Reviewerly/exHarmony huggingface dataset:
Machine learning with word embedding for detecting web-services anti-patterns

Software design Anti-pattern is the common feedback to a recurring problem that is ineffective and has a high risk of failure. Early prediction of these Anti-patterns helps reduce the design process’s efforts, resources, and costs. In earlier research, static code or Web Service Description Language (WSDL) metrics were used to develop anti-pattern prediction models. These source code metrics are calculated at either file-level or system-level. So, the values of these metrics are frequently dependent on assumptions that are not defined or standardized and might vary depending on the tools available. This study aims to develop a machine learning-based Anti-patterns prediction model using natural language processing techniques for representing the WSDL file as an input. In this research, the four-word embedding methods have been used to process the WSDL file. The processed outputs are used as input to the models trained using thirty-three classifier techniques. This study also uses eight feature selection techniques to remove ineffective features and five data sampling techniques to handle the class imbalance nature of the datasets. The results indicate that the developed models using text metrics perform better than the static code or WSDL metrics. Additionally, the results suggest that selecting features using feature selection and balancing data using sampling techniques helps improve the models’ performance.
Diffusion of Agricultural Ideas Through the Website of the Agricultural Extension and Training Department on the Social Networking “YouTube”

Abstract The research aim to identify the degree of diffused of agricultural ideas through the website of the Agricultural Extension and Training Department on the social network “YouTube”, as well as to find the correlation between the degree of diffusion of agricultural ideas and the variables: (video duration, agricultural idea specialization-Appropriate time of diffusion of the agricultural idea, the number of days since the diffusion of agricultural ideas, the independence of the agricultural idea. The researchers used the descriptive approach and the method of content analysis to achieve the objectives of the research, as the content of the website of the Department of Agricultural Extension and Training was analyzed on the YouTube, which is one of the formations of the Iraqi Ministry of Agriculture on the link and for the period extending from (11/12/2020 to 27/12/2021), through Follow up on agricultural ideas that have been spread through the site, as the YouTube is characterized by publishing videos of different lengths of time and followers can watch, like and comment on them. One numeric value for each view, two numerical values for each like, and three values for each comment. The data were analyzed using a number of statistical methods, including range, category length, Spearman correlation coefficient, Pearson correlation coefficient and (t) test. The results showed that the number of agricultural ideas that were diffused during the research period amounted to (30) agricultural ideas, and it was found that (93.34%) of them had a low degree of publicity. While the least popular agricultural ideas were my idea (breeding domestic chickens and identifying bird flu), as it was found that there is a significant relationship between the degree of spread of agricultural ideas and each of (the specialization of the agricultural idea, and Appropriate time of diffusion) The researchers recommended activating the role of the official website of the Agricultural Extension and Training Department in a better way by introducing the site through social sites and pages in order to benefit from the ideas presented more, with the dissemination of agricultural ideas within a short period of time and at a time that suits the beneficiaries of them.
"Begin with the End in Mind": Incorporating UX Evaluation Metrics into Design Materials of Participatory Design

Participatory Design (PD) aims to empower users by involving them in various design decisions. However, it was found that the PD’s evaluation criteria are usually set by the product team and used only at the end of a design process, without adequate user participation. To address this issue, we proposed introducing UX evaluation metrics into design materials at the participatory design INPUT phase. Using a case study of designing a chatbot for community members to report safety incidents, we studied the impact of this approach with 58 participants from two workshops. Our results showed that the integration of UX evaluation metrics efficiently rationalized participants’ contributions and helped identify key evaluation metrics when setting values for new AI systems, enhancing PD workshop insights. In addition to examining the use of the Program Theory Model to explain PD, our empirical investigation added a new dimension to this model.
Immersive Reading: Comparison of Performance and User Experience for Reading Long Texts in Virtual Reality

Specific use cases in virtual reality (VR) require users to read long texts on their headsets. We use VR to support researchers in the humanities, which includes the display of long text descriptions in VR. However, research on suitable user interface (UI) patterns for displaying and interacting with long texts in VR is scarce. To address this gap, we designed four text panel variants and conducted a within-participants study (N=24) to evaluate user experience (UX) and reading performance. Our findings suggest that there are no significant differences between conditions regarding reading performance. Yet, there are significant effects of conditions on some aspects of UX. Our work offers initial insights and future directions for research on the design of suitable UI patterns and the UX for reading long texts in VR.
From event perception and action to information states and information exchange

