Reviewerly/exHarmony huggingface dataset:
SINGLE-CENTER DESCRIPTIVE STATISTICAL ANALYSIS OF GLIAL TUMORS WITH REFERENCE TO GLIOBLASTOMAS

Gliomas are the most common primary intracranial brain neoplasms composing almost 80% of all cancerous brain tumours. Glioblastoma is the most common malignant brain tumour in the adult population and accounts in about 15% of all intracranial neoplasms. This observational analytical study gives a better overview of the current state of glial tumours in North Macedonia from 2021 to 2022 with special reference to glioblastomas. Epidemiological and statistical analysis of glial and other brain tumours in a specific time period with special reference to glioblastomas. This research is an observational statistical analysis that takes place on the Institute for Pathology in the Medical Faculty of ‘’Ss. Cyril and Methodius’’ University in Skopje using the digital data base of patients that had undergone surgery in the University Clinic of Neurosurgery and were diagnosed at the Institute of pathology during the time period 2021-2022. In the period from 2021 and 2022, at the Institute of pathology were diagnosed 106 (62%) glioblastomas out of which classic glioblastoma histomorphology confined 85 (80.19%) cases; 9 (8.49%) glioblastomas showed oligodendroglial component, 5 (4.72%) cases were gliosarcomas and 4 (3.77%) cases were diagnosed as giant cell glioblastomas. Three (2.83%) cases showed glioblastoma histomorphology with radionecrosis. When it comes to global statistics, we can conclude that Republic of North Macedonia follows the global trends regarding the incidence of glioblastoma associated with gender. Our results show that glioblastoma is more prevalent in males than females with the ratio male to female being 1.2:1, and this tumour is more prevalent in patients older than 55 years with the average age of diagnosis being 61 years. The results of this descriptive statistical analysis show that glial tumours, especially glioblastomas as most prevalent malignant tumour type deserves greater scientific attention which beside the impact on their diagnostic complexity, will expand the knowledge of this pathology field and strongly reflect on future scientific and diagnostic work. Keywords: Glioblastoma, glial tumors, statistics, analyzis, epidemiology. &nbsp; &nbsp
Underwater Target Tracking Algorithm Based on Optical Flow

Because of uneven underwater light and high contrast and background noise, the underwater target detection and location need higher requirements. In this paper, the optical flow target tracking algorithm based on Kanade-Lucas-Tomasi (KLT) algorithm is implemented on OpenCV platform, and the algorithm is verified in the water pipeline intelligent inspection competition environment. Aiming at underwater target detection and location, this algorithm uses optical flow method to achieve high detection certainty and fast operation speed of image frame difference method. Using the incremental PID control, the closed-loop control of underwater vehicle underwater motion is realized with a more stable control error.
Optical Quantum Computation without Optical Switch

We present an architecture of optical quantum computation that does not require optical switches. Our method utilizes quantum teleportation with multimode entanglement making it a scalable architecture that is compatible with the time-domain technology.
СТУДЕНТСЬКА ПРАКТИКА В ШКОЛІ ЯК ЗАСІБ АКМЕОЛОГІЧНОГО РОЗВИТКУ ПЕДАГОГАНАСТАВНИКА

This article highlights the features and role of pedagogical internship and student practice in the modern educational environment. The author emphasizes that the management of student practice and the mentoring process in an educational institution is a manifestation of the acmeological development of a teacher. But this is possible only when the educational community takes care of the interests of all participants in the educational process and performs its functions not formally, but conscientiously, in order to achieve a common goal — to educate a worthy citizen of its state
Automatic identification of smoking behaviour in public places based on improved YOLO algorithm

This paper proposes an automatic identification method of smoking behaviour in public places based on the improved You Only Look Once (YOLO) algorithm. The histogram of oriented gradient (HOG) feature method is used to detect the face part of smokers, taking the detected face image as the input, the edge of the image is extracted by using the structured edge detection operator of the edge box. The gesture dataset is re-clustered by K-means++ clustering algorithm to obtain a priori frame that is more suitable for gesture recognition; and in the traditional YOLO network, the SPP spatial pyramid pooling module is added to realise the fusion of local gesture features and global gesture features, to complete the automatic identification of smoking behaviour in public places. The results show that the highest recognition rate of this method is 94.3%, the accuracy and recall rate are higher than those of the traditional methods, and the recognition results are more comprehensive.
Research and Analysis of Video-Based Human Pose Estimation

