Reviewerly/exHarmony huggingface dataset:
TBAC: A Tokoin-based Accountable Access Control Scheme for the Internet of Things

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Overprivilege attack</b> , a widely reported phenomenon in IoT that accesses unauthorized or excessive resources, is notoriously hard to prevent, trace and mitigate. In this paper, we propose TBAC, a Tokoin-Based Access Control model enabled by blockchain and Trusted Execution Environment (TEE) technologies, to offer fine-grained access control and strong auditability for IoT. TBAC materializes the virtual access power into a definite-amount, secure and accountable cryptographic coin, termed “tokoin” (token+coin), and manages it using atomic and accountable state-transition functions in a blockchain. A tokoin carries a fine-grained policy defined by the resource owner to specify the requirements to be satisfied before an access is granted, and the behavioral constraints that describe the correct procedure to follow during access. The strong-auditability is achieved with blockchain and a TEE-enabled trusted access control object (TACO) to ensure that all access activities are securely monitored and auditable. We prototype TBAC by implementing all its functions with well-studied cryptographic primitives over different blockchain platforms, building a TACO on top of the ARM Cortex-M33 TEE microcontroller, and constructing a user-friendly APP for regular users. A case study is finally presented to demonstrate how TBAC is employed to enable autonomous and secure in-home cargo delivery.
Deep Multi-Task Multi-Agent Reinforcement Learning With Knowledge Transfer

Despite the potential of Multi-Agent Reinforcement Learning (MARL) in addressing numerous complex tasks, training a single team of MARL agents to handle multiple diverse team tasks remains a challenge. In this paper, we introduce a novel Multi-task method based on Knowledge Transfer in cooperative MARL (MKT-MARL). By learning from task-specific teachers, our approach empowers a single team of agents to attain expert-level performance in multiple tasks. MKT-MARL utilizes a knowledge distillation algorithm specifically designed for the multi-agent architecture, which rapidly learns a team control policy incorporating common coordinated knowledge from the experience of task-specific teachers. Additionally, we enhance this training with teacher annealing, gradually shifting the model's learning from distillation towards environmental rewards. This enhancement helps the multi-task model surpass its single-task teachers. We extensively evaluate our algorithm using two commonly-used benchmarks: StarCraft II micro-management and multi-agent particle environment. The experimental results demonstrate that our algorithm outperforms both the single-task teachers and a jointly-trained team of agents. Extensive ablation experiments illustrate the effectiveness of the supervised knowledge transfer and the teacher annealing strategy.
Aligning before Aggregating: Enabling Communication Efficient Cross-Domain Federated Learning via Consistent Feature Extraction

Cross-domain federated learning (FL), where data on local clients come from different domains, is a common case of FL. In such a cross-domain case, features extracted from the raw data of different clients deviate from each other in the feature space, leading to a so-called feature shift. This phenomenon can reduce feature discrimination and degrade the performance of the learned model. However, most existing FL methods are not specifically designed for the cross-domain setting. In this paper, we propose a novel cross-domain FL method named AlignFed. In AlignFed, each client model consists of a personalized feature extractor and a shared lightweight classifier. The feature extractor maps the features to a consistent space by aligning them to identical global target points. Inspired by recent studies in contrastive learning, AlignFed regards points that are uniformly distributed on the hypersphere as global target points. It then pushes features toward global target points of their corresponding classes and away from those of other classes to improve feature discrimination. The shared classifier aggregates knowledge across clients over the consistent feature space, which can mitigate performance degradation caused by feature shift while reducing communication cost. We conduct convergence analysis and perform extensive experiments to evaluate AlignFed.
Incremental Reinforcement Learning Via Performance Evaluation and Policy Perturbation

Rapid adaptation to the environment is the long-term task of reinforcement learning. However, reinforcement learning faces great challenges in dynamic environments, especially with continuous state-action spaces. In this paper, we propose a systematic Incremental Reinforcement Learning method via Performance Evaluation and Policy Perturbation (IRL-PEPP) to improve the adaptability of reinforcement learning algorithms in dynamic environments with continuous state-action spaces, which mainly includes three parts, i.e., performance evaluation, policy perturbation and importance weighting. Firstly, in performance evaluation, we apply the learned optimal policy to sample a few episodes in the original environment and use these samples to evaluate the policy applicability in the new environment. Then, in policy perturbation, the policy is perturbed according to the policy applicability to balance the trade-off between exploration and exploitation in the new environment. Finally, importance weighting is applied to weight the information to speed up the adjustment process of the policy. Experimental results demonstrate the feasibility and efficiency of the proposed IRL-PEPP method for continuous control tasks in comparison to the existing state-of-the-art methods.
Structural Analysis of the Additive Noise Impact on the $${\alpha \text {-tree}}$$

