Reviewerly/exHarmony huggingface dataset:
Image De-noising by an Effective SURE-Based Weighted Bilateral Filtering

The weighted bilateral filtering has been widely used in image de-noising, and there have been numerous improved bilateral filters. When dealing with the images with different noise levels, however, they may get de-noising results that are not robust and cannot remove salt and pepper (s&p) noise effectively. In this paper, we propose a new weighted bilateral filter, which preserves edge information and improves robustness, for image de-noising. The new filter introduces a new pseudo-median bilateral filter (NMBF) combined with a robust bilateral filter (RBF) in a weighted way for removing additive Gaussian and s&p noise. In addition, given the edge preserving, a robust estimation kernel function is applied in NMBF. Besides, to get optimal parameters, the modified filter involves the Stein’s unbiased risk estimate (SURE) method. A diversity of images polluted by various degrees of Gaussian, s&p, and mixed noise were used to evaluate the performance of this new bilateral filter.
Research on Template-Based Factual Automatic Question Answering Technology

With the increase of people’s demand for information retrieval, question answering system as a new generation of retrieval methods has attracted more and more attention. This paper proposes an automated template generation method based on remote supervision. In the process of generating template, in order to identify the keywords express the semantics of the problem, this paper designs an algorithm to mapping entity relationship to keywords by using pattern mining and statistical methods. This method implements automation of template generation, and makes the templates more accurate. For the template matching, by Deep-learning-based template matching algorithm, this paper use vector to represent words, and designs a tree-based convolutional neural network to extract features of the dependency syntax information. Building a neural network model for template prediction. Experimental results show that the above method can effectively improve the matching accuracy compared with the traditional template matching method.
A Deep Speaker Embedding Transfer Method for Speaker Verification

Recently a kind of deep speaker embedding called x-vector has been proposed. It is extracted from deep neural network and considered as a strong contender for next-generation representation for speaker recognition. However, training such DNNs requires a lot of data, usually thousands of hours. If we want to apply x-vector to Mandarin task but we only have a small amount of data, we can train DNNs on another language and fine-tune the DNNs with these little data. Firstly, we proposed a pure data driven method to transfer DNNs across languages and tasks. Secondly, we investigated the question that how to choose between training DNNs from scratch and reusing a pre-trained model by the transfer method we proposed. To answer the question, in this paper, we present the results of adapting a x-vector based speaker verification system from English to Mandarin by fine-tuning the front-end DNNs. We investigate the trend of performance improvement in these two training strategies when data size increasing. Experiment results show that adapting a pre-trained English model with a small amount of Mandarin data can easily reduce the equal error rate (EER). They also demonstrate that system trained from scratch is able to achieve better performance only when feed enough data. Finally, we test the performance of the two systems in noisy environment and found that system trained from scratch outperforms system fine-tuned with a pre-trained model.
A New Signal-Selective Wide-Band Ambiguity Function

A new signal-selective wide-band cross-ambiguity function is introduced. It performs a sinusoidally weighted correlation of a signal with a time-scaled and delayed version of a reference signal. If the reference signal is almost cyclostationary and the frequency of the weighting sinusoid is one of its cycle frequencies properly scaled, the new function, referred to as the wide-band cyclic cross-correlation function, exhibits the signal selectivity properties that are typical of cyclostationarity-based techniques. The new function is exploited for the localization and speed estimation of a wide-band moving source whose signal impinges on two sensors in a severe noise and interference environment.
Towards Standardization of Threshold Schemes at NIST

The Computer Security Division at the National Institute of Standards and Technology (NIST) is taking steps towards the standardization of threshold schemes for cryptographic primitives. These schemes, applicable to single-device and multi-party implementations, are designed with multiple components in a way to enable essential security properties even when up to a certain threshold number of the components are compromised. This offers a path to mitigate attacks on the implementations and operations of cryptographic primitives, and therefore enhance their security. Properties of interest include secrecy of keys (including resistance against side-channel attacks that exploit leakage from real implementations), integrity of computed values, and availability of operations.
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In this paper, we investigate free Hermitian self-dual codes whose generator matrices are of the form [I,A+vB] over the ring F2+vF2={0,1,v,1+v} with v2=v. We use the double-circulant, the bordered double-circulant and the symmetric construction methods to obtain free Hermitian self-dual codes of even length. By describing a new shortening method over this ring, we are able to obtain Hermitian self-dual codes of odd length. Using these methods, we also obtain a number of extremal codes. We tabulate the Hermitian self-dual codes with the highest minimum weights of lengths up to 50.
KNN-Based Pseudo-supervised RCNN Framework for Text Clustering

