Reviewerly/exHarmony huggingface dataset:
Aspects Extraction for Aspect Level Opinion Analysis Based on Deep CNN

Extracting aspect term is essential for aspect level sentiment analysis; Sentiment analysis collects and extracts the opinions expressed in social media and websites' comments and then analyzes them, helping users and stakeholders understand public views on the issues raised better and more quickly. Aspect-level sentiment analysis provides more detailed information, which is very beneficial for use in many various domains. In this paper, the significant contribution is to provide a data preprocessing method and a deep convolutional neural network (CNN) to label each word in opinionated sentences as an aspect or non-aspect word. The proposed method extracts the terms of the aspect that can be used in analyzing the sentiment of the expressed aspect terms in the comments and opinions. The experimental results of the proposed method performed on the SemEval-2014 dataset show that it performs better than other prominent methods such as deep CNN. The proposed data preprocessing method with the deep CNN network can improve extraction of aspect terms according to F-measure by at least 1.05% and 0.95% on restaurant and laptop domains.
A Web Platform and a Context Aware Recommender System for Active Sport Events

Customer recommendations have proved to boost sales, increase customer satisfaction and improve user experience, making recommender systems an important tool for businesses. While recommendations of items such as products or movies, when browsing online, are heavily examined and several recommendation algorithms and systems are developed, still recommendation systems for events present unique challenges. This becomes even more challenging when recommending active sport events to users, due to inherent restrictions and limitations. This paper presents a context aware recommender system developed and integrated to the ST76 web platform, which enables for the first time, to the best of our knowledge, to provide recommendations of users that are more likely to participate in an upcoming active sport event. Also, we showcase the importance of the ST76 platform and recommender system for sports tourism, through the analysis of the economic impact of an active sport event hosted on the platform.
Computing the auto regressive distributed lag (ARDL) method in forecasting COVID-19 data: A case study of NTB Province until the end of 2020

Abstract The purpose of this research is to conduct data forecasting on the spread of COVID-19 in West Nusa Tenggara Province, Indonesia at the end of 2020. The data used in this research is the COVID-19 data in NTB Province including the data of the number of positive patient, the number of patients recovered and the number of patient passed away. The forecasting process uses the Autoregressive Distributed Lag (ARDL) method of the GUI Multiple Forecasting System (G-MFS) based on MATLAB by determining the prediction on the next day of the graph equation generated after the data simulation. The forecasting of the COVID-19 spread in NTB at the end of the year of 2020 obtained that the number of confirmed patients amounting to 19, 614 people with an average increase of 1.48%, the number of patients healed are 6, 651 with an average increase of 1.10%, and the number of patients died amounting to 4, 953 people with an average increase of 2.42%. The average increase in the number of patients who died showed that the provincial government of NTB needs to give more serious attention to handling COVID-19 patients.
The joy of search: a google insider’s guide to going beyond the basics

"The joy of search: a google insider’s guide to going beyond the basics." The Information Society, 37(3), pp. 199–200
Quantum properties of two-dimensional linear harmonic oscillator in polar coordinate system

Abstract Using quantum theory and MATLAB software, the basic properties of two-dimensional linear harmonic oscillators in quantum mechanics are systematically studied in polar coordinate system, and obtain the visualized results. The results show that, in polar coordinate system, with the exception of special case n r =0, | m |=0, the degeneracy of two-dimensional linear harmonic oscillator is 2 n r +| m |+1, and the corresponding energy eigenvalues is ħω (2 n r +| m |+1). The number of intersection line between wave function and the plane with ψ =0 is 2 n r + m . In the case of n r =0, the maximum number of probability density distributions is 2| m |. The results of this visualization are in complete agreement with the theoretical results. The visualization results in different coordinate systems can be verified with each other, which opens up a new research idea and also provides an idea for other quantum theoretical models to be studied.
A Multi-Thread Auto-Negotiation Method for Value Conflict Resolution in Transboundary Service Design

When designing transboundary services, designers need to reasonably optimize the configuration of the service quality and service resources of each service participant to satisfy the value expectations of multiple parties.However, due to the constraints of the service quality offered, and resources owned by participants, the designed configuration may sometimes not meet all the value expectations.At this time, it is need to persuade the relevant parties to lower their value expectations, so as to realize resolution of value conflict.Therefore, for transboundary service design, a reasonable conflict resolution method is one of the key points.This paper proposes a multi-thread automatic negotiation method based on Stackelberg game theory and swarms intelligence algorithm to solve the problem of multi-party value conflicts.A numerical simulation on real-world transboundary service design was carried on to validate The rationality and validity of the proposed model.
Voice-controlled 3D modelling with an intelligent personal assistant

