Reviewerly/exHarmony huggingface dataset:
An FPGA-based Hardware Accelerator of RANSAC Algorithm for Matching of Images Feature Points

RANSAC algorithm is a powerful algorithm which is widely used to reject erroneous data in the field of automated image analysis and computer vision. In RANSAC, its iterative property leads to higher computation complexity. Therefore, the hardware implementations of RANSAC that have been proposed consume a large amount of resources. In this paper, an FPGA-based resource-efficient RANSAC hardware accelerator is proposed to eliminate false matches of feature points between two adjacent image frames. The proposed design adopts a single-line systolic array structure causing its lower on-chip resource consumption. It can work along with image feature points detection and matching modules on FPGA. The proposed design is evaluated on a ZYNQ XC7Z045 FPGA at 100MHz clock frequency. Processing 128 matching pairs, only needs 4,718 clock cycles. Experimental results demonstrate that its outstanding capability for screening matching pairs, our design can support real-time video processing.
Communication in Banking Sector: A Systematic Review

Background: Effective banking communication strengthens the relationship between customer, suppliers, stakeholders, manager, client, employees and board of directors. Banker’s experience on banking communication enhances banking system, employee’s behavior and core banking services facilities management. Banking communication improves organizational effectiveness through training, knowledge management, risk management, internal control system and data security management. In modern era, communication channel is transformed into electronic channel promoting e-banking which includes internet banking, mobile banking and e-payment system. Moreover, clerical work shifted into electronic form, which cut costs and satisfy customer.&#x0D; Objectives: This paper explores how banker’s experience effects on banking communication in commercial banks, which enhances their understanding level and determines effective communication in workplace.&#x0D; Methods: Extensive desk reviews followed by related literature werecarried out to gain better insight regarding the field of study.&#x0D; Findings: With the passage of time, computers and technologies have changed the traditional method of communication system. Banks are now using windows, world processing system, excel, computer operating system, DOS, database management system, data planning and database design, data security, internet, intranet, extranet service and email system which increase working performance.&#x0D; Conclusions: Bank managers need to understand importance of communication skills in order to increase effectiveness of internal communication between manager and employees.&#x0D; Implications: Commercial banks of Nepal requireto update in their communication practices and strategies in order to build proper channel which would help in communication between employees and management.
Recommender System for Responsive Engagement of Senior Adults in Daily Activities

Abstract Understanding and predicting how people change their behavior after an intervention from time series data is an important task for health recommender systems. This task is especially challenging when the time series data is frequently sampled. In this paper, we develop and propose a novel recommender system that aims to promote physical activeness in elderly people. The main novelty of our recommender system is that it learns how senior adults with different lifestyle change their activeness after a digital health intervention from minute-by-minute fitness data in an automated way. We trained the system and validated the recommendations using data from senior adults. We demonstrated that the low-level information contained in time series data is an important predictor of behavior change. The insights generated by our recommender system could help senior adults to engage more in daily activities.
Implementation and Evaluation of CNN Based Traffic Sign Detection with Different Resolutions

In this paper, we develop a traffic sign detection and recognition technique based on the YOLOv3 framework. The public traffic dataset TT100K and our own Taiwan road scene image dataset are used for network training. In the performance evaluation, we have extensively compared different input image resolution and ROI for network training and testing. In the experiments, the proposed method is able to achieve the mAP of 92.99% with 21 most common traffic signs on the road.
A Sparse Deep Factorization Machine for Efficient CTR prediction

Click-through rate (CTR) prediction is a crucial task in online display advertising. The embedding-based neural networks have been proposed to learn both explicit feature interactions through a shallow component and deep feature interactions using a deep neural network (DNN) component. These sophisticated models, however, slow down the prediction inference by at least hundreds of times. To address the issue of significantly increased serving delay and high memory usage for ad serving in production, this paper presents \emph{DeepLight}: a framework to accelerate the CTR predictions in three aspects: 1) accelerate the model inference via explicitly searching informative feature interactions in the shallow component; 2) prune redundant layers and parameters at intra-layer and inter-layer level in the DNN component; 3) promote the sparsity of the embedding layer to preserve the most discriminant signals. By combining the above efforts, the proposed approach accelerates the model inference by 46X on Criteo dataset and 27X on Avazu dataset without any loss on the prediction accuracy. This paves the way for successfully deploying complicated embedding-based neural networks in production for ad serving.
On the Consistency of k-means++ algorithm

