Reviewerly/exHarmony huggingface dataset:
DESIGN OF A WEB-BASED POINT RECORDING SYSTEM AT SMK NEGERI 1 TAKENGON

The Guidance and Counseling Website for Teachers is an online resource created with the express purpose of offering assistance and direction to teachers in carrying out their duties more successfully and improving their general well-being. This website attempts to offer pertinent materials, data, and counseling services catered to the unique requirements of instructors.Teachers frequently encounter a variety of difficulties in the increasingly difficult and complex educational environment, one of which is classroom management. This point-recording website was created to help teachers at SMK Negeri 1 Takengon capture students' points of failure more easily, avoiding data errors and enhancing the effectiveness and efficiency of their job.
Beyond Digital Financial Services: Exploring Mobile Money Agents in Tanzania as General ICT Intermediaries

Tanzanian mobile money and telecom agents (called wakala(s) in Swahili) have played a crucial role in expanding digital financial services (DFS) to rural areas. However, wakalas are losing their ability to financially sustain themselves providing intermediation services that their communities require. This work explores the potential for the wakala network to extend intermediation services to emerging ICTs beyond the scope of commercial DFS by uncovering the social and institutional factors that currently shape wakala practices. First, we investigate how two different models of intermediation from ICTD literature can inform broader strategies for intermediation through human infrastructures. We then complement this analysis with an on-the-ground quantitative survey and focus groups with community members and wakalas in Kagera, Tanzania. Our focus groups reveal that community members face challenges with new ICTs that require sustained intermediation and that wakalas encounter mounting financial instability and are thus receptive to intermediating for other ICTs. Finally, we present three factors that influence the broadening of the wakalas’ role of general ICT intermediaries: (1) aligning incentives and addressing the limits of pro bono actions, (2) providing appropriate training and a suitable support infrastructure, and (3) fostering trust-building and reciprocity.
Pengembangan E-LKPD Berbasis Inkuiri pada Materi Menganalisis Struktur Teks Negosiasi di Kelas X SMA Negeri 1 Gunungsitoli

Teaching materials are a tool to assist teachers and students in carrying out learning activities. However, the teaching materials used are still limited to package books only, making students less active in learning. The purpose of this research is to develop teaching materials in the form of inquiry-based E-LKPD on the material of analyzing the structure of negotiation text in class X SMA Negeri 1 Gunungsitoli. The method used in this research is Research and Development (R&amp;D). The development model used is ADDIE which consists of analysis, design, development, implementation, and evaluation stages. Based on the results of the study, the E-LKPD product obtained very valid criteria 99% material validators, 93% language validators and 96% design/media validators. It is stated that it is very practical obtained from the students' response questionnaire, namely individual trials of 92.8%, small group trials of 96.4% and field trials of 94.9%. Furthermore, it was declared very effective obtained from the learning test results, namely individual trials with classical completeness of 100%, small group trials of 83.3% and field tests of 95.8%. From the research results above, the inquiry-based E-LKPD developed has met the criteria of valid, practical, and effective for use in Indonesian language learning
Research on full-field vibration displacement measurement based on grid CCD moiré method

The development of full-field, accurate vibration testing techniques has been vastly valued by the academic and engineering community. However, due to the limited data transmission speed of digital cameras, the contradiction between speed and resolution remains in the traditional full-field vibration measurement systems based on image measurement, failing to acquire high-resolution images at high-speed simultaneously. In this paper, a high-speed and high-resolution full-field vibration displacement measurement system is established based on the grid CCD moiré method. A periodic grid is arranged on the surface of the sample as a deformation carrier, and the periodic pixel of the digital camera is used as a reference grating that ‘enlarges’ the grid on the surface of the sample to form a grid CCD moiré with a geometry significantly larger than the original, capturing images for deformation analysis. This approach lowers the requirement of digital image resolution for high-resolution deformation measurement, reducing the contradiction between speed and resolution in full-field vibration measurement. In this paper, the principle and implementation of full-field vibration measurement using the CCD moiré method are introduced, and the experiment results of full-field in-plane vibration displacement measurement are included, verifying the effectiveness and reliability of the system.
Efektivitas Penerapan Model Pembelajaran Problem Based Learning (PBL) Terhadap Kemampuan Numerasi Siswa