With the rapid development of computer vision and artificial intelligence, human pose estimation has become the subject of intense scholarly debate. In addition, ubiquitous video software and monitoring machines provide sufficient video data, and all kinds of key elements can be found in the visual information. However, due to different task subdivision scenarios as well as the confusing nature of the human actions, the scenario-oriented video detection techniques aim to establish standard libraries for distinct application scenarios, aggregating both original joint coordinates and composite features. In this paper, we present an innovative framework for detecting possible actions in various scenarios, table tennis training, pilot running planes and boot camp scenes, which covers vast range of social scenes. First, we call OpenPose for accurate and robust skeleton information. Then, manually constructed features of pose angles, relative displacements, moving pattern sequences and etc. are calculated. These informative features enlighten people of latent motion rules. Finally, we report how our framework is applied to realistic classification datasets. Through our work, an overall sketch for skeleton-based human pose estimation and a framework with practical application value is proposed, where people can gain a deep theoretical and practical understanding of a front field of computer vision.
Challenges of colorectal cancer screening in patients with learning disabilities: the way forward

People with learning disabilities (LD) are now living longer; thus, the incidence of cancer within this population is increasing.Available data indicate an excess of digestive tract cancers in people with LD, but colorectal cancer has rarely been specifically studied or extensively reviewed.This is despite risk factors such as being overweight, obesity, and lack of exercise being more frequent in people with LD.Published data showed that the frequency trends slightly higher than that found in the general population.Screening presents a unique opportunity to discover early colorectal cancer, but it is an underused utility in people with LD compared to the general population due to multiple reasons.Furthermore, the clinical presentation is frequently masked, particularly by challenging behaviours, and colorectal cancer is, therefore, often diagnosed late, making treatment difficult due to the advanced stage of these tumours.To improve the colorectal cancer detection rate in people with LD, we recommend that the government may have to review the screening age, provide more resources to support them and their caregivers, educate and increase awareness of the risk factors and signs and symptoms of colorectal cancer.
The Influence of Playing Speed and Mind Mapping Strategy on Learning Effect in Teaching Video

Playback speed; Subtitle; Cognitive processing; Learning effect
A VALUE-ORIENTED CREATIVE APPROACH TO A FUTURE SCIENCE TEACHER TRAINING AT THE PRESENT STAGE

Relevance. Preparing students for teaching is a rather complex and difficult process, it is associated with many reasons, such as the unattractiveness (non-prestige) of a teacher's profession for school graduates, the lack of competition for admission, the poor school preparation of first-year students, etc. These are just some of the reasons why it is quite difficult for many students to form and develop the motivation to master the profession in the real process. Formulation of the problem. How to make the university training of a future teacher the most effective? What conditions are necessary for the students to treat learning activities as preparation for their future profession? The purpose of the research is to determine the methodological ways and conditions for the formation of a value attitude towards one's future profession. The research methods used are theoretical analysis of the best practices for preparing students for a profession, a comprehensive pedagogical experiment. The results. When students pass the technological scheme: Motivation - Theory - Modeling - Reflection - Theory - Training - Reflection - Correction - Activity - Reflection - the image "I am a future teacher", there is an awareness of the importance of acquiring professional knowledge and skills, professional intentions are strengthened. Key findings. A creative value approach is one of the ways to form positive motivation, understanding the importance and value of professional knowledge, skills and competencies. At the same time, special conditions for active learning activities are created, a student sees the results of work himself, begins to be critical of his actions, paving the way for creativity and creative activity.
Research on 3D Face Reconstruction Based on Weakly Supervised Learning

Social robots may reshape the entire society in the future, and human-computer interaction is its key technology. The advent of virtual human enables the robot to show the realistic appearance of human face. A high-precision face model helps the robot to obtain a favorable impression of the interacting object and better realize the human-computer interaction. Because 3D face data is not enough, many existing face reconstruction algorithms first use traditional methods to construct 3D labels for 2D images as training data, but these synthetic data generally lack real facial features, which affects the reconstruction quality of the algorithm. Based on these questions, we first propose a weakly supervised model based on the multi-level loss to directly learn 3D features from a large number of images without 3D labels. In addition, to avoid the influence of wearing and face pose on the reconstruction, this paper uses the most advanced face segmentation network to preprocess the images and remove the invisible areas generated by face occlusion or rotation. The experimental results show that our model has achieved a certain improvement in reconstruction quality and reconstruction accuracy.
Amplifying the Work Engagement and Constructive Voice through the Leader Humility and Organizational Inclusion Climate

