Reviewerly/exHarmony huggingface dataset:
Trajectory-based Traveling Salesman Problem for Multirotor UAVs

This paper presents a new method for integrated time-optimal routing and trajectory optimization of multirotor unmanned aerial vehicles (UAVs). Our approach extends the well-known Traveling Salesman Problem by accounting for the limited maneuverability of the UAVs due to their kinematic properties. To this end, we allow each waypoint to be traversed with a discretized velocity as well as a discretized flight direction and compute time-optimal trajectories to determine the travel time costs for each edge. We refer to this novel optimization problem as the Trajectory-based Traveling Salesman Problem (TBTSP). The results show that compared to a state-of-the-art approach for Traveling Salesman Problems with kinematic restrictions of UAVs, we can decrease mission duration by up to 15%.
PEMANFAATAN KOMIK DIGITAL SEBAGAI SARANA BISNIS DIGITAL PADA MEDIA SOSIAL INSTAGRAM

Comics are increasingly being used as a means of doing business in today's era. Many companies, business entities and individuals do business through online media. The use of comics as a promotional medium in this digital era, of course, must pay attention to the advantages of using comics as a means of digital business. This study was conducted to determine the use of digital comics as a means of digital business on Instagram social media. The qualitative research method is carried out in an inductive way, namely examining the characteristics of each part in Digital Comics that is used as a Digital Business. The use of digital comics as a means of digital business is also a good choice, because by using digital comics the content will become more interesting because in the digital comics, illustrations and text balloons can be applied. Not only as an added attraction, but illustrations and text balloons can also be used as a means of delivering information in digital business activities.
Research on Gesture Recognition Based on YOLOv5

The gesture recognition technology based on YOLOv5 adopts the Backbone network to train and learn. The graphical interface developed by python Tkinter module has made the real-time gesture recognition of videos with high precision and frame rate achievable. In terms of the testing dataset, the overall average recognition precision of model can be up to 99.6%, which is up to the precision of current mainstream deep learning target detection algorithms. Moreover, it is superior to those algorithms in terms of the recognition speed. From the experimental results, it can be concluded that YOLOv5 takes account of both precision and speed requirements of recognition, which has an advantage in dealing with the natural interaction information. Thus the technology provided by this paper proposes effective means for the human-computer interaction techniques.
Explicit basis function kernel methods for cloud segmentation in infrared sky images

Photovoltaic systems are sensitive to cloud shadow projection, which needs to be forecasted to reduce the noise impacting the intra-hour forecast of global solar irradiance. We present a comparison between different kernel discriminative models for cloud detection. The models are solved in the primal formulation to make them feasible in real-time applications. The performances are compared using the j-statistic. The infrared cloud images have been preprocessed to remove debris, which increases the performance of the analyzed methods. The use of the pixels' neighboring features also leads to a performance improvement. Discriminative models solved in the primal yield a dramatically lower computing time along with high performance in the segmentation.
Schemes in Lean

We tell the story of how schemes were formalized in three different ways in the Lean theorem prover.
FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks

Federated Learning (FL) is a distributed learning paradigm that can learn a global or personalized model from decentralized datasets on edge devices. However, in the computer vision domain, model performance in FL is far behind centralized training due to the lack of exploration in diverse tasks with a unified FL framework. FL has rarely been demonstrated effectively in advanced computer vision tasks such as object detection and image segmentation. To bridge the gap and facilitate the development of FL for computer vision tasks, in this work, we propose a federated learning library and benchmarking framework, named FedCV, to evaluate FL on the three most representative computer vision tasks: image classification, image segmentation, and object detection. We provide non-I.I.D. benchmarking datasets, models, and various reference FL algorithms. Our benchmark study suggests that there are multiple challenges that deserve future exploration: centralized training tricks may not be directly applied to FL; the non-I.I.D. dataset actually downgrades the model accuracy to some degree in different tasks; improving the system efficiency of federated training is challenging given the huge number of parameters and the per-client memory cost. We believe that such a library and benchmark, along with comparable evaluation settings, is necessary to make meaningful progress in FL on computer vision tasks. FedCV is publicly available: https://github.com/FedML-AI/FedCV.
Pseudo-random number generator based on linear congruence and delayed Fibonacci method