Abstract This chapter introduces some basic notions of a theory of action based on TTR and applies TTR to basic notions of information update in dialogue. Here we build on seminal work on the KoS framework by Jonathan Ginzburg and also related computational implementation by Staffan Larsson leading to the information state update approach to dialogue systems. These ideas have been adapted in a way that allows us to pursue the questions of grammar and semantics in the remainder of the book. A central notion is that of the dialogue gameboard which we construe as a type of information state representing the current state of play in the dialogue from the perspective of a dialogue participant. It includes the dialogue participant’s view of what has been committed to as being true in the dialogue so far and what questions are currently under discussion.
How to Write Better CHI Papers (with LaTeX in Overleaf)

Writing and organizing research papers is a valuable skill that can make or break your academic career. This course will help you improve your skills in writing research papers for publication at CHI. In the past five years, my writing course at CHI has introduced you to everything you wanted to know about writing papers. Now, it is time to take your skills even further. This course is now also about how to use LaTeX and Overleaf to format your papers so that they are easy to read and have the most practical impact. It is broken up into four 75-minute online units that will help you structure your paper's research content and typographic design. The goal of the course is to help you write something that makes a contribution to the field of human-computer interaction and can be understood by other HCI researchers through engaging formatting and concise language.
A Framework for Daily Living Activity Recognition using Fusion of Smartphone Inertial Sensors Data

Recent years have seen rapid advancements in the human activity recognition field using data from smart sensor devices. A wide variety of real-world applications can be found in different domains, particularly health and security. Smartphones are common devices that let people do a wide range of everyday tasks anytime, anywhere. The sensors and networking capabilities found in modern smartphones enable context awareness for a wide range of applications. This research mainly focuses on recognizing human activities in the wild for which we selected an in-the-wild extra-sensory dataset. Six human activities i.e., lying down, sitting, standing, running, walking, and bicycling are selected. Time domain features are extracted and human activity recognition is performed using three different machine learning classifiers i.e., random forest, k-nearest neighbors, and decision trees. The proposed human activity recognition scheme resulted in the highest classification accuracy of 89.98%, using the random forest classifier. Our proposed scheme outperforms the state-of-the-art human activity recognition schemes in the wild.
Experience Data-enabled Design!

During the Experience Data-enabled Design! (DED) course attendees will learn about designing for intelligent ecosystems while using data as a creative material. After a brief introduction of the method, and (industrial) cases the participants will collect their data from the context of the conference using personalized design probes. This course offers a careful balance between hands-on work and DED theory. The learning outcomes focus on topics of (physical) prototyping, (remote) data collection and analysis, using data as a creative material, and designing remote interventions.
Augmented Reality for Interaction with a CubeSat nanosatellite (ARICS)

Abstract The development of national aerospace technology has aroused interest in different sectors, so there is a long road to transformation where our country must develop the necessary aerospace infrastructure for said technology. The educational sector has been interested in aerospace technology but the use of this type of technology is expensive. That is why, with the support of ICTs, we think about how to solve this problem and this is where augmented reality (AR) comes in, given that today most young people have a cell phone capable of displaying functions of AR through a mobile application. Currently AR in the aerospace industry is used to support the assembly of complex systems such as aircraft or satellites, and perform simulations of the operation of these systems. That is why this project proposes the use of augmented reality so that anyone has access to interaction with a CubeSat-type nanosatellite from their mobile devices.
Changes in the length of speeches in the plays of William Shakespeare and his contemporaries: A mixed models approach

Since 2007 a number of investigators have compiled statistics on the length in words of speeches in plays by William Shakespeare and his contemporaries, focusing on a change to shorter speeches around 1600. In this article we take account of several potentially confounding factors in the variation of speech lengths in these works and present a model of this variation in the period 1538-1642 through Linear Mixed Models. We confirm that the mode of speech lengths in English plays changed from nine words to four words around 1600, and that Shakespeare's plays fit this wider pattern closely. We establish for the first time: that this change is independent of authorship, dramatic genre, theatrical company, and the proportion of verse in a play's dialogue; that the chosen time span can be segmented into pre-1597 plays (with high modes), 1597-1602 plays (with mixed high and low modes), and post-1602 plays (with low modes); that some additional secondary modes are evident in speech lengths, at 16 and 24 words, suggesting that the length of a standard blank verse line (around 8 words) is an underlying unit in speech length; and that the general change to short speeches also holds true when the data is viewed through the perspective of the median and the mean. The change in speech lengths is part of a collective drift in the plays towards liveliness and verisimilitude and is evidence of a hitherto hidden constraint on the playwrights: whether or not they were aware of the fact, playwrights as a group were conforming to a structure for the distribution of speech lengths peculiar to the era they were writing in. The authors hope that the full modelling of this variation in the article will help bring this change to the attention of scholars of Shakespeare and his contemporaries.
A novel data-driven method for fuel-consumption prediction based on fast converged kernel extreme learning machine

