Reviewerly/exHarmony huggingface dataset:
An Exploratory Retrospective Assessment on the Usage of Bio-Inspired Computing Algorithms for Optimization

In the era of digitalization, every task is performed with the help of software-dependent applications.Therefore, the developed software is required to be robust, reliable, and fault free.Testing is performed to check the functioning of the developed software to evaluate whether the software product is error-free or not.Test cases play a vital role in the testing process.However, with the advancement of time, a particular test suite becomes so lengthy that the execution of all the test cases is not possible due to limited time and resources.Researchers have proposed diverse techniques to make the testing process an effective one.This study has worked towards finding the usage of bio-inspired computing algorithms used for optimization.The reason behind this is because these algorithms have performed exceptionally well in addressing complex problems to provide workable solutions in a reasonable time.It is observed that only a handful of these algorithms were applied in testing, such as ant colony optimization, bee colony optimization, neural networks, and genetic algorithms.Even progress is made in the limited area of these algorithms.This study was conducted with a motive to sort out the most popular bio-inspired algorithms and to explore their working principles, developments made till now, along with the scope of their application.This paper has discussed how the development of these algorithms has progressed from already explored algorithms to the development of many new ones such as cuckoo search, artificial bee colony, bat algorithm, firefly algorithm, flower pollination algorithm, and many more.This study will help the researchers to gain insight into choosing the algorithm and explore them in developing new techniques for optimization.
Arabic Morphological Analysis Techniques

Recently, activity surrounding Arabic natural language processing has increased significantly.Morphological analysis is the basis of most tasks related to Arabic natural language processing.There are many scientific studies on Arabic morphological analysis, yet most of them lack an accurate classification of Arabic morphology and fail to cover both recent and traditional techniques.This paper aims to survey Arabic morphological analysis techniques from 2005 to 2019 and to organize them into a reasonable and expandable classification system.To facilitate and support new research, this paper compares the currently available Arabic morphological analyzers, reaches certain conclusions, and proposes some promising directions for future research in Arabic morphological analysis.
Types of Thinking and Their Value for Ecological Education

In November, 2017 there was an event of (without exaggeration), universal scale: The Club of Rome published a report in time for the semicentennial anniversary on the subject of "Come on!Capitalism, short-sightedness, population and destruction of the planet" (von Weizsaecker & Wijkman, 2018).The idea of 'The New Enlightenment' became a key point of the report: the need to reject materialism and reductionism, to make a transition to a holistic outlook, and a fundamental transformation of thinking resulting in a radical paradigm shift of our civilization's development.Regarding the purpose of education in general, the Club envisions populations developing "literacy concerning the future", namely, a focus on sustainable development, the coherence of systems, and the cultivation of thinking that is integrated and beyond the analytical.
Differentiation of universities by the level of teaching staff income: correlation with the quality of education and research productivity

Г. А. Агарков, Д
Comparison of Accuracy between Long Short-Term Memory-Deep Learning and Multinomial Logistic Regression-Machine Learning in Sentiment Analysis on Twitter

The paper is about sentiment analysis research on Twitter.In this research data with the keyword, 'Russian Hacking' concerning the 2016 US presidential election on Twitter was taken as a dataset using Twitter API with Python programming language.The first process in sentiment analysis is the cleaning phase of tweet data, then using the Lexicon-based method to produce positive, negative, and neutral sentiment values for each tweet.Data that has been cleaned and classified will be processed in the Deep learning method with Long Short-Term Memory (LSTM) algorithm and Machine learning method with Multinomial Logistic Regression (MLR) algorithm.The accuracy of these two classification methods are calculated using the confusion-matrix method.The accuracy obtained from the LSTM classification method is 93 % and the MLR classification method is 92 %.Thus, it can be concluded that LSTM is better in classifying sentiments compared to MLR.
Predicting Book Sales Trend using Deep Learning Framework

A deep learning framework like Generative Adversarial Network (GAN) has gained popularity in recent years for handling many different computer visions related problems.In this research, instead of focusing on generating the near-real images using GAN, the aim is to develop a comprehensive GAN framework for book sales ranks prediction, based on the historical sales rankings and different attributes collected from the Amazon site.Different analysis stages have been conducted in the research.In this research, a comprehensive data preprocessing is required before the modeling and evaluation.Extensive predevelopment on the data, related features selections for predicting the sales rankings, and several data transformation techniques are being applied before generating the models.Later then various models are being trained and evaluated on prediction results.In the GAN architecture, the generator network that used to generate the features is being built, and the discriminator network that used to differentiate between real and fake features is being trained before the predictions.Lastly, the regression GAN model prediction results are compared against the different neural network models like multilayer perceptron, deep belief network, convolution neural network.
Multi-stage Aggregation and Lightpath Provisioning of Geo-distributed Data over EON Assisted by MEC

