Reviewerly/exHarmony huggingface dataset:
Improved Coupled Autoencoder based Zero Shot Recognition using Active Learning

Zero shot learning seeks to learn useful patterns in the source domain and identify novel concepts in the target domain. This transfer learning paradigm has recently gained immense popularity given the inherent limitations in data acquisition and subsequent annotation for a task (or domain). While typical zero shot learning methods utilize all the classes (and their instances) in the source domain in a passive way, we, in our work, actively use only a handful of relevant classes for learning in the source domain. With this intelligent data subset, we jointly learn the source and target domain parameters using coupled semantic autoencoders. This joint learning reduces the projection domain shift problem. We further extend the above model for word embedding based semantic space as well. For classes with no word embedding, we have solved prototype sparsity problem by training a neural network with all classes that has one. This neural network seeks to learn a mapping from attribute space to word embedding space. Experiments on AWA2 and CUB-UCSD datasets confirm the superiority of our hybrid approach over state of art methods by up to 16% and 8% in attribute and word embedding space respectively.
The Effectiveness of an Augmented Reality Guiding System in an Art Museum

Throughout the past decades, art museums have employed a number of digital technologyDigital technology aids in order to enhance the visitor’s experience and memory retainment. Augmented reality has proven to be among the most successful applications in this regard as studies have shown it to be the most effective in eliciting the interest of the visitors and enhance their learning. Most of these augmented realityAugmented Reality (AR) systems have been implemented on tablet computers or hand-held mobile devices. This chapter explores the effectiveness of such systems and presents an experiment conducted using an augmented realityAugmented Reality (AR) system operating on a head mounted display. Gathered results and user evaluation indicated that the system offered an enhanced museum experienceMuseum experience and improved the users’ concentration on exhibition, their art appreciationArt appreciation skills and more importantly elicited the visitor with a renewed interest in the artworks.
Research Based on Video Analysis the Integration of the Application of Mathematics Smart Class Teaching Media and Teachers' Knowledge Structure

There have been a great many of macro studies on the smart class teaching, but insufficient qualitative and quantitative researches on the media application and the teaching structure of the smart class at the micro level. Based on the research framework of classroom-based video analysis, this paper attempts to make a micro level empirical study on the relationship among the teaching behavior, the media application and the teacher's knowledge structure in the smart class by the scientific method of quantitative. On the case analysis of this framework, it will be put forward on a possible path to the deep integration of the media application and the teacher's knowledge structure in the smart class.
Android Apps Success Prediction Before Uploading on Google Play Store

Nowadays among all the mobile apps distribution platform Google Play Store is one of the most attractive and user effective platform for android apps. Every day this platform gets drenched with a huge amount of new android apps. Developers are always trying to make successful their new apps through their skilled independent work or team work. This platform is getting competitive day by day for the developers to retain their place in the market. But this growing competitive sector can be easier for the developers if they can determine the new app's success before uploading it on Google Play Store. As we know that usually an app's success is determined through the app's user rating and installation number, our work relates with the purpose to assume the new app's success through the prediction of user rating and installation number before uploading on the Google Play Store.
Application of Support Vector Machine Of Quantum Genetic Algorithm With Gauss Initialization In Multi-Class Classification

Support vector machine is a classical supervised machine learning algorithm, and it is also an important algorithm in statistical learning method, which is mainly used in binary classification problem. The maximum street width problem of SVM is often transformed into the optimization problem of dual problem. In order to improve the global optimization ability and increase the operation efficiency, this paper introduces the quantum genetic algorithm with Gaussian initialization into the optimization part of SVM, and then combines the OVR strategy to solve the multi-class classification problem.
An Automatic Cell Nuclei Segmentation based on Deep Learning Strategies

Automatic analysis of histopathology specimens images can be utilized in early extraction and detection of diseases such brain tumor, breast malignancy, colon cancer etc. The early detection of cancer may allow patients to take proper treatment. In this paper, an automatic cell nuclei segmentation based on deep learning strategies using 2-D histological images is proposed. In the proposed approach U-Net architecture is used and its hyper parameters are tuned to segment the cell nuclei. The proposed solution is built upon the highly adaptive nature of U - Net architecture. The task of nuclei segmentation in the proposed approach includes detection of nuclei in an image and extracting the foreground, while segmenting the connected foreground area into separated nuclei masks. In the experimental results the proposed approach is tested using the dataset having histopathological cell images of breast cancer. The results shows that the proposed deep learning based approach achieved the 86 % average accuracy in segmentation of cell nuclei and also outperforms the other deep learning architectures.
RSA Secured Web Based Steganography Employing HTML Space Codes And Compression Technique

