Reviewerly/exHarmony huggingface dataset:
eMarket for Local Farmers

The Covid-19 pandemic is a global health crisis that has brought an impact on everyone. A negative impact has made local farmers struggle to find their means of income other than selling their crops to their customers. The customers have had difficulties searching for good quality fresh produce other than from the supermarket. The purpose of this paper is twofold: firstly, to assess the manual and existing system on how consumers acquire their fresh produce during the Covid-19 pandemic, to design and implement a delivery system for local farmers to vend their fresh produce through a mobile application; and secondly, to test and evaluate the usability and functionality of the online farmers' market application. The existing mobile applications have changed everyone's approach in acquiring the daily essentials that the pandemic has brought. Therefore, the idea of developing an online marketplace for local farmers resulted in the proposed application, which will be explained in this paper. Rapid Application Development (RAD) was used as the methodology for the development of the eMarket application. A survey was conducted via Google form for twenty local farmers and another twenty potential customers during the pre-development stage. It was to collect data on the users' opinions regarding the proposed application. We then conducted the usability testing through Google form to collect the users' feedback on the eMarket application. Ten local farmers from Matang's market have participated in the testing phase. Additionally, ten students from a local university participated as potential customers in the usability testing. As a result of these testings, we have developed a usable farmer's market application that has been well-received by local farmers and customers.
A Study on Identification of Important Features for Efficient Detection of Fake Reviews

With the increasing advent of e-commerce, more and more people are engaged in online shopping. People rely on online reviews for making their purchasing decision as these reviews can provide a lot of useful information about the goods or services. These online reviews also help firms in understanding customer sentiment and behavior. However, fake or manipulated reviews are also posted to promote or demote the quality of the products or services which mislead the consumers and guide them to make wrong decision. Identification of fake review is difficult and their detection is currently an important issue. It is important to find out the characteristic feature which can be used for accurate detection of fake news. In this work, a framework for fake review detection has been proposed in which several types of features associated with the review, sentiment features, topic features and readability features are studied. The effectiveness of the features and their different combinations for discrimination of fake review from real review has been explored using different supervised classifiers. The simulation experiments are done with Amazon and Yelp benchmark review data set. It is found that the readability features are the most effective features. Some combination of readibility features and topic features can produce better performance with lesser number of features than only sentiment analysis for discriminating fake review from real one.
Research on Correction Method of Spoken Pronunciation Accuracy of AI Virtual English Reading

In order to improve the pronunciation accuracy of spoken English reading, this paper combines artificial intelligence technology to construct a correction model of the spoken pronunciation accuracy of AI virtual English reading. Moreover, this paper analyzes the process of speech synthesis with intelligent speech technology, proposes a statistical parametric speech based on hidden Markov chains, and improves the system algorithm to make it an intelligent algorithm that meets the requirements of the correction system of spoken pronunciation accuracy of AI virtual English reading. Finally, this paper combines the simulation research to analyze the English reading, spoken pronunciation, and pronunciation correction of the intelligent system. From the experimental research results, the correction system of spoken pronunciation accuracy of AI virtual English reading proposed in this paper basically meets the basic needs of this paper to build a system.
Development Of Video Based Action Recognition System For Item Taking From Shelf

This paper explores the development of video-based action recognition system for taking/returning item from/to the shelf that is fast enough to be used in real-time. For action classification, transfer learning is used on the Inflated 3D model with the ResNet50v1 backbone. Meanwhile, for object recognition, transfer learning is used with the YOLOv4 model because it includes components to determine the position of objects in the image. The system also adds a detector component so that the two classification components do not run continuously. Evaluation of the system produces an accuracy of 80% for the action classification component and 70% for the object recognition component. The entire system is capable of running on real-time video at speeds of up to 25 FPS.
The Effect of Urban Drainage on The Determination The Time of Storage In The City Development Area

Abstract Increasing development activities in various fields in Surabaya will impact the occurrence of flooding, where the growth of residential buildings changes the function of the drainage area into runoff land. Therefore, there is a need for regional drainage recommendations. This study aims to provide a reference for a safe number that follows the storage needs and the duration of the peak partial drainage required by the region, following the study of water resources science. The data needed are rainfall data, average area, land function, and land slope maps. This study uses a 5-year return period probability. Moreover, the Nakayasu Hydrograph method is also used. It has a grace period starting from the rain surface to the top of the hydrograph, the area of the watershed, and the length of the main river channel. The comparison of the length of time of concentration (Tc) of the area with the length of waiting for time (Tp) of the urban drainage hydrograph shows that the area’s Tc value is greater than the Tp value of the urban drainage.
Application of Virtual Reality in Cognitive Rehabilitation