Pseudo-random number generation techniques are an essential tool to correctly test machine learning processes. The methodologies are many, but also the possibilities to combine them in a new way are plenty. Thus, there is a chance to create mechanisms potentially useful in new and better generators. In this paper, we present a new pseudo-random number generator based on a hybrid of two existing generators - a linear congruential method and a delayed Fibonacci technique. We demonstrate the implementation of the generator by checking its correctness and properties using chi-square, Kolmogorov and TestU01.1.2.3 tests and we apply the Monte Carlo Cross Validation method in classification context to test the performance of the generator in practice.
An improved weighted optimization approach for large-scale global optimization

Abstract It is a great challenge for ordinary evolutionary algorithms (EAs) to tackle large-scale global optimization (LSGO) problems which involve over hundreds or thousands of decision variables. In this paper, we propose an improved weighted optimization approach (LSWOA) for helping solve LSGO problems. Thanks to the dimensionality reduction of weighted optimization, LSWOA can optimize transformed problems quickly and share the optimal weights with the population, thereby accelerating the overall convergence. First, we concentrate on the theoretical investigation of weighted optimization. A series of theoretical analyses are provided to illustrate the search behavior of weighted optimization, and the equivalent form of the transformed problem is presented to show the relationship between the original problem and the transformed one. Then the factors that affect problem transformation and how they take affect are figured out. Finally, based on our theoretical investigation, we modify the way of utilizing weighted optimization in LSGO. A weight-sharing strategy and a candidate solution inheriting strategy are designed, along with a better allocation of computational resources. These modifications help take full advantage of weighted optimization and save computational resources. The extensive experimental results on CEC’2010 and CEC’2013 verify the effectiveness and scalability of the proposed LSWOA.
Pre-service Teachers' Digital Experiences through Digital Pedagogical Practices in Norway

Digital skills are one of the key competences outlined in The European Reference Framework of Key Competences for Lifelong Learning prescribed by the EU in 2006. Integration of digital tools and resources into the classroom results in more platforms for teaching and learning activities. Teacher training programmes prepare pre-service teachers with pedagogical competencies and skills necessary for their future practices. This paper shows that pre-service teachers could overcome the pedagogical challenges during COVID-19 teaching by updating their present and future classroom teaching strategies around digital literacy. To explore further how these new teaching circumstances are understood and reflected on by pre-service teachers, the researchers collected written reflections of 52 pre-service teachers in Norway using an open-ended survey about their digital integration experiences in their practicum. This paper offers analyses of the reflections inductively to reveal the teachers’ process of development of their classroom teaching strategies as influenced by new digitalisation-related experiences. The findings show low levels of digital integration according to the SAMR model but moderate to high levels of satisfaction among pre-service teachers of digital practices. In the light of these findings, this study offers pedagogical technological implications for teachers and teacher educators who work with teacher education curricula.
Fine-tuning SalGAN and PathGAN for extending saliency map and gaze path prediction from natural images to websites

In recent years, researches dealing with the study of visual attention have become very popular thanks to the enormous increase of Artificial Intelligence. Machine Learning and, in particular, Deep Learning allowed researchers to propose new predictive models operating on natural images. In the meantime, an increasing number of websites has been made available on the Internet. However, few approaches, aiming at extending the results obtained on natural images to web pages, have been proposed. In this paper, we provide a contribution in this setting by applying fine-tuning and other refinements to two existing GAN-based approaches (i.e., SalGAN and PathGAN) originally proposed to predict the saliency maps and gaze paths on natural images. Our ultimate goal is defining some variants of them able to deal with websites. In particular, our SalGAN variant represents one of the first attempts to employ GANs for saliency map prediction on web pages, whereas our PathGAN variant is the first attempt to adopt GANs for gaze path prediction on websites Here, we present our proposals, highlight their main novelties, describe the tests done and the results obtained. We also highlight two further contributions of this paper, namely: (i) a new dataset, more complete than the existing ones, supporting the analysis of visual attention on websites, and (ii) a tool supporting a web page designer in her attempt to increase the visitor interest and curiosity.
Automated System for Restaurant Services

The study proposes a smart restaurant system and analyses its benefits to be able to determine system potential advantages in restaurants. Service time is one of the main criteria that can be improved to enhance the speed of the customer service as well as to increase the number of restaurant visitors. To develop the system, solutions found in scientific literature, software and their different architectures are analysed. It has been found out that it is possible to decrease the average restaurant service load time by 52.76 %. Two hypotheses have been proposed for further research in order to determine how a smart restaurant service system can increase chef’s efficiency and how the use of different algorithms can decrease chef’s workload during peak hours.
Application of a Task Stalling Buffer in Distributed Hybrid Cloud Computing