Web development has given its way to develop the strategies to secure the information available on the web. The data can be secured in numerous ways and one of the common way is to obnubilate the data through HTML web pages where the data is hidden in the source code of the web document. The web contains innumerable amount of personal data which needs to be hidden. We propose an algorithm which combines the techniques of cryptography and steganography which aids in securing the data by hiding it behind the source code of the HTML web pages. RSA encryption is used for encrypting the secret message. The encrypted data is compressed, mapped into Space codes and embedded under the HTML web pages by making mere changes in the code which cannot be noticed by the intruder as they are not suspicious. The robustness and security are enhanced.
Self-learning package development on the application of the internet of things to agriculture for the study application

This article aims to develop and to validate a self-learning package development on the application of the internet of things to agriculture for the study application. The developed training course consists of a training set, video online, a content sheet, a presentation and a test. The developed training course is implemented and tested with the selected 25 trainees as sampling group. The research results can be stated as follows: 1)the quality evaluation assessed by 5 experts obtains the average score of 4.49 (good), 2) the training efficiency using the developed training set is efficient in accordance with Meguigans's theory. The trainee satisfaction towards developed self-learning package obtains the average score of 4.65 (very good), and can be interpreted as very satisfactory. Therefore, the self-learning package can be applied effectively in training course as well in knowledge transfer.
Quasi-optimal processing simulation of ultrasonic signals

Abstract Magnetostrictive level gauges are widely used for measuring the level of bulk material and liquid medium including petroleum products, which determines the relevance of the measurement techniques and level gauges design improvement examination. The research objective is to modify the optimal signal processing classical methods for measuring the time intervals in the magnetostrictive level gauges by using the digital devices with limited computational resources. The article deals with the modification problems of the digital signal processing optimal algorithms providing the real time measuring devices operation. For addressing the challenges, the methods of numerical simulation and statistical processing of ultrasonic signals are used. Digital signal processing quasi-optimal algorithms for calculating the time intervals based on the use of cross-correlation functions leading to the operations number reduction, are described. The represented results can be applied in the level measurement and medicine for electrocardiograms analysis.
Mixture of Deep-Based Representation and Shallow Classifiers to Recognize Human Activities

Human action recognition is one of today's investigate issues that has engaged many researchers due to its significant and large applications. Newly, for the sake of the fame and success of the utilization of deep learning-based approaches in diverse fields such as machine vision, object-detection, and natural language processing, investigators have traveled from conventional hand-crafted techniques to deep learning methods. In this investigation, a higher-performance method is proposed using the transfer learning technique for recognizing human action, where a pre-trained deep neural network model is first used to extract features from the target dataset and then, the softmax classifier is used to classify the actions. It can be noticed that the transfer of learning from a huge dataset to the task of action diagnosis with the limited dataset, is well done. The suggested approach is evaluated on two datasets namely KTH and UCF Sports action. Comparative study shows that the suggested approach is superior to hand-crafted extraction-based techniques and other deep learning-based techniques, in terms of accuracy. Also, due to fewer parameters, it is possible to use this method for applications of human action recognition in mobile.
An Efficient Identification Scheme Based on Rank Metric

Using random double circulant codes, we design a rank metric version of Aguilar, Gaborit and Schrek (AGS) identification scheme which is resistant to attacks using quantum computers. We achieve optimum results in different scales comparing public key size, secret key size and communication cost with known identification schemes. Moreover, our protocol is more efficient and practical for different resource constrained devices such as smart cards or Radio Frequency Identification (RFID) tags. Furthermore, using the Fiat-Shamir paradigm, we design an efficient signature scheme.
AGI Protocol for the Ethical Treatment of Artificial General Intelligence Systems

The AGI Protocol is a laboratory process for the assessment and ethical treatment of Artificial General Intelligence systems that could be conscious and have subjective emotional experiences “theoretically”. It is meant for looking at systems that could have emotional subjective experiences much like a human, even if only from a theoretical standpoint. That is not to say that other ethical concerns do not also need to be addressed but this protocol is designed to focus on how we treat such systems in the lab. Other ethical concerns are out of scope. The protocol is designed to provide the basis for working with Artificial General Intelligence systems especially those that are modeled after the human mind in terms of systems that have the possibility of having emotional subjective experience from a theoretical standpoint. The intent is to create a reusable model and have it in the public domain so others can contribute and make additional suggestions for working with these types of systems.
Evolutionary-Based Ensemble Under-Sampling for Imbalanced Data