Virtual reality (VR) is defined as a simulation of the real world using computer graphics. The basic components of a VR application or program are interaction and immersion. Human-computer interaction is achieved through multiple sensory channels that allow individuals to explore virtual environments through senses. Immersion is considered the degree to which the individual feels engrossed or enveloped within the virtual environment. Scope of virtual reality is quite wide and varied, including technology, industry, education, and health. In the health sector, it has a significant role in assessment as well as intervention. Specific to human behavior and cognition, virtual reality's (VR) application is for cognitive assessment and rehabilitation. VR offers the potential to develop human testing and training environments that allow for the precise control of complex stimulus presentations in which human cognitive and functional performance can be accurately assessed and rehabilitated.
Novel public-key cryptosystems based on NTRU and algebraic structure of group rings

In this paper, we propose two novel public-key cryptosystems by combining the well-known NTRU public-key cryptosystem with the algebraic structure of group rings. We discuss the security analysis of these cryptosystems and show their enhanced security in comparison to the NTRU public-key cryptosystem. To be more precise, we show that the security of our novel cryptosystems is based on solving some recently discovered hard problems, for instance, Discrete logarithm problems in group rings, Inverse computation problems etc. We also discuss two examples to show the encryption and decryption procedure for both the cryptosystems.
A Preliminary Investigation on Learning Basic Chemistry using Virtual Reality

Many teaching and learning tools make use of existing technologies to help students learn more effectively. Three current digital technologies booming in mobile-based education are augmented reality, virtual reality, and mixed reality. Visualising the mechanism of atoms and chemical bonding is one of the challenges faced by students in Chemistry. This study developed an immersive mobile prototype application using virtual reality technology to help students visualise the process of chemical bonding, increasing their understanding and interest. The mobile application also includes a mini game to assess learners’ knowledge retention. Initially, five students were tested to gauge their reactions and satisfaction with the app. Overall, the proposed prototype application received positive feedback and will serve as a good foundation for future mobile learning applications. There was a positive impact on learners’ knowledge growth from the proposed application.
From off-site to on-site: A Flexible Framework for XR Prototyping in Sports Spectating

Developing and researching Extended Reality (XR) prototypes for sports spectating is challenging as on-site testing opportunities are limited and often cannot be used for development and debugging. In addition, conducting user studies and evaluating prototypes within such a large unconstrained live event environment can be problematic. These limitations created a need for a flexible XR prototype development framework that can be used off-site for testing and debugging as well as for user studies, while still providing the option to being directly applied on-site during a sports event.We developed a framework for a flexible XR prototyping process with different levels of fidelity. The framework integrated our experiences with the challenges of working remotely to create solutions for an on-site problem. We use our proposed framework to demonstrate how to develop different XR prototypes, such as a miniature lab prototype, a hybrid use-case using indirect AR, an off-site VR prototype and the actual on-site AR prototype.The proposed framework allows us to tackle our overall goal of using Augmented Reality (AR) to provide situated visualizations to on-site sports spectators. Our approach can be applied in AR and XR projects where access to the targeted environment is limited.
A Classifier for Reducing Numerical Errors using Ensemble Method

Nowadays, CNN have shown high identification accuracy in handwritten numeral recognition, but normal CNN learns image features from the pixel values of the image, so they could not learn the numerical information of the image. In this study, we propose a method to reduce the numerical error in handwritten numeric images in addition to improving the identification accuracy. Our proposed method improves performance by using an ensemble method that combines a CNN that learns to reduce numerical errors with a normal CNN. The results show that the proposed method produces higher identification rates and smaller numerical errors than the baseline method on the MNIST and KANNADA-MNIST datasets.
Application and Design of Intelligent Building Automation Control System

