Reviewerly/exHarmony huggingface dataset:
Implementation of ORB and Object Classification using KNN and SVM Classifiers

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
A Compression Objective and a Cycle Loss for Neural Image Compression

In this manuscript we propose two objective terms for neural image compression: a compression objective and a cycle loss. These terms are applied on the encoder output of an autoencoder and are used in combination with reconstruction losses. The compression objective encourages sparsity and low entropy in the activations. The cycle loss term represents the distortion between encoder outputs computed from the original image and from the reconstructed image (code-domain distortion). We train different autoencoders by using the compression objective in combination with different losses: a) MSE, b) MSE and MSSSIM, c) MSE, MS-SSIM and cycle loss. We observe that images encoded by these differently-trained autoencoders fall into different points of the perception-distortion curve (while having similar bit-rates). In particular, MSE-only training favors low image-domain distortion, whereas cycle loss training favors high perceptual quality.
An Optimal Path Planning Method for UCAV in Terminal of Target Strike

For unmanned combat aerial vehicle (UCAV), the strike trajectory must be calculated quickly after the target is determined in order to ensure the timeliness of the attack process. Aiming at the requirement of three-dimensional trajectories at the end of attack mission, a path planning method for UCAV in terminal of target strike is proposed in this paper. In the first part, the research background and the urgent requirements are analyzed. Then, four assumptions based on the features in terminal of target strike are proposed to simplify and model the problem. In the third part, the method of path planning with two tangent circles is presented in detail. The discussion of the feasibility and optimality in the projection plane, and path adjustment in three-dimensional space considering the kinematic constraints, are presented subsequently. Finally, simulation results under various conditions are given, to verify the performance of the proposed method.
An Adaptive Feature Channel Weighting Scheme for Correlation Tracking

In most of Discriminative Correlation Filter (DCF) based trackers, they used a fixed weight for each feature channel for all incoming frames. However, in the experiment, we find that different features have pros and cons under different scenarios. In this paper, we propose to couple the response of a DCF based tracker with the weights of different feature channels to strengthen their positive effects while weaken their negative effects simultaneously. This coupling is achieved by an adaptive feature channel weighting scheme. The tracking is formulated as a two-stage optimization problem: the tracker is learned using the alternative direction method of multipliers (ADMM) and the weights of feature channels are adaptively adjusted by a least-square estimation. We integrate the adaptive feature channel weighting scheme into two state-of-the-art handcrafted DCF based trackers, and evaluate them on two benchmarks: OTB2013 and VOT2016, respectively. The experimental results demonstrate its accuracy and efficiency when compared with some state-of-the-art handcrafted DCF based trackers.
Efficient Support Vector Regression with Reduced Training Data

Support Vector Regression (SVR) as a supervised machine learning algorithm have gained popularity in various fields.However, the quadratic complexity of the SVR in the number of training examples prevents it from many practical applications with large training datasets.This paper aims to explore efficient ways that maximize prediction accuracy of the SVR at the minimum number of training examples.For this purpose, a clustered greedy strategy and a Genetic Algorithm (GA) based approach are proposed for optimal subset selection.The performance of the developed methods has been illustrated in the context of Clash Royale Challenge 2019, concerned with decks' win rate prediction.The training dataset with 100,000 examples were reduced to hundreds, which were fed to SVR training to maximize model prediction performance measured in validation R 2 score.Our approach achieved the second highest score among over hundred participating teams in this challenge.
Toward a universal quantum programming language

Quantum computing may be more powerful than classical computing, but it has a radically different programming model. While current languages are in their infancy and future languages are likely to be different, now is a great time for language designers and implementers to try new ideas.
Caption-Based Region Extraction in Images

ImageAgrawal, Palash captioningYadav, Rahul and object detectionYadav, Vikas are some of the mostDe, Kanjar growing and popularPratim Roy, Partha research areas in the field of computer vision. Almost every upcoming technology uses vision in some way, and with various people researching in the field of object detection, many vision problems which seemed intractable seem close to solved now. But there has been less research in identifying regions associating actions with objects. Dense Image Captioning [8] is one such application, which localizes all the important regions in an image along with their description. Something very similar to normal image captioning, but repeated for every salient region in the image. In this paper, we address the aforementioned problem of detecting regions explaining the query caption. We use edge boxes for efficient object proposals, which we further filter down using a score measure. The object proposals are then captioned using a pretrained Inception [19] model. The captions of each of these regions are checked for similarity with the query caption using the skip-thought vectors [9]. This proposed framework produces interesting and efficient results. We provide a quantitative measure of our experiment by taking the intersection over union (IoU) with the ground truth on the visual genome [10] dataset. By combining the above techniques in an orderly manner, we have been able to achieve encouraging results.
Conceptual Model of User Interface Design for General Architectural Framework for Business Visual Analytics