Abstract An accurate fuel consumption prediction is significant to fuel-economy-oriented optimization, which can improve vehicle fuel economy. Based on the data-driven prediction framework, this paper proposes a real-time fuel consumption prediction model (RFCPM) using kernel extreme learning machine (KELM) which is optimized by fast converged grey wolf algorithm (FCGWA). A new steering function of FCGWA and a new activation function for KELM are presented to ensure the fast converge speed and higher accuracy performance. First, the characteristic variables of RFCPM are selected by reference to the model-based fuel consumption prediction method. Second, a KELM is adopted to predict fuel consumption. Third, FCGWA is adopted to select the best parameters of KELM using k-fold cross-validation method. Finally, the best model is chosen through real-vehicle test. Test results are compared with the original ELM and the wildly used WNN. The experiment shows that the proposed method significantly outperforms the original ELM and WNN in terms of both prediction accuracy and training time.
Water Resource Management

Water makes up about 70% of the earth’s floor and is one of the most vital sources essential to maintaining life. Speedy urbanization and industrialization have led to a deterioration of water best at an alarming fee, ensuing in harrowing illnesses. Water high- quality has been conventionally estimated through expensive and time-ingesting lab and statistical analyses, which render the cutting-edge belief of real-time tracking moot. The alarming results of bad water nice necessitate an alternative approach that is quicker and inexpensive. With this motivation, this study explores a sequence of supervised device mastering algorithms to estimate the water high-quality. The proposed methodology achieves affordable accuracy the use of a minimum number of parameters to validate the possibility of its use in real time water first-class detection systems. It demonstrates the overall maintenance and management of the water quality and quantity inside a plant using modern technologies. It based on the systematic working process to reduce the man power and cost maintenance for the industrialization development.
Traffic Sign Board Detection and Voice Alert System

Automated tasks have simplified practically everything we perform in today's world. Drivers often miss signage on the side of the road in an effort to focus on the road, which can be dangerous for them and others. This issue may be avoided if there was a quick way to alert the driver without requiring them to divert their attention. TSDR (Traffic Sign Detection and Recognition) is useful in this situation because it detects and recognizes signs, alerting the motorist to any forthcoming signs. This not only assures road safety, but also puts the driver at ease when driving on unfamiliar or difficult roads. Another issue that arises frequently is the inability to comprehend the sign's meaning. Drivers will no longer struggle to grasp what the sign is saying thanks to this Advanced Driver Assistance Systems (ADAS) application. In this paper, we propose a method for detecting and recognizing traffic signs that uses image processing for detection and an ensemble of Convolutional Neural Networks (LeNet) for recognition. Because LeNet CNN have a high recognition rate, they are ideal for a variety of computer vision tasks. For the CNN implementation, Tensor Flow is employed. And here for object detection we implement yolo frame work On the German data sets, we achieved recognition accuracies of more than 99 percent for circular signs.
Obstacle avoidance of a wheeled robotic swarm using virtual spring-damper mesh

Abstract Swarm robotics, in which groups of robots coordinate to achieve a common goal, has the potential to revolutionize a wide range of industries. However, one of the key challenges in developing effective swarm systems is enabling the robots to navigate around obstacles while maintaining cohesion. In this article, the problem of trajectory tracking for a self-organized swarm of wheeled robots in the shape of a regular polygon operating in an environment with obstacles is addressed. The goal is for the swarm to follow a reference point by the geometric center of the swarm, while avoiding collision with obstacles and maintaining a given distance between neighboring robots and the reference point. To solve this problem, a method based on virtual forces that act on each individual robot in the swarm is proposed. These virtual forces are derived from virtual spring-damper connections acting from the obstacles, adjacent robots, and the reference point. The virtual forces allow the robots to dynamically adjust their trajectories to avoid obstacles and maintain the shape of the swarm. The proposed approach is evaluated through simulation and experiment with a swarm of two-wheeled robots. During the experiment the maximum deviation of inter-robot distances was equal to 0.08 m. With the set limit distance to obstacles equal to 0.2 m, from which virtual forces start to push the robot away from the edge of the obstacle, the shortest distance to obstacles was 0.11 m. The results demonstrate the effectiveness of the method in enabling the swarm to accurately follow the reference point while avoiding obstacles and maintaining the desired distances between robots. Overall, the approach represents a step forward in the development of virtual force-based algorithms for obstacle avoidance in swarm robotics.
A practical study of post-quantum enhanced identity-based encryption

