Reviewerly/exHarmony huggingface dataset:
A Mathematically Generated Noise Technique for Ultrasound Systems

Ultrasound systems have been widely used for consultation; however, they are susceptible to cyberattacks. Such ultrasound systems use random bits to protect patient information, which is vital to the stability of information-protecting systems used in ultrasound machines. The stability of the random bit must satisfy its unpredictability. To create a random bit, noise generated in hardware is typically used; however, extracting sufficient noise from systems is challenging when resources are limited. There are various methods for generating noises but most of these studies are based on hardware. Compared with hardware-based methods, software-based methods can be easily accessed by the software developer; therefore, we applied a mathematically generated noise function to generate random bits for ultrasound systems. Herein, we compared the performance of random bits using a newly proposed mathematical function and using the frequency of the central processing unit of the hardware. Random bits are generated using a raw bitmap image measuring 1000 × 663 bytes. The generated random bit analyzes the sampling data in generation time units as time-series data and then verifies the mean, median, and mode. To further apply the random bit in an ultrasound system, the image is randomized by applying exclusive mixing to a 1000 × 663 ultrasound phantom image; subsequently, the comparison and analysis of statistical data processing using hardware noise and the proposed algorithm were provided. The peak signal-to-noise ratio and mean square error of the images are compared to evaluate their quality. As a result of the test, the min entropy estimate (estimated value) was 7.156616/8 bit in the proposed study, which indicated a performance superior to that of GetSystemTime. These results show that the proposed algorithm outperforms the conventional method used in ultrasound systems.
What is a User Interface, again? A Survey of Definitions of User Interface

A central object of study in Human-Computer Interaction is the User Interface (UI). The number of websites, papers, books, videos, and articles that attempt to explain the concept, attests this. In this study a collection of definitions of User Interface was assembled and classified seeking to identify parallels. Several queries were used in Google, Bing, digital libraries, and other sources. With more than 130 entries collected, we found that most of them concentrate in the elements of physical-sensorial contact between humans and artifacts. We called them thin definitions because they overlook elements of processing in the side of the computer, and perception, thinking, and understanding in the human side. Thick definitions, that dig deeper into the concept, considering human and machine capabilities as part of the UI, are less numerous but important to study, advance, and promote. We will argue for the need of a new definition and propose one.
Caring through Conversations: Voice User Interfaces For Everyday Stress Care

This paper presents a novel research study that explores the use of Voice User Interface (VUI) to mediate everyday stress care for women. In the preliminary phase of the study, we conducted need assessment activities which included a diary study, interviews, and a workshop that led to the development of user personas. Next, we developed system personas and crafted dialogues to manifest the personas. Then, we incorporated the dialogues into Voice User Interface (VUI) prototypes. We developed three VUI prototypes and conducted a validation workshop. We conclude the paper by discussing initial findings and implications for designing preventive stress-care. We hope to inspire the research community to explore novel approaches to designing technological interventions for preventative stress care using VUIs.
Reassessing the H in Human-Computer Interaction

We present the motivation and the organizational aspects of a discussion panel organized within the framework of MexIHC 2022 – The Ninth Mexican International Conference on Human-Computer Interaction. As the panel title suggests, the discussions revolve around the human aspects of HCI. As such, topics of discussion include ethics, legal issues, diversity and inclusion, and decolonizing perspectives, among others. Both our panelists come from different academic backgrounds, covering the main themes: technology and society.
Text-to-Text Multi-Task Learning to Generate Battlefield Situation Awareness Report

Advances in new communication technologies enable commanders to collect various information in battlefield situations. However, it is difficult to make quick and accurate decisions on the battlefield because of vast amount of information. To address this problem, several studies attempt to change tabular data into an easy-to-understand text format. Existing table-to-text studies are not suitable for battlefield situations because they use specific domain data such as WIKIBIO and WIKITABLETEXT. In this study, we propose a table-to-text transfer transformer (TaT4) that uses special tokens to transform log table data into a single sequence to preserve table information. Moreover, the proposed TaT4 uses multi-task learning that can leverage cross-task data of types in a single model to improve generalization performance. We conduct experiments on eight datasets generated from three Korean defense modeling and simulations (M&S) of battlefield situations in the Army, Air Force, and Navy. The proposed TaT4 outperforms the existing table-to-text models.
A Mutual Information Based on Ant Colony Optimization Method to Feature Selection for Categorical Data Clustering

