Reviewerly/exHarmony huggingface dataset:
Real-Time Online Goal Recognition in Continuous Domains via Deep Reinforcement Learning

The problem of goal recognition involves inferring the high-level task goals of an agent based on observations of its behavior in an environment. Current methods for achieving this task rely on offline comparison inference of observed behavior in discrete environments, which presents several challenges. First, accurately modeling the behavior of the observed agent requires significant computational resources. Second, continuous simulation environments cannot be accurately recognized using existing methods. Finally, real-time computing power is required to infer the likelihood of each potential goal. In this paper, we propose an advanced and efficient real-time online goal recognition algorithm based on deep reinforcement learning in continuous domains. By leveraging the offline modeling of the observed agent's behavior with deep reinforcement learning, our algorithm achieves real-time goal recognition. We evaluate the algorithm's online goal recognition accuracy and stability in continuous simulation environments under communication constraints.
Podcast-Based History of Indonesia Revolution Learning in Secondary Education

The objectives of this study are to: (1) Analyse and describe historical learning media that have been used for revolution material upholding the banner of the Republic of Indonesia in class XI at SMA 3 Semarang (a Senior High School); (2) Describe how the planning and development of podcast-based historical learning media for revolution material upholds the banner of the Republic of Indonesia; (3) Analyse the feasibility of using podcast-based historical learning media for revolution material upholding the banner of the Republic of Indonesia. This research conduct with Research and Development using a 4D development model from S. Thiagarajan, Dorothy Semmel, and Melvyn I Semmel Jerold which was modified by researchers which includes the stages of Define, Design, and Development. Media that teachers often use: power point, concept maps, and infographics. These learning media have weaknesses in the difficulty of students to review the material and understand historical events as a whole and chronologically because it only contains important points of the material. The results of this study show that podcast-based historical learning media for revolution material upholding the banner of the Republic of Indonesia is suitable for use in learning. This product received a rating of 98.07% from material experts, 87.5 from media experts, and 95.83% from practitioners. All assessments fall into the category of excellent and worthy of use in history learning in the classroom. Then strengthened by the results of product trials in the sample class through learning evaluation activities, students' understanding of the material in the podcast got an average score of 84.5% (Minimum Criteria is 75%).
FAIR Data Digest #18

On Reliable Research Software (Harvard Data Science Review) and announcements for the newsletter: frequency of posts and DOI identifiers
IMPROVING LEARNING OUTCOMES OF ECONOMIC ROLE MATERIAL GRADE 5 MI MIFTAHUL FALAH USING THE ROLE PLAY METHOD

The selection of learning methods, strategies, and media in teaching and learning activities is essential to produce maximum learning results. In social studies learning, many teachers still use the lecture method, which is no longer a favorite method for students because it seems boring, causing laziness in learning and significantly affecting student learning outcomes. Therefore, this study aims to determine the improvement of student learning outcomes if they use the role-play method in class 5 economic role material. The research method used is a type of classroom action research with descriptive analysis. The technique used for calculation is the sampling technique. The result of using the role-play method in class 5 economic role material is to increase the interest in learning students so that all students are enthusiastic about learning activities on the economic role material and have a good impact on their learning outcomes
PARSE: A personalized clinical time-series representation learning framework via abnormal offsets analysis

Background and objective: Clinical risk prediction of patients is an important research issue in the field of healthcare, which is of great significance for the diagnosis, treatment and prevention of diseases. In recent years, a large number of deep learning-based methods have been proposed for clinical prediction by mining relevant features of patients' health condition from historical Electronic Health Records (EHRs) data. However, most of these existing methods only focus on discovering the time series characteristics of physiological indexes such as laboratory tests and physical examinations, and fail to comprehensively consider the deviation degree of these physiological indexes from the normal range and their stability, thus greatly limiting the prediction performance. Methods: We propose a personalized clinical time-series representation learning framework via abnormal offsets analysis named PARSE for clinical risk prediction. In PARSE, while extracting relevant temporal features from the original EHR data, we further capture relevant features of abnormal condition as complementary information from the absolute offset of each physiological index's observed values from its normal value and the relative offset between each physiological index's observed values in two adjacent time steps. Finally, an adaptive fusion module is introduced to effectively integrate the above features to obtain the personalized patient's representations for clinical risk prediction. Results: We conduct an in-hospital mortality prediction task on two public real-world datasets. PARSE achieves the highest F1 scores of 48.1% and 40.3%, outperforming the state-of-the-art methods with a boost of 2.4% and 6.2% on two datasets respectively. Furthermore, the results of ablation experiments demonstrate that the two abnormal offsets and the proposed adaptive fusion method are contributing. Conclusions: PARSE can better extract the risk-related information from the EHRs data and improve the personalization of the patients' representations. Each part of PARSE improves the final prediction performance independently.
Special Issue on : Combinatorics, Cryptography, Computer Science and Computation

