Reviewerly/exHarmony huggingface dataset:
Ball and Goal Image Recognition on Humanoid Robot Darwin OP Using Faster Region-Based Convolutional Neural Networks (Faster R-CNN) Method

In humanoid robot soccer, the capacity to precisely track a ball is a crucial problem that is made challenging by processing limits and the subsequent inability to interpret all data from a high-definition image. This research suggests a method for locating and sizing balls in a computationally effective field setting. This research presents an enhanced, Faster Region-Based CNN-based deep learning architecture for multi-class ball and goal recognition. The proposed framework incorporates improved Faster RCNN model development, data argumentation, ball and goal image library building, and performance assessment. This study is a pioneer in employing 1000 real-world photographs to build a multi-labeled image class ball and goal. The convolutional and pooling layers are also improved for more precise and quick identification. The test findings reveal that the suggested method outperformed conventional detectors regarding detecting accuracy and processing speed. It has excellent potential for use in developing an autonomous, real-time image recognition system for humanoid robots.
A Scoping Study of Ethics in Artificial Intelligence Research in Tourism and Hospitality

Abstract As e-tourism scholars advance innovative research on the use and study of artificially intelligent systems, it is important to reflect on how well we are advancing transformative philosophies which ask that emerging fields consider issues of ethics, power, and bias. We conduct a scoping study of review papers published between 2015–2021 to understand the extent to which ethical and social bias issues are identified and treated in AI research in tourism. Results suggest that the potential for ethical and bias issues in AI in tourism is high, but identification and treatment of these issues by tourism researchers is weak. We summarize key implications of this trend and offer suggestions for pursuing a research agenda which increasingly identifies and treats issues of ethics and bias when advancing research on artificial intelligence (AI) in tourism.
Towards Recommender Systems in Augmented Reality for Tourism

Abstract Recent advances in augmented reality have enabled new ways of generating and presenting item recommendations. In tourism, AR applications can, for example, enhance points of interests (POIs) with virtual elements in AR and provide tourists with personalized recommendations for places to visit. In this paper, we present our prototype: a touristic AR application that augments various POIs with digital content and generates context-aware recommendations for POIs in the Niederdorf old town of Zurich, Switzerland. We demonstrate how useful information can be presented to users in an engaging way by combining AR technologies and recommender systems.
Legal Considerations on the Relationship Between Tourism Marketing and AR

Abstract Augmented reality (AR) is greatly changing the tourism scene in this decade. With the recent expansion of AR, it is now possible to obtain digital flyers and coupons by physically visiting a place. Various AR-based games, such as Pokémon Go, have encouraged many people to visit various locations in the real world. However, the use of AR as a marketing strategy to attract customers and induce movements in humans has created new legal issues, such as the appropriateness of placing coupons on private properties and its use in public parks by private companies for sales promotions. In addition, since AR can make valuable digital information appear in a space for a specific period, it raises new issues regarding time management. This paper considers the problems that arise in guiding people using AR, mainly from a legal perspective, and discusses the future direction.
Training reinforcement learning models via an adversarial evolutionary algorithm

When training for control problems, more episodes used in training usually leads to better generalizability, but more episodes also requires significantly more training time. There are a variety of approaches for selecting the way that training episodes are chosen, including fixed episodes, uniform sampling, and stochastic sampling, but they can all leave gaps in the training landscape. In this work, we describe an approach that leverages an adversarial evolutionary algorithm to identify the worst performing states for a given model. We then use information about these states in the next cycle of training, which is repeated until the desired level of model performance is met. We demonstrate this approach with the OpenAI Gym cart-pole problem. We show that the adversarial evolutionary algorithm did not reduce the number of episodes required in training needed to attain model generalizability when compared with stochastic sampling, and actually performed slightly worse.
Construction and Application of Task-based Blended Learning Model in a University EFL Listening and Speaking Course