This paper explores the application of recurrent convolutional neural networks (RCNN) to text clustering, an unsupervised task in natural language processing (NLP). The RCNN is trained with pseudo-labels that are generated by pre-clustering on unsupervised document representations. To enhance the quality of pseudo-labels, the K-Nearest Neighbors (KNN) algorithm is used to select training samples for the neural network. After the deep feature representations of all documents have been obtained using the trained RCNN, the agglomerative hierarchical clustering (AHC) algorithm is used to cluster them. The experimental results on two public databases show that the proposed approach significantly boosts the performance of text clustering.
Implementation of Naive Bayes for selection of green space areas

Abstract City development, population growth, and climate influence triggering a flood disaster. Green open space is one of the alternative flood prevention in urban areas. However, in determining the area of green open space is one of the alternative flood prevention in urban areas. However, in determining the area construction of the green open spaces in urban areas because it has undergone changes in the city, many parameters need to be considered for assessment, a simple way to solve it is by utilizing current technology and using data mining techniques. Data mining is an option that can be used to determine the area of green open space development based on existing data patterns. The Naïve Bayes Algorithm is the algorithm used in this research, the algorithm produces an average accuracy value is 53,94094% and gets the highest accuracy value at a data ratio of 80:20 with an accuracy value is 61%.
A Hybrid Technique for Unsupervised Dimensionality Reduction by Utilizing Enriched Kernel Based PCA and DBSCAN Clustering Algorithm

Selection of relevant features is a significant technique in the real time applications. It constitutes a number of data related to the ever-augmenting domains like financial organization, education etc. For recognition of relevant features, the results should be determined accurately. In the developed work Terry dataset is considered for unsupervised learning. Whereas significant features are selected by embedded method and the selected features are processed with enriched kernel based Principal component Analysis (PCA) for dimensionality reduction, which is further evaluated with Density-based spatial clustering of applications in the noise (DBSCAN) algorithm for developing a better performance.
Comparison of Breast Cancer Multi-class Classification Accuracy Based on Inception and InceptionResNet Architecture

Breast Cancer is the tumor that occurs most commonly in women and follows lung cancer in the most common cancers. Mortality rates caused by cancer can be reduced if early detection and treatment mechanisms are instituted. With recent advances in Deep Learning and Computer Vision, its application in diagnosis through pattern recognition is fast emerging. This paper compares the classification performance of two convolutional neural network models into benign and malignant subclasses for a breast cancer histopathological dataset. The first is built on Inception v3 architecture while the second contains residual connections in the Inception network called InceptionResNet v2. Performance has been enhanced by augmenting the data. Time taken to train and computational cost have been reduced through transfer learning. The results show accuracy from 85.9% to 91.3% on Inception v3 model. Inception Resnet v2 model performs better than Inception v3 with accuracies ranging from 89.8% to 94.6%.
Development of transmitter and receiver for fox hunting activity

Transmitter hunting, also known as fox hunting, is a popular activity amongst amateur radio hobbyists. The concept of this activity is based on radio direction finding. This study has two main parts, namely, the transmitter and the receiver. The transmitter uses Arduino Uno and Baofeng radios. GPS and GSM tracker is also added at the transmitter for location-monitoring purposes. The transmitter will be hidden in a designated area, and a Morse code that has been programmed in Arduino will be sent. For the receiver part, two receiver antennas with different designs and materials, namely, Yagi and Moxon, are used. These two antennas operate in a frequency range of 144 MHz and 145 MHz, respectively, and will be connected to an attenuator to reduce the strength of the received signal. Subsequently, these antennas will be connected to a Baofeng radio. The type of attenuator in this study is a step attenuator with three different gains, namely, 6, 10 and 20 dB. The use of an attenuator can reduce gain between 6.62 dB and 10.04 dB for Yagi antenna and between 2.66 dB and 12.64 dB for Moxon antenna. A test has been carried out at Parit Haji Rais area to compare the reception quality between Yagi and Moxon antennas. The test is carried out at four locations with different distances, namely, 350, 550, 700 and 900 m. The antennas can work well up to 550 m from the transmitter. Moxon antenna has a slightly better performance than Yagi antenna based on the distance.
Development of digital currency technology