Hierarchical representations are very convenient tools when working with images. Among them, the $${\alpha \text {-tree}}$$ is the basis of several powerful hierarchies used for various applications such as image simplification, object detection, or segmentation. However, it has been demonstrated that these tasks are very sensitive to the presence of noise in images. While the quality of some $${\alpha \text {-tree}}$$ applications has been studied, including some with noisy images, the noise impact on the whole structure has been little investigated. Thus, in this paper, we examine the structure of $${\alpha \text {-tree}}$$ s built on images in the presence of noise with respect to the noise level. We compare its effects on constant and natural images, with different kinds of content, and we demonstrate the relation between the noise level and the distribution of every $${\alpha \text {-tree}}$$ node depth. Furthermore, we extend this study to the node persistence under a given energy criterion, and we propose a novel energy definition that allows assessing the robustness of a region to the noise. We finally observe that the choice of the energy has a great impact on the tree structure.
Computer Vision for Pose Estimation in Real Time

In a photograph or video, a human pose estimate attempts to predict the pose of several human body components. Since certain human motions frequently produce posing movements, understanding such poses is crucial for action recognition. This chapter focuses on recent developments in action recognition and their application to human pose estimation. With the use of pose estimating techniques and human pose depth estimates, the authors attempt to provide a detailed overview of the top-down and bottom-up models currently employed for action detection. In the framework of real-time posture estimation, this chapter explores traditional computer vision applications. Due to severe injuries, many fitness enthusiasts are unable to achieve their goals. Poor training posture and poor mental health are the causes. These people can modify their posture in real time and perform better in a computer vision-based environment. This chapter sheds light on sports, the necessity to correct posture, and the application of technology in this area.
The Indian Search Algorithm

Searching algorithms deal with searching for key $$\mathcal {K}$$ in a heap of data. In this work, an indigenous Searching Algorithm—“The Indian Search Algorithm” has been proposed that will be efficient enough to search for a key in the order $$\begin{aligned} \textrm{log}_{\left( k+\frac{1}{k+\frac{1}{k+\frac{1}{k+\frac{1}{k+\frac{1}{\ddots }}}}}\right) }{\left( n-1\right) }+\textrm{log}_2{\left( n\right) } \end{aligned}$$ where $$\left( k+\frac{1}{k+\frac{1}{k+\frac{1}{k+\frac{1}{k+\frac{1}{\ddots }}}}}\right) $$ is the kth metallic ratio. The Search Algorithm would be an efficient one at least for an Ordered List. In the paper, we have tried to incorporate two children from the same hierarchy—with the parent being the Generic Search with kth metallic ratio, which are Indo-Pellian Search, following the Pell Series, and Indo-Fibonaccian Search following the Fibonacci Series. The Computational Complexity of both have been evaluated.
Linking of Ontologies for Composition of Semantic Web Services Using Knowledge Graph

To enhance Web Services interoperability for better composition, Semantic Web Services (SWS) architecture offers an opportunity to add higher semantic levels in the existing frameworks using ontologies. Semantically described services will provide better service discovery and allow easier composition and interoperation. Knowledge graphs (KG) use ontologies as the core model to represent formal semantics in knowledge representations and therefore can be effectively utilized in the composition of SWS. Reasoning over knowledge graphs is the new area of research to infer consistent and reliable information from existing data. In this paper, we have proposed and implemented a framework for reasoning over KG using subclass inference that has achieved an average precision of 79.87% and an average query response time of 2.02 s for 37 user queries from 9 domains in the OWL-S dataset.
Research on the Mechanism of Large Sports Events Promoting the Development of Urban Influence

Large sports events can drive the growth of traditional industries in the host area and directly drive the development of the three major industries in the urban construction system. From the current situation of sports events, it can be seen that they are beneficial for stimulating people's health and sports awareness, and inspiring people's longing for the host city. However, the hosting of sports events also has drawbacks, which can harm the interests of those who do not participate in sports events and hinder their resettlement after the event. This article attempts to analyze the mechanism by which large-scale sports events promote the development of urban influence. The rapid development of large-scale events is relatively difficult for some host cities, as the operational capacity, estimated operating costs, and investment in event operations cannot be properly handled. At the same time, it brings an increased financial burden to the government. In this regard, large-scale sports events require a lot of manpower, material resources, and financial resources to be held. Therefore, in the preparation stage, organizers must fully budget and plan, and prepare emergency plans to prevent the occurrence of unexpected events that affect the smooth holding of sports events.
Evaluating Regular Path Queries on Compressed Adjacency Matrices