We prove in this paper that the expected value of the objective function of the k-means++ algorithm for samples converges to population expected value. As k-means++, for samples, provides with constant factor approximation for k-means objectives, such an approximation can be achieved for the popula tion with increase of the sample size. This result is of potential practical relevance when one is considering using subsampling when clustering large data sets (large data bases).
7-DOF Robotic Manipulator for Autonomous Segregation using Transfer Learning

7 DOF Robotic arm provides an efficient solution for segregating objects using transfer learning a deep-learning method. Segregating objects though may not sound a difficult task, but when automated requires complex detection algorithms and proper set-up. This complex setup is useful when segregating hazardous substances which human hands cannot do. The robotic arm used comprises 7 dynamixels, giving it 7 degrees of freedom (DOF). There are many solutions for this task, but this paper mainly focuses on using a transfer learning technique for detecting objects and then segregating. Transfer learning is used because deep-learning has much more time complexity than transfer learning. Hence this proves to be an efficient method.
A hybrid algorithm based on cuckoo search and differential evolution for numerical optimization

Cuckoo search (CS) is a new intelligent bionic algorithm, but it is easy to fall into the local optimum. In this paper, a hybrid algorithm based on cuckoo search and differential evolution (CSDE) is proposed. In the global optimization, a control factor is introduced to judge the optimization state of CS, further determine whether it is necessary to combine the differential strategy to improve the population diversity. Besides, different evolutionary strategies are used to enhance the local search. Numerical experiments, carried on 28 benchmark functions from CEC 2013, demonstrate that CSDE successfully achieves better optimization performance than other competitive optimization algorithms.
Bilattice logic of epistemic actions and knowledge

Baltag, Moss, and Solecki proposed an expansion of classical modal logic, called logic of epistemic actions and knowledge (EAK), in which one can reason about knowledge and change of knowledge. Kurz and Palmigiano showed how duality theory provides a flexible framework for modeling such epistemic changes, allowing one to develop dynamic epistemic logics on a weaker propositional basis than classical logic (for example an intuitionistic basis). In this paper we show how the techniques of Kurz and Palmigiano can be further extended to define and axiomatize a bilattice logic of epistemic actions and knowledge (BEAK). Our propositional basis is a modal expansion of the well-known four-valued logic of Belnap and Dunn, which is a system designed for handling inconsistent as well as potentially conflicting information. These features, we believe, make our framework particularly promising from a computer science perspective.
Combination Analysis of Data Encryption Standard (DES) Algorithm and LUC Algorithm on File Security

Cryptography is a science of encryption techniques, where the data to be encrypted will be encrypted using a key into data that is difficult to read for other parties who do not have a decryption key. DES algorithm is a symmetric cipher block cryptographic algorithm that uses a block size of 64 bits and a key size of 56 bits. LUC algorithm is an asymmetric cryptographic algorithm that uses two primes to generate public keys and secret keys. Hybrid crypto systems can provide a greater level of security. In this study the DES algorithm will be used to encrypt the message, while the LUC algorithm is used to encrypt the DES external key before the key is sent to the recipient. The results of the study are the length of the file character is directly proportional to the time of the encryption and decryption process. In combination the DES algorithm and the LUC algorithm are very dependent on the LUC key generation process. If the p value and the q value used are greater the longer the LUC key generation process. The repeated encryption process with the same DES external keywords using the LUC algorithm will produce different cipherkeys. Message security using hybrid DES and LUC algorithm cryptography is better because the cipherkey generated from DES external key encryption is different and longer.
Removal of High-Density Impulsive Noise in Giemsa Stained Blood Smear Image Using Probabilistic Decision Based Average Trimmed Filter

The chapter focuses on the removal of salt and pepper noise from the contaminated giemsa stained blood smear image using Probabilistic Decision Based Average Trimmed Filter (PDBATF). The experiments outcomes are recorded and compared with recently reported algorithms. The proposed algorithm provides better accuracy level in terms of peak signal to noise ratio, image enhancement ratio, mean absolute error and execution time.
Label-Smooth Learning for Fine-Grained Visual Categorization