By improving feature extraction techniques, high-dimensional datasets emerge more frequently, in which irrelevant or redundant features may appear. This curse of dimensions will affect the performance of clustering algorithms. Motivated by this, there is an increasingly necessity of selecting the most informative features. On the other hand, high time complexity of brute force methods makes heuristic techniques a better substitute. In this paper, we apply a mutual information based on ant colony optimization technique to select the most informative features by omitting redundant and irrelevant features. In our proposed method, MIMRFS (Maximum Informativity Minimum Redundancy Feature Selection), each ant individually decides to choose a subset of best features. Then the attitude of all ants is gathered and the final selected subset is chosen. This selected subset will be used to cluster whole instances. Experimental results show the high performance of MIMRFS. According to empirical results, the accuracy of MIMRFS is 96%, 83%, and 85% on three datasets Engine, Chess, and Lymphography which have an increase in comparison with other categorical clustering techniques.
Development of a Multi-Fan System (MFS) in a Plant Factory with Artificial Light

A plant factory is a factory that grows plants indoors. These indoor farms could be the key to solve food shortages in the world. Plant factories are operated in indoor spaces under controlled cultivation conditions such as light, temperature and humidity. Then, a multi-fan system (MFS) for single culture beds. The MFS had four fans which were installed on both the front and back sides of culture beds to generate airflow from two opposite horizontal directions by using the Internet of Things (IoT) via the access and connection of smartphone devices. The fans that push the air into the culture bed were air inlets while those that pull the air out of the culture bed were air outlets. The main problem is in plant factories with artificial light, a heat that is usually used to control the environmental parameters and the air velocity is generally lower than the optimum range required for plant growth. Compare to a plant factory without using a multi-fan, it no circulation of air in the container to ensure continuous gas exchange. This reduction in gas exchange can impact calcium uptake by the plants. The gas exchange makes the tip burn. Tip burn can have a significant impact on the salability of a lettuce crop. Based on the limitations that have been highlighted previously, this research has been carried out by using multi-fan and without multi-fan. To get the data that need to be compared. Then, to improve the airflow in a plant factory with artificial light and prevent tip burn occur on the lettuce itself. In a nutshell, this prototype is expected to help plant factories reduce tip burn symptoms on leaf lettuce and the airflow can improve the growth of indoor cultured lettuce.
On Restricted Computational Systems, Real-time Multi-tracking and Object Recognition Tasks are Possible

Intelligent surveillance systems are inherently computationally intensive. And with their ever-expanding utilization in both small-scale home security applications and on the national scale, the necessity for efficient computer vision processing is critical. To this end, we propose a framework that utilizes modern hardware by incorporating multi-threading and concurrency to facilitate the complex processes associated with object detection, tracking, and identification, enabling lower-powered systems to support such intelligent surveillance systems effectively. The proposed architecture provides an adaptable and robust processing pipeline, leveraging the thread pool design pattern. The developed method can achieve respectable throughput rates on low-powered or constrained compute platforms.
Method for the Semantic Modelling of the Product Context Using Text Mining for the Derivation of Innovation Potentials

Increased competitive pressure requires companies to increase their own innovative capacity. In particular, innovation management must identify and analyze the external dimensions of influence that affect the product. Although large amounts of text data are available for this purpose, there is yet no possibility of evaluating them in a structured way and linking the information obtained with each other in order to generate knowledge about the product environment. The presented method enables the derivation of innovation potentials from these text data. First, the external dimensions of influence that affect the product and are thus relevant for the innovation process are identified. Each influence dimension also defines data sources that can be used for the analysis. When analyzing the document collection from the data sources, applications from the field of text mining extract the topic areas of the available documents. Finally, a semantic network links the knowledge gained and shows the dependency of the identified topics in order to generate knowledge about future innovation potentials. The method was validated with a use-case from the sports equipment industry.
Improving a Recommendation Engine for Traditional Trade Between Wholesalers and Retailers Using Association Rules

This paper explores the data collection and mining of association rules to generate over 8,500 association rules which improve an existing recommendation engine used in an eCommerce platform between traditional trade wholesalers and traditional trade retailers. This improved recommendation engine allows traditional trade retailers to receive personalized recommendations based on items in their cart, and improves the current recommendation engine which only recommends most sold products. The improved recommendation engine helps traditional trade retailers purchase the right products for their stores and allows traditional trade wholesalers to increase the revenue of their stores, thereby providing both traditional trade wholesalers and traditional trade retailers with tools to help compete against modern trade outlets.
Automated commentary generation based on FPS gameplay analysis