The purpose of this study was to determine the effectiveness of the application of the Problem-Based Learning model which was characterized by differences in student’s numeracy skills in the Surface Area and Volume of Flat Sided Spaces topic at SMP Negeri 4 Kuningan between the Problem-Based Learning model and the conventional model. This research is a static group pretest-posttest design. The sample in this study was class VIII F, totaling 25 students as the experimental class, and class VIII E, totaling 32 students as the control class. Data collection techniques used are subjective tests and questionnaires. Data analysis techniques in this study were hypothesis testing and student questionnaire responses. Based on the results of statistical testing, the increase in students’ numeracy skills is in the medium category. The percentage of student’s responses to Problem-Based Learning is 51.60%. This study concludes that the numeracy skills of the students using the Problem-Based Learning model are better than the numeracy abilities of the students using conventional learning models, the improvement of student’s numeracy skills is in the medium category, and more than half of the student’s respond well to the learning model, so it can be said that Problem-Based Learning is quite effective
Simulations of quantum nonlocality with local negative bits

We propose a simple simulation of nonlocal quantum correlations among $N$ qubits using a local hidden variable source with a positive probability distribution, given that each of the $N$ observers has access to a local negative bit. Notably, unlike the Toner-Bacon protocol, no exchange of classical bits between the observers is required. Moreover, our simulation can be extended to include Popescu-Rohrlich box correlations.
Self-Generating Evaluations for Robot’s Autonomy Based on Sensor Input

Reinforcement learning has been explored within the context of robot operation in different environments. Designing the reward function in reinforcement learning is challenging for designers because it requires specialized knowledge. To reduce the design burden, we propose a reward design method that is independent of both specific environments and tasks in which reinforcement learning robots evaluate and generate rewards autonomously based on sensor information received from the environment. This method allows the robot to operate autonomously based on sensors. However, the existing approach to adaption attempts to adapt without considering the input properties for the strength of the sensor input, which may cause a robot to learn harmful actions from the environment. In this study, we propose a method for changing the threshold of a sensor input while considering the strength of the input and other properties. We also demonstrate the utility of the proposed method by presenting the results of simulation experiments on a path-finding problem conducted in an environment with sparse rewards.
Enhancing 6-DoF Object Pose Estimation through Multiple Modality Fusion: A Hybrid CNN Architecture with Cross-Layer and Cross-Modal Integration

Recently, applying the utilization of RGB-D data for robot perception tasks has garnered significant attention in domains like robotics and autonomous driving. However, a prominent challenge in this field lies in the substantial impact of feature robustness on both segmentation and pose estimation tasks. To tackle this challenge, we proposed a pioneering two-stage hybrid Convolutional Neural Network (CNN) architecture, which connects segmentation and pose estimation in tandem. Specifically, we developed Cross-Modal (CM) and Cross-Layer (CL) modules to exploit the complementary information from RGB and depth modalities, as well as the hierarchical features from diverse layers of the network. The CM and CL integration strategy significantly enhanced the segmentation accuracy by effectively capturing spatial and contextual information. Furthermore, we introduced the Convolutional Block Attention Module (CBAM), which dynamically recalibrated the feature maps, enabling the network to focus on informative regions and channels, thereby enhancing the overall performance of the pose estimation task. We conducted extensive experiments on benchmark datasets to evaluate the proposed method and achieved exceptional target pose estimation results, with an average accuracy of 94.5% using the ADD-S AUC metric and 97.6% of ADD-S smaller than 2 cm. These results demonstrate the superior performance of our proposed method.
Enhancing IoT Security: An Innovative Key Management System for Lightweight Block Ciphers

This research paper presents a study on designing and implementing a robust key management scheme for lightweight block ciphers in Internet of Things (IoT) networks. Key management is a critical concern for IoT devices due to their limited resources and susceptibility to security threats. The proposed scheme utilises partial key pre-distribution to achieve lightweight and secure key management. The protocol's security has been analysed against various attacks, demonstrating its resistance. Performance evaluation results indicate that the proposed key management technique is suitable for resource-constraint IoT networks, as it reduces communication overhead, power consumption, and storage space requirements. The methodology employed in this research includes designing and implementing the proposed key management scheme and conducting scenario-based analyses of its functionality. The results affirm that the proposed solution effectively ensures secure communication in IoT networks. Overall, this research contributes to developing a secure and efficient key management scheme for lightweight block ciphers in IoT networks.
Multichannel One-Dimensional Data Augmentation with Generative Adversarial Network

