Reviewerly/exHarmony huggingface dataset:
Troika GAN vs Decoupled GAN: An Investigation on the Impact of Subnetwork Weight Sharing for Data Augmentation

Notable advancements in the field of computer vision have transpired through the application of Generative Adversarial Networks (GANs). A new GAN variant, the Troika GAN (T-GAN), was recently proposed for data augmentation and was shown to be superior to the Coupled GAN (CoGAN) and the classic techniques of rotation and affine transformation. This paper describes our further investigation on T-GAN, specifically the impact of its subnetworks weight sharing. We decoupled the weight-sharing subnetworks of T-GAN to form three independent GANs, which we refer to collectively as the Decoupled GAN and whose weights are trained separately. We then used T-GAN and the Decoupled GAN to augment a set of words with limited instances from the IAM Handwriting Database. The resulting augmented datasets were applied to train the three types of Artificial Neural Network (ANN) classifiers: Vanilla ANN, Deep ANN, and Convolutional Neural Network (CNN). Results showed that the best accuracies from each of the 3 classifier types were obtained when these were trained with datasets augmented by a T-GAN. For example, the CNN classifier registered 89.76% as its best performance using T-GAN while recording only 87.47% accuracy from utilizing Decoupled GAN. A paired t-test between the 10-fold cross-validation results of these yielded a statistically significant p-value of 0.0075 in favor of the T-GAN augmentation. This clearly indicates that the sharing of weights is a vital factor in the generation of better synthetic data. With its significant impact on improving handwriting classification networks, T-GAN can be an ideal data augmentation approach to build robust systems where there is a scarcity of training dataset instances.
Development of a robot partner system to support the elderly based on sensor data

In recent years, the number of elderlies living alone has been increasing year by year due to the declining birthrate and aging problem, which has become a major problem especially in developed countries. There is a great demand for health support systems and monitoring systems using sensors to support the elderly in such situations. In this paper, we propose a robot partner system that can communicate based on sensor data in such a situation.
An Analysis of the Third Year Students’ Difficulties in Writing Argumentative Essay at English Department of FKIP Bung Hatta University

This research aimed to find out students' difficulties in writing an argumentative essay. The research used descriptive method; the population was the third year students of the English department, FKIP Bung Hatta University Padang. They were divided into four classes, A, B, C and D. Sample of this research was class B, and the size of the sample was 35 students.  The writer used the cluster random sampling technique to get a representative sample. The research result showed that that is 21(60%) students have difficulties in writing an argumentative essay and the result of the writing test. Specifically, from data analysis of writing task showed that 21(60%) students had difficulties in organization of idea, 22(62.85%) students in grammar, 20 (57.14%) students vocabulary, 21 (60%) students in using transition signal, and 21(60%) students in mechanics.
Deep Learning Approaches to Image Classification

Face recognition methodology (human face) have made many improvements in the emerging world have made many face identification is a technic of detecting a face through a technicality. It likens the image with a dataset of known images to encounter a match. Face identification authenticates personal identity, as well as raising secrecy challenges. Artificial neural networks (ANN) are used extensively in facial processing of images. Back-propagation neural networks (BPNNs) are mainly used for classification of facial images and also it achieves high accuracy. Nevertheless, there is a variation due to different neural designs and implementations. Accordingly, investigators usually need to do several observational trials before they can finalize the neural network design, but this is a slow process which considerably mitigates the efficiency of using the above-said method (BPNN) and the final pattern still not be quintessential. Despite the precision of image identification system is a biometric mechanism is lower than iris or fingerprint identification, it is commonly accepted its noncontact procedures. Newly, it had also been made widespread as a commercial and a business tool. As a result, there are certain guidelines for a valid design and operationalization of BPNNs. This work endeavours to find and abbreviate certain guidelines recommended by various researchers through specialized literature and observations. To contribute academicians with history and framework, somewhat ANN basics are also initiated.
Contrast improvement of medical images using advanced fuzzy logic-based technique

