Reviewerly/exHarmony huggingface dataset:
Cross Task Temporal Consistency for Semi-supervised Medical Image Segmentation

Semi-supervised deep learning for medical image segmentation is an intriguing area of research as far as the requirement for an adequate amount of labeled data is concerned. In this context, we propose Cross Task Temporal Consistency, a novel Semi-Supervised Learning framework that combines a self-ensembled learning strategy with cross-consistency constraints derived from the implicit perturbations between the incongruous tasks of multi-headed architectures. More specifically, the Signed Distance Map output of a teacher model is transformed to an approximate segmentation map which acts as a pseudo target for the student model. Simultaneously, the teacher’s segmentation task output is utilized as the objective for the student’s Signed Distance Map derived segmentation output. Our proposed framework is intuitively simple and can be plugged into existing segmentation architectures with minimal computational overhead. Our work focuses on improving the segmentation performance in very low-labeled data proportions and has demonstrated marked superiority in performance and stability over existing SSL techniques, as evidenced through extensive evaluations on two standard datasets: ACDC and LA.
Adopting the Europeana Data Model (EDM) for Describing Resources in Cultural Heritage Systems: A Case Study of Taiwan Memory

Taiwan Memory (TM) is a digital library that stores digital cultural heritage (CH) resources from the National Central Library and other CH institutions in Taiwan and provides public access. In this paper, we adopt the Europeana Data Model (EDM) as the foundation and map the metadata elements of TM to EDM classes in order to improve the current metadata landscape for the system and to make the digital objects more discoverable and accessible on the Internet. Four postcards of Taiwanese aboriginal Amis are selected to demonstrate the feasibility of the proposed Taiwan Memory Conceptual Data Model in describing the TM resources. This study provides experiences of adopting EDM for an existing CH system. The processes of EDM mapping, entity enrichment, institution identification, and resource description are elaborated. It is hoped that the implication and the adoption of EDM in this study might provide invaluable information for the future development of TM or be beneficial to other CH systems that are planning to adopt EDM or other semantic data models.
Offline-Online Actor-Critic

Offline-online reinforcement learning (RL) can effectively address the problem of missing data (commonly known as transition) in offline reinforcement learning. However, due to the effect of distribution shift, the performance of policy may degrade when an agent moves from offline to online training phases. In this paper, we first analyze the problems of distribution shift and policy performance degradation in offline-online reinforcement learning. Then, in order to alleviate these problems, we propose a novel RL algorithm offline-online actor-critic (O2AC) algorithm. In O2AC, a behavior clone constraint term is introduced into the policy objective function to address the distribution shift in offline training phase. In addition, in online training phase, the influence of the behavior clone constraint term is gradually reduced, which alleviates the policy performance degradation. Experiments show that O2AC outperforms existing offline-online reinforcement learning algorithms.
Research of the efficiency of the automatic system of detection and recognition of objects on radar image using neural networks

The main factors influencing the efficiency of an automatic object recognition system in a radar image obtained with a synthetic aperture radar are considered A clustering algorithm of selecting objects on a radar image has been developed. The results of applying the radar image clustering algorithm, which makes it possible to increase the probability of correct recognition of objects, are presented. An analysis of the effectiveness of the application of the clustering algorithm depending on the threshold detection is given. The structure of the two-stage system of the algorithm of the automatic recognition of objects on a radar image has been developed and presented.
Classification of Body Posture on TikTok Videos Using a Representation Based on Angles

Representing complex phenomena, such that coming from video sources, in order to build models for posture detection involves challenges such as: avoiding loss of relevant information, rely on that the representation fits the expectative, and avoid cost for specialized hardware [1, 4, 5, 8]. According to Patel et al. [7] detecting corporal movement is gaining strength for tasks such as: personal health care, environmental awareness, human-computer-interaction and surveillance systems. On the other hand, as we previously said, acquiring information based on corporal movement to feed the algorithms that build the models can be costly in time, computational resources, human participation, as well as the variety of representative movements to be studied. However, considering crowdsourcing coming from social networks which are focused in creating contents such as TikTok has facilitated collecting data from this nature. In particular, the contents in this social network are focused on corporal movements such as dancing, walking, speaking in sign language etc. which can be used to integrate large and diverse data sets. The trending in the platform, which are mainly oriented to dancing, make it possible to collect postural data sets which are arranged into similar observations. Nevertheless, there exists a challenge to analyze these sources due to the diversity to record the videos, for example we can find different resolution, backgrounds, source light, clothes, spatial position from the individual, etc. In this sense, preprocessing video is one of the first tasks to tackle noisy or diverse information and provide reliable information in the subsequent stage of the analysis. Therefore, in this article we propose a novel technique for extracting information that can be used to generalize postural information from the human body in TikTok videos without being affected by the differences in their capture methodology. To build a data set with the initial features from the videos we used the algorithm proposed by Cao et al. [2]. In our case, the novel technique is based on the representation of relationships between the corporal features which can form triangles in their significant positions within the posture. Due to such representation it is possible to search for equivalence in angles through different data sets. In that way, we reduce the number of variables used to find similarities, enabling the possibility to create generalized models with lower costs. In this case we used a Naïve Bayes Network to verify the level of generalization and performance.
New approaches to scientific and academic libraries automation in the modern context

