Reviewerly/exHarmony huggingface dataset:
OptiDepthNet: A Real-Time Unsupervised Monocular Depth Estimation Network

With the development of deep learning, the network architectures and algorithm accuracy applied to monocular depth estimation have been greatly improved. However, these complex network structures can be very difficult to realize real-time processing on embedded platforms. Consequently, this study proposed a lightweight encoding and decoding structure based on the U-Net model. The depthwise separable convolution was introduced into the encoder and decoder to optimize the network structure, further reduce the computational complexity, and improve the running speed, the implementation algorithm being more suitable for embedded platforms. When the accuracy of similar depth images was achieved, the network parameters could be reduced by up to eight times, and the running speed could be more than doubled. The research showed the proposed method to be very effective, having a certain reference value in monocular depth estimation algorithms running on embedded platforms.
Workshop 2: Spherical Tensors and Spin Hamiltonians

In this workshop, the reader can practice with building spherical tensors from combinations of other tensors, using Clebsch–Gordan coefficients, calculating the effects of a change of frame, using radiofrequency Hamiltonians, and applying motional averaging.
Attention-Based Multimodal Image Matching

We propose a method for matching multimodal image patches using a multiscale Transformer-Encoder that focuses on the feature maps of a Siamese CNN. It effectively combines multiscale image embeddings while improving task-specific and appearance-invariant image cues. We also introduce a residual attention architecture that allows for end-to-end training by using a residual connection. To the best of our knowledge, this is the first successful use of the Transformer-Encoder architecture in multimodal image matching. We motivate the use of task-specific multimodal descriptors by achieving new state-of-the-art accuracy on both multimodal and unimodal benchmarks, and demonstrate the quantitative and qualitative advantages of our approach over state-of-the-art unimodal image matching methods in multimodal matching. Our code is shared here: Code.
Self adaptive random generated certificateless signcryption identity with load balancing for secured cloud data communication

In cloud, data communication is performed among the servers and cloud users where security plays a major role for protecting the data from unauthorised users. The several techniques have been developed to provide the security for cloud data. But, it failed to consider both data security as well as load balancing in cloud. This is the major concern in decreasing the growth of cloud computing. A self adaptive random generated certificateless signcryption identity based weighted heuristic search load balancing (SARGCSI-WHSLB) technique for improving the load balancing efficiency with higher security in cloud data access is proposed. This technique performs the encryption and signature verification process where only the authorised cloud user accesses the data from the cloud server. Based on the results and discussions, SARGCSI-WHSLB technique improved the data security as well as load balancing efficiency in cloud environment than the other methods.
Building a software model to preserve intellectual property and identify the authorship of a text

This article describes a software model algorithm that was implemented in order to determine the author of an unknown document based on the previously presented data and also studies the degree of accuracy of each of the measures for determining the authorship of a text. A comparative analysis is carried out based on the dependence of the quality of the identification of the text's creator on his stylistic affiliation. The problem of determining the authorship of anonymous text in this programming model has been solved using a set of metrics, for each of which the following parameters are required: analysis of text frequency, total number of characters in the text, and dimension of the alphabet. These parameters are required for anonymous text and for the library of famous authors.
Steff: A Novel Spatio-Temporal System for Dynamic Texture Classification Using Efficientnet on Outdoor Scenes

In recent years, dynamic texture classification has become an important task for computer vision due to its unknown spatial and temporal nature. Deep learning-based approaches would still be required for performance improvement. In this context, we propose a novel spatio-temporal approach (STEFF) for dynamic texture classification that combines the representation power of motion and appearance using the difference and average operators between video sequences. Then, we extract deep texture features from outdoor scenes and integrate both spatial and temporal features into a pre-trained Convolutional Neural Network model, namely EfficientNet, with a fine-tuning and regularization process. The robustness of the proposed approach is reflected in the promising result when comparing our method to 17 proposed architectures and other existing models. The experimental results on three datasets demonstrate the effectiveness and efficiency of the proposed approach. The accuracy percents are 95.95, 94.09, and 98.01 on the Yupenn, DynTex++, and Yupenn++ datasets, respectively.
Analysis of the Negative Impact of Internet Tweets on Readers' Values and the Positive Measures

As a new way of reading with the help of network media, network tweets not only cover most of the characteristics of traditional reading materials, but also show the characteristics of content liberalization, scope expansion and concept diversification.Different from the characteristics of traditional paper-based reading materials, such as high publishing threshold and inconvenient reading, the new era of network tweets has low creative threshold, confused creator identity and uneven ability, which brings many benefits to people's reading, at the same time, as a new thing, it also breeds some negative effects, such as misleading, virtual and general.It is of great significance to reduce the negative impact on readers' values and further play the positive role of network tweets by taking reasonable guidance measures, such as building a defense line from the platform, creators and the national level, carrying out systematic education for readers, and mobilizing the main role of readers in the field of network tweets.
Radio Advertising in Italy at the Time of the Pandemic