Medical images like magnetic resonance imaging (MRI) images, hematoxylin and eosin (HE) stain images, and others play a vital role in the automatic medical diagnosis process. It hastens the process of the diagnosis as well as improving the accuracy over the manual process. But medical images often seem to suffer from low contrast problems, and an accurate result can be expected only from a good quality image. Hard-based techniques are applied for contrast improvement as they involve less complexity, but they are not able to bring the result to a satisfactory level. The main reason behind this is a high level of fuzziness involved in medical images. Because traditional techniques are not able to deal with this level of fuzziness, an advanced fuzzy logic–based technique is introduced in this chapter for improving the contrast of different medical images like HE stain, MRI, and so on. Also, as noise puts a very bad effect on the overall quality of medical images, before going for contrast improvement, in the proposed approach, noise removal using an improved median filter is implemented first. This imparts a very good impact on the final result of the enhancement process. The experimental analysis claims the efficiency of the proposed technique.
Information Security Using Multimedia Steganography

Nowadays, use of data is growing exponentially with the advancement cyber technology. Hence, the security of the data is one of the major areas of concern as a hacker has set to work to crack this data for their cause. Steganography implements the technique for hiding the confidential information into a cover file in order to maintain the security, which is difficult to extract for the third party. The proposed approach includes two different forms of multimedia steganography namely image and video. Video steganography is also an efficient technique for storing a large amount of data. We have used least significant bit (LSB)-based techniques for image and video-based steganography. The degree of steganography can hide a large capacity of data and security provided by it through algorithm used. The results show the effectiveness of our model.
Integrated architecture of sensor services, Internet of things and big data

At present, there are more and more applications of wireless sensors, this service can be used for Internet sensors, while cloud services can be used for data storage and analysis. In order to improve the usability of devices envisaged by the Internet, the potential of traditional WSN is limited by a relatively small number of deployments. The influence of heterogeneity and Limited (or private) is. The development environment and interface bring difficulties. The development environment and interface bring difficulties constraints. This paper presents an information system solution for the ubiquitous integration of wireless sensor networks and related services. It takes account of the data flow from sensor to service. The architecture provides a set of abstraction for different sensors and services. It is designed for implementation on resource constrained nodes and can be extended to server environment. This article introduces the implementation of the core architecture of C. It includes services on Linux and Contiki (using restricted application protocol (COAP)) and Linux HBase data storage service integrated.
Discovering informative features in large-scale landmark image collection

One of the key problems in image retrieval systems is the presence of irrelevant and noisy image content. Such content can cause significant confusion for the system. Therefore, there is a need to represent images with only informative features in order to improve the retrieval performance of the system or any subsequent process. In this article, we propose a method to identify the informative features in a large-scale image collection. We apply the frequent itemset mining (FIM) approach to extract visual features patterns from a list of images of the same object. Then, we generate feature pairs to measure the significance of each feature depending on the co-occurrence with its neighbouring features. In addition, we apply this feature selection technique to localise the landmark in the image. The performance of the proposed method is evaluated in terms of average precision (AP) on two benchmark data sets and found that it gives a comparable retrieval performance over the bag of visual words baseline system and the previous methods.
Framework for Mining Hybrid Automata from a Constrained Machine Learning Architecture

As learning enabled cyber physical systems become more prolific, modeling for the purposes of verification and validation (V&V) becomes a primary barrier. A key challenge is to model cyber physical systems at the appropriate level of abstraction while maintaining a clear and understandable linkage between the human designer and the system under design. Fuzzy logic is a framework to synthesize linguistic, natural language requirements into machine learning models. It has been shown that fuzzy logic is a suitable method to learn the behavior of a nonlinear system when a model is not present. The implementation of a constrained fuzzy inference system presented in this paper is a specific class of machine learning architecture that provides a mechanism to relate a system model to human specified requirements. It is not at all dissimilar to more popular neural network architectures. The goal of this paper is to introduce a framework for constrained learning that enables both fast approximation of real valued systems while simultaneously enabling the modeling and analysis of safety properties. This paper focuses on a foundational constrained intelligent learning framework. The framework describes an alternative and constrained machine learning architecture capable of producing highly efficient structures that are also amenable to verification. This paper will focus on the efficiency of the computational structure while future papers will demonstrate the verifiability.
A mixed integer programming formulation for Smashed Sums puzzle: Generating and solving problem instances

Playing mind games and puzzles has 2500 years of known history. Puzzles and games constitute a research domain that is attracting the interest of scientists from numerous disciplines such as artificial and computational intelligence, neural networks etc. All types of puzzles and games contain their own logic and mathematics. Able to know the science behind them and modelling the logic that a person uses to solve them would shed light to some decisional concepts. This is particularly true from the perspective of computational intelligence. In this paper a logic-based puzzle game called Smashed Sums is considered. The binary integer linear programming formulation is proposed to use in solving and generating the puzzles. Illustrative examples are given to show the validity of the formulation. Some experimental computations are conducted to analyze the puzzle and its complexity. And several open problems are concluded for the further researches.
Building of Directed Weighted Networks of Terms for Decision-Making Support During Information Operations Recognition

