Reviewerly/exHarmony huggingface dataset:
Malay SMS Spam Detection Tool Using Keyword Filtering Technique

Abstract Message Service (SMS) Spam is one form of mobile device attack that can affect mobile user’s security and privacy. This study developed a Malay SMS Spam detection framework specifically for Malay language. The paper discusses about the spam word detection using keywords filtering technique and security question to enable authorized user reset password when forgot the password. The development of the detection tool is done using the Object Oriented Methodology (OOM). There are four phases in OOM which is Requirement Phase, Analysis Phase, Design Phase and Coding Phase. This detection tool is then further analyzed to evaluate the functionality. This project also compares the existing tool to know the lack of current existing tool. Most of the existing spam tools are capable of identifying English text-based messages but there are close to none to filter the Malay language text-based SMS spam. It is expected that the developed tool is able to detect the SMS spam by using keywords filtering technique in Malay language.
RETRACTED: Sentimental analysis using Machine learning techniques on python

Abstract Really, a sentiment alludes to the mirrored image of feelings of people. The modern-day globe seems concerning the strings of feelings. People pleasure this is specific trouble, love, contempt and so on thru precise activities. Division of feelings i.e. good, terrible and nonpartisan, is called emotional analysis. The goal this is essential to research how textual content exam techniques may be nicely used to dive right into a percent of the supplied facts in a improvement of posts zeroing in on diverse styles of tweets dialects, tweets volumes on twitter. The project this is complete finished the use of Python.
A Data Feature Recognition Method Based On Deep Learning

Aimed at the problems of traditional data feature recognition schemes such as feature extraction difficulties, poor classification and recognition effects, and high sensitivity, a data feature recognition scheme based on deep learning is proposed to realize feature extraction and classification of the data information automatically. By analyzing the characteristics of the data and the problems of the deep neural network, the attention mechanism is introduced in the convolutional neural network to extract more important networks, and the long and short-term memory network is improved, reducing the complexity of the model, and avoiding the problem of information loss caused by long sequences.
An Observation of Usability Website for Down Syndrome Children

Abstract This study focuses on quality in a view of usability on website for Down Syndrome Children (DSC). The website specially designed for children with Down Syndrome to gain benefit and to learn how easy to use the website. This study wants to know how easy of exist website which are focus on DSC and really easy to use with using usability principles: learnability, efficiency, memorability, errors and satisfaction. The usability principles used to evaluate the website. The result shows that the website applies usability approach successfully through games which resembles learning material in it for children with Down Syndrome to learn.
Monitoring Early Warning Signs Evolution Through Time

In excessive business competition, detecting weak signals is very important to anticipate future changes and events. The process of detecting weak signals is very challenging, and many techniques were proposed to automatize this challenge but still needs the intervention of experts’ opinion. Understanding those detected signals and their evolution in time is crucial to reveal the alertness of possible future events and warnings. For this reason, this paper proposes a new algorithm to strengthen weak signals into early warning signs. The proposed algorithm aims to monitor and track weak signals’ evolution within time. The output will be a list of early warning signs and visualization to illustrate their evolution in time. Finally, to adequately understand the early warning signs obtained and enhance their semantic alertness, we used Word2Vec modeling to provide semantically similar words to these warning signs and improve their contextual alertness. We tested this algorithm on a web news dataset of 2006-2007 to detect early warning signs related to the 2008 financial crisis ahead of time. We obtained prominent results in strengthening and monitoring the evolution of early warning signs related to this crisis.
Semi Nonnegative Matrix Factorization Recommendation Algorithm Based On Bayesian Probability Model

Despite model-based Collaborative Filtering (CF) methods are popular in recommendation systems, the latent factorization is extensively concerned. However, due to lack of nonnegative constraints and intrinsic probability semantics, traditional latent factorization methods have problems of low internal semantic interpretability and prediction accuracy with unsatisfactory running time per iteration times. In this paper, we propose a semi nonnegative matrix factorization algorithm based on Bayesian probability model. Using semi supervised learning method, the original rating matrix is initialized into positive and negative mixed which guarantees intrinsic interpretable semantics. Then, in order to add probability interpretation and improve the accuracy of prediction, we utilize the Gauss-Wishart distribution as a prior distribution and use Markov Chain Monte Carlo (MCMC) to achieve Bayesian random approximation inference. The whole experiment mainly considers the number of latent factors D and differences between best results and final results under a certain number of iterations. Facing the sparsity and imbalance in large data sets, the proposed algorithm guarantees the internal interpretability and has high prediction accuracy in the real world data set under the different number of latent factors and iterations with a small convergence time.
Embedded Learning Based on RNN network and RotatE strategy for Relation Prediction in Knowledge Graph

