Reviewerly/exHarmony huggingface dataset:
Research on Bone Stick Text Recognition Method with Multi-Scale Feature Fusion

Bone sticks are composed of thin slices of animal bones created by ancient people, which mainly served the functions of fixing books, writing scripts, and divination. The bone stick script is an essential material for studying the history of Chinese Western Han script. Using a neural network for text recognition can quickly interpret ancient text, while extracting deeper semantic information, neural networks will also lose superficial image details. After multi-layer convolution and pooling of bone sticks, the continuous loss of superficial details affects classification accuracy. At the same time, the unbalanced distribution of bone stick quantity leads to a low recognition rate with small samples of bone sticks. Aiming to solve the above problems, a bone stick recognition method based on multi-scale features and focal loss function is proposed. Firstly, based on the residual network ResNet, the output features of the first layer and four Conv_x layers are pooled globally to reduce the feature dimension of each channel, and the channel splicing method is used to add different depths of base information to the original high-level features, which improves the detail feature extraction ability of the model. Secondly, in view of the unbalanced distribution of the bone stick data, the original cross-entropy loss function is replaced by the focus loss function, which increases the penalty for classification errors and improves the recognition rate of classes with few training samples. Experimental results show that the recognition accuracy of the proposed method on the bone stick data set is up to 90.5%.
Data and knowledge dual-driven architecture for autonomous networks

The vision of autonomous network has become an industry consensus. Leading operators have moved from network automation to network intelligence, and the deep integration of network and AI technology as the main technical method enters the scale adoption in production networks. At the same time, the third-generation AI technology has ushered in a research and development boom driven by both data and knowledge. To build an architectural consensus to further guide technical standards for accelerating industrial cooperation, a data and knowledge dual-driven autonomous network architecture, as well as its design principles, functional modules and deployment options, are given to unleash the power of technology innovation and industry transformation.
Digital Anthropological photography

Despite the fact that in recent years the anthropologist's arsenal has significantly expanded due to the intro-duction of digital 3D scanning, computed tomography, microtomography, etc. into the practice of anthropological research, for most researchers photography remains an important part of the scientific process. Moreover, the resulting images are increasingly subject to higher requirements, since they often appear in scientific circulation much faster than before, bypassing editors and professional retouchers of publishers thanks to various kinds of Internet resources, such as presentations, on-line Internet conferences, reports, etc. In this new digital reality, the researcher acts as both an expert, a director, and an operator of a photo session and is solely responsible for the quality of the result and for its compliance with the goals of the shooting. The high intelligence of modern digital cameras creates a false impression in the beginner’s mind that camera can always be given freedom in making decisions regarding the shooting parameters. However, as shown in the article, there are a number of shooting situations when targeted manual management of shooting parameters is necessary to obtain a positive result. The following information will help the photographer do this. The purpose of our article is to help the researcher anthropologist qualitatively solve his problems using a digital camera. We will try to give an idea of those basic concepts, features of technology and techniques that determine the work of a photographer within the digital space. To this end, the article discusses the main technical and methodological techniques of anthropological photography within the digital space. A brief definition of the basic concepts of the “digital world” and the most important technical characteristics of modern digital cameras are given. The main part of the article is devoted to photography of paleoanthropological materials. Particular attention is paid to the shooting of the skull and odon-tological materials. Specific recommendations are given on the management of shooting parameters and on the organization of the shooting process, the use of which will allow the researcher to obtain high-quality digital pho-tographs of the studied anthropological objects that meet both the requirements of modern printing and the re-quirements of representation on Internet resources.
Robot navigation with predictive capabilities using graph learning and Monte Carlo tree search

This article develops a prediction and path planning system based on the graph neural network to navigate a robot in a complex dynamic environment. In particular, the core of this method is to predict those aspects of the future that are directly relevant for planning, including their value, state, and policy. A graph neural network-based method is introduced to encode the interaction between the robot and the surrounding environment. Then, the dynamic model of the environment is learned through the model-based reinforcement learning, and the path is planned using the Monte Carlo tree search method according to the learned model. Finally, simulation studies are given to evaluate the validity and advantage of the obtained algorithm compared with the most recent methods. It has been shown that the proposed method achieves a higher success rate within a less time. Meantime, the oscillatory and freezing problems caused by the short-sightedness of the robot are avoided.
Fixed point sets in digital topology, 1