This research aims to produce digital technology through bitcoin platform. By using blockchain technology, people can use decentralized peer-to-peer transactions that can be used by users. The method that is used in this research are qualitative and quantitative methods, where the research data in the form of numbers and analysis using statistics. Sampling technique used simple random sampling and data processing tool in the form of questionnaire. From the research results obtained are digital technology and digital data can produce data in accordance based on user needs. Quantitative analysis techniques are also called statistical techniques and used to analyze data in the form of questionnaire numbers. The results of this research can be a recommendation for people who want to keep abreast of digital currency technology to be as digital assets.
Representative News Generation using Automatic Clustering in Big Data Environment

There are 43,000 online media in Indonesia which publish at least one until two news every hour. The amount of information exceeds human processing capacity, resulting in several impacts on humans such as confusion and psychological stress. In this research we propose a new system for processing incremental news data and provide a mechanism for determining representative news by applying Automatic Clustering algorithm. The system consists of 4 main functions: (1) Data Acquisition and Preprocessing, (2) Keyword Feature Extraction, (3) Data Aggregation, Automatic Clustering, and (4) Incremental Clustering. The news is grouped in to same information based on information-retrieval. This system runs on big data environment to process large amount of data. There are 3,000 news collected in database by the system in a whole day in database. The collected news are processed using Automatic Clustering and then aotumatically grouped into 389 clusters. A cluster is identified as the unknown cluster and the clusters are evaluated without enclosing single member clusters. For experimental study, the system performed 93,51%.
Non-Linear Enhancement Techniques for Mammograms

Non-linear enhancement techniques encompass various categories of approaches. Those specific to or commonly applied for processing of medical images include morphological filtering, fuzzy-based enhancement and non-linear filters.
How Consistent Is Your GUI Design?

Maintaining design consistency is very important for IT organizations with complex products. In order to improve design consistency, we need a way to measure it first. Here we describe a method to evaluate GUI consistency, and we then develop a consistency score to quantify the consistency performance. This approach is initially designed towards our online cloud platform and we believe it can be applied to a variety of user interface types.
Research on College Students’ Academic Early Warning System Based on PCA-SVM

Nowadays, how to use education industries’ data is a hot topic. Taking the scores of 70 courses of 216 students in the College of Mathematics and Data Science as the sample, the Principal Component Analysis-Support Vector Machine (PCA-SVM) model for early warning system is established by selecting the first-year course scores and graduate grades. The PCA-SVM is better than Support Vector Machine (SVM) in accuracy, probability of false alarm and F1-score. The PCA-SVM model for early warning system can provide students with more accurate academic guidance, and can provide scientific basis for teaching reform and management decision making.
Evaluating the Utility of the Usability Model for Software Development Process and Practice

Processes and practices are tools that organizations use to improve their capabilities. Agile transformations are very popular, as are process and practice improvement and adoption initiatives, but they face many challenges, including low adoption rates. Improving process and practice usability might increase adoption rates and effective use. This idea led us to define a Usability Model for Software development Process and Practice (UMP), consisting of characteristics and metrics, in the quest to improve the work experience of software development practitioners and the effectiveness of process and practice adoption initiatives. The goal of this paper is two-fold: (1) to present the refined version of the UMP and (2) to describe a study on the application of the UMP to the Visual Milestone Planning (VMP) method in order to evaluate UMP’s utility, specifically its ability to produce useful feedback in a real-life scenario. The study produced preliminary confirmation that the UMP is applicable to the VMP, along with specific feedback on improvement opportunities for the VMP. An interview with the VMP creator confirmed that the UMP model and the evaluation feedback were valuable for enhancing VMP adoption. In summary, we can conclude that the empirical results obtained show that UMP can be useful. Nonetheless, more studies are needed to provide further confirmation in different scenarios.
Leveraging Social Media Linguistic Features for Bilingual Microblog Sentiment Classification

Social media and microblogs have become an integral part of everyday life. People use microblogs to communicate with each other, express their opinion about a wide range of topics and inform themselves about issues they are interested in. The increasing volume of information generated in microblogs has led to the need of automatically determining the sentiment expressed in microblog comments. Researchers have worked in systematically analyzing microblog comments in order to identify the sentiment expressed in them. Most work in sentiment analysis of microblog comments has been focused on comments written in the English language, whereas fewer efforts have been made in predicting the sentiment of Greek microblog comments. In this paper, we propose a lexicon-based sentiment analysis algorithm for the sentiment classification of both Greek and English microblog comments. The proposed method uses a unified approach for determining the sentiment of comments written in both languages and incorporates techniques that exploit the distinctive features of the language used in microblogs in order to accurately predict the sentiment expressed in microblog comments. Our approach achieves promising results for the sentiment classification of microblog comments into positive, negative or neutral.
WebEL: Improving Entity Linking with Extra Web Contexts

