Reviewerly/exHarmony huggingface dataset:
Construction Algorithm for Adaptive Morphological Structuring Elements Based on the Neighborhood Gray Difference Changing Vector Field and Relative Density

Structuring elements of fixed shape and size are used in most conventional mathematical morphology operations, which makes the border of image targets shift, produces new image artifacts and loses small image objects due to the diversity and complexity of the image targets. In this paper, a new construction algorithm for adaptive structuring elements is proposed based on the neighborhood gray difference changing vector field and relative density. The proposed structuring element is able to adaptively change shape according to the gray and edge characteristics of an image. This algorithm involves first incorporating the gray difference changing vector field to smooth the local image region and make the gray level within the image target more uniform and then defining a border degree function based on relative density to determine whether the center pixel of the local image region is a border pixel. The adaptive structuring element is composed of all the strong border pixels found in a local image region. Dilation and erosion operations and other derivative operations are proposed with this new adaptive structuring element based on conventional morphology operation principles. The experimental results show that this proposed algorithm is able to effectively suppress the shifting effect of the image target borders while accurately locating the border of the image target region. Additionally, other effective image information is retained and image distortion is reduced while weakening the image details.
Parallel Gpu Based Offline Signature Verification Model

Handwritten signature has a key role in personnel authentication today. Institutions such as banks are using the handwritten signatures to control false transactions. Due to an increase in access by people to institutions such as banks, insurance companies etc., the volume of signatures are increasing day by day and need to be verified in real time. Hence, there is always a demand for a reliable and fast signature verification system. Researchers have contributed several techniques and ideas for signature verification. However, it has been investigated that the models are inefficient and their real-time implementation is a difficult task. In the present manuscript, a GPU based offline signature verification model has been proposed. The proposed signature verification model uses Hidden Markov Model (HMM) for both training and verification of the signatures. It has been found that the proposed GPU based model has more than 5 times faster response. The quantitative evaluations have been done using the average false rejection rate (FRR) and false acceptance rate (FAR). The lower values have been obtained which indicates that the proposed model provides higher reliability.
A Model to Enhance Security Of Digital Transaction

Factor such as Authentication, security, confidentiality & integrity are some of the most dominant Important factors required for online transaction. Online banking 24/7 availability and responsiveness has added this service into our day to day life due to location independence UPI mobile wallet. Digital financial transaction is under various threats like phishing, pharming, malware, Man-In-The-Middle (MITM) attack. Attacker would find under different attack ways to keep on attacking the digital transaction. Three factor authentications are a game changer. Security over the digital transaction continues to be a key focus area for monetary establishment. Firms must guarantee consistence strategies to meet the increasing regulatory law to safeguard the Digital transaction and save hard earned money and expressing the limitation for various authentication techniques. By using the three-factor authentication where we use a combination of three authentication techniques such as password certificate and biometric OR TOTP. We can secure the transaction from the inception to the end.
Security on Data Auditing Protocols for Cloud Storage Data

Cloud Computing (CC) has attained a great interest from various researchers because of its services to end user according to the requirements posed by the Cloud Service Providers (CSP). The problem of storage space can be resolved by using Cloud Storage Service (CSS) that tends to provide a vast storage space to the local end users. As CSS does not have its own storage to secure the user's data, it poses many data security challenges in the cloud computing paradigm. However, some traditional systems don't fulfil the security requirements for enterprises and consumers in Service Level Agreements (SLAs). This remains a significant bottleneck for service adoption. The discussion of recent developments on the security of cloud computing paradigm is provided in this research work. To deal with security threats, various existing approaches along with its comparative analysis are reviewed. The various security problems and its solution space associated with clouds are understood by this research work and further it also provides an effective future work to solve the emerging cloud computing challenges.
Adaptive Texts Deconvolution Method for Real Natural Images

Understanding of real scenes is an important task in augmented reality (AR). Identifying and comprehension of texts from real scene images is useful in implementing robust AR devices. Therefore, improving the quality of text images for better readability is very important and text images deconvolution is useful to increase the accuracy of AR pattern recognition algorithms. In this work, we propose an estimation method for the filtering operator within total variation deconvolution model. This method is applied for the texts image deconvolution problem from natural images. In the experiments, we use NIQE score - the blind quality assessment metric - to assess the deconvolution quality. We further compare the proposed method with other image deconvolution models such as the blind deconvolution, the Lucy and the Wiener methods. The experimental indicate that our deconvolution method works effectively for texts enhancement across different scenes with high quality results.
Implementation of a Biometric Interface in Voice Controlled Wheelchairs