The purpose of the research is to create a hybrid cloud platform that performs distributed computing tasks using high-performance servers and volunteer computing resources. The proposed platform uses a new task scheduling method, which is also presented in this paper. It uses a task stalling buffer to manage workload among the two grids without any additional information about the tasks. Since efficient task scheduling in these distributed systems is the actual problem, the system reliability issue is solved using a hybrid cloud architecture when both high-performance servers and volunteer computing resources are combined. The results of the experiment showed that the proposed solution solves the problem of balancing workload between two grids better than the standard scheduling algorithm. Computer study and experiments also showed that the proposed hybrid cloud tasks scheduling method with a task stalling buffer reduces up to 47.3 % of total task execution time. The outcome of this paper provides a background for future research on a task stalling buffer in hybrid cloud computing.
Undecidability of QLTL and QCTL with two variables and one monadic predicate letter

We study the algorithmic properties of the quantified linear-time temporal logic QLTL in languages with restrictions on the number of individual variables as well as the number and arity of predicate letters. We prove that the satisfiability problem for QLTL in languages with two individual variables and one monadic predicate letter in Σ 11 -hard. Thus, QLTL is Π 11 -hard, and so not recursively enumerable, in such languages. The resultholds both for the increasing domain and the constant domain semantics and is obtained by reduction from a Σ 11 -hard N×N recurrent tiling problem. It follows from the proof for QLTL that similar results hold for the quantified branching-time temporal logic QCTL, and hence for the quantified alternating-time temporal logic QATL. The result presented in this paper strengthens a result by I. Hodkinson, F. Wolter, and M. Zakharyaschev, who have shown that the satisfiability problem for QLTL is Σ 11 -hard in languages with two individual variablesand an unlimited supply of monadic predicate letters.
The educational possibilities of augmented and virtual reality technologies

The article reveals the role and place of augmented and virtual reality technologies in classroom and extracurricular activities. Despite the fact that, as educational practice shows, these technologies have a number of advantages over traditional teaching methods, there are a number of problems of their effective implementation in the educational process. In particular, many teachers experience difficulties in using AR technologies in their professional activities, associated with determining their place and role in the specific circumstances of the educational process. The aim of the work is to study the possibilities of using augmented and virtual reality technologies as an innovative technology for organizing the educational process in real school practice. The text discusses various approaches to the implementation of these technologies in the educational space of the school, technological and methodological features of their development within the school informatics course, gives a comparative description of programs and applications for creating augmented reality, presents the development of some elements of augmented reality and reveals the methodological features of their use in lessons and extracurricular activities. As a result of the study, it became possible to consider AR/VR-technologies as an integral part of school educational tools.
Water surface object detection using panoramic vision based on improved single-shot multibox detector

Abstract In view of the deficiencies in traditional visual water surface object detection, such as the existence of non-detection zones, failure to acquire global information, and deficiencies in a single-shot multibox detector (SSD) object detection algorithm such as remote detection and low detection precision of small objects, this study proposes a water surface object detection algorithm from panoramic vision based on an improved SSD. We reconstruct the backbone network for the SSD algorithm, replace VVG16 with a ResNet-50 network, and add five layers of feature extraction. More abundant semantic information of the shallow feature graph is obtained through a feature pyramid network structure with deconvolution. An experiment is conducted by building a water surface object dataset. Results showed the mean Average Precision (mAP) of the improved algorithm are increased by 4.03%, compared with the existing SSD detecting Algorithm. Improved algorithm can effectively improve the overall detection precision of water surface objects and enhance the detection effect of remote objects.
Accelerating the Race to Autonomous Cars – A Case Study