A multi-stage aggregation and lightpath provisioning algorithm is proposed for geo-distributed data in EON assisted by MEC. Simulation results show the algorithm can reduce the job completion time and bandwidth consumption.
GRAPHICAL PASSWORD STRATEGY

The graphical password strategy could really change how a typical user would insert their password and how secure it could be, it still has its flaws and limitations. One of the limitations of a graphical password strategy is that it could be prone to shoulder surfing. Without having a password field just like an alphanumeric password would have, a graphical password could be physically observed and especially in public places and the attacker has a clear visual of the password being inserted for multiple times, they could easily crack the password which is quite a severe flaw. Another potential limitation of a graphical password strategy is that it is prone to guessing as well. Same as an alphanumeric password, if the user only registered a short and predictable password, the chances of it being prone to guessing would increase. To overcome these potential limitations, a shoulder surfing resistant technique could be implemented such as including multiple mouse cursors when the users are logging in to their account which would confuse the attacker from identifying which one is the legitimate mouse cursors and which click points has the user clicked. Next, same as an alphanumeric password, a requirement of at least 10 click points must be clicked to make the graphical password stronger could be implemented to the system to ensure that the user does not just enter a sloppy password which would decrease the chances of an attacker to guess the password of the user dramatically.
BLOCK CHAIN TECHNOLOGY FOR ELECTRONIC VOTING

Election is a way to choose the representatives through a fairness, integrity, and democratic rules. During the election, the voting system allows authorized voters to cast their vote for candidates. In essence, the voting system can directly influence several aspects that are political science, social science, and economics. Thus, the concept of the voting system must respond and rigorously considered before the election is held. The voting system begins evolving from paper voting, then E-voting and the latest will be I-voting. Concisely, E-voting is simply an electronic system, while I-voting is nothing but remote E-voting (internet accessible). E-voting is the most adopted worldwide and it is a tool that frequently represents democracy of the election. Therefore, most of the countries continue to research and improve the E-voting process. Eventually, the current voting system is however far from what it should deliver. Blockchain technology provides a decentralized architecture that distributes digital information synchronously among the P2P network without a central database. Because of technology advancement, Blockchain has represented a standard technology that to the challenges like perfect online privacy and Web 3.0 dApps. Hence, the study proposes Blockchain based Online Voting System (BOVS) to enhance the integrity, optimize the voting process, produce consistent voting results, and strengthen the transparency of the voting system. Finally, the research addressed the weaknesses of the current I-voting system and successfully applied Blockchain technology to resolve those weaknesses.
Question Answering System based on Food Spot-Check Knowledge Graph

Question answering system based on food knowledge graph can help people understand and analyze the information and potential problems about food safety, This paper crawled the data of food spot-check data in recent years from the Official website, and designed the extraction algorithm of food general entities, food domain entities and relationships between entities for these data. The extracted entity pairs were stored in the gStore database. This paper also built a question answering system which classified the possible problems of users through grammar analysis and designed an explanation for each type of problem. At last, each explanation was defined as a template and mapped to the SPARQL language for querying in knowledge graph, the results were shown as lists and data graphs on the web pages.
Fuzzy Sugeno Algorithm for Clustering Document Management

The administration process exists in every department of the company, and it has caused a large quantity of the documents.Before the documents are processed, the administration staff need to classify which document should be processed first.Because the clustering process has not integrated into a system, it takes a long time, and it caused the administration process got delayed.With this problem, it is expected the document management system using Fuzzy Sugeno will help to reduce the problems.Fuzzy Sugeno works on clustering the document by the value of the category and the acceptance input by the user.The Fuzzy Sugeno helps administration staff to know which documents are in high priority and low priority to prevent the delay of the administration process.The document management system using Fuzzy Sugeno is help the administration staff to process the documents efficiently and it is improving the administration process in the company.
Real-time pedestrian detection method based on improved YOLOv3