Despite the attitudinal and behavioral outcomes of organization inclusion climate that have been studied generally, leader humility as antecedent is relatively ignored in research. This paper investigated leader humility as a precursor of dehumanization and its direct and indirect impact on work engagement and constructive voice in telecommunication sector. Data was collected from 380 respondents working in telecom sector via self-administrated questionnaires at one point in time. Data was analyzed for hypothesized direct and indirect relationships through SPSS 25 and the PROCESS Macros Model 4. The results showed that leader humility increases employee work engagement and constructive voice through organization inclusion climate. Implications for research and practice in telecom are discussed.
Virtual tourism: A literature review

Humans have spent their lives in a technological era where every aspect of society and every economic activities depends on technological factors. Among the recent cutting-edge technologies, virtual reality (VR) has emerged as a new consideration in tourism development. Virtual tourism researching has appeared since the late 90s of the 20th century, but up to now, scholars have continuously discussed this topic in the context of technology application in tourism. In addition, the research on virtual tourism has still had many disagreements because there have been various definitions of virtual technology. This study employed a descriptive review method to explore some popular and growing themes in virtual tourism research and analyze different perspectives of scholars. Research results shown a number of different points of view on virtual tourism including: Technology context and the formation of virtual tourism; concept; role, advantages, limitations, relationship with traditional tourism, future development trends. At the end, some suggestions for future research are presented.
Penggunanaan Metoda Fuzzy Logic Tsukamoto Untuk Memprediksi Jumlah Penerimaan Peserta Didik Baru ( Studi Kasus TK. Bina Mulia )

The Bina Mulia Indonesia Education Foundation, currently overseeing the education of young children, is one of the Kindergartens (TK) in the residential area of Villa Mutiara Jaya, Wanajaya Village, Cibitung District which still has the potential to absorb new students based on the large amount of initial data obtained from the academic year. 2018/2019 to 2021/2022, with conditions in recent years greatly affecting the number of new student admissions which has decreased. In this study, a comparative analysis of the Tsukamoto fuzzy logic algorithm method was used, to predict the number of registrants next year seen from the number of students who graduated and registered from the previous year and compared the calculations using the average. the value of the results obtained in the three Tsukamoto fuzzy methods
Rancang Bangun Aplikasi Jasa Cuci Sofa Berbasis Web Menggunakan Metode Prototype Pada PT. Sinar Berkah Sofa

This research was made with the aim of reducing the problems that the company has from ordering washing services which are still done manually and there is no price list contained in this service. Therefore, the author aims to design an Application Using Web-Based Sofa Washing Services Prototype Method. The method used in this study is in the form of data collection by conducting observations, interviews, and literature studies, then for the method of analysis using the prototype method, in an object-based method, namely the Unified Modeling Language (UML) which consists of Use Case Diagrams, Activity Diagrams, and sequence diagrams, Class Diagram. Then to design the program using the Hyper Text Preprocessor (PHP) programming language, Sublime Text 3 as a text editor, MySQL as a database. The results of this study are customers can see the price list without having to ask the service and the ordering process no longer uses manual ordering.&#x0D;
Experimenting with experiments

From the introduction:&#x0D; This special issue is dedicated to the exploration of experiments and experimentation. It follows a PhD. course entitled “Exploring and performing experiments” that we organized at Department of Digital Design and Information Studies in spring 2019. The course was attended by 12 PhD fellows, and during the course we and the participants decided to produce a special issue based on the participants’ PhD research projects. The literature for the course included a variety of texts and research articles focusing on experiments mainly from the field of Science and Technology Studies (STS). The readings included the work of Ian Hacking, Andy Pickering, Bruno Latour, Steven Shapin and Simon Schaffer, Isabelle Stengers, Shirley Strum and Brian Eno among others. In the call for papers for this issue authors were asked to draw on the literature in the field of STS in order to explore the role of experiments and experimentation in their own projects, and to consider their articles as vehicles for bringing insights from STS to their own fields. The spirit of this special issue is thus one of ‘STS pollination’ by bringing STS to other fields, rather than necessarily being contributions to STS itself. Hopefully it will generate novel insights and contributions and perhaps cross-pollination
Pengembangan Fungsi Clay Sebagai Pengharum Mobil

“Craftivity” is a business engaged in the creative industry that make orproduce toys made from clay shaped figure and has a function as car fragrances. Currently no one has sold clay based figures that have car fragrances function. So the purpose of this study is to find out the target market demand. Using Quantitative and qualitative research methods. What we get is that most of the consumers agree on custom product figures that suit their own desires to add car fragrances functionality. This can be a business opportunity for craftivity.
Cryptocurrency price forecasting method using long short-term memory with time-varying parameters