The article focuses on a specific form of advertising – radio advertising – at a time when it was called upon to change itself, namely during the first phase of the Coronavirus pandemic. Taking into consideration a corpus of radio advertisements broadcast during the programme La Zanzara, on air on Radio24, the main rhetorical, morpho-syntactic and lexical aspects are considered. The linguistic profile that emerges is not particularly lively, nor do the adverts show that they have fully adapted to the pandemic.
Prediction of Large-Scale Instrument Usage Based on Catboost Algorithm for Science and Education Integration in Local Universities

Under the background of the "Double First-Class" university plan, the sharing mechanism of large-scale instruments plays an indispensable role in science and education integration in universities.This paper first collects usage data (from September 2020 to September 2022) of large campus-shared devices in a real-world setting.Second, based on the machine learning algorithm, CatBoost, this paper constructs a method to predict the usage of devices.The results show that the best prediction is achieved when the input time step is 3 (with MAE, MSE, and RMSE being 56.10, 7445.43, and 86.28, respectively).Based on the obtained prediction results, corresponding policy recommendations are proposed further.Finally, taking Guilin University of Electronic Science and Technology as an example, this paper illustrates the use cases of large campus-shared devices in the real world.The method in this paper provides concrete decision support for large campus-shared equipment managers to estimate the busy and idle periods of equipment usage and develop equipment maintenance plans.
Perbedaan Motivasi Belajar Ditinjau Dari Faktor Demografi Mahasiswa Yang Mengikuti Perkuliahan Daring Universitas Negeri Padang

The Differences In Learning Motivation In View Of The Demographic Factors Of Student Taking Online Lectures In State University Of Padang. This study aims to find out the difference in Learning Motivation In View Of The Demographic Factors Of Student Taking Online Lectures In State University Of Padang.. Quantitative approaches of comparative types are the methods used in this research. This study's population consists of students enrolled at the State University of Padang. In this study, random sample techniques were used, and subjects obtained as many as 150 people.In this data collection, we used the motivation to learn and the socioeconomic scale. The data were analyzed using a factorial ANOVA technique. The results showed a level of significance of sig = 0.351 (sig 0.05), meaning that there are no differences in learning motivation in view of the demographic factors of students taking online lectures at the State University of Padang.
E-Role of Blockchain in the Cyber-Physical System IoT-Based Healthcare System

The Internet of Things (IoT) is a constantly growing environment for connecting hardware, software, physical things, and communicating devices altogether or individually over a network with or without human interventions. It collects, computes, and exchanges data together. This quality of IoT makes the platform flexible for the variety of applications that work on this technology to get incredible benefits, including healthcare domains. In the olden days, the medical system does not have any digital file system, clinical data monitoring, and cyber-physical systems. Cyber Physical System (CPS) is the nucleus of IoT. The smart parameters of IoT create an interesting thing on the Internet of Healthcare System (IoHT) and keep the best place in revenue. Apart from smart homes, cities, and smart devices, IoT established a vital position in IoT. The security and the privacy of Healthcare data should be managed properly. Thus, making a new concept into it called, “Blockchain.” This not only marks its step in cryptocurrencies, but it also tights the security and privacy of all types of data, and it supports heterogeneous applications, especially IoT-based applications. This article describes the Blockchain-IoT-based healthcare applications, security of the Internet of Medical Things (IoMT), and the future direction in the healthcare system based on Blockchain.
CPS Support IoMT Cyber Attacks, Security and Privacy Issues and Solutions

The Internet of Things steps into the medical field, which enhances traditional Medicare into smart medicare or SIoMT (Smart Internet of Medical Things), which gives us the Smart Healthcare System (SHS). The constant and everlasting increase in data storage, management, and collaboration, interpretation. It should be elaborated widely. This progress may result in security and privacy disturbances on a large amount of IoMT data. The cyber threats affect this IoMT data based on their different solutions. The Cyber Physical system supports IoT by providing sensors, actuators, etc., where the physical system of IoT was easily prone to compromise in terms of security. The IoMT connects and interprets with many enabling technologies to provide standard procedures to overcome this issue. Many security measures have been designed so far to protect and increase the prerogative of IoMT. Data security, patient privacy, and many other factors to be considered. This article describes the security issues of IoMT, cyber-attacks of IoMT, and security solutions for IoMT have been discussed. The IoMT declares remote treatment instead of traditional treatment, and in recent pandemics, this IoMT provides a remarkable place and powerful impact on everything. The IoMT has a vibrant capacity to hold and interconnect the heterogeneous platforms to support the massive amount of applications in the healthcare system. The IoMT is prone to many cyber threats on data. The healthcare system supports intelligent and automatic functionalities with the support of IoMT.
Clear Vision