In this paper, we examine some properties of the fixed point set of a digitally continuous function. The digital setting requires new methods that are not analogous to those of classical topological fixed point theory, and we obtain results that often differ greatly from standard results in classical topology. We introduce several measures related to fixed points for continuous self-maps on digital images, and study their properties. Perhaps the most important of these is the fixed point spectrum $F(X)$ of a digital image: that is, the set of all numbers that can appear as the number of fixed points for some continuous self-map. We give a complete computation of $F(C_n)$ where $C_n$ is the digital cycle of $n$ points. For other digital images, we show that, if $X$ has at least 4 points, then $F(X)$ always contains the numbers 0, 1, 2, 3, and the cardinality of $X$. We give several examples, including $C_n$, in which $F(X)$ does not equal $\{0,1,\dots,\#X\}$. We examine how fixed point sets are affected by rigidity, retraction, deformation retraction, and the formation of wedges and Cartesian products. We also study how fixed point sets in digital images can be arranged; e.g., in some cases the fixed point set is always connected.
Classification of Russian Texts by Genres Based on Modern Embeddings and Rhythm

The article investigates modern vector text models for solving the problem of genre classification of Russian-language texts. Models include ELMo embeddings, BERT language model with pre-training and a complex of numerical rhythm features based on lexico-grammatical features. The experiments were carried out on a corpus of 10,000 texts in five genres: novels, scientific articles, reviews, posts from the social network Vkontakte, news from OpenCorpora. Visualization and analysis of statistics for rhythm features made it possible to identify both the most diverse genres in terms of rhythm: novels and reviews, and the least ones: scientific articles. Subsequently, these genres were classified best with the help of rhythm features and the neural network-classifier LSTM. Clustering and classifying texts by genre using ELMo and BERT embeddings made it possible to separate one genre from another with a small number of errors. The multiclassification F-score reached 99%. The study confirms the efficiency of modern embeddings in the tasks of computational linguistics, and also allows to highlight the advantages and limitations of the complex of rhythm features on the material of genre classification.
Detecting Mentions of Green Practices in Social Media Based on Text Classification

The paper is devoted to the task of searching for mentions of green practices in social media texts. The relevance of this task is dictated by the need to expand existing knowledge about the use of green practices in society and the spread of existing green practices. This paper uses a text corpus consisting of the texts published on the environmental communities of the VKontakte social network. The corpus is equipped with an expert markup of the mention of nine types of green practices. As part of this work, a semi-automatic approach is proposed to the collection of additional texts to reduce the class imbalance in the corpus. The approach includes the following steps: detecting the most frequent words for each practice type; automatic collecting texts in social media that contain the detected frequent words; expert verification and filtering of collected texts. The four machine learning models are compared to find the mentions of green practices on the two variants of the corpus: original and augmented using the proposed approach. Among the listed models, the highest averaged F1-score (81.32%) was achieved by Conversational RuBERT fine-tuned on the augmented corpus. Conversational RuBERT model was chosen for the implementation of the application prototype. The main function of the prototype is to detect the presence of the mention of nine types of green practices in the text. The prototype is implemented in the form of the Telegram chatbot.
A Security and Privacy Aware Computing Approach on Data Sharing in Cloud Environment

Today, the role of cloud computing in our day-to-day lives is very prominent. The cloud computing paradigm makes it possible to provide demand-based resources. Cloud computing has changed the way that organizations manage resources due to their robustness, low cost, and pervasive nature. Data security is usually realized using different methods such as encryption. However, the privacy of data is another important challenge that should be considered when transporting, storing, and analyzing data in the public cloud. In this paper, a new method is proposed to track malicious users who use their private key to decrypt data in a system, share it with others and cause system information leakage. Security policies are also considered to be integrated with the texts encrypted to ensure system safety and to prevent the violation of data owners ' privacy. For this purpose, before sending the data to the cloud, it must be encrypted in such a way that operations such as max, min, etc. can be performed on it. The proposed method uses order-preserving symmetric encryption (OPES), which does not require decryption or re-encryption for mathematical operations. This process leads to a great improvement in delay. The OPES scheme allows comparison operations to be performed directly on encrypted data without decryption operands. According to the results, it is obvious that the proposed strategy is in a better position compared to the base paper in terms of the system's ability to find the malicious elements that cause the problem of leakage and in terms of system security to prevent the violation of privacy.
Research on the Construction of Modern Agricultural Industrial System in Daqing