Using knowledge graph-based relationship prediction methods usually uses a path-oriented strategy and ignores the differences between nodes neighborhood relations, resulting in excessive path redundancy information, which reduces the prediction accuracy. This paper proposes a method based on RNN network and RotatE strategy, which improves the accuracy of relationship prediction by embedding learning and accurately expressing the neighborhood relations of nodes. First of all, through the RNN network, the data association characteristics of different nodes' neighborhood are effectively learned, so that the parameters contain the information of the neighbor nodes; Then, the RotatE score strategy is used to describe the differences in the relationship between the nodes, which makes the node relationships at the encoding end more clearly distinguishable. The strategy, which combines relationship and path information, uses the neighborhood similarity of nodes and path information as the main basis for relationship prediction, thereby improving the accuracy of relationship prediction. Experiments show that the method proposed in this paper is effective, and compared with the latest algorithm, its prediction accuracy has been significantly improved.
Generation of Objections Using Topic and Claim Information in Debate Dialogue System

In recent years, systems with a dialogue interface are attracting wide attention [1, 2]. We propose a dialogue system that can debate with users about news broadcasts on TV or radio and help users to understand the meaning deeply. We previously reported a debate system that collected opinions from the Web [4], vectorized them, and finally selected the most appropriate supporting/opposing opinion among them for debating. In this paper, we propose a Neural Network Language Model that can generate objections instead selecting one opinion for debating. The model generates sentences by putting claim information (supporting/opposition) in the input layer of Long Short-Term Memory (LSTM) [3]. We conducted experiments by BLEU score and Human Evaluation, and both showed the effectiveness of our method.
Curriculum Reform and Practice of “UI Interface Design” Based on “Information Visualization”

Through effective UI design, the design front-end and back-end coding are effectively combined, and the PC and mobile terminals are used as carriers for information visualization interface design research, including interface layout, color, font size, icon graphics, and data information interface vision Design presentation. At this stage, UI interface design has a good development prospect, large social demand, a wide social audience, and a shortage of professional design talents. How to quickly train UI interface design talents is particularly important. This course reform aims to introduce computer talents with programming background into the front-end design concept, and integrate the design of information visualization interface.
A Performance Benchmark for Stream Data Storage Systems

Modern business intelligence relies on efficient processing on very large amount of stream data, such as various event logging and data collected by sensors. To meet the great demand for stream processing, many stream data storage systems have been implemented and widely deployed, such as Kafka, Pulsar and DistributedLog. These systems differ in many aspects including design objectives, target application scenarios, access semantics, user API, and implementation technologies. Each system use a dedicated tool to evaluate its performance. And different systems measure different performance metrics using different loads. For infrastructure architects, it is important to compare the performances of different systems under diverse loads using the same benchmark. Moreover, for system designers and developers, it is critical to study how different implementation technologies affect their performance. However, there is no such a benchmark tool yet which can evaluate the performances of different systems. Due to the wide diversities of different systems, it is challenging to design such a benchmark tool. In this paper, we present SSBench, a benchmark tool designed for stream data storage systems. SSBench abstracts the data and operations in different systems as “data streams” and “reads/writes” to data streams. By translating stream read/write operations into the specific operations of each system using its own APIs, SSBench can evaluate different systems using the same loads. In addition to measure simple read/write performance, SSBench also provides several specific performance measurements for stream data, including end-to-end read latency, performance under imbalanced loads and performance of transactional loads. This paper also presents the performance evaluation of four typical systems, Kafka, Pulsar, DistributedLog and ZStream, using SSBench, and discussion of the causes for their performance differences from the perspective of their implementation techniques.
Review on Privacy Preservation Methods in Data Mining Based on Fuzzy Based Techniques