Abstract Intelligent building is a safe, comfortable, efficient, energy-saving, convenient and flexible modern building designed and built by organically combining high-tech and architectural art. Building automation control system is an important part of intelligent building, which realizes its own development through the development platform of intelligent building. It can improve the quality of building entities and supporting equipment and the quality of building environment in intelligent building to varying degrees. The building automation system in the intelligent building takes the humanized interface as the emphasis, and takes the combination of mechanical and electrical system, fire control system, access control system and parking management system as the method to create a convenient and comfortable living and working environment for building users. This paper mainly studies the building automation control system of intelligent building, and then describes the content of the building automation control system in intelligent building, and at the same time, carries out in-depth research on related issues, hoping to provide reference for researchers in this field.
A Mixed Reality Supervision and Telepresence Interface for Outdoor Field Robotics

Collaborative human-robot field operations rely on timely decision-making and coordination, which can be challenging for heterogeneous teams operating in large-scale deployments. In this work, we present the design of an immersive, mixed reality (MR) interface to support sense-making and situational awareness based on the data collection capabilities of both human and robotic team members. Our solution integrates state-of-the-art methods in environment mapping and MR so that users may gain rapid insights regarding the working environment, the current and previous locations of human and robot team members, and the environment data such team members have collected. We describe the implementation of our system, share lessons learned in collaborating with emergency responders throughout our design process, and offer a vision for the use of immersive displays for human-robot field team deployments in large-scale outdoor environments.
Improving users' mental model with attention‐directed counterfactual edits

Abstract In the domain of visual question answering (VQA), studies have shown improvement in users' mental model of the VQA system when they are exposed to examples of how these systems answer certain image‐question (IQ) pairs. In this work, we show that showing controlled counterfactual IQ examples are more effective at improving the mental model of users as compared to simply showing random examples. We compare a generative approach and a retrieval‐based approach to show counterfactual examples. We use recent advances in generative adversarial networks to generate counterfactual images by deleting and inpainting certain regions of interest in the image. We then expose users to changes in the VQA system's answer on those altered images. To select the region of interest for inpainting, we experiment with using both human‐annotated attention maps and a fully automatic method that uses the VQA system's attention values. Finally, we test the user's mental model by asking them to predict the model's performance on a test counterfactual image. We note an overall improvement in users' accuracy to predict answer change when shown counterfactual explanations. While realistic retrieved counterfactuals obviously are the most effective at improving the mental model, we show that a generative approach can also be equally effective.
Processing Words Effectiveness Analysis in Solving the Natural Language Texts Authorship Determination Task

The previously developed method establishes the natural language texts authorship based on frequency analysis, supplemented by indicators of text complexity and recurrent analysis.The authorship indication problem is reduced to the pattern recognition classical theory.To account for the different individual indicators information content, their weights are taken into account.They are determined according to the maximum number of the correctly established texts authorship from the training sample using a genetic algorithm.This method is used to study the effectiveness of the author's style representation that is based on different types of words processing: two types of words stems and 4-grams.To obtain stems, the adapted Porter stemmer is used and creating a dictionary of the foundations of the Ukrainian language original method is applied, respectively.Taking into account the calculated indicators weights, the reliability of establishing the text authorship in the control sample reached 85-91%.
User-Centered Design of Visualizations for Software Vulnerability Reports

Today’s software systems are created by software development processes that naturally include mistakes, some of which can be exploited by attackers and are therefore called vulnerabilities. Automatic software scanners enable developers to analyze their applications to detect vulnerabilities and alert them of their presence. But often these reports are hard to understand, include false positives or overwhelm users due to the sheer number of alerts, since a report may contain hundreds to thousands of vulnerabilities. Developers must undergo a process called vulnerability triage to find the relevant vulnerabilities to fix. This paper presents two interactive visualizations for developers and security experts to gain an overview of the security state of their application. Users can see the distribution of vulnerabilities, find the most relevant ones, and compare differences between application versions. Our visualization design is inspired by an initial preliminary study and has been evaluated by domain experts to investigate the usability and appropriateness.
Semantic-Based Integrated Plagiarism Detection Approach for English Documents

The proposed work models a novel plagiarism detection system based on the semantic features to uncover the cases of plagiarism. The system constructs the dynamic relation matrix for each suspicious and source sentence pair to measure the degree of similarity using semantic features. Two Weighted Inverse Distance and GlossDice procedures show several text properties (synonyms, shortest path, etc.) to overcome the limitations of the existing features and new similarity metric for plagiarism detection are presented in this paper. Moreover, this research investigates the independent performance of various features to detect plagiarized cases and combine the best features by assigning different weight contributions to further enhance the system performance. Weighted Inverse Distance integrated with SynJaccard boosts the system performance and shows promising results. Initially, all the experiments were performed on PAN-PC-11dataset, and then PAN-14 text alignment dataset was used to validate the results of the proposed system. The effectiveness of the proposed system has been measured using standard performance measures i.e. Precision, Recall, F-measure, Granularity, and Plagdet score. The proposed system has outperformed the other baseline systems with precision (0.9459), recall (0.8861), f-measure (0.8917), and plagdet (0.8857) on the PAN-PC-11 dataset. For PAN-14 text alignment, the system exhibits precision (0.9257), recall (0.9055), f-measure (0.8931), and plagdet (0.8806).
Unsupervised authorship attribution using feature selection and weighted cosine similarity