Data augmentation is one of the most important problems in deep learning. There have been many algorithms proposed to solve this problem, such as simple noise injection, the generative adversarial network (GAN), and diffusion models. However, to the best of our knowledge, these works mainly focused on computer vision-related tasks, and there have not been many proposed works for one-dimensional data. This paper proposes a GAN-based data augmentation for generating multichannel one-dimensional data given single-channel inputs. Our architecture consists of multiple discriminators that adapt deep convolution GAN (DCGAN) and patchGAN to extract the overall pattern of the multichannel generated data while also considering the local information of each channel. We conducted an experiment with website fingerprinting data. The result for the three channels' data augmentation showed that our proposed model obtained FID scores of 0.005,0.017,0.051 for each channel, respectively, compared to 0.458,0.551,0.521 when using the vanilla GAN.
Editorial on the New Scope of Dynamics

We are thrilled to introduce the new scope of the Dynamics, a platform that will unravel the captivating world of diverse dynamics and their multifaceted applications [...]
Hospital Readmission and Length-of-Stay Prediction Using an Optimized Hybrid Deep Model

Hospital readmission and length-of-stay predictions provide information on how to manage hospital bed capacity and the number of required staff, especially during pandemics. We present a hybrid deep model called the Genetic Algorithm-Optimized Convolutional Neural Network (GAOCNN), with a unique preprocessing method to predict hospital readmission and the length of stay required for patients of various conditions. GAOCNN uses one-dimensional convolutional layers to predict hospital readmission and the length of stay. The parameters of the layers are optimized via a genetic algorithm. To show the performance of the proposed model in patients with various conditions, we evaluate the model under three healthcare datasets: the Diabetes 130-US hospitals dataset, the COVID-19 dataset, and the MIMIC-III dataset. The diabetes 130-US hospitals dataset has information on both readmission and the length of stay, while the COVID-19 and MIMIC-III datasets just include information on the length of stay. Experimental results show that the proposed model’s accuracy for hospital readmission was 97.2% for diabetic patients. Furthermore, the accuracy of the length-of-stay prediction was 89%, 99.4%, and 94.1% for the diabetic, COVID-19, and ICU patients, respectively. These results confirm the superiority of the proposed model compared to existing methods. Our findings offer a platform for managing the healthcare funds and resources for patients with various diseases.
Improved Multi-Person 2D Human Pose Estimation Using Attention Mechanisms and Hard Example Mining

In recent years, human pose estimation, as a subfield of computer vision and artificial intelligence, has achieved significant performance improvements due to its wide applications in human-computer interaction, virtual reality, and smart security. However, most existing methods are designed for single-person scenes and suffer from low accuracy and long inference time in multi-person scenes. To address this issue, increasing attention has been paid to developing methods for multi-person pose estimation, such as utilizing Partial Affinity Field (PAF)-based bottom-up methods to estimate 2D poses of multiple people. In this study, we propose a method that addresses the problems of low network accuracy and poor estimation of flexible joints. This method introduces the attention mechanism into the network and utilizes the joint point extraction method based on hard example mining. Integrating the attention mechanism into the network improves its overall performance. In contrast, the joint point extraction method improves the localization accuracy of the flexible joints of the network without increasing the complexity. Experimental results demonstrate that our proposed method significantly improves the accuracy of 2D human pose estimation. Our network achieved a notably elevated Average Precision (AP) score of 60.0 and outperformed competing methods on the standard benchmark COCO test dataset, signifying its exceptional performance.
Reward-respecting subtasks for model-based reinforcement learning

To achieve the ambitious goals of artificial intelligence, reinforcement learning must include planning with a model of the world that is abstract in state and time. Deep learning has made progress with state abstraction, but temporal abstraction has rarely been used, despite extensively developed theory based on the options framework. One reason for this is that the space of possible options is immense, and the methods previously proposed for option discovery do not take into account how the option models will be used in planning. Options are typically discovered by posing subsidiary tasks, such as reaching a bottleneck state or maximizing the cumulative sum of a sensory signal other than reward. Each subtask is solved to produce an option, and then a model of the option is learned and made available to the planning process. In most previous work, the subtasks ignore the reward on the original problem, whereas we propose subtasks that use the original reward plus a bonus based on a feature of the state at the time the option terminates. We show that option models obtained from such reward-respecting subtasks are much more likely to be useful in planning than eigenoptions, shortest path options based on bottleneck states, or reward-respecting options generated by the option-critic. Reward respecting subtasks strongly constrain the space of options and thereby also provide a partial solution to the problem of option discovery. Finally, we show how values, policies, options, and models can all be learned online and off-policy using standard algorithms and general value functions.
Comparative analysis, classification, and segmentation of the handwritten Gujarati conjuncts depending on the structural properties of the constituent characters