Abstract Pedestrian detection in image or video data is a very important and challenging task in security surveillance. The difficulty of this task is to locate and detect pedestrians of different scales in complex scenes accurately. To solve these problems, a deep neural network (RT-YOLOv3) is proposed to realize real-time pedestrian detection at different scales in security monitoring. RT-YOLOv3 improves the traditional YOLOv3 algorithm. Firstly, the deep residual network is added to extract vehicle features. Then six convolutional neural networks with different scales are designed and fused with the corresponding scale feature maps in the residual network to form the final feature pyramid to perform pedestrian detection tasks. This method can better characterize pedestrians. In order to further improve the accuracy and generalization ability of the model, a hybrid pedestrian data set training method is used to extract pedestrian data from VOC data set and train with INRIA pedestrian data set. Experiments show that the proposed RT-YOLOv3 method achieves 93.57% accuracy of mAP (mean average precision) and 46.52f/s (number of frames per second). In terms of accuracy, RT-YOLOv3 performs better than Fast R-CNN, Faster R-CNN, YOLO, SSD, YOLOv2 and YOLOv3. This method reduces the missed detection rate and false detection rate, improves the positioning accuracy, and meets the requirements of real-time detection of pedestrian objects.
Reservoir-based sampling over large graph streams to estimate triangle counts and node degrees

Reservoir sampling is widely employed to characterize large graph streams by producing edge samples. However, existing reservoir-based sampling methods mainly focus on counting triangles but perform poorly in analyzing topological characteristics reflected by node degrees. This paper proposes a new method, called triangle-induced reservoir sampling, or T-Sample, to count triangles and estimate node degrees simultaneously and efficiently. While every edge in a graph stream is processed only once by T-Sample, a dual sampling mechanism performing both uniform sampling and non-uniform sampling is carefully designed. Specifically, T-Sample’s uniform sampling is used to count triangles by a newly proposed method with smaller estimation variances than existing reservoir-based sampling methods; whereas, its non-uniform sampling ensures that edge samples are connected. Experimental results driven by real datasets show that T-Sample can count triangles with smaller estimation errors and variances than the state-of-the-art reservoir-based sampling methods while obtaining much more accurate information about node degrees at smaller time and memory costs.
Development of Adobe Flash CS6 Learning Media in Short Story-Based on Learning Text of Advanced Local Community of Batak Toba Students in Tanjungmorawa

This study aims to determine the results of the development of Adobe Flash CS6 Learning Media on Short Story Text Learning based on local wisdom. This type of research is research and development based on the ADDIE development model. After going through the development process, a learning media produced Short Story Text based on Local community of the Batak Toba Class XI students of SMA Negeri 1 Tanjung Morawa consists of 4 (four) main menus namely Learning Competencies, Short Story Materials, Learning Quiz and Profile. The results showed that (1) the validation of the material experts included aspects of content Feasibility assessment with a percentage of eligibility of 92.73% with a predicate of Very Good (SB), a Presentation aspect with a percentage of eligibility of 86% with a predicate of Very Good (SB), a Language aspect with a percentage of 100% and the title of Very Good (SB), the aspect of Graphic with a percentage of 90% with the title of Very Good (SB); (2) validation of media design experts includes aspects of the assessment of Guidelines and Information with a percentage of eligibility of 100% and the predicate of Very Good (SB), Operational aspects of Software with a percentage of eligibility of 100% and the predicate of Very Good, Systematic aspects, Aesthetics and Media Principles with a percentage of 98.40% and a predicate of Very Good (SB); (3) The results of the Field Trial of Teachers and Students obtained a response of 96.15% with the criteria "Very Good"; (4) Short Text Learning outcomes before using adobe flash CS6 learning media is 51 while Short Text learning outcomes after using adobe flash CS6 learning media is 81. The difference obtained is 58.23% from before and after using adobe flash CS6 learning media. So, it can be concluded that the quality of learning media made is "very good".
A Product Recommendation System Based on User Complaint Analysis Using Product Reviews

In recent years, e-commerce attracts attention to the spread of online shopping sites, many users who refer to product review comments to choose the products and higher-rated products can be recommended from users' purchase histories. However, traditional recommendation systems cannot recommend alternative products when existing products' reviews contain user complaints (e.g., price, battery, design, function, etc), it is still difficult to satisfy users' requirements. Therefore, in this work, we propose a novel product recommendation system that analyzes two kinds of information: Complaint information and satisfaction information from review comments on e-commerce. It is possible to recommend alternative products to satisfy your requirements that can solve the complaint information on the product when you browsing. In this paper, we describe the complaint information extraction based on product review analysis by extracting both negative information and positive information from product reviews, and we also explain alternative product recommendation method that can solve complaints, and we verify the effectiveness of complaint information extraction and alternative product recommendation.
ADPE: Adaptive Dynamic Projected Embedding on Heterogeneous Information Networks