Class imbalance is a prevalent problem in the real world, which mainly refers to the uneven distribution of samples of each class, and it will lead to a serious decline in the learning performance. The classification results will seriously bias to the majority class and ignore the minority class. However, the accuracy of the minority class is usually the focus of attention. Therefore, in this paper, an evolutionary-based ensemble under-sampling (EEU) algorithm is proposed to solve this problem. Specifically, evolutionary algorithm is used to under sample the data and multiple base classifiers are trained by ensemble learning. The advantage of this algorithm lies in that it can improve the accuracy of minority class. Comparison experiments are performed on five UCI datasets, and the results demonstrate that EEU outperforms other sampling methods.
Implications of vocabulary density for poetry: Reading T. S. Eliot’s poetry through computational methods

Abstract By incorporating computational methods into reading literary texts, this study examines the literary implications of the ‘vocabulary density’ and frequency of nouns and adjectives in T. S. Eliot’s poetry. This study analyzes 4,689,655 words from forty-seven poets available on Project Gutenberg, a catalog spanning from the eighteenth century to the early twentieth century. The data illustrate both the continuity and discontinuity found in English and American poetry dependent on conventional divisions between literary movements: eighteenth century, Romanticism, Imagism, and Modernism. The findings shed light on the similarities and differences between Eliot’s poetry and others’, particularly in terms of Franco Moretti’s concept of ‘modern epic’ and his methodology of ‘distant reading’. Through this combined quantitative and qualitative research, this article ultimately upholds the notion that the linguistic distinction of Eliot’s high modernist poetry lies, by and large, in his use of invented and equivocal words that reflects and represents an artistic response to modern human, cultural, social conditions, and experiment with poetic diction and polyphonic voice in the early twentieth century.
A Technique to Reconstruct Wavelet-Based Watermark Immune Against Salt &amp; Pepper Noise

This paper proposes a technique to reconstruct watermark immune against salt & pepper noise attack. In watermarking technique, a particular cover embeds an original image as a watermark. To transform the cover image, it uses DWT that gains the LL sub-band, and later, transforms by DCT. To generate and embed the watermark, it modifies the transformed coefficients and the cover image by varying scaling factors. Now to obtain the watermarked image, it applies inverse discrete cosine with DWT on the reformed sub-bands. Next, to extract the watermark, an extraction process is needed. Besides, if high-density salt & pepper noise is enforced on a watermarked image, it exploits the `switching threshold algorithm' for noise elimination, that is implemented in two steps. At first, it detects corrupted pixels. Then using an adaptive length window, it projects new pixel-values only for corrupted pixels. Finally, to validate the effectiveness, the developed technique is assessed.
Single Image Super-Resolution Using Frequency-Dependent Convolutional Neural Networks

In this paper, we propose a single image super-resolution (SR) method based on frequency-dependent training of convolutional neural networks. Several researchers have focused on the reconstruction of super-resolution images by training a single convolutional neural network. In the proposed method, we divided the input images into three sub-frequency groups and then trained a convolutional neural network for each sub-frequency group. Then, the final output images were reconstructed by combining the SR images from the multiple networks. Experimental results show that the proposed training method produces promising performance.
Text Categorization with Fractional Gradient Descent Support Vector Machine

Abstract Text documents on the web are an incredible resource including one example of big data, large size and so many variations that it becomes difficult for humans to choose meaningful information without the help of a computer. Text categorization job is to automatically classify text documents into standards class based on their content. The objective of this research is to implement a classifier with optimization based on the Fractional Gradient Descent in text classification. In our research, we propose using the Fractional Gradient Descent to optimize the SVM classifier so that it can increase the speed of training data. We explore a batch of different training data to compare the speed of the UCI ML text dataset training process with the SVM- SGD and SVM-FGD classifiers. This research concludes that using SVM-FGD will optimize the training time for text dataset in the activity of data classification.
Improvement of Knowledge Search Method for Numeral Terms in Power System Based on Multi-source Fusion

Abstract In the process of energy and power research, it is often necessary to find numeral data information from multiple ways based on searching words, so the domain information can better analyzed and understood, visually discover hidden relationships between information and ultimately improve search efficiency and experience. The article designs a set of numeral entity discovery methods based on NLP and improves a visual search engine. Using CRF for named entity pre-processing extraction, using TF-IDF, etc. to complete multi-source information combination search and information mining method establishment, the power system energy database is used as the information source of the search engine, enabling users to locate the document data of the searching words in the energy database and the context information associated with them quickly, generate summary information, extract the central words based on this, and image the information in the form of word cloud and chart demonstration. Experiments have shown that the searching result are of interest to users, quickly locate the literature and context-related information containing the numeral hotspots, and the search recall rate is significantly improved.
Mining multiple informational text structure from text data