Abstract: In higher education today, blended learning has been acknowledged as one of the most popular trends in language teaching since it combines the strengths of both online learning and traditional teaching methods. This study explored the construction and application of a task-based blended learning model in a university English listening and speaking course in an English as a foreign language (EFL) context. At the end of the 15-week study, data were collected via online records and two survey questionnaires from 110 participants regarding their feedback on this learning model. The quantitative and qualitative analysis of the data revealed that the majority of the students displayed a positive attitude towards the new teaching approach and recognized a growth in their English listening and speaking skills, as well as a boost in their learning participation, interaction, interests, motivation, and self-confidence through fulfilling tasks in this blended learning model. The study has implications for the future lesson planning of other English courses, in addition to the listening and speaking course, and also for the development of task-based learning models.
DenMG: Density-Based Member Generation for Ensemble Clustering

Ensemble clustering is a popular approach for identifying clusters in data, which combines results from multiple clustering algorithms to obtain more accurate and robust clusters. However, the performance of ensemble clustering algorithms greatly depends on the quality of its members. Based on this observation, this paper proposes a density-based member generation (DenMG) algorithm that selects ensemble members by considering the distribution consistency. DenMG has two main components, which split sample points from a heterocluster and merge sample points to form a homocluster, respectively. The first component estimates two probability density functions (p.d.f.s) based on an heterocluster’s sample points, and represents them using a Gaussian distribution and a Gaussian mixture model. If random numbers generated by these two p.d.f.s are deemed to have different probability distributions, the heterocluster is split into smaller clusters. The second component merges clusters that have high neighborhood densities into a homocluster. This is done using an opposite-oriented criterion that measures neighborhood density. A series of experiments were conducted to demonstrate the feasibility and effectiveness of the proposed ensemble member generation algorithm. Results show that the proposed algorithm can generate high quality ensemble members and as a result yield better clustering than five state-of-the-art ensemble clustering algorithms.
Highly Parallel Linear Forest Extraction from a Weighted Graph on GPUs

For graph matching, each vertex is allowed to match with exactly one other vertex, such that the spanning subgraph of the matching has a maximum degree of one, i.e., the subgraph is a [0,1]-factor. In this work, we provide a highly parallel algorithm to extract a spanning subgraph with a maximum degree of n (the subgraph is a [0,n]-factor) and demonstrate the efficiency of our GPU implementation for n=1,2,3,4 by expressing the algorithm in terms of generalized sparse matrix-vector products. Moreover, from the [0,2]-factor, we compute a maximum linear forest (union of disjoint paths) by breaking up cycles and permuting the subgraph with respect to the vertex order within the paths. Both tasks execute efficiently on the GPU because of our novel parallel scan implementation, which does not require a random access iterator. As an application of linear forests, we demonstrate the algebraic creation of enhanced tridiagonal preconditioners for various large matrices from the Sparse Matrix Collection and report runtimes in the order of milliseconds for graphs with millions of edges and vertices on an RTX 2080 Ti.
Mentha: Enabling Sparse-Packing Computation on Systolic Arrays

Generalized Sparse Matrix-Matrix Multiplication (SpGEMM) is a critical kernel in domains like graph analytic and scientific computation. As a kind of classical special-purpose architecture, systolic arrays were first used for complex computing problems, e.g., matrix multiplication. However, classical systolic arrays are not efficient enough when handling sparse matrices due to the fact that the PEs containing zero-valued entries perform unnecessary operations that do not contribute to the result. Accordingly, in this paper, we propose Mentha, a framework that enables systolic arrays to accelerate sparse matrix computation by employing a sparse-packing algorithm suitable for various dataflow of systolic array. Firstly, Mentha supports both online and offline methods. By packing the rows or columns of the sparse matrix, the zero-valued items in the matrix are significantly reduced and the density of the matrix is improved. In addition, acceleration benefits can be obtained by the adaptation scheme even with limited resources. Moreover, we reconfigure PEs in systolic arrays at a low cost (1.28x in area, 1.21x in power) and find that our method outperforms TPU-like systolic arrays by 1.2~3.3x in terms of SpMM and 1.3~4.4x in terms of SpGEMM when dealing with moderately sparse matrices (sparsity < 0.9), while its performance is at least 9.7x better than cuSPARSE. Furthermore, experimental results show a FLOPs reduction of roughly 3.4x in the neural network.
Computer and Web Technology in Teaching Management System of Colleges and Universities