Entity Linking is the task of determining the identity of textual entity mentions given a predefined Knowledge Graph (KG). Plenty of existing efforts have been made on this task using either "local" information (contextual information of the mention in the text), or "global" information (relations among candidate entities). However, either local or global information might be insufficient especially when the given text is short. To get richer local and global information for entity linking, we propose to enrich the context information for mentions by getting extra contexts from the web through Web Search Engines. Based on the intuition above, two novel attempts are made. The first one adds web-searched results into an embedding-based method to expand the mention's local information, where an attention mechanism is applied to help generate high-quality web contexts, while the second one uses the web contexts to extend the global information, i.e., finding and utilizing more extra relevant mentions from the web contexts with a graph-based model. Finally, we could combine the two models we proposed to use both extended local and global information from the extra web contexts. Our empirical study based on six real-world datasets shows that using extra web contexts to extend the local and global information could effectively improve the performance of entity linking.
An efficient generalized shift-rule for the prefer-max De Bruijn sequence

One of the fundamental ways to construct De Bruijn sequences is by using a shift-rule. A shift-rule receives a word as an argument and computes the symbol that appears after it in the sequence. An optimal shift-rule for an (n,k)-De Bruijn sequence runs in time O(n). We propose an extended notion we name a generalized-shift-rule, which receives a word, w, and an integer, c, and outputs the c symbols that comes after w. An optimal generalized-shift-rule for an (n,k)-De Bruijn sequence runs in time O(n+c). We show that, unlike in the case of a shift-rule, a time optimal generalized-shift-rule allows to construct the entire sequence efficiently. We provide a time optimal generalized-shift-rule for the well-known prefer-max and prefer-min De Bruijn sequences.
A Semantic Taxonomy for Weighting Assumptions to Reduce Feature Selection from Social Media and Forum Posts

Numerous researchers have worked on the knowledge-based semantics of words to clarify the ambiguity of (https://github.com/alimuttaleb/Ali-Muttaleb/blob/master/Synonym.txt) synonyms in various natural-language processing fields, such as Wikipedia, websites, and social networks. This paper attempts to clarify ambiguities in the lexical semantics of taxonomy in social media. It proposes a new knowledge-based semantic representation approach that can handle ambiguity and high dimensionality issues in text mining. The proposed approach consists of two main components, namely, a feature-based method for incorporating the relationships between lexical sources and a topic-based reduction method to overcome high dimensionality issues. These components help weight and reduce the relevant features of a concept. The proposed approach captures further lexical semantic similarity between words. It also evaluates the use of (https://wordnet.princeton.edu) WordNet 3.1 in text clustering and constant weighting assumption in the feature-based method used to select concepts/words from social media. To address ambiguity, the semantics of concepts with small feature subset size reduction are represented, and the performance of the semantic similarity measurement is improved. The proposed method evaluates word semantic similarity using the (https://github.com/alimuttaleb/semantictaxonomy/blob/master/mc30.txt) MC30 dataset in WordNet and obtains the following results for semantic representation: r = 0.82, p = 0.81, m = 0.81, and nz = 0.96.
Unidirectional Sparse Tensor Based Model for the Noise Removal of Remote Sensing Image

In this paper, we mainly focus on a quite challenging denoising problem in remote sensing images, which is to simultaneously remove Gaussian noise and sparse noise that mainly include stripes and salt-pepper noise. We propose a convex unidirectional sparse model based on mode-3 tensor modeling to remove the mixture noise. A proximal alternating direction method of multipliers (ADMM) based algorithm is designed to effectively solve the given minimization model. Comparing with some recent state-of-the-art denoising methods, the proposed method shows the best performance from visual and quantitative aspects.
Extended Fibonacci Series for Selection of Carrier Samples in Data Hiding and Extraction

Now a days data sensitivity and information security remains as a key challenge for every IT industry. Most of the industries have their focus on data security. The existing data security techniques like cryptography, steganography are used only to protect the data from unauthorized access. Recently, it is found that cryptography is not enough to protect the data when it is transmitted over wireless network. Proposed concept focuses on the Data Hiding mechanism, which enables the proper use of sample selections of carrier channel through the use of extended Fibonacci series. Proposed concept have achieved better results and generates less noise in carrier object after the concealing of secret information in it.