Industrial prosperity is the key to the Rural Revitalization Strategy.It also requires all regions to expand industrial space, innovate industrial forms, promote industrial cross integration, accelerate the integration of rural primary, secondary and tertiary industries, and then explore the formation of a diversified and characteristic modern rural industrial system based on local resource endowment and market demand.Therefore, the construction of modern rural industrial system is not only related to the development pace of agricultural and rural modernization, the consolidation of poverty alleviation achievements, but also affects the overall promotion of Rural Revitalization Strategy.
Detecting and locating trending places using multimodal social network data

Abstract This paper presents a machine learning-based classifier for detecting points of interest through the combined use of images and text from social networks. This model exploits the transfer learning capabilities of the neural network architecture CLIP (Contrastive Language-Image Pre-Training) in multimodal environments using image and text. Different methodologies based on multimodal information are explored for the geolocation of the places detected. To this end, pre-trained neural network models are used for the classification of images and their associated texts. The result is a system that allows creating new synergies between images and texts in order to detect and geolocate trending places that has not been previously tagged by any other means, providing potentially relevant information for tasks such as cataloging specific types of places in a city for the tourism industry. The experiments carried out reveal that, in general, textual information is more accurate and relevant than visual cues in this multimodal setting.
APPLICATION OF AHP AND GIS FOR OPTIMAL SOLAR SITE IDENTIFICATION IN MADHESH PROVINCE, NEPAL

Solar-based renewable energy adoption is in its early stage in the power system of Nepal complying with its commitment to carbon neutrality. The government of Nepal has declared a goal of setting up solar power plants of at least 200 MW in Madhesh Province, but the selection of optimal sites will be the decisive factor in achieving this goal. Therefore, rigorous investigation is necessary for optimal site selection. A three-step framework combining the Analytic Hierarchy Process (AHP) and the Geographical Information System (GIS) has been adopted to identify the optimal location for solar power plant installation in Madhesh Province. The framework includes the creation of weighted individual raster images for the different criteria, images of restricted areas for solar installation, and a combination of all the rasters into a single raster using GIS-based software. The weights of the criteria and sub-criteria have been evaluated using the AHP model, which is an expert judgments-based model. The criteria considered were solar irradiance, annual mean temperature, distance from the road, distance from the substation, distance from the urban area, elevation, aspect, and land use. The results were graded from the least preferred area to the most highly suitable area. The most highly suitable areas were in the Saptari, Siraha, Dhanusa, and Mahottari districts, while the districts in the western region of the province had less suitable areas. Thus, the province’s eastern region is most suitable for installing solar power plants.
What makes the unsupervised monocular depth estimation (UMDE) model training better

Abstract Current computer vision tasks based on deep learning require a huge amount of data with annotations for model training or testing, especially in some dense estimation tasks, such as optical flow segmentation and depth estimation. In practice, manual labeling for dense estimation tasks is very difficult or even impossible, and the scenes of the dataset are often restricted to a small range, which dramatically limits the development of the community. To overcome this deficiency, we propose a synthetic dataset generation method to obtain the expandable dataset without burdensome manual workforce. By this method, we construct a dataset called MineNavi containing video footages from first-perspective-view of the aircraft matched with accurate ground truth for depth estimation in aircraft navigation application. We also provide quantitative experiments to prove that pre-training via our MineNavi dataset can improve the performance of depth estimation model and speed up the convergence of the model on real scene data. Since the synthetic dataset has a similar effect to the real-world dataset in the training process of deep model, we finally conduct the experiments on MineNavi with unsupervised monocular depth estimation (UMDE) deep learning models to demonstrate the impact of various factors in our dataset such as lighting conditions and motion mode, aiming to explore what makes this kind of models training better.
Pembelajaran Berdiferensiasi dan Kreativitas Menulis Cerpen Peserta Didik Program Sekolah Penggerak Angkatan Pertama Jenjang SMP Kota Probolinggo

This study aims to describe the tendency of creativity in writing short stories for students of the first batch of Probolinggo junior high school program students with the application of differentiated learning. The research method used is descriptive quantitative with data analysis using percentages. The research subjects of the first batch of PSP Junior High School students in Probolinggo City for the 2021/2022 academic year were 60 respondents. Collecting data using a questionnaire in the form of a statement using a Likert scale. Differentiated learning outcomes on students' short story writing creativity are in the very positive category. The results of the analysis of each variable indicator of creativity in writing short stories are in the positive category. Each indicator shows the results of the analysis, namely: 1) Being able to get story ideas from their own experience or from others, the percentage of the result is 77.5%, 2) Being able to arrange a story line according to the structure by pulling the percentage of the result is 75.41% 3) Able to write stories using creative language the result is 71.66%. By using differentiated learning, it is easier for students in the learning process.
Criteria for Quality Assessment and Inclusion in SCOPUS: A Comparison of Bibliometric Indicators in Three BH Journals