Approaches to designing and developing of automated library and information systems (ALIS) are discussed. The need to increase and develop online functionality for technologists and users is emphasized. Independence of ALIS soft– and hardware from external resources including free software is critically important.
Applications of Cultural Algorithms in Different Branches of Science

CAs have been employed to solve a variety of problems in other branches of science, such as computer, physical, social, and biological sciences. The main aim of this chapter is to review the problems in these areas and discuss related mathematical formulations.
Quantum Key Distribution: Ekert E91 Protocol Derivations

The Ekert protocol proposed by Artur Ekert in 1991 is a method for quantum key distribution. The protocol is based on the principle of quantum entanglement and a clever use of the identities prescribed by Bell's test to entangled qubits. We show below the derivations to detect an eavesdropper, Eve, when Alice and Bob are trying to exchange keys for secure communication.
Pelatihan Penulisan Artikel ilmiah pada Prosiding dan Jurnal Nasional untuk mahasiswa di Wilayah Bodetabek

The purpose of this activity is to improve students' ability to write scientific articles. Training and mentoring is carried out to improve students' ability to write scientific articles in journals and proceedings nationwide. This activity was conducted online using Zoom media and Youtube Education channel. Training and mentoring time from June 7 to October 30, 2020. Methods used are lectures, discussions, q&amp;A and assignments online. This training was attended by 1,476 participants from various professions, but the target remains on students in bodetabek area. Training and devotion results show the average student's ability tends to be good at a score of 84.05 with some students reaching a score of 100, although some still score below 70
Impact of Gamification Element Towards Continuance Intention at Indonesia Marketplace

In this era, the marketplace needs to use various strategies in their business and marketing to compete with other businesses to have a competitive advantage that can attract customers. One of the strategies is to use a new concept called gamification in digital marketing. Shopee Tanam is one of the most popular gamification features in Indonesia. The research goals were to define and clarify the effectiveness of applying Shopee Tanam gamification elements in the Shopee marketplace. Also, finding gamification elements that have the most significant impact on user Continuance Intention of Shopee Tanam game on the Shopee marketplace. This study uses Partial Least Square-Structural Equation Modelling (PLS-SEM) as study research with a purposive sampling method; data was gathered by online questionnaire from 399 respondents in November 2022 and respondents are located in Greater Jakarta and other cities around Indonesia. In this research, Intrinsic Motivation and satisfaction influence Continuance Intention, while Competence, Autonomy, Relatedness, and Reward affect Intrinsic Motivation.
Charles Dodge (b. 1942)

White and Pritchett place Dodge’s work within early digital-to-analog (DAC) computer music, and describe how he brought a uniquely human sensibility to computer composition. Dodge’s background includes the Columbia studio with Ussachevsky, then the Princeton studio with Godfrey Winham, and with the IBM 360 at Columbia. He describes his first work with a DAC processor (Extensions for Trumpet and Tape), and his current work, a videotape opera using the processor and speech synthesis. He notes the emotional and referential aspects of speech as a source, that computer processing expands possibilities for sound manipulation with increased emotional affect. They discuss the ideal listener and degrees of listening, returning to past works, the human element in technology, and the American Composers Alliance and Composers Recordings.
Barrier Function Enhanced Geometric Controller for Safe Control of a Quadrotor UAV

Appropriate safety constraints, especially the state constraints, of a quadrotor unmanned aerial vehicle (UAV) are fundamental for safe control. Following the prevalent geometric controller, a new safe geometric control approach is proposed with state constraints for a quadrotor UAV, wherein the control barrier function (CBF) is integrated into the geometric controller to achieve safe control of the quadrotor UAV. Specifically, the CBF with respect to the states is constructed to establish the corresponding safety region. To satisfy the safety constraints, i.e., confining the states of the quadrotor UAV in the safety region rigorously, the control inputs calculated from geometric controller are refined with minimal modification by utilizing quadratic programming. The simulation study demonstrates the remarkable performance of the proposed approach in terms of the safety motion of a quadrotor UAV.
Universal skills for the VUCA world: how to develop and evaluate? Experience of the All-Russian Olympiad “I am a professional”