Network embedding (NE) aims to represent network information appropriately by learning low-dimensional and dense vectors for the nodes and edges of information network. Actually, the real world is almost full of heterogeneous information networks, which stimulates the emergence of heterogeneous information networks (HINs) embedding models. However, parts of existing HIN embedding models like meta-path-based methods only capture limited and aggregated information of relations, whereas some models based on metric or distance learning are usually of high computational complexity and slow training speed. In this paper, we present a novel heterogeneous information network embedding model, which applies dynamic projection metrics and translation mechanisms to the complicated heterogeneous information networks including multiple nodes and different relations. In order to overcome the imbalance of the distribution of relations in HIN and optimize the training process, we introduce an adaptive loss function for model optimization. Further more, we propose a hybrid model with baseline method as the initialization of the model. Experiments have been implemented on some real-world HIN datasets. And empirical results show that our model significantly outperforms the state-of-the-art representation learning models.
Cross-modal Hashing Retrieval Based on Density Clustering

Cross-modal hashing retrieval methods have attracted much attention for their effectiveness and efficiency. However, most of the existing hashing methods have the problem of how to precisely learn potential correlations between different modalities from binary codes with minimal loss. In addition, solving binary codes in different modalities is an NP-hard problem. To overcome these challenges, we initially propose a novel adaptive fast cross-modal hashing retrieval method under the inspiration of DBSCAN clustering algorithm, named Cross-modal Hashing Retrieval Based on Density Clustering(DCCH). DCCH utilizes the global density correlation between different modalities to select representative instances to replace the entire data precisely. Furthermore, DCCH excludes the adverse effects of noise points and leverages the discrete optimization process to obtain hash functions. The extensive experiments show that DCCH is superior to other state-of-the-art cross-modal methods on three benchmark bimodal datasets, i.e., Wiki, MIRFlickr and NUS-WIDE. Therefore, the experimental results also prove that our method DCCH is comparatively usable and efficient.
Compositional Semantics Network With Multi-Task Learning for Pun Location

A pun is always humorous and has strong interactive value in people's daily communication.It creates a humorous effect in a certain context, in which a word implies two or more meanings by using polysemy (homographic pun) or phonological similarity to another word (heterographic pun).Pun location is a task to identify the pun word in a given text, which is of great significance to understand humorous texts.Existing methods generally adopt single long sequence structure but cannot well capture the rich semantics of pun words in sentences.We present an approach that considers long-distance and short-distance semantic relations between words simultaneously.For the long-distance semantic relation, we introduce multi-level embeddings to represent the most relevant aspects of the data.For the short-distance semantic relation, we exploit the complex-valued model with a self-adaptive selection mechanism based on multi-scale of input information.Meanwhile, we propose a new classification task to distinguish the homographic pun and heterographic pun.We introduce it as an auxiliary to jointly train the original pun location task, which first learns the location of different types of puns together.Experiment results show that the latest state-of-the-art results can be achieved through our model.
It’s not just about the money

Across the globe, we continuingly hear financial commentators and academics highlighting the importance of small and medium-sized enterprises (SMEs) and the role they play in various countries in e...
Variational Quantum Demodulation for Coherent Optical Multi-Dimensional QAM

We introduce a hybrid quantum-classical variational algorithms to realize quasi-ML decision of high-dimensional modulation (HDM) in fiber-optic communications, motivated by the recent advancement of quantum processors. Our Ising Hamiltonian model for demodulation is demonstrated on a real quantum processor.
Integrated Quantum Photonics on Silicon Platform

We present our recent study on silicon integrated quantum photonics, from single photon sources to applications of quantum communication, generation and manipulation of high-dimensional quantum entanglement states, and sampling of quantum state of light.
IT Students Selection and Admission Analysis using Naïve Bayes and C4.5 Algorithm

Admission to college and selection of applications have probably become an integral part of some colleges and universities in their enrolment process, yet it is girded by controversy and skepticism.A new area of research that uses techniques of data mining is known as Educational Data Mining.It incorporates machine learning algorithms and statistical methods to help for the interpretation of student's learning habits, academic performances, and further improvements-if needed.This paper focuses on the predictive values of certain academic variables, admission tests, high school academic records as related to the performance of Information Technology (IT) students at the end of the first year.For this reason, 221 data were used, and C4.5 and Naive Bayes algorithms are applied to generate a prediction on the students' performance.The C4.5 classification gained 98.64% in 10-folds cross-validation and 96.97% in the 70% training and 30% testing percentage split compared to Naïve Bayes which only gained 89.14% and 86.36% for both 10-folds cross-validation and 70% training and 30% testing percentage split respectively.The comparative analysis of the result shows that senior high school track and academic data and admission test results are the influential attributes to the performance of IT students in their first year.This paper recommends for future studies to add different data from different years to increase the accuracy of the prediction.