The 7th International Conference on Combinatorics, Cryptography, Computer Science and Computation was held on November 16-17th 2022 at Iran. These four interconnected play crucial roles in both theoretical and practical applications. Combinatorics studies combinations, permutations, counting problems, and finite set structures, with applications in biology, computer science, and physics. Cryptography focuses on encrypting and decrypting information for security and integrity, protecting data privacy in electronic transactions. Computer science studies computer design, development, and operation, enabling advanced data analysis tools and efficient automation systems. Computation involves calculating or manipulating information, which is essential for software development and applications in data science, artificial intelligence, and cybersecurity. These fields provide a foundation for understanding information nature, security, and the development of cutting-edge digital technologies.
A personality-centred design approach for virtual humans on correspondence with roles and behaviors

With the gradual lowering of technological threshold and the increasing penetration rate of virtual reality and augmented reality devices, virtual humans (VHs) are increasingly intermediating users and information systems in various contexts. As a projection of VH's anthropomorphic stability, a set of personality traits is used to present contents and guide the generation of human-like behaviors. The large variety of VHs poses a challenge to their systematic design. This paper tries to establish an approach to practising a personality-centred design with consideration of other accompanying components. At first, the integrity of the VH personality is constructed, along with a personality model of VHs different from those of real humans. Then two studies are carried out to reveal the correspondence between personalities and roles of VHs, and between personalities and VH behaviors. Study 1 provides design cards for VH roles in specific contexts, as a set of proper personalities and typical behaviors accordingly. The perception experiment of Study 2 provides clues for utilizing multimodal signals in certain behaviors and enriches the design cards. This paper aims to lay specifications and references when designing for digital experience, with VHs as the main human-computer interface.
Enhancing throughput using channel access priorities in frequency hopping network using federated learning

Abstract The data are sent by the nodes taking part in frequency hopping communications (FHC) utilising carrier frequencies and time slots that are pseudo-randomly assigned. Because of this, a high degree of protection against eavesdropping and anti-interference capabilities is provided. When using FHC in an environment, sharing time and frequency resources, avoiding collisions, and differentiating services are all made more complex as a result of this. A protocol for FHC that is based on dispersed wireless networks is presented by the authors of this research. It is a mechanism for multiple access control, which is prioritised and distributed. The ratio of empty channels metric can be found in the previous sentence. It is possible to provide priority in channel access by assigning different preset ratios of empty channel thresholds to the various traffic classes. Frames from frequency spread segments that have a partial collision are included as well. An analytical model is simulated for the analysis in terms of collision probability, transmission probability, and frame service time in order to carry out a theoretical examination of the performance of FHC. The objective of this inquiry is to determine how well FHC works. The analytical model has been proven correct by the exhaustive simulations as well as the theoretical findings. Cloud platforms are often used in the instruction of the most cutting-edge machine learning techniques of today, such as deep neural networks. This is done in order to take advantage of the cloud's capacity to scale elastically. In order to satisfy the criteria of these sorts of applications, federated learning, has been proposed as a distributed machine learning solution. This is done in order to fulfil the requirements of these kinds of applications. In federated learning (FL), even though everyone who uses the system works together to train a model, nobody ever shares their data with anybody else. Each user trains a local model with their own data, and then communicates the updated models with a FL server so that the data can be aggregated and a global model can be constructed. This process ensures that each user's model is unique. This process is repeated until a global model has been developed. This kind of training not only reduces the amount of network overhead that is necessary to transfer data to a centralised server, but it also safeguards the personal information of the users. Within the framework of this work, we looked at the feasibility of using the FL technique of learning on the many devices that are part of the dispersed network. On a centralised server, we conduct an analysis of the performance of the FL model by comparing its accuracy and the amount of time it takes to train using a range of various parameter value combinations. Additionally, the accuracy of these federated models may be made to reach a level that is comparable to that of the accuracy of central models.
Versatile NISQ processors