In order to assist physically handicapped persons in their movements, we developed an embedded isolated word speech recognition system (ASR) applied to voice control of smart wheelchairs.However, in spite of the existence in the industrial market of several kinds of electric wheelchairs, the problem remains the need to manually control this device by hand via joystick; which limits their use especially by people with severe disabilities.Thus, a significant number of disabled people cannot use a standard electric wheelchair or drive it with difficulty.The proposed solution is to use the voice to control and drive the wheelchair instead of classical joysticks.The intelligent chair is equipped with an obstacle detection system consisting of ultrasonic sensors, a moving navigation algorithm and a speech acquisition and recognition module for voice control embedded in a DSP card.The ASR architecture consists of two main modules.The first one is the speech parameterization module (features extraction) and the second module is the classifier which identifies the speech and generates the control word to motors power unit.The training and recognition phases are based on Hidden Markov Models (HMM), K-means, Baum-Welch and Viterbi algorithms.The database consists of 39 isolated speaker words (13 words pronounced 3 times under different environments and conditions).The simulations are tested under Matlab environment and the real-time implementation is performed by C language with code composer studio embedded in a TMS 320 C6416 DSP kit.The results and experiments obtained gave promising recognition ratio and accuracy around 99% in clean environment.However, the system accuracy decreases considerably in noisy environments, especially for SNR values below 5 dB (in street: 78%, in factory: 52%).
Distilling Knowledge for Distant Speech Recognition via Parallel Data

In order to improve the performance of distant speech recognition tasks, this paper proposes to distill knowledge from the close-talking model to the distant model using parallel data. The close-talking model is called the teacher model. The distant model is called the student model. The student model is trained to imitate the output distributions of the teacher model. This constraint can be realized by minimizing the Kullback-Leibler (KL) divergence between the output distribution of the student model and the teacher model. Experimental results on AMI datasets show that the best student model achieves up to 8.5% relative word error rate (WER) reduction when compared with the conventionally-trained baseline models.
An Unsupervised Method for Detecting Style Breaches in a Document

In this paper, we propose an unsupervised method for identifying style breaches in a given document, also known as intrinsic plagiarism detection. In fact, plagiarism is one of the major challenges in various domains. Supervised learning techniques fail to capture the stylistic changes in a text effectively and this is mainly due to the static segmentation of the text. For this reason, we present in this paper our proposed method for intrinsic plagiarism detection, we experimented with the unsupervised algorithm Kmeans and the similarity measure Cosine. Our results are comparable to best systems presented in PAN@CLEF competitive conference.
Development of Faculty Profiling and Capability Enhancement Advisor System using Intelligent Predictive String Search Algorithm

Faculty personnel nowadays are faced with a change of roles and responsibilities. As a result, they must involve themselves in the current trend of professional growth. Faculty development enables personal and professional growth so there is a great need for faculty development plans. The authors of this study developed a Faculty Profiling and Capability Enhancement Advisor System that can generate a faculty development plan according to the profiles and qualifications of the faculty members based on their individual Personal Data Sheets. The system can also recommend further trainings for faculty members based on their specializations. These outputs are accurately generated by the system through utilizing the Intelligent Predictive String Search Algorithm (IPSSA). The developmental and descriptive methods of research were adopted in the study, both in the development and evaluation of the system. The evaluation form included the following criteria, based on ISO 9126 standards: Functionality, Reliability, Efficiency, Maintainability, and Portability. The overall evaluation of the system was "Acceptable", based on the combined inputs of the respondents comprised of faculty members, school administrators, and ICT experts. Improvements to the code and security of the system through implementation of Secure Socket Layer (SSL) encryption are recommended. Another enhancement that can be added is a function that provides the system flexibility in updating forms as they are amended.
Optimization Sentiments of Analysis from Tweets in myXLCare using Naïve Bayes Algorithm and Synthetic Minority Over Sampling Technique Method

Abstract Text mining can be used to classify opinions about complaints or not complaints experienced by XL customers. This study aims to find and compare classifications in the sentiments of analysis from the view of XL customers. This dataset was derived from tweets of XL customers written on myXLCare Twitter account. In text mining techniques, “transform case”, “tokenize”, “token filters by length”, “n-gram”, “stemming” were used to build classification and sentiments of analysis. Gataframework tools were used to help during preprocessing and cleansing processes. RapidMiner is used to help create the sentiment of analysis to search and compare two different classifications methods between datasets using the Naïve Bayes algorithm only and Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE). The results of the two methods in this study found that the highest results were using the Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE) with an accuracy of 86.33%, precision 82.85%, and recall ratio 92.38%.
On k-Error Linear Complexity of Binary Sequences Derived From Euler Quotients Modulo 2p