This study aimed to distinguish the various types of informational text structure present in the text data. Classification of informational text structure in a given text is an essential area of research for discovering knowledge present in the text content. Several previous studies defined a set of categories of informational text structure which can be identified based on their respective signal words. The paper proposed a methodology for automatic extraction of those text informational structures from school textbook data. The task was to classify a text into one or more of the given predefined categories. Human annotators have performed the categorization, who have sufficient domain knowledge about the subjects of the book. For automatic classification, the occurrence frequency of the signal words was used as a feature vector. A Naïve Bayes based classifier was trained using 120 manually annotated text data. Forty text data was used to test the classifier. The classifier had a precision rate of 92% and F1 score of 95.6%.
Learning to Navigate in Unknown Environments Based on GMRP-N

Navigation in unknown environments can be formulated as POMDP which remains a challenging problem till now. In this paper, a grid map based random policy networks (GMRP-N) is proposed to explictly map the past observation to actions. The GMRP-N is a CNN based deep neural network, which first maps the unknown world and then make decision based on the map. High sample efficient imitation learning method is used to train GMRP-N. During training, uniform random unknown environments are generated and dijkstra algorithm is adopted as expert. A grid map based deterministic policy networks (GMDP-N) is also constructed as comparision in this paper. We find that the random policy is very key for successfull navigation in unknown environments, which can help the robot jumping out of the trap. After well trained, the success rate of navigation to target in random unknown enviroment is higher than 97.5% for GMRP-N which shows the GMRP-N is very valid. Finally the generalization ability is tested and the success rate remains high even though the environment is very different from the training environments.
Building a cognizant honeypot for detecting active fingerprinting attacks using dynamic fuzzy rule interpolation

Abstract Dynamic fuzzy rule interpolation (D‐FRI) technique delivers a dynamic rule base through the utilisation of fuzzy rule interpolation to infer more accurate results for a given application problem. D‐FRI offered dynamic rule base is very useful in security areas where network conditions are always volatile and require the most updated rule base. A honeypot is a vital part of any security infrastructure for directly investigating attacks and attackers in real‐time to strengthen the overall security of the network. However, a honeypot as a concealed system can only function successfully while its identity is not revealed to any attackers. Attackers always attempt to uncover such honeypots for avoiding any trap and strengthening their attacks. Active fingerprinting attack is used to detect these honeypots by injecting purposefully designed traffic to a network. Such an attack can be prevented by controlling the traffic but this will make honeypot unusable system if its interaction with the outside world is limited. Alternatively, it is practically more useful if this fingerprinting attack is detected in real‐time to manage its immediate consequences and preventing the honeypot. This article offers an approach to building a cognizant honeypot for detecting active fingerprinting attacks through the utilisation of the established D‐FRI technique. It is based on the use of just a sparse rule base while remaining capable of detecting active fingerprinting attacks when the system does not find any matching rules. Also, it learns from current network conditions and offers a dynamic rule base to facilitate more accurate and efficient detection.
Integral Kinesiology Feedback for Weight and Resistance Training

Existing physical fitness systems are often based on kinesiology. Recently Integral Kinesiology has been proposed, which is the integral kinematics of body movement. In this paper, we apply integral kinesiology to the bench press, commonly used in weight and resistance training. We show that an integral kinesiology feedback system decreases error and increases time spent lifting in the user. We also developed proofs of concept in aerobic training, to create a social physical activity experience. We propose that sharing real time integral kinematic measures between users enhances the integrity and maintenance of weight, resistance, or aerobic training.
Blockchain and its Alternative Distributed Ledgers - A Survey

Blockchain as a distributed data structure or ledger is perceived to provide solutions in manifold areas beyond their prominent form of digital currency; they also showed several challenges. As enhancements to the existing blockchain systems are being proposed and developed, alternative underlying data structures were driven to solve some of the challenges facing them whilst maintaining their key ideas. We start by explaining the key concepts of the blockchain as implemented in Bitcoin and Ethereum. From there, we explain the challenges and survey on the alternative data structures along with their consensus mechanisms. We find a generalization in using hash pointers in the alternative data structures and unfold the current research and implementation directions for solutions to the challenges.