In this paper we look into the feasibility of controlling the process of 3D modelling with the help of voice commands.The main motivation is represented by the potential increase in the accessibility of cluttered graphical interfaces to novice users, helping them to smoothen the learning curve.Throughout this paper, we present a solution that we have implemented to augment the interface of an existing 3D modelling tool for the purpose of creating and editing 3D objects.We describe the overall architecture of the solution, detailing the structure of the command set it employs and the main challenges it was designed to overcome.Finally, we seek to demonstrate the potential of the voice-interaction model by employing its functionality in a 3D modelling use-case and analyzing its relative performance when compared to the use of the graphical interface.
Research on semantic Retrieval Framework Design based on Ontology

With the continuous development of network technology, network education has also received more and more attention, the application of teaching platform is more and more common. This teaching method has obvious advantages over the traditional teaching method. People can arrange their study time freely and gain more knowledge in their limited time. All such advantages of the network teaching platform are closely related to the teaching resource database module in the teaching platform. However, there are still some problems in the construction of teaching resource bank: Users have low efficiency in retrieving resources of the resource database and cannot quickly obtain the resources they need. Different teaching platforms have their own teaching resource pools, but these resource pools cannot be reused, which results in the repeated development of the resource pools. These two aspects of the problem, also gradually become a hindrance to the further development of the network teaching platform. This paper mainly studies Chinese word segmentation and constructs a more general resource management module. Finally, a semantic retrieval framework based on ontology is designed.
The Coupling Relationship Between Rural Revitalization and New Urbanization Is Based on Time Series Analysis: Case Study of Ya'an

The coordinated promotion of new urbanization strategy and Rural Revitalization Strategy is the basic path to achieve the integration of urban and rural development, the key to the integration of urban and rural development lies in the dual drive of Rural Revitalization and new urbanization. However, the quantitative research on the coupling relationship between Ya'an Rural Revitalization and new urbanization is almost blank. This paper uses the system coupling coordination degree model to construct the evaluation index system of Rural Revitalization and new urbanization subsystem, and takes the index data of Ya'an City from 2011 to 2018 as samples to conduct empirical analysis on the coupling degree of the two. The results show that the coupling degree of subsystems of the two strategies is always at the high level of 0.9, but the coupling coordination degree is relatively low. According to the increasing trend of high coupling degree and coordination degree year by year, it can be predicted that with the passage of time, the coupling coordination of Rural Revitalization and new urbanization system in Ya'an City will gradually evolve into the benign resonance type of "high coupling high coordination" through long-term antagonism and running in, and guide the two systems to develop in a more orderly and coordinated direction.
A Model using Agile Methodologies for Defining Metrics to be used by the Public Sector in Brazil to set Remuneration for Outsourced Software Development

The process of contracting software factories within the scope of the Federal Public Administration (APF, in Portuguese) in Brazil has undergone some changes due to legislative alterations and a model has been proposed to improve the rendering of services regarding delivering results. Software factory contracts based on predictive processes and on the metrics of Function Points (FP) are the target of criticisms and have problems in achieving results, as verified in previous research. The initial objective of the study now proposed will be to define a process model for formulating metrics that can be used in agile contracts as opposed to those of standardized FP which have already proved to be quite problematic and difficult for the contractor to manage. Thus, in line with the theme of agile contracts for software development companies, the study proposed will seek to better understand the problem of software development and its maintenance by public bodies by means of contracts using agile methodologies and appropriate metrics for remunerating these services.
Dynamic Guided Network for Monocular Depth Estimation

Self-attention and encoder-decoder have been widely used in the deep neural network for monocular depth estimation. The self-attention mechanism is capable of capturing long-range dependencies by computing the representation of each image position by a weighted sum of the features at all positions, while the encoder-decoder can capture detailed structural information by gradually recovering spatial information. In this work, we combine the advantages of both methods. Specifically, our proposed model, DGNet, extends EMANet [1] by adding an effective decoder module to progressively refine the coarse depth map. In the decoder stage, we design a dynamic guided upsampling module that employs dynamically generated kernel conditioned on low-level features to guide the upsampling of the coarse depth map. Experimental results demonstrate that our method obtains higher accuracy and generates visually pleasant depth maps.
Adaption of Smart Devices and Virtual Reality (VR) in Secondary Education