The user interface is the means by which the user is able to interact with the software and perceive it with his/her senses, thus enabling the user to take advantage of the software capabilities. The graphical user interface rich in details, provides enhanced software functionality but requires a higher-level training and experience to be able to use the tool. Visually facilitated graphical user interface provides users with a faster, easier and more sensible visual communication. In a company, different business users need different levels of data and information. The information that the manager or director/CEO need is much more complex and more voluminous compared to the data employees need. Managers need information on the overall workflow to be able to take strategic business decisions in a timely manner. Employees use a limited set of Business Analytics features. For this group of business users, lower levels of complexity of user interfaces are required, as they often do not require professional knowledge in the field of data science and information technology. Designing a user interface that is appropriate to the business needs and professional knowledge of different business users is one of the key factors to characterizing and improving the efficiency and effectiveness of user interfaces for business analytics. Inspired by these challenges, the paper: provides analysis of user interfaces, their role and importance for business users and their application in the field of business analytics. The paper proposes a conceptual model of complexity levels in user interface design by segmentation and classification of business users in the context of the third layer of the General Architectural Framework for Business Visual Analytics (GAF for BVA). The new conceptual model presents the three main categories: Visual Friendly-UI, Advanced-UI, and Professional-UI. The name of the conceptual model is FAP-UI (Visual Friendly, Advanced and Professional-UI). This paper relates to previous research publications of the author presenting the concept of GAF for BVA. This paper contributes to further architectural development of this business analytics framework.
MMOG User Participation Based Decentralized Consensus Scheme and Proof of Participation Analysis on the Bryllite Blockchain System

Proof of Work (PoW) based blockchains have limitations in throughput, time consumption, and energy efficiency.In these systems, a miner will consume significant time and resources to obtain a reward for contributing to the blockchain.To overcome these limitations, recent research on blockchains are focused on accelerating the speed, scalability, and enhancing the security level.By enhancing specific procedures of blockchain system, the level of data integrity supported by the blockchain can become more robust, and efficient.In this paper, a new blockchain consensus model based on the Bryllite Consensus Protocol (BCP) is proposed to support a hyper-connected massively multiplayer online game (MMOG) ecosystem.The BCP scheme enables users to participate directly in new consensus processes through a Proof of Participation (PoP) algorithm.In this model, the consensus algorithm has a simpler form while maintaining high security level.In addition, because the BCP scheme gives users an equal chance to make a contribution to the blockchain, rewards are distributed in an equal fashion, which motivates user participation.The analysis of the proposed scheme is applied to the Bryllite consortium blockchain system (homed in Hong Kong), which is a new blockchain network developed for international game industries, gamers, and game events.
Design Analysis of Web-Based Furniture Marketing Information System

In the industrial world and with the progress of the times, technology will greatly help a small industry in managing beneficial information for consumers. Not all wood furniture industries have stores. However, the majority of shop owners in Pringsewu do not produce their own furniture. The owners who do not produce their own furniture receive merchandise from entrepreneurs who do not have shops. The owners provide furniture model orders needed by customers and users. In this case study, the data had been computerized so that consumers can receive information only by accessing the desired information. Furniture stores that build Web-Based Furniture Marketing Information Systems id to participate in providing data and information to people and can be accessed quickly, anywhere, and anytime.
Some Note on Artificial Intelligence

This introductory chapter discusses some basics of artificial intelligence and in particular some theoretical issues concerning neural networks that are still open problems and need further investigations. This is because, independently of the large use of neural networks in several fields, using neural networks and similar artificial intelligence techniques to solve problems of non-polynomial complexity is by itself a creative and intelligent problem, not rigidly tied to procedural methods and fully explainable on theoretical bases.
Functional Monitoring and Control in Electronic Information and Educational Environment