Today’s noisy intermediate-scale quantum (NISQ) era processors have reached an interesting stage in their development where over 50 qubits are routinely available for us. Traditionally these processors have used the gate-based model of quantum computation, however other useful models including quantum walks and analog approaches can be used. As we want versatility for our processors, we will show how they can be used to implement multi photon quantum walks as well digital-analog models for quantum discrimination. In particular we will introduce a model called quantum neural sensing where one can distinguish different states of matter with the measurement of only one qubit.
Histological Features, Risk Factors, and Classification of Breast Cancer: A Rapid Review

Breast cancer (BC) is developing from cells of breast tissue and is caused by the presence of malignant cells, which are characterized by uncontrolled cell division, leading to abnormal growth (in situ carcinoma), and their ability to invade normal tissue locally. BC's diagnosis and treatment strategies have recently improved, but the incidence rates continue to increase. Worldwide, BC is the most common cancer in women. The aim of this study is to combine histological, classification, and risk factors in a small review for easy searching by clinical researchers. Currently, there are many studies on BC in Iraq or in other regions of the world. Therefore, combining these studies in one work may give an advantage in the clinical trials of BC. In conclusion, BC is the first of the ten top cancers in Iraqi women up to 2018.
Optimized Reversible Logic Multiplexer Designs for Energy-Efficient Nanoscale Computing

Nano- and quantum-based low-power applications are where reversible logic really shines. By using digitally equivalent circuits with reversible logic gates, energy savings may be achieved. Reducing garbage output and ancilla inputs is a primary emphasis of this study, which aims to lower power consumption in reversible multiplexers. Multiplexers with switchable 2:1, 4:1, and 8:1 ratios may be built using the SJ gate and other simple reversible logic gates. The number of ancilla inputs has been cut in half from four to zero, and the amount of garbage output has been cut in half as well, from eight to three, making the 2:1 multiplexer an improvement over the prior design. New 4:1 multiplexer has 10' ancilla inputs, up from 2' in the previous designs. The proposed 4:1 multiplexer also cuts waste production in half from the current 5-to-6 bins per day. The 8:1 multiplexer has two ancilla inputs and nine trash outputs, while the current architecture only has one of each. The functionality of the VHDL and Xilinx 14.7-coded designs is validated by ISIM simulations.
Keigo Learning in Building Omotenashi no Kokoro through a Communicative Language Teaching Approach

In building interpersonal relationships through omotenashi no kokoro (Excellent Service), Keigo is a crucial aspect to consider when communicating in Japanese. Keigo, both in terms of grammar and its usage concept, is quite complicated. Therefore, the role of instructors in delivering Keigo learning materials is expected to motivate students to improve their communication skills while mastering the art of creating interpersonal connections. Communicative Language Teaching (CLT) is chosen as the teaching method. This research aims to provide an alternative teaching experience focusing on omotenashi no kokoro. The research method used is descriptive with a qualitative approach. The research subjects are students of the Japanese Language Program at Universitas Teknologi Yogyakarta (UTY) taking the hospitality course. The research is conducted through observation by analyzing CLT-based learning activities using three steps: mechanical practice, meaningful practice, and communicative practice. The study results indicate that with the CLT method, students can use Keigo through word or phrase repetition, understand the meaning of words and expressions in sentences, and freely actualize themselves through improvisation and exploration of conversational contexts. A new finding from this research is that the CLT teaching method is effective when applied to the hospitality course, emphasizing building interpersonal relationships through omotenashi no kokoro.
An application of neural network algorithm model based on improved multi-expression programming in English language education practice

In the field of English education, neural network algorithm can effectively predict and evaluate teaching, and significantly improve the quality of education and teaching. Therefore, a neural network English teaching evaluation prediction model based on multi expression programming is proposed. Through the research on neural network and genetic algorithm (GA), it is found that flexible neural tree cannot optimise parameters and results at the same time. Therefore, a neural network algorithm model (MEP) based on multi expression programming is proposed to solve the problem, and the MEP-NN English teaching evaluation model is constructed by optimising the model parameters with evolutionary algorithm. The model is applied to the English teaching process to achieve the evaluation of English teaching quality. The results show that in the mean square error performance test of multiple algorithms, achieving convergence after 500 iterations, with an MSE value of 0.02 and the best error performance; in the English class comprehensive quality prediction, the proposed MEP-NN algorithm has the best prediction accuracy, with a prediction mean of 86.56 points, closest to the actual value of 86 score, with a prediction accuracy of 94.56%. This shows that the proposed MEP-NN algorithm has excellent performance.
Map-Aided Particle Filter for Improved Multi-hypothesis Ambiguity Resolution