Recently, the scale of running colleges and universities has continued to expand, the professional environment is very flexible, the teaching methods are diverse, and the school-enterprise cooperation is frequent, which undoubtedly increases the workload of teaching management. Thus, it is imperative to fully use modern information technology, establish a teaching management system, and improve the level of teaching management and school operation efficiency. The purpose of this paper is to study the college teaching management system based on computer and Internet technology. It expounds the general situation of teaching management in colleges and universities, formulates the design scheme of teaching management system in colleges and universities, and designs in detail around the modules of teaching management, examination management, system management, and student management. Using ASP.NET technology to create and manage Web application platform, using the combination of B/S and C/S as the development language, developed and realized 7 functional modules of SQL Server database system. Finally, the performance test and system operation test are gotten. The results show that the average response time of each information input test is 2.5 seconds, and the CPU utilization rate is 27%, indicating that the information input is correct and the query and save results are correct.
Education Platform System on Account of Association Rule Algorithm

Education platform system in the social and economic development of the environment, all parties have put forward the implementation plan can be in a variety of complex conditions. Now, from the perspective of system design, the system principle of designing the education platform system is described, and the technology and the overall composition are adopted to adapt to the interactive teaching and teaching management under the mode of online network and on-site education, and the tool or method is used to replicate the current education process to the greatest extent. This paper introduces the components and functions of the design education platform system in detail, and explains the mechanism of using algorithm in the design education platform system based on association rule algorithm. This paper studies the related contents of the design education platform system based on association rule algorithm, and focuses on the role of the design education platform system based on association rule algorithm to improve the performance of the education platform system. The data show that the system research of design education platform based on association rule algorithm is more excellent in response time and efficiency than other system research of design education platform.
Adaptive and Efficient GPU Time Sharing for Hyperparameter Tuning in Cloud

Hyperparameter tuning (HPT), which chooses a set of optimal hyperparameters for a learning algorithm, is critical to machine learning training. Unfortunately, the current resource provisioning approaches for HPT are unable to adjust resources adaptively according to the upward trends of HPT accuracy at runtime, resulting in low GPU utilization or HPT accuracy. On the other hand, dynamic resource provisioning approaches based on checkpointing are inefficient for HPT, because of high overhead of context switching and job restarting.
A Fast and Secure AKA Protocol for B5G

With the popularity of mobile devices, the mobile service requirements are now changing rapidly. This implies that the micro network operator dedicated to a specific sector of users has the potential to improve the 5G architecture in terms of scalability and autonomy. However, the traditional AKA protocol does not allow the micro operator to authenticate mobile users independently. To solve this problem, we propose the Fast AKA protocol, which disseminates a subscriber’s profile among base stations via a Blockchain and mutually authenticates the subscriber and serving base station locally for roaming. The proposed architecture speeds up the authentication process, provides forward/backward secrecy, and resists replay attack as well as man-in-the-middle attack. We believe that Fast AKA can serve as a cornerstone for B5G.
Pelatihan dan Pendampingan Informasi Geospasial Berbasis Sistem Informasi Geografi (SIG) dalam Meningkatkan Kompetensi Guru Geografi

The rapid development of GIS geospatial information technology demands proficiency from geography teachers, but teachers tend to have minimal knowledge of the technology, and even the impression that teaching and learning activities are not being used optimally. I have. GIS geospatial information technology is very useful in describing environmental problems, metropolitan areas, floods, and various activities to establish trends, determine best locations, and identify patterns and models. The purpose of this activity is the enhancement and application of geographic information technology based on geographic information systems for geography teachers in the form of training and instruction, including data entry, management, analysis and presentation. In carrying out this activity, the participants, while receiving a strengthening of GIS concepts and GIS components, created maps of slopes, elevations and landslides in operational demonstration activities, then classified and scored them, and evaluated them. Click Overlay (Overlay) to create a new map with the new classification. The results of this activity show better control as well as higher enthusiasm and responsiveness during the training activity. The whole operation went smoothly. In addition, GIS education can enhance the effectiveness of geography education through application in schools.
Outtopological Digraph Space and Some Related Properties