One of the most expensive processes inside Public-Key Infrastructure (PKI) is certificate management. Identity-based Encryption (IBE) is seen as an alternative to alleviate the associated cost. This is especially relevant in many mission-critical applications characterized by resource-constrained endpoints. MIKEY-SAKKE is a protocol globally endorsed to secure mission-critical applications through the use of IBE. However, its security is threatened by the rapid development of quantum computers. Therefore, quantum-secure IBE should be deployed to meet the security and performance boundaries of the different applications. In order to develop quantum-secure IBE, traditional protocols can be enhanced by integrating Post-Quantum Cryptography (PQC) primitives, such as the finalists of the NIST PQC Round 3 standardization process, or by deploying fully dedicated post-quantum IBE constructions, such as the DLP-IBE scheme. To this end, this paper presents the following contributions. First, it evaluates the performance of MIKEY-SAKKE in constrained embedded devices. Second, it extracts the requirements that post-quantum cryptographic primitives should meet to allow a plug-and-play replacement of the traditional security primitives with quantum-secure primitives. Third, it benchmarks the different alternatives for deploying post-quantum IBE, which includes the NIST PQC Round 3 finalists for Key Encapsulation Mechanism (KEM) and digital signature, as well as the DLP-IBE scheme. Finally, it analyses the impact of the post-quantum primitives on quantum-secure MIKEY-SAKKE. The results show that none of the examined quantum-secure primitives meet all the specified requirements to achieve a post-quantum plug-and-play approach. Combining the different post-quantum KEM/IBE and signature schemes yields a range of trade-offs compared to the current MIKEY-SAKKE, either having slower computation or larger keys and ciphertexts/signatures or both.
Phase stability analysis and phase equilibrium calculations in reactive and nonreactive systems using new hybrids of Pelican and Gorilla troops algorithms

Phase stability and equilibrium calculations are very significant in the process industries, ranging from process simulation, equipment design, operation and design. These problems involve several systems of complex and nonlinear equations, which are mathematically challenging. An alternative solution is formulating the problems into a global optimisation problem. A robust and reliable optimisation technique is required to solve these global optimisation problems. In this study, two recently developed stochastic global optimisation algorithms, POA and GTO were applied to phase stability and equilibrium problems. The need to enhance their performance necessitated the development of two modified algorithms, PGOA1 and PGOA2, which are the hybridisation of POA and GTO. All the algorithms' abilities were explored for solving computationally challenging and multidimensional phase stability and equilibrium problems, and their performances were compared. The implementation of POA and GTO was good in solving most problems, though GTO slightly outperformed POA. The hybridisation of both to develop PGOA1 and PGOA2 was worthwhile as both outperformed the POA and GTO. PGOA2 is more robust and reliable in solving phase stability and equilibrium, and its performance superseded the performances of the other algorithms.
The method of the real-time human detection and tracking

Today, data collected from video surveillance systems require processing. Video content analysis (VCA) or video analytics (VA) has found applications in security systems, retail, the automotive industry, smart home technologies, etc. The tasks of video analytics include the detection and tracking of objects. Usually, video analytics systems are specialized software for analyzing video data collected from webcams and intelligently assessing the situation. There are also separate video surveillance cameras with built-in video analytics functions. Software approaches to human detection and tracking are quite diverse, special applications and web applications or separate software modules are created. This work presents an approach to solving these problems using convolutional neural networks. The aim of the study is to increase the efficiency of human detection and tracking in video sequences. For this purpose, an overview of existing methods of detecting objects in images was conducted, in particular: the Viola-Jones algorithm, the histogram of oriented gradients. The choice of convolutional neural networks for solving the subtask of object detection is substantiated. The working principles, advantages and disadvantages of Faster R-CNN, YOLO, SSD and RetinaNet networks are considered. Their comparative analysis was carried out according to the indicators of speed and accuracy of recognition on the HABBOF dataset. A hybrid neural network for human detection and tracking has been developed: a convolutional neural network of the YOLO type has been improved. The created network achieved an accuracy of 39.2% at 43 frames per second. Experiments were carried out with the created network in order to evaluate the operation of the network in various conditions. It has been established that it works well in poor lighting conditions, but the issue of detecting small objects remains open.