A new video-based transit-lane enforcement project in New Zealand is not only able to distinguish buses from cars, it can also detect the number of passengers in a car, and is fully relocatable
Relationship between Science Academic Passion and Positive Experience about Science of Elementary General and Science-Gifted Students

This study investigated the relationship between science academic passion and positive experience about science of elementary general and science-gifted students. To do this, 4th-6th grade students (n=90) at elementary schools and 4th-6th grade students (n=126) at gifted science education institutes in Seoul were selected. Tests for science academic passion and positive experience about science were then administered and correlation analysis between the two variables was performed. Analysis of the results revealed all subcategories of science academic passion had significantly positive relationships with all subcategories of positive experience about science. The degree of correlation between subcategories of science academic passion and subcategories of positive experience about science differ significantly in some correlations (correlations between ‘importance’ and ‘science academic emotion’, ‘importance’ and ‘science learning motivation’, and ‘harmonious passion’ and ‘science-related self-concept’) between general students and science-gifted students. The degree of correlation between science academic passion and positive experience about science were also somewhat different according to each subcategory of the two variables. And this tendency was similar between general students and science-gifted students in the subcategories (‘importance’, ‘like’, and ‘time/energy investment’) corresponding to “intensity” of science academic passion, but there was a slight difference between general students and science-gifted students in the subcategories (‘harmonious passion’ and ‘obsessive passion’) corresponding to “type”. Educational implications of these findings are discussed.
Indonesian Madrasah Competency Assessment: Students’ numeracy based on age

[English]: This study aims to analyze primary madrasah students’ numeracy based on their age difference; 10-12 years old and to confirm whether the older the students, the better their academic ability when compared to younger students as prior studies suggest. This study used the results of the year 2022 Indonesian Madrasah Competence Assessment (AKMI) in East Java comprising 7.356 students. Students’ numeracy was categorized into five proficiency levels: need intervention, basic, proficient, skilled, and need creative space. Data shows that students who participated in AKMI were dominated by 11-year-old students. In each age group, the spread of the proficiency level is similar, for example, the largest number of students in each group is at the proficient level. Moreover, students in the 11-year-old group have the highest average score, but the difference between the group is not significant. This indicates that they have slightly better numeracy skills than the other groups. Despite the slight difference, the distinct characteristics of each age group could be identified, for example, the number of students, proficiency levels, and average numeracy scores. [Bahasa]: Penelitian ini bertujuan menganalisis numerasi siswa Madrasah Ibtidaiyah (MI) berdasarkan perbedaan umur; 10-12 tahun dan mengonfirmasi apakah siswa yang lebih tua memiliki kemampuan akademik yang lebih baik daripada siswa yang lebih muda sebagaimana ditunjukkan oleh penelitian terdahulu. Penelitian ini menggunakan data hasil Asesmen Kompetensi Madrasah Indonesia (AKMI) di Jawa Timur yang melibatkan 7.356 siswa. Numerasi siswa dikelompokkan dalam lima kategori kemahiran, yaitu perlu intervensi, dasar, cakap, terampil, dan perlu ruang kreasi. Data menunjukkan bahwa peserta AKMI terbanyak adalah siswa usia 11 tahun. Pada setiap kelompok umur, sebaran level kemahiran tidak berbeda, misalnya, sebagian besar siswa pada setiap kelompok umur berada pada level cakap. Selain itu, siswa pada kelompok umur 11 tahun memiliki rata-rata nilai numerasi tertinggi, namun perbedaan setiap kelompok tidak signifikan. Hal ini menunjukkan bahwa siswa usia 11 tahun memiliki numerasi yang sedikit lebih baik. Walaupun memiliki perbedaan yang tidak signifikan, perbedaan karakteristik setiap kelompok umur tetap dapat diidentifikasi, misalnya jumlah siswa pada tiap kategori usia, tingkat kemahiran, dan rata-rata skor numerasi.
Research on storage and compression method of time series data in cement production

The phenomenon of data redundancy and large occupation of storage space is easy to be caused, because the cement production process is continuous and short cycle, resulting in a large number of production process timing data. Furthermore, this phenomenon will make the data can not be utilized effectively. According to the data feature and alteration frequency, this paper designs the corresponding storage compression method based on technology about sub-table storage, the periodic compression and changed-storage compression which can effectively optimize the data storage space and improve the data application efficiency.
Research on global path planning method based on improved ant colony algorithm