This work was supported in part by the Projects of International Cooperation and Exchange National Natural Science Foundation of China and the Russian Foundation for Basic Research (NSFC-RFBR) under Grant 61911530130, in part by the National Natural Science Foundation of China under Grant 61373140. The work of Vladimir Edemskiy was supported by the RFBR-NSFC according to the research project under Grant 19-51-53003. The work of Chunxiang Xu was supported in part by the National Natural Science Foundation of China under Grant 61872060, in part by the National Key Research and Development Program of China under Grant 2017YFB0802000, and in part by the Key Laboratory of Financial Mathematics of Fujian Province University (Putian University) under Grant JR201901.
FIR Filter Design using Wavelet Coefficients

The recent developments in the field of digital signal processing makes use of the wavelet mathematical function. The multi-resolution ability of the wavelets makes it outperform Fourier transformations in terms of data compression and quality. This paper shows that wavelet coefficients can be considered as digital Finite Impulse Response (FIR) filter coefficients in the FIR filter design. Other most popular FIR filter design methods are the windowing method, Frequency sampling method and optimal filter design method. The paper also presents the comparison of the popularly used rectangular window method filter design with that of the wavelets.
Design and Implementation of a Program Trading Platform for Futures

With the increase of trading volume and frequency in futures market, manual trading is unable to meet the requirements of investors in many cases. Meanwhile, program trading has advantages of quick reaction and objectivity, which is suitable for realizing complex trading strategies. Therefore, we proposed a program trading platform for futures, making it possible for investors to realize their strategies by programs and execute them automatically. The platform consists of four layers of interface, processing, strategy and user. In the processing layer, we designed an event-driven engine as the processing core of the platform to ensure high instantaneity, stability and concurrency. Then we explained how users realize and manage their trading strategies in the strategy layer. After optimizing the design, we implemented the program trading platform and did system testing on it. The results show that the platform can access the exchanges through the Internet to trade futures, and can achieve our objectives in instantaneity, stability and concurrency.
SRCS: A New Proposed Counting Sort Algorithm based on Square Root Method

Counting sort is one of the basic sorting algorithm in computer science which has a time complexity of $\mathcal{O}(N + K)$ where N is the number of elements and K is the maximum value among those N elements. It is superior when it comes to sort countable objects those come from a discrete set of values, such as bounded integers. But, it fails to provide efficiency if the range of K is significantly greater than N. For this reason, it is less used in practical fields of computer science though it has an extensive use as a sub-routine in other sorting algorithms. Several extension approaches on counting sort algorithm have been proposed in the literature but none of those aims to increase the limit of K. This paper proposed an extension of counting sort algorithm that is named Square Root Counting Sort (SRCS) which has an increased limit of K. Specifically, the proposed approach can handle the maximum value of K2 where the classic one is only able to solve K. The first phase of the approach is to prepare some blocks of elements using square root technique and then sort each of the blocks simultaneously to get final sorted array. The proposed extension of counting sort outperforms than classic counting sort as well as bubble, selection and insertion sort.
Implementation of the Bilingual Intercultural Education Program in a Lima-Peru School

The objective of this study is to evaluate the level of implementation of the Bilingual Intercultural Education Program (BIE) in the Bilingual Intercultural School “Shipibo Community”. The participants were 24 students of primary level (11 of 5th grade and 13 of 6th grade). We conclude that the level of implementation of the BIE program is low because, according to the dimensions evaluated, such as: teachers, curriculum content and educational materials, the basic characteristics of an intercultural school are not met. In this sense, teachers speak and write very little during classes in the Shipibo language. These include, to a lesser extent, the linguistic, technical knowledge and values of the Shipibo-Konibo population during the teaching-learning processes. In addition, with respect to educational materials, these are often found in Spanish, rarely in the Shipibo language and are also decontextualized. Finally, there is a significant difference in the level of knowledge and satisfaction that Shipibo and non-Shipibo students feel about the language, since there is a positive attitude, from the first students, towards that Amazonian language.
Optimal Feature Selection for Decision Trees Induction Using a Genetic Algorithm Wrapper - A Model Approach