Numerous research have been done to predict cryptocurrency prices since cryptocurrency prices affect global economic and monetary systems. However, investigations using linear connection approaches and technical analysis indicators frequently fall short of providing an explanation for changes in the pattern of BitCoin pricing. This paper is proposed to study time-varying parameters with long short-term memory (LSTM). The study is investigated on a dataset retrieved from Binance from March 2022 to April 2022. The proposed LSTM used a variety of hyperparameter settings, particularly time parameters, to predict the cryptocurrency price (BTC/USDT) on the dataset. Additionally, it is evaluated in terms of mean absolute percentage error (MAPE) in comparison to smooth moving average (SMA), weighted moving average (WMA), and exponential moving averages (EMA). From the investigation, using the previous 3 days for prediction gives the lowest of the MAPE values and the proposed LSTM outperformed the other models. When considering the last three days' value of pricing, the indicated LSTM offers the best accurate prediction, with a MAPE percentage of 0.0927%.
Integrated approaches in a morphological analyzer of the Arabic language

This article presents a systemof a morphological analyzer of the Arabic language, by integrating several approaches and the viterbi algorithm. First approach is based on database for all thesurface patterns in the Arabic language, second approach is Buckwalter Arabic morphological analyzer and the last approach is based on finite state automaton. With the integration of correspondence tables between affixes in these approaches. The combination between these approaches in our analyzer is very important. Our analyzer is tested on a morphological corpus of 200,000 words, which generalize the words of the Arabic language. The effectiveness of the proposed approaches is demonstrated experimentally and the results obtained are comparable to the state of the art. Moreover, it shows the interest and the advantages of integrating these approaches are to improve our morphological analyzer.
Analyzing animal escape data with circular nonparametric multimodal regression

Analyzing the escape direction of animals subject to covariates is a problem that requires statistical techniques beyond classical regression methods. Apart from the periodicity of the angle of direction, which demands the use of circular statistics, animal escape data usually call for the exploration of the preferred orientations rather than the expected orientation. In this paper we propose the use of a nonparametric method to estimate the conditional local modes of the escape directions of animals from a regression perspective. We present the estimation algorithms and study the asymptotic properties of the estimators as well as its finite sample performance through some simulation experiments. Our proposal is used to model the escape behavior of a group of larval zebrafish escaping from a robot predator. More broadly, the approach presented in this paper can be applied to many existing problems related to animal behavior or other fields.
Source-Aware Embedding Training on Heterogeneous Information Networks

Abstract Heterogeneous information networks (HINs) have been extensively applied to real-world tasks, such as recommendation systems, social networks, and citation networks. While existing HIN representation learning methods can effectively learn the semantic and structural features in the network, little awareness was given to the distribution discrepancy of subgraphs within a single HIN. However, we find that ignoring such distribution discrepancy among subgraphs from multiple sources would hinder the effectiveness of graph embedding learning algorithms. This motivates us to propose SUMSHINE (Scalable Unsupervised Multi-Source Heterogeneous Information Network Embedding) — a scalable unsupervised framework to align the embedding distributions among multiple sources of an HIN. Experimental results on real-world datasets in a variety of downstream tasks validate the performance of our method over the state-of-the-art heterogeneous information network embedding algorithms.
Zero-photon catalysis based eight-state discrete modulated measurement-device-independent continuous-variable quantum key distribution

Zero-photon catalysis (ZPC) introduces noiseless attenuation and can be implemented by existing technologies in quantum key distribution (QKD) protocols. In this paper, we present a ZPC-based eight-state measurement-device-independent continuous-variable QKD (MDI-CV-QKD) combined with discrete modulation and reverse reconciliation. This ZPC-involved eight-state protocol shows better efficiency in terms of optimal modulation variances, secret key rates, transmission distances, tolerable excess noises, and reconciliation efficiency compared to the eight-state protocol without ZPC, the four-state protocol without ZPC, and the four-state protocol with ZPC, at a low signal-to-noise ratio (SNR).
Two-dimensional phase unwrapping based on Fourier transforms and the Yukawa potential spectrum

The two-dimensional phase unwrapping problem (PHUP) has been solved with discrete Fourier transforms (FTs) and many other techniques traditionally. Nevertheless, a formal way of solving the continuous Poisson equation for the PHUP, with the use of continuous FT and based on distribution theory, has not been reported yet, to our knowledge. The well-known specific solution of this equation is given in general by a convolution of a continuous Laplacian estimate with a particular Green function, whose FT does not exist mathematically. However, an alternative Green function called the Yukawa potential, with a guaranteed Fourier spectrum, can be considered for solving an approximated Poisson equation, inducing a standard procedure of a FT-based unwrapping algorithm. Thus, the general steps for this approach are described in this work by considering some reconstructions with synthetic and real data.