The paper deals with the monitoring of the functioning and control of the effectiveness of the use of electronic information and educational environment of higher education. Particular attention is paid to the registration of events and their analysis in the Moodle learning management system from the perspective of the organization of educational interaction and analytical activities of the management. It is noted that the learning management system Moodle can not without additional plugins to provide information about site visitors, traffic sources, load indicators on the system. External tools, such as «Yandex.Metrics». By integrating this information system into the electronic information and educational environment of the University, it is possible to analyze additional statistical data (number and age of visitors, devices used to enter the environment). The obtained information allows not only to optimize the educational process from the position of control over the study of educational material, but also to significantly improve the efficiency, convenience and quality of work of students in the electronic information and educational environment of higher education.
A Graph Kernel Based Item Similarity Measure for Top-N Recommendation

The item neighborhood-based graph kernel (INGK) has recently been proposed to compute item similarity on the Linked Open Data (LOD) graph and then produce top-N recommendations (the similarity measure is abbreviated as INGK-LOD). This paper explores how to use the graph kernel to compute item similarity on the basis of user-item ratings. We transform the user-item ratings matrix into an undirected graph called a user-item ratings graph, and define the graph kernel based on the graph, which can be used to compute item similarity (the similarity measure is abbreviated as INGK-UIR). We applied INGK-UIR, INGK-LOD and two baseline similarity measures, Cosine and Pearson correlation coefficient, to top-N recommendation, and conducted experimental evaluation of recommendation accuracy using the MovieLens 1M benchmark dataset. The results show that our INGK-UIR significantly outperforms INGK-LOD and the two baseline measures in terms of precision and recall.
Histology Tissue Classification

Now it’s time to put all we have learned together and see how the techniques we have learned so far can be used on a real dataset. We will use a dataset that I have used successfully as my end project in my university course on deep learning: the "collection of textures in colorectal cancer histology". This dataset can be found on several websites:
Cost Functions and Style Transfer

In this chapter we will look in more depth at the role of the cost function in neural network models. In particular, we will discuss the MSE (mean square error) and the cross-entropy and discuss their origin and their interpretation. We will look at why we can use them to solve problems and how the MSE can be interpreted in a statistical sense, as well as how cross-entropy is related to information theory. Then, to give you an example of a much more advanced use of special loss functions, we will learn how to do neural style transfer, where we will discuss a neural network to paint in the style of famous painters.
Computational Intelligence Generation of Subject-Specific Knee and Hip Healthy Joint Angles Reference Curves

Backpropagation Neural Network (BNN) and Extreme Learning Machine (ELM) can generate subject-specific joint angle reference profiles based on a subject’s height, weight, age and walking speed. These reference profiles are useful in various fields such as biomechanics and medicine, for detection of gait pathologies and rehabilitation. A common procedure used to identify abnormal gait is comparing an individual’s knee and hip curves against healthy reference curves. These reference curves are usually obtained from a heterogeneous sample of healthy subjects and might lack the specificity required to obtain accurate results. It is why the generation of reference curves according to an individual’s height, weight, age and gait speed shall result in a better comparison and diagnosis. The main objective of the present study is to observe which of the two Computational Intelligence (CI) methods, BNN and ELM, present more accurate results when used to generate reference curve profiles based on subject height, weight, age and gait speed for the knee and hip joint angles.
A Conceal Fragment Visible Image Broadcast Through Montage Images with Revocable Colour Alterations

Information security is increasing progressively essential in the advanced world. Protected Image Broadcast has a capability of being received for mass correspondence. A few stenographic systems for transmitting data without bringing doubt are found up in [1–5]. Anyway another protected photograph Broadcast procedure is proposed, known as anonymous part unmistakable montage photograph which enables the client to safely transmit a photograph under the front of another photograph of same size, This paper exhibits a methodology called Reversible data hiding with LSB technique where montage photograph age has done by isolating the anonymous photograph into pieces and changing their separate shading attributes into relating squares of the objective photograph. Use of the Pixel shading changes yields the lossless recouped photograph in view of the untransformed shading space esteems. Age of the key assumes an essential job to regain the anonymous photograph from the montage photograph in lossless way. At last a similar methodology can be performed on recordings likewise which wipes out the flashing ancient rarity to accomplish the lossless information recuperation in movement related recordings. The exploratory outcomes demonstrate great powerful conduct against all coincidental and unintentional assaults and contrast with the regular calculations.
BSIL: A Brain Storm-Based Framework for Imbalanced Text Classification