The study is devoted to automated processing of documents of a subject domain for formation of text corpus, construction of networks of terms and using of these networks for building of knowledge bases of decision support systems during information operations recognition. In this work, for building the undirected networks of terms the Horizontal Visibility Graph algorithm is used. A new approach for determining the directions and the weights of links in the network of terms that correspond to certain concepts of the considered subject domain is proposed. By its application, an ontological model of the subject domain related to the information operations recognition was built. A new approach for building the knowledge bases of decision support systems during the information operations recognition using the directed weighted networks of terms of subject domain is proposed. Using it for building of knowledge bases allows saving time and financial resources by reducing the use of expert information and make it possible to detect gaps in the knowledge bases of decision support systems.
Relevance Ranking for Web Search

Relevance ranking is a core problem of Information Retrieval which plays a fundamental role in various real world applications, such as search engines. Given a query and a set of candidate text documents, relevance ranking algorithms determine how relevant each text document is for the given query. This degree of relevance allows them to rank the text documents and perform actions such as returning the best matching documents for the query. As in other machine learning and computational intelligence disciplines, deep learning techniques have recently achieved state of the art results by successfully capturing relevance matching signals between query-textual document pairs. This paper focuses on the PositionAware Convolutional-Recurrent Relevance Matching approach. On a first phase, it reimplements the original work, reproduces the published results and performs a number of additional experiments that identify potential model limitations. On a second phase, it explores possible model improvements based on deep learning techniques such as soft self-attention and deep transfer learning. Experiments on the well-known TREC Web Track data show that it is possible to obtain small improvements over the original model and point to a number of limitations of the general approach due to the information bottlenecks involved.
Network Representation Learning Based on Topological Structure and Vertex Attributes

Network Representation Learning (NRL) is an essential task in the field of network data analysis, which tries to learn the distributed representation of each vertex in the network for downstream vector-based data mining tasks. NRL is helpful in solving the computationally expensive or intractable problems of large-scale network analysis. Most related NRL methods only focus on encoding the network topology information into vertex representation. However, vertices may contain rich attributes that directly impact the network formation and measure the attribute-level similarity between vertices. Additionally, encoding the vertex attributes information into the representation vector may improve the performance of the representation. This paper proposes a general NRL framework TAFNE that can effectively retain both network topology and vertex attributes information. For complex types of vertex attributes, we design two different information fusion methods that take both training efficiency and generality into account. The proposed TAFNE framework is extensively evaluated through various data analysis tasks, including clustering, visualization and node classification, and achieves superior performance compared with baseline methods.
Dynamic TSK Systems Supported by Fuzzy Rule Interpolation: An Initial Investigation

Takagi-Sugeno-Kang (TSK) Systems form one type of conventional fuzzy rule inference system, providing an effective approach for performing prediction and regression tasks. In a real-world application, the inputs are usually varying against time, thereby requiring dynamically maintaining the rule base in order to maintain and possibly improve the efficacy of such a system. Situations may become more complicated if the training data does not sufficiently cover the problem space. Fuzzy Rule Interpolation (FRI) systems may help, whilst most of which follow a static approach, tending to process a large amount of interpolated rules which are generally discarded once the results are derived. Yet, the interpolated rules may contain potentially useful information. This paper presents a dynamic TSK system by exploiting such rules to support subsequent inference and promote rule bases. The obtained intermediate rules are directly added into the sparse rule base until it reaches a certain size. Afterwards, a clustering algorithm is employed to categorise rules into different groups so that an interpolated conclusion can be computed using the closest rules selected from a small number of closest rule clusters. Through systematic experimental comparisons with the conventional static approach, it is demonstrated that the proposed dynamic TSK system not only improves the overall reasoning accuracy but also reduces the interpolation overheads by avoiding the need for interpolations of experienced similar observations.
Generative Adversarial Networks Enhanced Extreme Learning Machine to Classify Faults in Rolling Bearings