The aim of this paper is to describe an approach to a sophisticated model of optimised subsets of data classification. This effort refers to a seemingly parallel processing of two algorithms, in order to successfully classify features through optimization processing, using a wrapping method in order to decrease overfitting and maintain accuracy. A wrapping method measures how useful the features are through the classifier’s performance optimisation. In cases where big datasets are classified the risk of overfitting to occur is high. Thus, instead of classifying big datasets, a “smarter” approach is used by classifying subsets of data, also called chromosomes, using a genetic algorithm. The genetic algorithm is used to find the best combinations of chromosomes from a series of combinations called generations. The genetic algorithm will produce a big number of chromosomes of certain number of attributes, also called genes, that will be classified from the decision tree and they will get a fitness number. This fitness number refers to classification accuracy that each chromosome got from the classification process. Only the strongest chromosomes will pass on the next generation. This method reduces the size of genes classified, eliminating at the same time the risk of overfitting. At the end, the fittest chromosomes or sets of genes or subsets of attributes will be represented. This method helps on faster and more accurate decision making. Applications of this wrapper can be used in digital marketing campaigns metrics, analytics metrics, website ranking factors, content curation, keyword research, consumer/visitor behavior analysis and other areas of marketing and business interest.
Video Stream Processing and Compression with Codec Choice Ability

The paper covers problem of creation of application with a graphical user interface for processing and compression of video stream by the selected codec. The application will contain a list of codecs installed on the computer, the ability to select them, as well as the ability to save the compressed file to a directory of your choice which leads to economy of HDD space.
Integrating expert system with a full-text search to solve growers' problems

This paper proposes a new approach to find the most relevant solution for growers' problems. The proposed approach utilized the full-text search of Microsoft SQL Server to index and search growers' problems. Also, it integrates the expert system with the result of full-text search by annotating the results with tips for more information, in addition to indexing the knowledge base and applying full-text search on it. We applied the proposed approach in VERCON's growers' problems module; this module contains a database of growers' problems for different crops. The experiments have been applied on wheat, a dataset of 874 problems. The results clearly indicate that the proposed approach is capable to retrieve the most relevant solution at the top 5 rankings with a high degree of accuracy.
Symmetric Reed–Muller Codes

In this paper, a variant of the Reed-Muller code, termed symmetric Reed-Muller (SRM) code, is proposed. We show that the bivariate version of SRM code forms the best-known locally-correctable codes in certain parameter regimes. Then, the minimum distance of SRM codes is provided in certain cases. Finally, the multivariate version of SRM codes is introduced.
Facilitating Data Curation: a Solution Developed in the Toxicology Domain

Toxicology aims to understand the adverse effects of chemical compounds or physical agents on living organisms. For chemicals, much information regarding safety testing of cosmetic ingredients is now scattered in a plethora of safety evaluation reports. Toxicologists in our university intend to collect this information into a single repository. Their current approach uses spreadsheets, does not scale well, and makes data curation and querying cumbersome. Semantic technologies (e.g., RDF, OWL, and Linked Data principles) would be more appropriate for this purpose. However, this technology is not very accessible to toxicologists without extensive training. In this paper, we report on a tool that supports subject matter experts in the construction of an RDF-based knowledge base for the toxicology domain. The tool is using the jigsaw metaphor for guiding the subject matter experts. We demonstrate that the jigsaw metaphor is a viable option for generating RDF. Future work includes investigating appropriate methods and tools for knowledge evolution and data analysis.
Improving Question Classification with Hybrid Networks

Question classification is a basic work in natural language processing, which has an important influence on question answering. Due to question sentences are complicated in many specific domains contain a large number of exclusive vocabulary, question classification becomes more difficult in these fields. To address the specific challenge, in this paper, we propose a novel hierarchical hybrid deep network for question classification. Specifically, we first take advantages of word2vec and a synonym dictionary to learn the distributed representations of words. Then, we exploit bi-directional long short-term memory networks to obtain the latent semantic representations of question sentences. Finally, we utilize convolutional neural networks to extract question sentence features and obtain the classification results by a fully-connected network. Besides, at the beginning of the model, we leverage the self-attention layer to capture more useful features between words, such as potential relationships, etc. Experimental results show that our model outperforms common classifiers such as SVM and CNN. Our approach achieves up to 9.37% average accuracy improvements over baseline method across our agricultural dataset.