Background/Purpose: Every automaker is racing to generate self-driving innovations and some slew of fantastic tech firms and start-ups doing the same. The vehicle industry has a long history of implementing cutting-edge technologies to bring efficient, creative, and reliable vehicles to market, all while working to reduce production costs. Such innovations involve machine learning and computational intelligence, which are essential to automobiles progress. Machine learning (AI) technologies have made the innovative concept of self-driving vehicles an actuality. Today, global automotive rulers such as BMW, Volvo, and Tesla use intelligent automation to enhance production, raise production efficiency, and actually drive secure, extra relaxed, expanding, and increasingly enjoyable. This article provides a comprehensive analysis of Companies in the development of Autonomous vehicles and used ABCD analysis to examine the key parameters. Objective: Analyses the technology and business strategies of the companies in the Race of Autonomous cars. Design/Methodology/Approach: The information for this case study were gathered from various scholarly articles and websites. Findings/Result: The technological details of Artificial Intelligence, Self-driving car companies, laws and restrictions of different companies for using Self-driving vehicles, Autopilot driving features, sales volume and financial expansion, Impact of COIVID-19 on Autonomous vehicles business are studied. The impacts of COVID-19 on the autonomous car business are analysed using the ABCD framework. Originality/Value: The result provides a brief overview of different self-driving vehicle companies and self-driving technology building companies in the competitive race. Paper type: A Research Case study paper - focuses on companies in a race of producing Autonomous vehicles and the growth of those companies.
Quantum non-Markovian “casual bystander” environments

Quantum memory effects can be induced even when the degrees of freedom associated to the environment are not affected at all during the system evolution. In this paper, based on a bipartite representation of the system-environment dynamics, we found the more general interactions that lead to this class of quantum non-Markovian "casual bystander environments." General properties of the resulting dynamics are studied with focus on the system-environment correlations, a collisional measurement-based representation, and the quantum regression hypothesis. Memory effects are also characterized through an operational approach, which in turn allows to detect when the studied properties apply. Single and multipartite qubits dynamics support and exemplify the developed results.
Usability analysis taking into consideration the aspects of accessibility of selected university websites

The goal of this paper was to evaluate selected web services of universities in terms of user experience, with particular emphasis on usability and accessibility. The research was conducted using eye-tracking and questionnaire methods. Ten people participated in this study. The objects of the study were three university websites: the Catholic University of Lublin (KUL), the Cracow University of Technology (PK) and the West Pomeranian University of Technology (ZUT). The eye-tracking data were subjected to qualitative and quantitative analyses, while the data from questionnaires were subjected to quantitative analysis. The results of individual analyses are presented in the form of heat maps, scan paths, charts and tables.
A CNN Hardware Accelerator Designed for YOLO Algorithm Based on RISC-V SoC

YOLO (You Only Look Once) has been widely used in the field of object detection because of its extremely fast real-time calculation speed and good migration ability. In recent years, the design of artificial intelligence systems with high real-time and low energy consumption has become a research hotspot. In this paper, we propose a CNN hardware accelerator specifically designed for YOLOv3-Tiny to increase the calculation parallelism while reducing the frequency of memory access. The design is configured and controlled by T-Head C910, a state-of-art open source multi-core processor based on RISC-V architecture. Experimental results show that the design can provide effective throughput improvement for small embedded systems with limited resources.
Digital public goods: Enablers of digital sovereignty

Digital sovereignty helps countries maintain control over their decision making and implementation of services. Proprietary technology can lead to technological lock-ins and silos that threaten digital sovereignty, but digital public goods, a type of open-source solution, allow countries to adopt, adapt and scale technology in ways that maintain their flexibility. As uptake of digital public goods – and thus digital sovereignty – spreads, it is reshaping the patterns of development co-operation.
Disinformation, Deepfakes and Democracies: The Need for Legislative Reform

Rapid technological advancement is changing the way that political parties, voters, and media platforms engage with each other. This along with cultural change has led to an emerging era of disinformation and misinformation driven by both domestic and foreign actors. Political deepfakes, videos created through the use of artificial intelligence, allow individuals to rapidly create fake videos indistinguishable from true content. These videos have the capacity to undermine voter trust and could alter electoral outcomes. Regulating disinformation however raises significant free speech concerns, as well as questions about where liability should fall. In particular, holding large technology and media platforms accountable for content could lead to unintended chilling effects around freedom of expression, harming rather than protecting democratic institutions. Proposed regulations should therefore be carefully analysed through the framework of the implied freedom of political communication, ensuring that any new laws are proportionate and tailored to the threat they seek to prevent. This article analyses how current Australian law interacts with political deepfakes and proposes two targeted amendments to our federal electoral regulations to reduce the threat they pose to elections.