Video games evolved into competitive sports within a short span of time. Commentary plays a key role in any sport. Commentary is useful to understand the game as well as to capture key moments of the game. The balance between play-to-play commentary and color commentary creates the whole value of commentary. Additionally, it has been proven that contribution factors play a major role in the meaning of an automated commentary as well. The proposed commentary generator takes all three factors into account to generate a commentary track for the first-person shooter game Valorant. Human evaluation along with numerical figures are taken to evaluate the quality of the generated commentary. The evaluation results suggest that the proposed model performs on par with the human commentary.
Walkable Space Estimation for Visually Impaired Persons in 3D Simulated Environment

Environmental perception is the process by which visually impaired people tend to sense, understand, and build awareness of the walking environment and the surrounding objects. Walkable space includes any space that the visually impaired person is physically capable of walking. The task of estimating the walkable space is performed by estimating pixels belonging to the ground plane in the scene. Plane parameters are estimated based on the input of 3D point coordinates of pixels belonging to the sidewalk in the outdoor environment. Moreover, the curb line is estimated using the output of the walkable space calculation to determine whether the visually impaired navigator is walking along the sidewalks or on the road. Proof of concept and evaluation experiments are conducted on a 3D simulated environment with the use of RGB, semantic and depth images taken from the wearable RGB camera, segmentation camera and depth camera. The results are benchmarked with the existing image processing-based methods and showed that the proposed method can be successfully implemented for the safe navigation of visually impaired users with minimum computational complexity in image processing.
Prioritizing Customer Requirements for Science and Technology Service Platform Based on Improved TF-IDF and Sentiment Analysis

With the development of the modern service industry, some problems are also created by prosperity. It is difficult to sufficiently match the supply and demand of science and technology service platforms, which leads to poor user experience. This paper puts forward a method of extracting users’ STS requirements based on improved TF-IDF and sentiment analysis. Firstly we identify user keywords by text cutting, then we calculate user requirement weight by TF-IDF. Considering semantics, we use Word2Vec to screen synonyms in to improve the accuracy of TF-IDF. Finally, the weight of user requirements is modified by sentiment analysis. The experiment demonstrates the efficiency of our method.
A Dual CNN Architecture for Single Image Raindrop and Rain Streak Removal

Visual quality of rainy images are considerably poor due to the raindrops in camera lens and the rain streaks in the background scenes. Although the raindrops and rain streaks are appeared together in real-world rainy images, most of the previous approaches are proposed to remove either of them. In this paper, we have proposed a novel CNN model architecture to remove raindrops and rain streaks together. The proposed CNN model architecture has two branches and it consumes two formats of a rainy image via an encoder-decoder network and a dense CNN network. At the end of the architecture, outputs of both branches are combined to produce a high-visibility rain-free image with natural colours. In addition, internal and external skip connections are introduced in the blocks of these branches to improve the performance further. The proposed model is trained and then tested on RainDrop, Rain100H, (d) Rain100L, and Rain12 benchmarks and showed excellent performance than the state-of-the-art approaches.
A Conceptual Framework for Blockchain-based Cannabis Traceability in Supply Chain Management in an Emerging Country

The cannabis industry is facing challenges with the traceability of product standards and regulations, including lack of trust, visibility, and tracking system immutability. Blockchain technology (BCT) is a potential driver of cannabis supply chain traceability for improving the tracking system process, credibility, immutability, and decentralized application of the development process. This research focuses on exploring blockchain-based traceability in supply chain management (SCM) and developing an appropriate model. The findings of this study highlight the various elements involved in cannabis supply chain traceability. A conceptual framework for blockchain-based cannabis traceability in SCM is proposed in this paper, comprising four layers: (I) cannabis supply chain, (II) data interface, (III) traceability system, and (IV) blockchain. This approach can be integrated into the smart contract, consensus algorithm, blockchain application, and digital storage.
A Conceptual Framework for Assessing Risks for Data Protection Impact Assessment Process in Maritime Industries

Personal data is used to define customer requirements. Organizations should securely collect and process such data, using data protection policies aligned with the applicable regulations. The General Data Protection Regulation (GDPR), an EU data protection law, has include a data protection assessment method called Data Protection Impact Assessment (DPIA) to ensure personal data security. The maritime industry is also concerned about personal data protection. However, there is a still a lack of practical methods to assess data protection risks. This article aims to introduce the conceptual framework for a new method for risk assessment in maritime systems, using DPIA and various systems-theoretic risk approaches as a conceptual basis. The ICT system is a central system in which personal data is utilized in the architecture of maritime systems. In this article, this system will be taken as a basis for illustrating the newly proposed method for personal data security risk assessment in a DPIA context. The conceptual framework will be further concretized and tested in follow-up research.
Sorting System for Ship Outfitting Pipes Based on Enhanced Object Detection