The most significant motivation behind calculations in data mining will play out excavation on incomprehensible past examples since the extremely large data size. During late occasions there are numerous phenomenal improvements in data assembling because of the advancement in the field of data innovation. Lately, Privacy issues in data Preservation didn't get a lot of consideration in the process mining network; nonetheless, a few protection safeguarding procedures in data change strategies have been proposed in the data mining network. There are more normal distinction between data mining and cycle mining exist yet there are key contrasts that make protection safeguarding data mining methods inadmissible to mysterious cycle data. Results dependent on the data mining calculation can be utilized in different regions, for example, Showcasing, climate estimating and Picture Examination. It is likewise uncovered that some delicate data has a result of the mining calculation. Here we can safeguard the Privacy by utilizing PPT (Privacy Preservation Techniques) strategies. Important Concept in data mining is privacy preservation Techniques (PPT) because data exchanged between different persons needs security, so that other persons didn't know what actual data transferred between the actual persons. Preservation in data mining deals that not showing the output information / data in the data mining by using various methods while the output data is precious. There are two techniques used for privacy preservation techniques. One is to alter the input information / data and another one is to alter the output information / data. The method is proposed for protection safeguarding in data base environmental factors is data change. This capacity has fuzzy three-sided participation with this strategy for data change to change the first data collection.
Sign Language Detection Using Image Processing

Sign language is an effective means of communication for the deaf and dumb. But those who cannot understand this language find it almost impossible to get a grip of what the other person is trying to communicate. The proposed research helps non-sign-language speakers in identifying gestures used in Sign Language. The methodology described in this paper is implemented using MATLAB. In this method, firstly, the signs are captured with the help of a webcam. The images captured are then processed further and the features are extracted from the captured images using different structural features. Comparison of the features is done using SVM Classifier can use this document as both an instruction set and as a template into which you can type your own text.
An execution environment as a service for adaptive long-running workflows

Purpose This paper aims to present Long4Cloud (long-running workflows execution environment for cloud), a distributed and adaptive LRW execution environment delivered “as a service” solution. Design/methodology/approach LRWs last for hours, days or even months and their duration open the possibility of changes in business rules, service interruptions or even alterations of formal regulations of the business before the workflow completion. These events can lead to problems such as loss of intermediary results or exhaustion of computational resources used to manage the workflow execution. Existing solutions face those problems by merely allowing the replacement (at runtime) of services associated with activities of the LRW. Findings LONG4Cloud extends the previous works in two main aspects, namely, the inclusion of dynamic reconfiguration capabilities and the adoption of an “as a service” delivery mode. The reconfiguration mechanism uses quiescence principles, data and state management and provides multiple adaptive strategies. Long4Cloud also adopts a scenario-based analysis to decide the adaptation to be performed. Events such as changes in business rules or service failures trigger reconfigurations supported by the environment. These features have been put together in a solution delivered “as a service” that takes advantage of cloud elasticity and allows to better allocate cloud resources to fit into the demands of LRWs. Originality/value The original contribution of Long4Cloud is to incorporate adaptive capabilities into the LRW execution environment as an effective way to handle the specificities of this kind of workflow. Experiments using current data of a Brazilian health insurance company were carried out to evaluate Long4Cloud and show performance gains in the execution of LRWs submitted to the proposed environment.
Deep Learning Model for Efficient Mammogram Analysis

Breast cancer is one of the main causes of death due to cancer in women all over the world. There is an urgent need for effective identification of the onset of breast cancer so that it can be treated in its early stages, and thus, save many lives. Breast cancer can be detected at a treatable stage with the aid of screening mammography. However, these tests often suffer from false-positive and false-negative cases due to their limited sensitivity and specificity. CAD systems have been used for a long time, to produce digitized mammograms and to perform an automated analysis for the detection of suspected regions. This has helped to provide a second opinion to radiologists, thereby increasing the diagnosis rate. Although CAD systems were equipped with hand-crafted features and machine learning algorithms, they failed to solve the problem of false-positive cases, and also spent more time on image interpretation. Deep Learning uses a complex architecture comprising of neural networks, and is becoming a favored method for medical image analysis. Deep Neural Networks can process an enormous amounts of data, perform automatic feature extraction, and give better results than the existing state-of-the-art methods. In this work, we have proposed a model which uses deep learning for an efficient analysis of mammograms. We have addressed the issue of unnecessary biopsies being conducted due to improper mammogram analysis. Our model focuses on three main areas—micro-calcification detection, analysis of lesions, and tissue texture analysis.
Wave–particle duality relation with a quantum N-path beamsplitter*

The wave–particle duality relation derived by Englert sets an upper bound of the extractable information from wave and particle properties in a two-path interferometer. Surprisingly, previous studies demonstrated that the introduction of a quantum beamsplitter in the interferometer could break the limitation of this upper bound, due to interference between wave and particle states. Along the other line, a lot of efforts have been made to generalize this relation from the two-path setup to the N -path case. Thus, it is an interesting question that whether a quantum N -path beamsplitter can break the limitation as well. This paper systemically studies the model of a quantum N -path beamsplitter, and finds that the generalized wave–particle duality relation between interference visibility and path distinguishability is also broken in certain situations. We further study the maximal extractable information’s reliance on the interference between wave and particle properties, and derive a quantitative description. We then propose an experimental methodology to verify the break of the limitation. Our work reflects the effect of quantum superposition on wave–particle duality, and exhibits a new aspect of the relation between visibility and path distinguishability in N -path interference. Moreover, it implies the observer’s influence on wave–particle duality.
Intelligent Hybrid Chatbot Solution for Archaeological Sites Tracking