The outtopological digraph space, a novel topological constraint imposed by a subbasis, is introduced in this work. A subbasis $\overleftarrow{\textrm{I}}^{\alpha}_{\underset{\cdot}{e}}$ is a set contains one vertex such that the edge ẹ is outdegree of it. As a result, various theorems have been established. A characterization and some examples are provided to describe this new structure.
Google Meet's Ubiquitous and Pervasive Application with BigBlueButton

In this pandemic period, the education processes apply online distance learning. There are many educational learning media that can be used and one is by using Google Meet. A platform for video conferences is called Google Meet. With the ability to host video conferences and host presentations, it is an excellent platform for cooperation. so that you can attend meetings straight from an email or Calendar event. Google Meet is important to inspect teachers' perceptions and attitudes. When they have positive perceptions about online education, teachers also adopt positive attitudes toward educational technology. They would thus be open to change and resist the move to online virtual education. Google Meet is now experiencing a pandemic with various difficulties. This research paper aims to introduce Google Meet, how to utilize the application of Google Meet, how to generate Google meet links, and their benefits. This paper focuses on the effect of the Google Meet media-assisted lecture method on building student knowledge.
Analysis of geothermal potential in Hangjiahu area based on remote sensing and geographic information system

Geothermal resources are one of the most valuable renewable energy sources because of their stability, reliability, cleanliness, safety and abundant reserves. Efficient and economical remote sensing and GIS (Geographic Information System) technology has high practical value in geothermal resources exploration. However, different study areas have different geothermal formation mechanisms. In the process of establishing the model, which factors are used for modeling and how to quantify the factors reasonably are still problems to be analyzed and studied. Taking Hangjiahu Plain of Zhejiang Province as an example, based on geothermal exploration and remote sensing interpretation data, the correlation between the existing geothermal hot spots and geothermal related factors was evaluated in this paper, such as lithology, fault zone distance, surface water system and its distance, seismic point distance, magmatic rock and volcanic rock distance, surface water, farmland, woodland temperature and so on. The relationship between geothermal potential and distribution characteristics of surface thermal environment, fault activity, surface water system and other factors was explored. AHP (Analytic Hierarchy Process) and BP (Back Propagation) neural network were used for establishing geothermal potential target evaluation models. The potential geothermal areas of Hangjiahu Plain were divided into five grades using geothermal exploration model, and most geothermal drilling sites were distributed in extremely high potential areas and high potential areas. The results show that it is feasible to analyze geothermal potential targets using remote sensing interpretation data and geographic information system analysis databased on analytic hierarchy process analytic hierarchy process and back propagation neural network, and the distribution characteristics of surface thermal environment, fault activity, surface water system and other related factors are also related to geothermal distribution. The prediction results of the model coincide with the existing geothermal drilling sites, which provides a new idea for geothermal exploration.
Boomerang uniformity of power permutations and algebraic curves over 𝔽<sub>2</sub> <i> <sup>n</sup> </i>