Based on the selected corpus of journals, this paper aims to analyse the criteria for their inclusion in the bibliographic and citation database Scopus. The paper takes a sample of three scientific journals from Bosnia and Herzegovina – Social Sciences and Humanities Studies, Philologist – Journal of Language, Literary and Cultural Studies and Historical Views. The results of this paper show that the DHS journal follows the official Scopus guidelines, which indicates the lack of a causal relationship between the journal indexing criteria and its implementation in practice – indexing Philologist and Historical Views while rejecting Social Sciences and Humanities Studies. The importance of these results lies in the systematization of criteria for indexing scientific journals in a specific database, as well as in a better understanding of the Scopus inclusion process, guided by a comparison of recent bibliometric indicators.
Freedom of speech and hate speech: an american perspective

Freedom of speech is a fundamental right in the United States, however, it is not unlimited. Although the Supreme Court initially supported the government in its pursuit of speech limitation, more recently the Court has positioned itself as the protector of the First Amendment rights. The Court prevented the government from regulating speech on the basis of content and provided strict parameters regarding the lack of protection of freedom of speech. The article aims to examine the evolution of the US jurisprudential understanding and how the idea of hate speech fits into the often ambiguous categories established in the decisions.&#x0D;
Deep Conservation: A latent-dynamics model for exact satisfaction of physical conservation laws

This work proposes an approach for latent-dynamics learning that exactly enforces physical conservation laws. The method comprises two steps. First, the method computes a low-dimensional embedding of the high-dimensional dynamical-system state using deep convolutional autoencoders. This defines a low-dimensional nonlinear manifold on which the state is subsequently enforced to evolve. Second, the method defines a latent-dynamics model that associates with the solution to a constrained optimization problem. Here, the objective function is defined as the sum of squares of conservation-law violations over control volumes within a finite-volume discretization of the problem; nonlinear equality constraints explicitly enforce conservation over prescribed subdomains of the problem. Under modest conditions, the resulting dynamics model guarantees that the time-evolution of the latent state exactly satisfies conservation laws over the prescribed subdomains.
Novel qutrit circuit design for multiplexer, De-multiplexer, and decoder

Abstract Designing conventional circuits present many challenges, including minimizing internal power dissipation. An approach to overcoming this problem is utilizing quantum technology, which has attracted significant attention as an alternative to Nanoscale CMOS technology. The reduction of energy dissipation makes quantum circuits an up-and-coming emerging technology. Ternary logic can potentially diminish the quantum circuit width, which is currently a limitation in quantum technologies. Using qutrit instead of qubit could play an essential role in the future of quantum computing. First, we propose two approaches for quantum ternary decoder circuit in this context. Then, we propose a quantum ternary multiplexer and quantum ternary demultiplexer to exploit the constructed quantum ternary decoder circuit. Techniques to achieve lower quantum cost are of importance. We considered two types of circuits, one in which the output states are always restored to the initial input states and the other in which the states of the output are irrelevant. We compare the proposed quantum ternary circuits with their existing counterparts and present the improvements. It is possible to realize the proposed designs using macro-level ternary gates that are based on the ion-trap realizable ternary 2-qutrit Muthukrishnan–Stroud and 1-qutrit permutation gates.
On the Employment of Machine Learning in the Blockchain Selection Process

Given the growing increase in the number of blockchain (BC) platforms, cryptocurrencies, and tokens, non-technical individuals face a complex question when selecting a BC that meets their requirements (e.g., performance or security). In addition, current approaches that aid such a selection process present drawbacks (e.g., require specific BC knowledge or are not automated and scalable), which hinders the decision process even further. Fortunately, techniques such as Machine Learning (ML) allow the creation of selection models without human interaction by identifying the BC features that match the requirements provided by the user in an automated and flexible manner. Thus, this work presents the design and implementation of an ML-based BC selection approach that employs five ML models to select the most suitable BC given user requirements (e.g., BC popularity, fast block inclusion, or Smart Contract - SC support). The approach follows an ML-specific data flow and defines a novel equation to quantify the popularity of a BC. Furthermore, it details the models’ accuracy and functionality in two distinct use cases, which shows their good accuracy (>85%). Finally, discussions on (a) the ML usefulness, (b) advantages over rule-based systems, and (c) the most relevant features for the BC selection are presented.