This paper presents the implementation of a map-aiding algorithm for estimating the full geometry-based float solution in the differential carrier phase positioning of a land vehicle. The focus is on evaluating the effect of map-matching on the convergence of ambiguity states. The estimation is performed within a Bayesian framework using a Sequential Importance Resampling (SIR) Particle Filter (PF). The SIR PF estimates the position, velocity, acceleration states, and float ambiguities using L1 GPS carrier phase and pseudorange observations. Three ambiguity resolution methods, namely Rounding, Bootstrapping, and Integer LeastSquares (ILS), are applied to determine the most likely integer values based on the estimated float ambiguities. The empirical covariance matrices for ambiguity resolution are obtained by analyzing the particle distribution with and without the map matching and transformed using the decorrelation Z transformation of the Least Squares Ambiguity Decorrelation Adjustment (LAMBDA) method. The results demonstrate the benefits of incorporating map information in the ambiguity resolution process, particularly in improving ambiguity convergence. The success rates of the three search methods are computed and compared to show the positive effect of the map-aiding algorithm on the ambiguity domain. The proposed algorithm is tested on 4 segments of data from a land vehicle test chosen to demonstrate ambiguity re-initialization and convergence during different vehicle maneuvers with and without map-aiding.
Design of dynamic ski motion capture and human posture detection model based on spatio-temporal Transformer and wireless device tracking technology

As a result of significant advancements in living conditions, individuals have redirected their attention towards physical exercise. Skiing, as a widely popular sport, necessitates the real-time maintenance of correct posture during movement. Therefore, we present a dynamic skiing motion capture and human posture detection model that leverages wireless device tracking. Primarily, personnel tracking is enabled through the construction of service base stations and the utilization of wireless device tracking technology. Subsequently, a human posture detection model is formulated in the form of human posture key points, employing the image information of each frame obtained via wireless devices. Finally, we introduce a spatio-temporal Transformer structure that facilitates the detection and recognition of human posture in consecutive frames. Our results demonstrate that our approach can precisely locate and track the position of skiing personnel. Compared to the latest Blip and Conformer methods, our technique yields F values that surpass them by 1.20% and 4.51%, respectively. Moreover, our model can achieve convergent model parameters and accomplish training objectives more efficiently, thus enabling posture detection and dynamic capture of skiing personnel via image and video information.
Preserving Privacy: How Governments and Digital Services Can Harness Zero-Knowledge Proofs for Secure Identification

Amidst rapid technological advancement and digital transformation, ensuring privacy and data security is paramount. Governments and digital service providers face the challenge of establishing secure identification systems that protect individuals' personal information while enabling reliable authentication and seamless user experiences. Traditional identification methods often require individuals to disclose sensitive personal information, leading to privacy risks and potential data breaches. Zero-knowledge proofs (ZKPs) have emerged as a promising solution to address these concerns. By leveraging ZKPs, individuals can authenticate their identities or assert specific attributes without revealing sensitive data. This approach holds great potential for preserving privacy while enabling efficient and trustworthy verification processes. This paper explored ZKPs and how governments and digital service providers can utilize this technology to achieve secure identification while upholding privacy. A key focus was prototyping a secure identification protocol using ZKPs. Through practical implementation, this research aimed to demonstrate the reliability and effectiveness of ZKPs in real-world scenarios. Keywords : zero-knowledge proofs, privacy, digital identity, governments, digital services. DOI: 10.7176/ISDE/13-2-06 Publication date: September 30 th 2023
Augmented wheelchair control for collision avoidance

An electric wheelchair gives people with disabilities the ability to move independently and participate in social activities. However, the control of wheelchairs requires concentration, foresight, and spatial orientation, which often deteriorate with the progression of a disease and with age. The loss of these efficiency can be significantly compensated by the safety system. The research presented on augmentation in manual control of the wheelchair is carried out. Although most of the existing propositions focus on stopping before an obstacle, bypassing it, or automatic navigation to the indicated area, they do not resolve safe motion in tight spaces and areas with many obstacles. This article presents a new approach to collision controller for manual control. The proposed solution takes into account the situation in which the driver of the electric wheelchair uses it as a tool to move moveable objects in its surroundings. The developed and tested system provides safe and controlled contact with the obstacle. As a result, the intent to move resulting from joystick deflection causes a controlled low-speed motion of the object. This article contains descriptions of the control method and the research stand with the wheelchair, simulation results and experimental studies. The simulation model was used to analyse dynamics of the controlled object, select collision controller parameters and test controller operation. The results of wheelchair tests and simulations are similar and consistent with the assumed mode of operation, confirming correctness of the proposed control method.