Learning is an essential process for all individual spans from cradle to graveyard. Biology is one of the subjects where most of the students have been facing difficulties to study and memorize its content due to its complicated environment and structures. The effective education to the students is necessary, and it could be achieved by adopting of new technological concepts and application to their syllabi. Virtual reality is one of the outstanding modes to the students to learn and memorize the subject quickly. In this research, quantitative and qualitative methods were used to determine the factors of adaption of smart devices and virtual reality (VR) in secondary education with a pretest–posttest group. Additionally, the reliability of the collected data was analyzed with Cronbach’s alpha test. Two hundred students from ten schools were randomly selected for the pretest while 100 were selected for the posttest among these 200 students; where these 100 students allowed to use VR technology for their educational purposes, and these samples were selected using a stratified random sampling technique. In conclusion, the research shows that the performance of the students who used the VR tool in their studies was significantly increased; male students are very fond of technology and tools, thus score more marks compare with female students. At the meantime, female students are needed to be trained (to use the VR system) more in order to learn the subjects. Also, it is suggested to introduce this VR system to all the other subjects with proper training to both students and teachers not only education sector but also in various domain where the training is essential part to the employees.
A Study on Confidence: An Unsupervised Multiagent Machine Learning Experiment

Uncovering the true nature of confidence is a complex problem. This article proposes a step in this direction, designing a socially inspired experiment to further investigate the nature of confidence in the context of self-learning.
Build a Regression Model to Predict the ODDS of Credit Card Application

As a kind of credit loan business without collateral, credit card business has a high risk. The scale of bank credit card business is growing rapidly, but the credit management of bank for all levels of credit card is not mature, resulting in the risk of credit card business and related business problems. An Australian credit card transaction data is proposed to predict the occurrence of credit card default. The results show that the bank card application is mainly affected by six key factors. The bank could focus on influential factors and consider whether to accept the customer's credit card application according to the fitting model.
3R: Word and Phoneme Edition based Data Augmentation for Lexical Punctuation Prediction

Existing Lexical Punctuation Prediction methods are mainly trained on the standard clean data while losing the generalization in practical automatic speech recognition (ASR) system with ubiquitous transcription errors. To bridge the gap between clean training data and noisy testing data, we propose three random (3R) data augmentation strategies: random word deletion (RWD), random word substitution (RWS), and random phoneme edition (RPE) in both word and phoneme levels on the training dataset. Specifically, we contribute an acoustically similar vocabulary with phoneme level editions for acoustically similar word substitution. In addition, we first introduce the RoBERTa-large model into a punctuation prediction task to capture the semantics and the long-distance dependencies in language. Extensive experiments on the English dataset IWSLT2011 yield to a new state-of-the-art comparing to the prevalent punctuation prediction methods.
An End-to-End Approach for Automatic and Consistent Colorization of Gray-Scale Videos Using Deep-Learning Techniques

Automated colorization of black and white videos has been subject to much research within the image processing and machine learning communities. We are aware of the fact that video colorization, until now, is a computer-based process requiring user intervention at many steps. We present a deep-learning-based process that colorizes black and white videos without any human assistance. The primary objective of this work is proposal of a system that aims to create a fully automated and highly consistent video colorization system. Our primary goal is to demonstrate the viability of our methodology in creating consistently colored videos and reveal promising avenues for future work. Current methods in existence incorporate either frame by frame image colorization which leads to inconsistent results like blurred videos or they use user scribbles which are accurate but are time-consuming and require user intervention. We on the other hand propose a method for colorizing videos using key-frame extraction, colorizing the independent key-frames, and then propagating the colors from key-frames to semantically similar frames.
On the Implementation of Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation

This article describes detailed notes on the practical implementation of our paper Planar 3D transfer learning for end to end unimodal MRI unbalanced data segmentation (ICPR 2020, Milan), which deals with a problem of multiple sclerosis lesion segmentation from a unimodal MRI flair brain scan by applying a planar 3D transfer learning backbone weights to an autoencoder segmentation neural network. Our source code is published online under an open-source license, and we provide step-by-step instructions for the reproduction of our results.
USB Traffic Analyzer - digUSB

The USB protocol is prone to vulnerabilities raised from the lack of device authentication and authorization that could allow a USB device to impersonate other peripheral devices and the lack of a security mechanism that needs to verify the data or the messages received from a device. Thus, USB traffic analysis tools are needed to identify the USB devices built to exploit the mentioned vulnerabilities. The digUSB is a USB traffic analyzer tool that leverages the power of ELK Stack and Wireshark to provide increased USB traffic analysis capability to discover USB devices hidden features. It is organized into a modular structure and covers all the analysis cycle starting from capturing the USB traffic, processing and viewing low level transactions and identifying badUSB devices trough defined charts. It integrates a built-in data extraction module that can be used to recover keystrokes and small files from PCAP files.
An Interpretable Graph-Based Mapping of Trustworthy Machine Learning Research