The article examines different approaches to assessing new transformational changes in the educational sphere, determined by the general patterns of a rapidly changing VUCA world. By 2030, 14% of employees of organizations around the world may need to change careers and acquire new competencies. The problem of competency shortages will affect everyone – employers, students, and even entire families. Educational technologies are also changing. New approaches and innovative technologies are being developed in schools, universities, and corporate training. Most expert organizations, commissions, and councils involved in educational transformation agree that the new digital world needs a broader range of competencies, not just digital skills. In the sphere of higher education, the transformation of the educational process and the development of universal skills are especially relevant for students of management specialties such as “Management” and “Public Service”. The necessity to introduce into the process of both university and corporate training the new approaches and methods for assessing the universal competencies of future managers is obvious. At the same time, higher education institutions still focuse on acquiring contextual skills based on the so-called explicit knowledge, and not on developing universal skills.
Investigation and Simulation of an Actual System for Aerial Retrievals of Low-Speed Falling Objects

In a previous study, mid-air retrieval with fixed-wing UAVs to a low-speed falling object was considered, and the approach trajectory generation using USFC (updating final-state control) was proposed. However, the effect of the delay on the control law has not been investigated for the real system. In particular, unlike the feedback attitude control by PID control using IMU, the trajectory generation using GNSS system is expected to have relatively large delay due to sensing and control calculation. In this study, a control law that allows for the delay caused by those processes was designed. Moreover, the effectiveness of the designed control law was confirmed under some conditions by simulation. In addition, based on the simulation results, an experimental system capable of parallel processing was constructed to realize the control by UFSC.
Supervised and Unsupervised Methods in Depth Estimation

Monocular depth estimation from single images has gained increasing attention in recent years, considering that this technique is one of the most important techniques in autonomous driving. Since the contrast and parameters of the indoor images internally differ from outdoor. this work presented two methods for optimizing depth estimation using convolutional neural networks. In the first method, the indoor images were dealt by mask prediction using an encoder-decoder structure (DRN) and by proposing three separate networks as depth estimator (ResNet-50, DenseNet-161 and ResNet-152). In the second method, which depends on outdoor images, depth estimated by CNN with no ground truth depth maps by using image reconstruction technique, with left-right disparity consistency check and autoencoder architecture (Resnet-18 model). Both proposed methods showed good performance compared to the reference studies.
НЕЧЕТКАЯ КОГНИТИВНАЯ МОДЕЛЬ СИСТЕМЫ ПРОЕКТНОЙ ДЕЯТЕЛЬНОСТИ ВУЗА

The article summarizes the preliminary results of modeling a complex system of student project activity using a fuzzy cognitive map. For effective management of any process, to achieve a certain effect in the future, mathematical models of various processes and phenomena are needed. But where accurate quantitative measurements are impossible, where only qualitative expert assessments are applicable, management proceeds under conditions of uncertainty. The fuzzy cognitive model (map) allows us to consider heterogeneous semantic structures in the process of mutual influence: the value-semantic sphere of the personality of the future teacher; project activity through subjective experience and social and material results; the infrastructure of the educational space of the university formed in the process of project activity. As a result of computer research, the conditions of applicability of the proposed model are clarified, the dynamics of changes in a complex system is analyzed; steps to refine numerical data and modeling prospects are determined.
Modelling an Adaptive Clustering Model for the Mining Community Using Learning Approaches

Data clustering is a crucial phase in data analysis, widely concentrated by data mining communities. Many previous algorithms based on data clustering are related to the endless models that look for sparsity and higher dimensional issues and try to avoid the sequence of information and the data structural data. The recurrent and convolutional neural networks work on deep learning-based models concerning the data as sequences. Yet, the explanation of outcomes and the supervised signals are lacking. The adaptive data clustering model (ADCM) technique is proposed in this system to incorporate the pre-trained data encoders into data clustering tasks. This model depends on the representation of a sequence that breaks the dependencies on the supervision. The proposed system provides experimental outcomes that perform better than the traditional data clustering algorithm and the modern data model, pre-trained on the complete datasets. Additionally, the clustering result explains the significant understanding of the deep learning technique principles. The clustering approach proposes the description model that assists the users in understanding the quality and meaning of the outcome of the clustering process.
Analysis of Hand Movements and Fingertip Reaction Forces on Knitting Shape for Evaluation of Knitting Skill

Aging of skilled craftspeople and the successors lack have become serious issues for inheritance of traditional craft works. Solutions are demanded to preserve the craftspeople skills in a form to be passed on for lasting culture. In this study, we aim to extract skills of craftspeople as quantified knowledge through analysis of their works. We focus on knitting works, which require manual dexterity. we analyze the knitting skill by measuring hand motions and reaction forces of fingertips. In particular, we analyze effects of differences of hand motion on knitted patterns. Through measurement experiments, we consider hand motions of skilled work.
Nutritional Status, Wash in Schools, and the Academic Achievement of Public Elementary School Pupils