Fine-Grained Visual Categorization (FGVC) is challenging due to the superior similarity among categories and the large within-category variance. Existing work tackles this problem by designing self-localization modules in an end-to-end DCNN to learn semantic part features. However the model efficiency of this strategy decreases significantly with the increasing of the number of categories, because more parts are needed to offset the impact of the increasing of categories. In this paper, we propose a label-smooth learning method that improves models applicability to large categories by maximizing its prediction diversity. Based on the similarity among fine-grained categories, a KL divergence between uniform and prediction distributions is established to reduce model’s confidence on the ground-truth category, while raising its confidence on similar categories. By minimizing it, information from similar categories are exploited for model learning, thus diminishing the effects caused by the increasing of categories. Experiments on five benchmark datasets of mid-scale (CUB-200-2011, Stanford Dogs, Stanford Cars, and FGVC-Aircraft) and large-scale (NABirds) categories show a clear advantage of the proposed label-smooth learning and demonstrate its comparable or state-of-the-art performance. Code is available at https://github.com/Cedric-Mo/LS-for-FGVC .
Single Remote Sensing Image Super-Resolution with an Adaptive Joint Constraint Model

Remote sensing images have been widely used in many applications. However, the resolution of the obtained remote sensing images may not meet the increasing demands for some applications. In general, the sparse representation-based super-resolution (SR) method is one of the most popular methods to solve this issue. However, traditional sparse representation SR methods do not fully exploit the complementary constraints of images. Therefore, they cannot accurately reconstruct the unknown HR images. To address this issue, we propose a novel adaptive joint constraint (AJC) based on sparse representation for the single remote sensing image SR. First, we construct a nonlocal constraint by using the nonlocal self-similarity. Second, we propose a local structure filter according to the local gradient of the image and then construct a local constraint. Next, the nonlocal and local constraints are introduced into the sparse representation-based SR framework. Finally, the parameters of the joint constraint model are selected adaptively according to the level of image noise. We utilize the alternate iteration algorithm to tackle the minimization problem in AJC. Experimental results show that the proposed method achieves good SR performance in preserving image details and significantly improves the objective evaluation indices.
AN ENSEMBLE MODEL FOR STATISTICAL MONITORING OF PATTERNS IN BIVARIATE PROCESSES BASED ON MULTIPLE ARTIFICIAL NEURONAL NETWORKS

Multivariate control graphics detect signals out of control of the process. These signals are special patterns of joint variation, but they do not allow to determine what types of variation patterns take place in the individual variables. The referred problem has been treated through models of pattern recognition (PR) by Artificial Neural Networks (ANN). There are important advances in solving the problem to univariate cases, but not so in multivariate cases. There is no research which affirms that a single ANN can identify a multivariate out-of-control signal and recognize the special variation types of the variables individually. This research presents a model of PR of special variation in bivariate processes, and is based on an organized assembly of different types of ANN which are activated sequentially. With this work, it is possible to obtain a diagnosis of the bivariate process control that simultaneously recognizes the type of variation of the variables involved. This novel model provides the basis of new knowledge about statistical control of bivariate processes by PR through ANN. The model had two stages of training: experimental and industrial. The first one worked with data generated by Montecarlo simulation and the second one with data from a process that performs manufactured operations on metal bars used in the speed system in automobiles. Keywords: Statistical quality control, Pattern recognition, Artificial neural networks, Perceptron, Backpropagation
The Effectiveness of Noise in Data Augmentation for Fine-Grained Image Classification

Recognizing images from subcategories with subtle differences remains a challenging task due to the scarcity of quantity and diversity of training samples. Existing data augmentation methods either rely on models trained with fully annotated data or involve human in the loop, which is labor-intensive. In this paper, we propose a simple approach that leverages large amounts of noisy images from the Web for fine-grained image classification. Beginning with a deep model taken as input image patches for feature representation, the maximum entropy learning criterion is first introduced to improve the score-based patch selection. Then a noise removal procedure is designed to verify the usefulness of noisy images in the augmented data for classification. Extensive experiments on standard, augmented, and combined datasets with and without noise validate the effectiveness of our method. Generally, we achieve comparable results on benchmark datasets, e.g., CUB-Birds, Stanford Dogs, and Stanford Cars, with only 50 augmented noisy samples for every category.
End-to-End Topic Classification without ASR

This document explores an end-to-end model for topic classification without automatic speech recognition(ASR) system. In general, we always employ the ASR system to convert the speech recording to text and then use the standard natural language processing(NLP) knowledge to complete the topic identification task. However for low-resourced language, the lack of transcribed text and good language model results in the absence of practical speech recognition system. In this case, our paper proposes an end-to-end system for topic modeling based on mel-frequency cepstrum coefficients(MFCCs) feature. Comparing with the lexical discovery methods (such as segment dynamic time warping(DTW)), our method which can be applied to large-scale dataset which significantly reduces training time and model complexity.
Organization of Research Work of Future Pedagogues in Higher Education Institutions: Experiential Learning Results