In mobile robot navigation, the design of path planning algorithms is a key issue, because the fast and efficient work of mobile robots must rely on reasonable and reliable path planning algorithms. The ant colony method is used to study the path planning of mobile robots in this work. The mathematical model of various ant colony method parameters has been updated and optimized. The direction guiding factor is introduced to avoid the blindness of the traditional ant colony algorithm. An adaptive pheromone updating rule was proposed to improve the disadvantages of the traditional ant colony algorithm, which is easy to fall into premature and outputs local optimum. The adaptive adjustment pheromone volatile factor is adaptively to make the algorithm level more distinct, highlight the task focus of each stage, and consider the quality and efficiency of the path planning. Finally, complex and open environment maps are designed, and simulation experiments are carried out to verify the advance of the improved algorithm in the path planning distance and convergence speed.
Copyright ownership challenge arising from AI-generated works of art: A time to stand and stare

With the advent of artificial intelligence, artistic creativity is no longer an exclusive privilege of human authors, as AI systems are able to model several types of human behaviour in terms of computational processes, among which, the creation of artistic works. This chapter explores the copyright ownership challenge related with AI-generated works. It argues that ownership of AI-generated works is a real challenge for the current copyright regime. It assesses the grounds of this challenge, related with the multi-stage and multi-agent algorithmic creative process as well as with the creative autonomy of AI systems. Then it proposes a taxonomy of available options of response towards ownership challenge, as parameterized on the basis of two assumptions: the threshold of autonomy of AI systems and the desirability of privatization of AI-generated works. Last, it supports that this challenge shall be seen as an opportunity to enhance the human-centric approach of copyright law globally.
Electric Energy Meter Information Recognition System Based on Deep Learning

A new type of electric energy meter information recognition system based on deep learning is proposed. The system is mainly divided into OCR character recognition system and electric meter information verification system. OCR character recognition system mainly includes two parts: character detection and character recognition. The text detection uses the CTPN model, and the text recognition uses the CRNN network in deep learning for recognition, and then uses the CTC loss function for sequence processing to improve the accuracy of text recognition. Through the RCTW-17 data set training, an OCR text recognition system with high accuracy, strong stability and fast speed is obtained. The identified results are automatically checked with the information in the background database, and finally an electric energy meter information identification system is obtained. The verification of the RCTW-17 data set and the actual photo identification of the electric energy meter prove the effectiveness of this method.
RankspeQ: An R Package Platform for Genotype Characterization and Performance-Ranking Based on MultispeQ Measurements

With the new technologies in plant phenotyping, robust and reliable tools are still required to analyze large-scale and multivariate datasets. The RankspeQ is a novel R package developed to evaluate genotype performance and support selection-driven decisions based on leaf traits and also environment-related variables measured by the MultispeQ device. The presented software consists of 3 main functions: the data cleaning, genotype ranking based on its performance and comparison of accessions against grain yield or another crop trait. Optionally, the evaluation can be done by groups which can be defined by the user such as genepools, families, races, etc. The software development as well as the data evaluation was made with datasets of Phaseolus spp. experiments. However, R code - with easy modifications - can be used on any other type of crop. The tool will help to understand the hidden potential of MultispeQ equipment and identify important crop traits useful in a genotype characterization in particular environments. The tool has direct potential for physiologists, breeders etc. as he identifies the best performing accessions, however, also target false positive results with high yield but low photosynthetic performance. We also propose to use a new efficiency index (Phi Index). Further updates will include new algorithms (e.g. trait heritability) and a shiny interface to make the software user friendly.
Capability Level Information Technology Governance with COBIT 5 Framework in the EDM04, APO01, and APO07 Processes at PT Future Mediatrix Group

PT Future Mediatrix Group is a company that does business in the field of information technology that provides social media services, digital activation, digital marketing, and website creation. The interviews show that the current obstacles are the lack of resource optimization, management of the IT management framework, and human resource management at PT Future Mediatrix Group. Considering the crucial function of information technology, a governance evaluation is needed to determine the capabilities possessed by information technology. In this study, the framework used was the COBIT 5 framework with the Assessment Process Activities stage. The domains used in this study are EDM04 (Ensure Resource Optimization), APO01 (Manage the IT Management Framework), and APO07 (Manage Human Resources) obtained from problem mapping. This study's results obtained the capability level in each domain. In the EDM04 (Ensure Resource Optimization) domain for the current condition, a capability value of 1.94 is obtained, which can be interpreted as being at capability level 2. In the APO01 (Manage the IT Management Framework) domain, a capability value of 2.45 is obtained for the current condition, which can be interpreted as being at capability level 2. In the APO07 (Manage Human Resource) domain for the current condition, a capability value of 2.27 is obtained, which can be interpreted as being at capability level 2. Therefore, PT Future Mediatrix Group is recommended to maximize resources and IT management framework.