The purpose of the research was to assess the effect of nutritional status and Water, Sanitation, and Hygiene in Schools (WINS) on the academic achievement of pupils among public elementary schools in Libona and Manolo Fortich, Bukidnon during the academic year 2019-2020. The study utilized descriptive-correlational and causal research design to gather the necessary data needed. In response to the study’s statement of the problem, mean and standard deviation, paired t-test, Pearson product-moment correlation, and multiple regression were used. Based on the findings , the feeding program implemented by DepEd to identify severely wasted and wasted pupils has helped the majority of the pupils to attain normal nutritional status. The school officials have highly implemented the WINS in five areas, namely water access, waste management, hygiene, deworming, and health education, but moderately implemented sanitation. The feeding program implemented by the schools has produced better nutritional status to the participants of the study, and the level of academic performance of the pupils has increased after the program implementation. There is no significant relationships between the pupils’ academic achievement and implementation of nutritional status, water access, waste management, deworming, and health education. Whereas sanitation and hygiene are the best significant predictors of students’ academic achievement. Nutritional status, water access, waste management, deworming, and health education failed to predict academic performance, while sanitation and hygiene emerged as the best predictors. Keywords : nutritional status, water, sanitation and hygiene in schools, academic achievement
Estimation of Center of Gravity and Contact Point for the Manipulation of Line -shaped Flexible Objects

As robots are becoming popular labor force, we can expect more robots to work in places more familiar to our daily lives such as restaurants. This paper aims at realizing a manipulation of line-shaped flexible objects to achieve its appetizing looks. As a first step, we try to develop a system for handling and manipulating a line-shaped flexible object: a method for controlling the bias of the object: a method for estimating the center of gravity and contact point to further estimate the distribution of line-shaped flexible objects in the robotic hand. The efficacy of the developed system was validated by several experiences.
Analisis Kebutuhan Pengembangan Media Pembelajaran Autoplay dengan Evaluasi Permainan Congklak pada Materi Sistem Indera

Humans are social creatures that allow each other to interact directly so that the rate of spread of the Covid-19 pandemic is increasing rapidly. This is certainly felt heavy by educators and students. Especially educators are required to be creative in delivering material through online learning media. Media as one component in the system has a function as a means of non-verbal communication. As a component of the system, it means that the media absolutely must exist or must be used in every learning. Technological developments in the current era make students only fixated on gadgets so that students begin to forget the culture or local wisdom that exists in their respective regions. Therefore, it would be better if education today can introduce a little more local culture to students. Congklak traditional game is one of them, this game can improve students' cognitive and motor development.
Implementasi Metode K-Nearest Neighbor Dalam Mengklasifikasikan Jenis Suara Berdasarkan Jangkauan Vokal

Humans have voice characteristics with different vocal ranges, namely the male voice consists of Tenor, Baritone, and Bass, while the female voice consists of Soprano, Mezzosoprano, and Alto. Determining the voice range, especially for a singer, requires a vocal trainer or musical instrument that is quite difficult to access. Therefore, a sound classification system created based on vocal range using the Harmonic Product Spectrum (HPS) feature extraction method and the K-Nearest Neighbors (KNN) classification method uses k parameters from 1 to 40. The test gets the highest accuracy on parameter k=8, which is 88.88%, so that from the resulting accuracy to prove the K-Nearest Neighbor (KNN) method gives good results in classifying the type of voice.&#x0D; Keywords: Classification, Vocal range, Harmonic Product Spectrum, K-Nearest Neighbors
The Comparative Landscape of Chinese and Foreign Applications on Blockchain in E-government

This research assessed the use of blockchain in e-government, to provide a useful overview for future research. To compare the research landscapes in China and abroad, We select a few publications from the CNKI and Web of Science Core Collections and choose to manually audit these publications by reading the abstracts to ensure the data’s reliability. To examine the information in the title catalog that fits the requirements, as well as to study the research hotspots and research trends eligible, visual analysis tools such as Citespace are utilized. The following are the major findings: Firstly, foreign research on e-government involving blockchain focuses more on technical and practical analysis, while Chinese research focuses more on theoretical analysis, with specific technical practice yet to be implemented. Secondly, a foreign study in this subject was undertaken earlier than Chinese research. Finally, the Chinese should strengthen exchanges with international counterparts and institutions, and aim to synthesize experience that can be coupled with China’s distinctive reality to better promote the deployment and application of blockchain in China’s e-government.