All neural networks are not always effective in processing imbalanced datasets when dealing with text classification due to most of them designed under a balanced assumption. In this paper, we present a novel framework named BSIL to improve the capability of neural networks in imbalanced text classification built on brain storm optimization (BSO). With our framework BSIL, the simulation of human brainstorming process of BSO can sample imbalanced datasets in a reasonable way. Firstly, we present an approach to generate multiple relatively balanced subsets of an imbalanced dataset by applying scrambling segmentation and global random sampling in BSIL. Secondly, we introduce a parallel method to train a classifier for a subset efficiently. Finally, we propose a decision-making layer to accept “suggestions” of all classifiers in order to achieve the most reliable prediction result. The experimental results show that BSIL associated with CNN, RNN and Self-attention model can performs better than those models in imbalanced text classification.
Explaining and improving a machine-learning based printer identification system

Counterfeiting of currency globally remains a significant problem to this day. According to the authorities, a large portion of this fake currency is produced by Small Office or Home Office inkjet printers. In this paper, we explain why a previously developed machine learning based Printer Identification System works with high accuracy and we investigate to improve the stability and generalization of the classifier. We study the print patterns of 8 inkjet printers from different manufacturers. We look at the features of the data by reducing its dimensions using Principle Component Analysis. This shows significant separation between printers which implies that the Deep Neural Network was able to pick up on key differences. The results are also comparable with that of reducing dimensions with Linear Discriminant Analysis. The model however does have some limitations regarding ink density and print media. It always classifies an image amongst the trained printers and does not show anomalies. For this, we consider the Gaussian distribution of target printers to see how the probabilities fared when trained and fitted separately for each printer, thus having a set of images that do not fit in with any of the printers for which the classifier was trained. The results acquired from these methods have contributed to making a more real-world implementation of our classifier, named LAPIS (machine-Learning Applied Printer Identification System), for printer forensics.
Structure Learning of Similar Ising Models: Information-theoretic Bounds

This paper considers the problem of estimating the structures of a pair of structurally similar graphs associated with two distinct Ising models. It is assumed that the graphs have the same number of nodes with unknown structures, with the additional side information that a known subset of nodes have identical structures (connectivity) in both graphs. The objective is the exact recovery of the structures of both graphs. The bounded degree and bounded edge sub-classes of Ising models are investigated, and necessary and sufficient conditions on the sample complexity for bounded probability of error under the two criteria are established. Furthermore, the results are compared with the conditions on the sample complexity of recovering the graphs independently. One major observation is that by judicially leveraging the information about the identical sub-graphs by jointly recovering both structures, the sample complexity reduces by a factor cp2, where p is the number of nodes in the graph and c is some constant.
Solar Elevation Angle and Solar Culmination Determination using Celestial Observation; A Case Study Of Hadejia Jigawa State, Nigeria

The world typically depends on renewable resources because of its zero-emission. The most important and available resource is solar energy, which is found almost accessible in each and every location of the world. Solar energy is among the abundant renewable resources and is the alternative most fundamental sources of energy. It is mandatory to determine the solar azimuth angle, solar altitude angle together with the understanding of solar culmination relatively to the time and collection device location. This work was conducted in Hadejia Jigawa state of northern Nigeria. The main objective of this study was to identify the maximum and minimum solar elevation angle (SEA), solar azimuth angle (SAA), solar culmination (SC) and questions of time (EOT) of the study area. Keisan online calculator was used as a method to track the sun path/position and corresponding data such as solar altitude and azimuth angle, culmination and equations of time. Keisan calculator can be used trace sun specific location for any spatial location of the earth. The results shows&nbsp;178&nbsp;0&nbsp;SAA, maximum SC 12:34:02, maximum SEA 850&nbsp;and maximum EOT 16.323 respectively. The findings of this paper will help decision-makers and engineers understand the peak sun angle, sun path/position and also use for solar harvesting technology in the study, especially during solar panels installation.
Advanced CNNs and Transfer Learning

In this chapter, we look at more advanced techniques typically used when developing CNNs. In particular, we will look at a very successful new convolutional network called the inception network that is based on the idea of several convolutional operations done in parallel instead of sequentially. We will then look at how to use the multiple cost function, in a similar fashion as what is done in multi-task learning. The next sections will show you how to use the pre-trained network that Keras makes available, and how to use transfer learning to tune those pre-trained networks for your specific problem. At the end of the chapter, we will look at a technique to implement transfer learning that is very efficient when dealing with big datasets.