Not only in the country like Sri Lanka, but also most of the countries have numerous historically valued places and monuments that provide great survival and civilization history. While searching information about those places, there exists a lack of information and trusted information sources. Even though some information is available, it does not include the convenient and efficient ways to retrieve the information. The proposed system contributes a solution to the aforementioned problem with Artificial Intelligence [AI] & Deep Learning [DL] concepts. The proposed chatbot solution helps to enhance the user experience while retrieving the available information about the archeological places from the system. It can automate the searching task by enabling a methodology for chatting with the user via a conversational interface. The proposed voice detection module, natural language processing model and the dialog management model leverages a higher accuracy rate and it will showcase the power of search assistants. Furthermore, it shows how it can be an alternative to the usage of the application and enhance the user experience without any hesitation.
Estimating Phonetic Probability in Etymology

An etymological proposition is often said to be probable or improbable from the phonetic point of view, and it is not rare for opinions to diverge on which it is. The estimation is typically purely intuitive, based on perceived similarity and no more than a handful of analogous examples. This paper proposes a method for quantifying the phonetic probability of an etymology and comparing it to the alternative hypothesis. It is intended to be used with sizable datasets, to produce a well-supported, objective verdict.
DIC-IR Analysis of Transient Thermal Stresses

This paper shows the feasibility of determining transient thermal strains and stresses using simultaneous time-dependent full-field temperature and displacement measurements along the surface of simple and complex structures. Temperature and displacements were experimentally determined by a set-up composed of a thermographic infrared (IR) micro-bolometer camera coupled to a stereo Digital Image Correlation (DIC) system. A polycarbonate flat disc-shaped specimen was tested under transient thermal inputs. The thermal expansion coefficient of polycarbonate was also measured using the same experimental set up. The mechanical or net strain distributions were determined from the total or gross DIC measured strains by subtracting the free induced temperature strains. The final strain distributions were compared with analytical and finite element solutions determined by using the IR test-measured temperature distributions.
Handwritten Digits Recognition Using an Ensemble Technique Based on the Firefly Algorithm

This paper develops a multi-step procedure for classifying Farsi handwritten digits using a combination of classifiers. Generally, the technique relies on extracting a set of characteristics from handwritten samples, training multiple classifiers to learn to discriminate between digits, and finally combining the classifiers to enhance the overall system performance. First, a pre-processing course is performed to prepare the images for the main steps. Then three structural and statistical characteristics are extracted which include several features, among which a multi-objective genetic algorithm selects those more effective ones in order to reduce the computational complexity of the classification step. For the base classification, a decision tree (DT), an artificial neural networks (ANN) and a k-nearest neighbor (KNN) models are employed. Finally, the outcomes of the classifiers are fed into a classifier ensemble system to make the final decision. This hybrid system assigns different weights for each class selected by each classifier. These voting weights are adjusted by a metaheuristic firefly algorithm which optimizes the accuracy of the overall system. The performance of the implemented approach on the standard HODA dataset is compared with the base classifiers and some state-of-the-art methods. Evaluation of the proposed technique demonstrates that the proposed hybrid system attains high performance indices including accuracy of 98.88% with only eleven features.
RTL development of a parameterizable Reed–Solomon Codec

Error correction coding (ECC) methods have been considered essential constituents of data transmission systems. Reed–Solomon (RS) codes are a core ECC technique that have been adopted in numerous applications and standards. Several register-transfer level (RTL) architectures for RS codecs have been proposed to address specific demands and overcome scalability challenges in speed and area. However, the influence of the main RS codec parameters on the corresponding hardware design has been undervalued by literature. The authors propose an open access intellectual property (IP) of a parameterizable RS codec and explore key aspects of its RTL development using IEEE 802.15.7 standard as illustration. Herein, it is demonstrated that formal verification has the potential to be solely used to attest the correctness of the developed IP for the RS codec configurations specified by IEEE 802.15.7. Furthermore, synthesis reports for the target field-programmable gate array devices indicate that the proposed IP is able to cope with throughput requirements in IEEE 802.15.7.