This research paper presents a comprehensive analysis, classification, and segmentation of Gujarati conjuncts, with the aim of providing a deeper understanding of the intricate conjuncts in the Gujarati script. The study investigates the coverage area and joining patterns of conjuncts to categorize them based on their distinct structural properties. The coverage area refers to the spatial extent of a conjunct and is classified into three categories: full box, upper half box, and lower half box characters. The joining patterns offer insights into how consonants are connected or merged within a conjunct, including possibilities such as horizontal lines, curves. Accurate segmentation of conjuncts is crucial for retrieving their constituent components. This paper also discusses a segmentation algorithm that considers information from neighboring pixels, as well as the joining patterns and coverage area of conjuncts. The research study incorporates 728 frequently used handwritten conjuncts of the Gujarati script. Experimental analysis is conducted on a substantial dataset of 45,000 conjuncts. The experimental results demonstrate that conjuncts falling into the lower half box category or those connected with a horizontal line or a curve exhibit the highest success rate of over 85%. Furthermore, statistical analysis reveals that the success rate remains consistent and comparable across the various character groups, providing further support for the findings.
Cooperative situational awareness of multi-UAV system based on improved D-S evidence theory

Situational awareness (SA) of unmanned aerial vehicles (UAVs) has been a research hotspot over the decades. Existing research mainly focuses on the SA of a single UAV in two-dimensional planes, whereas obstacles are assumed a priori. To eliminate these constraints, this study considers the cooperative situational awareness (CSA) problem of multi-UAV systems in the scenario of crossing a three-dimensional (3D) obstacle belt while no prior information of obstacles is required. First, the distribution models of the multi-UAV system and the obstacles are built based on two reference frames. Second, various types of uncertainties are characterized, which reflect an urgent need for CSA. Thus, a centralized CSA scheme is proposed and conducted on multiple UAVs and at different times, and the Dempster-Shafer (D-S) evidence theory is introduced to address information uncertainties and achieve high-accuracy information fusion. Next, to deal with the high-conflict evidence situations that are common in practice, a modified D-S fusion method is further developed. A modified Pearson coefficient is utilized to measure the correlation between different pieces of evidence. Both information credibility and uncertainty are taken into account to evaluate the evidence from different perspectives, and a novel evidence weight assignment method is presented to treat high-conflict situations. Numerical simulations validate the effectiveness of the proposed CSA method. Compared to existing studies, the proposed method is applicable to different trust paradoxes and achieves the best performance among various fusion methods.
PIRB: Privacy-Preserving Identity-Based Redactable Blockchains with Accountability

In this paper, we propose a privacy-preserving identity-based redactable blockchain (PIRB), the first identity-based redactable blockchain that supports flexible policies while maintaining accountability. Based on digital identities, PIRB enables a knowledge owner to set one policy for a batch of users while preserving policy privacy. Furthermore, similar to state-of-the-art solutions, PIRB draws inspiration from the proxy re-encryption technique to enforce user accountability. The design of PIRB entails addressing two primary technical challenges: firstly, achieving a flexible policy while upholding policy privacy; secondly, establishing accountability measures. To tackle the former challenge, we propose an enhanced identity-based encryption scheme that integrates polynomial function techniques. To address the latter challenge, a distinct identifier is generated for each user and subsequently concealed within the user’s secret key. Specifically, following existing schemes, we present the first scheme PIRB-I to cater to one-way access control scenarios, empowering owners to define access policies for designated editors. Additionally, recognizing the needs on the editor side for owner selection, we enhance PIRB-I through the introduction of matchmaking encryption, thereby supporting bilateral access control in a framework denoted as the second scheme PIRB-II. Notably, PIRB-I and PIRB-II involve a trade-off between computational and communication complexities. Specifically, when contrasted with PIRB-I, PIRB-II facilitates editors in owner selection, thereby mitigating editors’ communication overheads at the cost of increased computational overheads during policy generation and matching. Theoretical analysis demonstrates the inherent trade-off complexity and the resilience exhibited by PIRB-I and PIRB-II against chosen-plaintext attacks. Extensive experimentation on the FISCO blockchain shows that, compared with the state-of-the-art works, PIRB-I and PIRB-II achieve 200 times and 100 times computational efficiency improvements and 50 times and 60 times communication efficiency improvements on average, respectively.