Sorting pipe fittings are a necessary process before ship outfitting. During the sorting process, workers check the quantity and the ID number of the pipe fittings and sort the pipe fittings into different pallets, which are then transported to different assembly areas. Manual searching for paper documents takes up most of the sorting time. To overcome this problem, this paper proposes a digital system incorporating an object detection model to assist in the recognition of pipe fittings. YOLOV4 is adopted as the object detection model. A large number of synthetic 3D model images of pipes are generated to enhance the training dataset for object detection and have improved MAP by 7.64%. The digital system further filters the object detection result by the inputted manual empirical information and has finally achieved a detection accuracy of 82.93%. The proposed system helps to efficiently identify pipe fittings and has reduced time spent on the ship’s pipe sorting process.
Intelligent Wheelchair with Emotion Analysis and Voice Recognition

Intelligent wheelchairs are becoming more and more prevalent in contemporary life, and the peaceful interaction of humans with wheelchairs is one of the most popular research topics. The development of a voice recognition and emotion recognition based intelligent wheelchair framework is being addressed here for truly impaired/disabled people who are unable to operate the wheelchair by hand. The patient can operate the wheelchair using voice commands, and the wheelchair’s Emotion Analysis module recognizes the patient’s face and records the patient’s emotions before sending the information to a cell phone application. A portion of the intelligent wheelchair is made to gather crucial information given by other units and send out emergency calls or notifications to the caregivers. Face recognition technology uses image processing to identify facial expressions by detecting the patient’s face and facial expressions. This helps the other components collect and send data via Internet of Things technologies. Speech – to –Text and Text – to-Speech Methodology is used in the voice recognition module and it captures the voice command data set and extracts the features of the commands.The model is already built and trained to recognize the commands and to send action request to the relevant unit.The Responsive AI auto starts the timer when the patient moves away from the wheelchair, recognizes time and responses back. This unit auto also sends the alert and calls to the guardian when the user has no response.
Automobile Product Ranking based on the Singlish Comments in Social Media Platforms

In today’s world, many customers buy or choose products based on online reviews. The internet contains a vast collection of natural language. People share their subjective thoughts and experiences with one another in various social media platforms. Product reviews can be analyzed to determine how people feel about a particular product. In Sri Lanka, people widely use Singlish (Sinhala-English) to comment and give reviews on products, rather than a single pure language.Therefore in this research it has extracted data from social media platforms on various brands in the automobile industry and propose a system to rank the automobile brands in Sri Lanka based on the social media comments which are written on Singlish. When ranking products, it is not practical to rank products based only on the frequency of the products. Because a brand having the highest number of comments does not necessarily indicate that it has good market perception compared to other brands. In order to get an accurate overview, the study have considered both the people’s sentiment towards the particular brand and the frequency of comments. When ranking the products research has done several rankings based on different aspects namely market value, country of origin and second hand market,vehicle performance, product features which people pay their most attention in the automobile industry and also an overall ranking considering all these aspects together. With that it is possible to identify which vehicle type or brand has the highest and lowest demand in the market, and the automobile manufacturer can get a good understanding where a particular product stands out comparative to other brands and apply their strategies accordingly. When implementing the ranking system 100000 social media comments were extracted and annotated. Convolutionary neural network was used to develop the main model, and out of the different methods tried to predict the sentiment as the part of the main model, random forest method gave a higher accuracy of 96.7 making it a more sophisticated combination.
Identification of Factors Affecting the Adoption of Home Intelligent Robots

Understanding the factors driving the adoption of intelligent robots at home is crucial to improve the technology development given the era of Industry 4.0. This study aims to identify the influencing factors on the adoption of home intelligent robots. Eleven factors are recognized and Interpretative Structural Modeling (ISM) method was used to analyze the data that collected through questionnaires. In addition, fuzzy-ISM model is built to extend the experiments. The results found that price is the most dependent influencing factor, and after-sales service and appearance are the fundamental driving factors in regular ISM model, while price and after-sales service remain in the top and bottom levels in the fuzzy-ISM results.