based algorithm is proposed to solve the multi-label classification problem, named as ML-SOM. SOM follows an unsupervised training process to learn the topological structure of the training points. At testing-phase, a testing instance can be mapped to a specific neuron in the network and it's label can be determined using the training instances mapped to that specific neuron and nearby neurons. Thus in this paper, we have considered the neighborhood information of SOM to determine the label vector of testing instances. Experiments were performed on five multi-labeled datasets and performance of the proposed system is compared with various state-of-the-art methods showing competitive performance. Results are also validated using statistical significance t-test.
Technical Fixes for Legal Uncertainty in the 1980s Software Cracking Scene

During the 1980s, many software developers and enthusiasts reacted to software's unclear status within the U.S. copyright law by pursuing technical, rather than legislative fixes. This paper applies forensic methods to recover records of conflict between commercial software developers and the enthusiasts who sought to “crack” commercial programs’ copy protection.
Alteration Hydrothermal Stage Division and its Application in Geological Prospecting Using CASI-SASI Airborne Hyperspectral Data: Taken the Baixianishan Area in Liuyuan Town of Gansu Province as an Example

Abstract The division of the hydrothermal period based on alteration minerals extracted from airborne hyperspectral data has been rarely researched. This work processed CASI-SASI airborne hyperspectral data, and optimized reference spectra from image endmembers based on the expert knowledge in the Baixianishan area. The mixed tuned matched filtering method was utilized to map alteration minerals, and the distribution of limonite, short-wavelength sericite, middle-wavelength sericite and long-wavelength sericite was obtained. Distribution patterns of limonite and three sericite subclasses were analyzed, and three hydrothermal stages were divided. It is considered that the second hydrothermal activity is closely related to the gold deposit. It is proposed that prospecting factors of the Baixianishan gold deposit are NEE-trending fault structure, the vein-like distribution of limonite and short-wavelength sericite. Based on the discuss above, a favorable prospecting section is found in the south of the Xiaobaishan area. It is indicated that the CASI-SASI airborne hyperspectral data can quickly and accurately provide more effective remote sensing criteria at the regional scale for both basic geological survey and mineral resources exploration, which has a wide application prospect in the geological field.
Prediction of wind disaster using kriging spatial interpolation and internet of things

Abstract The Indonesian Disaster Management Agency (BNPB, Badan Penanggulangan Bencana) estimates that wind dissaster are increasing in some parts of Indonesia. The occurrence of wind dissaster can not be predicted, when and where they occur, because the cause of natural factors, namely differences in pressure. Disasters that occur in Indonesia is caused by human factors, namely changes in land use and a way of life that does not pay attention to environmental conservation. Public preparedness in the face of the possibility of disaster increases, when disaster occurs they immediately evacuate themselves to a safer place. Therefore it is necessary socialization and understanding to the community against the possibility of disaster, another thing to think about is preparing early warning facilities and infrastructure. Yogyakarta is one of the areas that must be wary of the threat of a tornado, especially when there will be seasonal changes. The damage caused by the wind disaster is increasing, indicating that wind speed plays an important role in relation to the impact of damage caused. In this research methodology of system development that will be used is System Development Life Cycle (SDLC). For estimation of direction and wind speed will be used Kriging Spatial Interpolation Method. The IoT technology used consists of an anemometer and a microcontroller device and GSM (Global System for Mobile Communications) to obtain or capture real-time data of wind speed and direction sent over the internet to the server.
It was found that…: Introductory it Patterns by Native and Non-Native Authors

Introductory it pattern, as in it was found that, is of significance in academic writing but the use of introductory it might be challenging especially for native-and non-native students and non-native academic writers.However, few studies have been conducted to compare the use of introductory it pattern by native and non-native scholars.This study investigates the frequencies, variability and functions of the introductory it patterns in the research articles of native and non-native academic professionals.The study uses data from the MCRA-L1 and MCRA-L2 corpora of MCRA (Multilingual Corpus of Research Articles) corpus.The size of each corpus was one million words.In order to extract introductory it patterns, four-word lexical bundles were searched for through WordSmith Tools with a cut-off point of 5 times per million words for 4-, 5-and 6-word bundles.The results revealed that there were 38 different introductory it patterns in the MCRA-L1 and 66 in the MCRA-L2, and the frequency and percentages showed the tendency of the Turkish authors to overuse the introductory it-structures in their research articles.