This paper presents a computational model for the unsupervised authorship attribution task based on a traditional machine learning scheme. An improvement over the state of the art is achieved by comparing different feature selection methods on the PAN17 author clustering dataset. To achieve this improvement, specific pre-processing and features extraction methods were proposed, such as a method to separate tokens by type to assign them to only one category. Similarly, special characters are used as part of the punctuation marks to improve the result obtained when applying typed character n-grams. The Weighted cosine similarity measure is applied to improve the B3 F-score by reducing the vector values where attributes are exclusive. This measure is used to define distances between documents, which later are occupied by the clustering algorithm to perform authorship attribution.
Global Solar Radiation Forecasting using Artificial Neural Network and Support Vector Machine

Abstract Global solar radiation (GSoR) forecasting involves predicting future energy from the sun based on past and present data. Literature reveals that not all meteorological stations record solar radiation, some equipments are faulty, and are not available in every location due to high cost. Hence, the need to predict and forecast using predictors such as land surface temperature (LST). Satellite data when were used to complement ground-based stations have been yielding good results. Different artificial intelligence (AI) methods such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) present different forecasting performances. Motivated by existing literature-related contradictions on the performance superiority of ANN and SVM in GSoR forecasting, the two techniques were compared based on several statistical tests. Experimental results show that ANN outperformed SVM by 2.9864% accuracy, making it superior in the forecast of GSoR.
An Empirical Study on the Co-Occurrence of Design Smells in the Same Software Module:God Class Case Study

Design smell detection is one of the essential activities for improving software quality. Mainly, detecting the co-occurrence of design smells contributes to reducing future maintainability costs. In this study, we empirically investigate the co-occurrence between God Class and 13 types of design smells in a set of 24 software systems. A group of five design smell detection tools and 42 human experts have been used for evaluation. The results indicate a strong co-occurrence between the pairs of (God Class, Feature Envy) and (God Class, Long Method).
Remote communication in Scrum teams – a COVID-19 preventive measure or work time optimisation?

The article presents the impact of remote work, resulting from the COVID-19 pandemic, on the functioning of Scrum teams. Attempts have been made to analyse the positive and negative aspects of remote work. The article also looks at the impact of remote work on the level of communication and efficiency of Scrum teams. For this purpose, the author conducted research on a sample of 40 organisations that declared to use Scrum methodology, using 187 questionnaires as the research material. The study was carried out at the turn of April and May 2021 and was carried out using the CAWI technique. The obtained results were analysed using the Principal Component Analysis and Cluster Analysis methods, and enable defining a picture of an organisation’s readiness to work remotely. In addition, they also allowed for an assessment of how the infrastructure used for remote work communication translates into the organisation of Sprint meetings. This paper presents conclusions aimed at counteracting the observed irregularities detected during the tests. At the end, the author proposes solutions that could improve communication within Scrum teams, with remote work in mind.
Secure Contactless Mobile Payment System

Near-field communication technology is a great enabler of contactless applications, which has resulted in exponential growth of contactless payment systems. While the NFC technology has an advantage over other wireless technologies like Bluetooth and barcode due to the close communication proximity and speed, it is still susceptible to security threats such as eavesdropping, relay, and replay attacks. Cryptographic protocols resist most attacks mounted on the NFC systems, but relay attacks remain evasive to such attempts. We propose a relay attack countermeasure that ensures mutual authentication of paring devices during contactless payment. The protocol combines proximity token and challenge/response key exchange to verify the communicating devices&#x2019; proximity before authentication. In this paper, we detail the architecture of the proposed contactless payment system, discuss its fully functional prototype, and present the experiment results when subjected to relay attacks. The experiment results show additional time while the user device terminates the communication where the relay attack was detected. From the experiment, the protocol is secure, easy to use, and practical.