The article is devoted to the results of experimental training in forming the readiness of future pedagogues to successfully perform their professional tasks by means of research work.Also this article deals with the author's approach to the application of methodological tools for the formation of professional competence in higher education applicants at all stages of university education.The article is focused on the need to build modern higher education based on the principles of organizing research activities with a practical and pragmatic orientation.The factors of systemic influence on the effective research activity of future pedagogues are identified and analyzed.The experimental study was conducted in a parallel structure, thus, two homogeneous objects were identified -experimental and control groups.In experimental groups, the author's method of teaching future teachers was used; in the control groups, the educational process followed the traditional scheme of studying in HEIs.The comparison of both objects of the study was performed both before and after the experiment.It is proved that successful implementation of the experimental programme will contribute to meet the needs of future educators in quality higher education, expand their professional competence, create the conditions for scientific research.The results of comparisons and contrasts of initial and final characteristics of the formation of the future pedagogues' readiness to successful perform professional tasks by means of research are presented; the efficiency and effectiveness of the above-mentioned process are proved.
Continuous high-resolution observation system using high-speed gaze and focus control with wide-angle triangulation

High-resolution image-based observations of 3D motion from multiple objects are useful for individual identification and monitoring of crowds of objects/creatures. However, using only a wide-angle camera does not allow suitable observations given the tradeoff between angle of view and resolution. In contrast, a high-speed gaze controller and a high-speed variable focus lens can provide image recognition with high-resolution observations. On the other hand, simultaneous control can be inefficient for single-target tracking and multiple-target switching against automatic gaze/focus scanning. Therefore, we propose a hybrid high-speed gaze/focus control tracking system and cooperative operation with a wide-angle camera. Triangulation from the tracking system and wideangle camera based on a fundamental matrix and a lookup table enables the fast convergence of the gaze/focus to different targets, resulting in efficient continuous high-resolution observation of multiple dynamic objects. We experimentally verified the calibration accuracy, the high temporal response of gaze/focus during tracking onset, and the suitable identification performance for a very small moving marker under high-resolution tracking observation. We have also confirmed the long and detailed tracking of freely swimming fish under continuous observation.
Characteristic polynomials of the curve y2 = x7 + ax4 + bx over finite fields

In this work, we list all possible characteristic polynomials of the Frobenius endomorphism for genus 3 hyperelliptic curves of type y 2 = x 7 + ax 4 + bx over finite field F q of characteristic p > 3.
The Development of Augmented Reality Educational Media Using Think-Pair-Share Learning Model For Studying Buginese Language

Language is a tool used for communication. Indonesia has a variety of languages, one of which is the Buginese language. As a language whose level of speakers exceeds five million, the popularity of this language begins to fade and is rarely used over the times. The Buginese language has its own letter called Lontara which consists of 23 letters. In addition to learning it difficult, educational media to learn this language, especially how to write letters is rarely developed. Learning this language is mandatory for formal school students ranging from elementary school to high school. However, a number of teachers noticed that Lontara writing lessons were very difficult for students to understand. Because of the difficulty level of writing, this research was conducted to answer and overcome these problems by developing an educational media called Lontara Augmented Reality. The research and development method (RnD) was utilized as the method in this study and it collaborates with ASSURE as the media development model. ASSURE model is used in the media design process. This media has an embedded learning model which called think-pair-share. The presence of this learninf model aims to enhance user skill in learning due to it provides learning with pair features. Educational media design has been validated by four media experts and four content materials validators. In the first iteration of validation, the average value of 3.90 was obtained with an average total score of 206.5 and a total percentage of around 78% (good category). After media being revised, an increase of 11% from the former iteration became 89% with 4.45 in average and total score of 235.75 (very good category). Furthermore, expert material content obtained an average value of 4.74 with a total average score of 99.5. with the percentage of the feasibility of the media reaching 95%. The percentage of usability testing results shows a great result with 96.91% in average and functionality tests around 100% which means that all of these functions this educational media application can work properly. in addition, performance testing and compatibility results show a normal activities. Memory and CPU usage are in normal conditions and not disturbing the applicaton performance. Lontara Augmented reality compatible in many android smartphones. Around 15 types of devices have been installed and it can run normally in these devices.