Abstract We obtain the Boomerang Connectivity Table of power permutations <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mi>F</m:mi> <m:mo stretchy="false">(</m:mo> <m:mi>x</m:mi> <m:mo stretchy="false">)</m:mo> <m:mo>=</m:mo> <m:msup> <m:mi>x</m:mi> <m:mrow> <m:msup> <m:mn>2</m:mn> <m:mi>m</m:mi> </m:msup> <m:mo>−</m:mo> <m:mn>1</m:mn> </m:mrow> </m:msup> <m:mtext> of </m:mtext> <m:msub> <m:mi mathvariant="double-struck">F</m:mi> <m:mrow> <m:msup> <m:mn>2</m:mn> <m:mi>n</m:mi> </m:msup> </m:mrow> </m:msub> </m:mrow> </m:math> $F(x)={{x}^{{{2}^{m}}-1}}\text{ }\!\!~\!\!\text{ of }\!\!~\!\!\text{ }{{\mathbb{F}}_{{{2}^{n}}}}$ with m ∈ <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mrow> <m:mo>{</m:mo> <m:mrow> <m:mn>3</m:mn> <m:mo>,</m:mo> <m:mfrac> <m:mrow> <m:mi>n</m:mi> <m:mo>−</m:mo> <m:mn>1</m:mn> </m:mrow> <m:mn>2</m:mn> </m:mfrac> <m:mo>,</m:mo> <m:mfrac> <m:mrow> <m:mi>n</m:mi> <m:mo>+</m:mo> <m:mn>1</m:mn> </m:mrow> <m:mn>2</m:mn> </m:mfrac> <m:mo>,</m:mo> <m:mi>n</m:mi> <m:mo>−</m:mo> <m:mn>2</m:mn> </m:mrow> <m:mo>}</m:mo> </m:mrow> <m:mo>.</m:mo> </m:mrow> </m:math> $\left\{ 3,\frac{n-1}{2},\frac{n+1}{2},n-2 \right\}.$ In particular, we obtain the Boomerang uniformity and the Boomerang uniformity set of <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mi>F</m:mi> <m:mo stretchy="false">(</m:mo> <m:mi>x</m:mi> <m:mo stretchy="false">)</m:mo> <m:mtext> at </m:mtext> <m:mi>b</m:mi> <m:mo>∈</m:mo> <m:msub> <m:mi mathvariant="double-struck">F</m:mi> <m:mrow> <m:msup> <m:mn>2</m:mn> <m:mi>n</m:mi> </m:msup> </m:mrow> </m:msub> <m:mo>∖</m:mo> <m:msub> <m:mi mathvariant="double-struck">F</m:mi> <m:mn>2</m:mn> </m:msub> <m:mo>.</m:mo> </m:mrow> </m:math> $F(x)\text{ }\!\!~\!\!\text{ at }\!\!~\!\!\text{ }b\in {{\mathbb{F}}_{{{2}^{n}}}}\setminus {{\mathbb{F}}_{2}}.$ Moreover we determine the complete Boomerang distribution spectrum of F ( x ) using the number of rational points of certain concrete algebraic curves over <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:msub> <m:mi mathvariant="double-struck">F</m:mi> <m:mrow> <m:msup> <m:mn>2</m:mn> <m:mi>n</m:mi> </m:msup> </m:mrow> </m:msub> <m:mo>.</m:mo> </m:mrow> </m:math> ${{\mathbb{F}}_{{{2}^{n}}}}.$ We also determine the distribution spectra of Boomerang uniformities explicitly.
An Exact and Practical Classical Strategy for 2D Graph State Sampling

Abstract Constant‐depth quantum circuits that prepare and measure graph states on 2D grids are proved to possess a computational quantum advantage over their classical counterparts due to quantum nonlocality and are also well suited for demonstrations on current superconducting quantum processor architectures. To simulate the partial or full sampling of 2D graph states, a practical two‐stage classical strategy that can exactly generate any number of samples (bit strings) from such circuits is proposed. The strategy is inspired by exploiting specific properties of a hidden linear function problem solved by the target quantum circuit, which in particular combines traditional classical parallel algorithms and an explicit gate‐based constant‐depth classical circuit together. A theoretical analysis reveals that on average each sample can be obtained in nearly constant time for sampling specific circuit instances of large size. Moreover, the feasibility of the theoretical model is demonstrated by implementing typical instances up to 25 qubits on a moderate field programmable gate array platform. Therefore, the strategy can be used as a practical tool for verifying experimental results obtained from shallow quantum circuits of this type.