Complete faulty data is significance in fault classification. However, in engineering applications, the data we obtained is usually incomplete and unbalanced. In the present, a new generative adversarial networks (GANs) enhanced extreme learning machine (ELM) is proposed. Using GANs, numerous new samples similar to the raw signal are generated and further combined with the raw signal to obtain synthetic samples. ELM is activated by the synthetic samples as training samples to detect unknown faulty signals (test samples). Experiments show that the proposed method has better performance than ELM to overcome the shortcomings of lower classification accuracy.
Augean Artificial Neural Network

There is always a big influence of robotics and natural language processing in the field of automation. The current scenario of natural language processing (NLP) focuses on machine analysis and machine interpretation. By the term machine analysis, accuracy becomes an important phenomenon. So, it is named Augean that refers to higher accuracy for natural language processing which is difficult to attain involving the use of neural networks. Thus far named as Augean artificial neural network. It is one of the key branches of machine learning which is peculiar from the other branches speculating more towards artificial intelligence. NLP is now one of the key aspects in making machine learn and letting it speak. The current framework of these discusses more of deep learning. One of the biggest challenges is to make the machine learn and speak is known. Thus far processing becomes the most crucial part. Therefore, when it comes to processing there is no better idea than the brain to be involved. Now the current scenario is about making machine learn to the best of the accuracy and if there is something of great accuracy then it involves a real complex algorithm. The machine works possibly best only if the accuracy stays high and stepping on to the precision of work with better learning ability machines are remarkable. So, to frame this, artificial neural network and neuroscience have been used to make the model more robust increasing the accuracy with at least five percent to the previous models involved.
Design &amp; Implementation of Vertical Surface Climbing Cleaner Robot

This paper presents a vertical surface climbing robot for cleaning of dust from high rise building. Usually, people clean their wall of outer surface of building by human. Often it becomes risky and costly also. A portable robot having the ability to climb on vertical surface has been expected for a long time. To protect human’s life we designed a prototype of vertical surface climbing robot for cleaning and other multipurpose operation. Some combination of technology has been shown such as smart wireless control with all direction moving capability. Four Dc motors are used to move the robot and one Dc motor is used to rotate the cleaning brush. There is one Electric Ducted fan (EDF) which helps the robot to stick to the surface against gravitational force. An Arduino uno is used as a controller for different There is a Bluetooth modular which has been used to connect with smart phone and take command from user. Four motor helps it to move in up, down, right and left direction by the according commands given by smart phone. The full robot is power by using DC battery and AC to DC converted adapter combined. To observe the outcome of the project, we analyzed the performance of the robot by climbing in different surfaces with different nature. The climbing and stick to the surface differ because of grip. Griping is an important issue for this robot. Most of the surface except glass its performance satisfied us.
Research on Portable Multi-Bar Code Recognizer Based on Digital Image Processing Technology

At present, in the logistics industry, the increasing number and speed of collecting product identifications in batches is an important link to improve the production efficiency of enterprises. Based on this, this paper uses multi-computers and high-definition cameras to use digital image processing technology to achieve multiple barcode collection, identification and storage. First of all, an overview of digital image processing technology is introduced, and its main features and advantages are introduced. Then the development process and current status of barcode recognition are analyzed. Finally, a multi-barcode identifier is designed and implemented. It mainly explains its camera selection, image pre-processing, multiple image stitching, single barcode segmentation, multiple barcode positioning, and motion scanning stitching. The instrument can be easily carried and used.
S-DolLion-MSVNN: A Hybrid Model for Developing the Super-Resolution Image From the Multispectral Satellite Image

Abstract Super-resolution offers a new image with high resolution from the low-resolution (LR) image that is highly employed for the numerous remote sensing applications. Most of the existing techniques for formation of the super-resolution image exhibit the loss of quality and deviation from the original multi-spectral LR image. Thus, this paper aims at proposing an efficient super-resolution method using the hybrid model. The hybrid model is developed using the support vector regression model and multi-support vector neural network (MSVNN), and the weights of the MSVNN is tuned optimally using the proposed algorithm. The proposed DolLion algorithm is the integration of the dolphin echolocation algorithm and lion optimization algorithm that exhibits better convergence and offers a global optimal solution. The experimentation is performed using the datasets taken from the multi-spectral scene images. The optimal and effective formation of the super-resolution image using the proposed hybrid model outperforms the existing methods, and the analysis using the second-derivative-like measure of enhancement (SDME) ensures that the proposed method is better and yields a maximum SDME of 67.6755 dB.