There is an increasing interest in ensuring machine learning (ML) frameworks behave in a socially responsible manner and are deemed trustworthy. Although considerable progress has been made in the field of Trustworthy ML (TwML) in the recent past, much of the current characterization of this progress is qualitative. Consequently, decisions about how to address issues of trustworthiness and future research goals are often left to the interested researcher. In this paper, we present the first quantitative approach to characterize the comprehension of TwML research. We build a co-occurrence network of words using a web-scraped corpus of more than 7,000 peer-reviewed recent ML papers—consisting of papers both related and unrelated to TwML. We use community detection to obtain semantic clusters of words in this network that can infer relative positions of TwML topics. We propose an innovative fingerprinting algorithm to obtain probabilistic similarity scores for individual words, then combine them to give a paper-level relevance score. The outcomes of our analysis inform a number of interesting insights on advancing the field of TwML research.
An Attack Scheme of RSA Encryption System with Protocol Failure

RSA encryption algorithm is widely used in information and communication systems to protect the security of information. But the protocol failure would bring a lot of risks to RSA encryption systems. In this paper, the security of RSA encryption system and detailed risks brought by protocol failure which is result of improper choices of parameters discussed. Furthermore, an at-tack scheme of these risks and corresponding pseudo-code are proposed in this paper. This attack scheme would provide warnings for designers and users of RSA encryption systems.
RED: A Real-Time Datalogging Toolkit for Remote Experiments

The ability to conduct experiments on virtual reality systems has become increasingly compelling as the world continues to migrate towards remote research, affecting the feasibility of conducting in-person studies with human participants. The Remote Experiment Datalogger (RED) Toolkit is an open-source library designed to simplify the administration of remote experiments requiring continuous real-time data collection. Our design consists of a REST server, implemented using the Flask framework, and a client API for transparent integration with multiple game engines. We foresee the RED Toolkit serving as a building block for the handling of future remote experiments across a multitude of circumstances.
Implementation of Proof-of-Work Concept Algorithm using SimBlock Simulator

Blockchain Technology is a new database technology that can solve data integrity without a third party's need by doing cryptography on data transactions. The cryptographic hash data transaction in each block is linked, and it contains the information, time-stamps, and hash data of the previous block. So, it is impossible to manipulate the existing data. There are currently several blockchain simulators available to make it easier for us to understand how blockchain networks work. The blockchain simulator used and developed in this paper is the SimBlock simulator. This application can simulate the blockchain network and visualize the block propagation process that occurs. However, this simulator does not support the actual hash computation process as in the Proof-of-Work (PoW) concept. In this paper, we propose to add the PoW concept to the SimBlock simulator to provide more detailed information about the block structure on the blockchain network, the relationship between one block and another, to show how long the mining process takes, and to show the hashing value created. The aim is to educate the users with detailed information and understand how the Blockchain network works using the PoW concept. Our experiment tried various nodes from 125 to 1,000 and block combinations from 1,250 to 10,000. We use leading-zero functions to solve the mathematical problems required in the PoW concept. The goal of math problems is to find one leading-zero or two leading zeros. The compile-time result from the SimBlock modification is 0.2 to 2.2 seconds slower when using one leading-zero and 1 to 7.2 seconds slower when using two leading-zero. With this result, using the PoW concept algorithm requires higher resources so that the compilation process will take longer. However, the users still can see the hashing process that occurs.
Data Balancing for Credit Card Fraud Detection Using Complementary Neural Networks and SMOTE Algorithm

This Research presents an innovative approach towards detecting fraudulent credit card transactions. A commonly prevailing yet dominant problem faced in detection of fraudulent credit card transactions is the scarce occurrence of such fraudulent transactions with respect to legitimate (authorized) transactions. Therefore, any data that is recorded will always have a stark imbalance in the variety of minority (fraudulent) and majority (legitimate) class samples. This imbalanced distribution of the training data among classes makes it hard for any learning algorithm to learn the features of the minority class. In this thesis, we analyze the impact of applying class-balancing techniques on the training data namely oversampling (using SMOTE algorithm) for minority class and under sampling (using CMTNN) for majority class. The usage of most popular classification algorithms such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), Logistic Regression (LR), Random Forest (RF) are processed on balanced data and which results to quantify the performance improvement provided by our approach.