Regular Path Queries (RPQs), which are essentially regular expressions to be matched against the labels of paths in labeled graphs, are at the core of graph database query languages like SPARQL. A way to solve RPQs is to translate them into a sequence of operations on the adjacency matrices of each label. We design and implement a Boolean algebra on sparse matrix representations and, as an application, use them to handle RPQs. Our baseline representation uses the same space as the previously most compact index for RPQs and excels in handling the hardest types of queries. Our more succinct structure, based on $$k^2$$ -trees, is 4 times smaller and still solves complex RPQs in reasonable time.
A masking-based federated singular value decomposition method for anomaly detection in industrial internet of things

The industrial internet of things (IIoT) is a flexible and scalable manufacturing system that can collect and analyse data from sensors based on machine learning, cloud, and edge computing. Recommendation systems can identify patterns in big data and reduce irrelevant data, with the singular value decomposition (SVD) algorithm being commonly used. Based on the found regularities in the data, it is possible to predict the most probable future events, such as emergency shutdowns of equipment, the occurrence of emergencies, etc. This paper explores the SVD method for anomaly detection in IIoT and proposes the federated singular value decomposition (FedSVD) method, which better protects large-scale IIoT data privacy. Results show FedSVD has greater accuracy and duration of calculations. A masking-based FedSVD method is proposed for anomaly detection and data protection. Choosing the optimal algorithm for IIoT and recommendation systems can automate the processing of critical parameters and improve efficiency.
Perencanaan Pembangkit Listrik Tenaga Surya Sistem Off-Grid di Desa Bungku Kecamatan Bajubang Kabupaten Batanghari Jambi

This study aims to plan a solar power plant with an off grid system that will be used for electricity needs in Hamlet Kunangan Jaya 2 RT.28 which is one of the Hamllet in Bungku Village. Bungku Village is included in the 3T area, which is left behind, foremost, and outermost.Hamle Kunangan Jaya 2 RT.28have not enjoyed electricity from PLN due to difficult access and far from urban areas.The research was conducted with a descriptive research method. The selection of this type of method is used on the basis of the consideration that the nature of the research describes a plan carried out to meet the electricity needs of public facilities. Studies field were carried out to determine the electricity needs of public facilities: Mosques, Polides, and Elementary Schools, find out the number of panels needed, measure solar radiation and measure the area of the building. Calculation of electricity requirements is carried out in order to find out how much electricity is needed in public facilities, carry out design plans and calculate a budget plan to describe the planned installation. The results of solar data measurement for Optimal Global Irradiation in Bungku Village are 4,549kWh/m2 per day, the required amount of solar cell power is 6,000WP, three 40A Solar Charger Controllers, 2,800Ah batteries and 8000Watt Inverters. The total load required for mosques, polides, and elementary schools is 25,126Wh. The total RAB for PLTS development using an off-grid system requires a cost of IDR 138,647,000.
Innovation in Science Learning Using Augmented Reality in Animal Metamorphosis Materials

Currently, there are still many science lessons that use the lecture method and are teacher-centred. There are still many students' natural science scores whose grades are below the KKM; the cause of low grades in this era is monotonous and unpleasant learning. Teacher-centred learning also makes students easily bored and bored, so they are not interested in learning related to the material being presented. Regarding this problem, the authors created a learning media in the form of an augmented reality application that functions to help students get to know and interact in real-time about Natural Sciences subjects, especially on animal metamorphosis material. The author uses the Multimedia Development Life Cycle (MDLC) development model through six stages, namely concept, design, collecting, assembly, testing, and distribution. From the results of making this application, animal metamorphosis learning will be easily recognized through augmented reality media, and students can enthusiastically learn using this application. Therefore, it is essential to make this learning media because students will be interested in learning new things, primarily related to 3D from augmented reality. Moreover, this technology is still rarely encountered and used in learning today.
Research on the Application of Mind Map to Improve Teaching reading at College

The paper aims to apply a new English reading teaching way at college based on mind map. The paper first analyses the present situation of English reading teaching at college. Then it introduces the definition and the kinds of mind map. Thirdly, it tries to use mind maps on teaching reading in college English. By using mind up, learners can have a clearer idea of the structure, better understand the writers' purpose and finally have their own thinking after reading.
English Teachers’ Strategies in Teaching Listening Comprehension for Senior High School in MAN Serdang Bedagai

The article deals with the teachers’ strategies in teaching listening comprehension for Senior High School in MAN Serdang Bedagai. The aims at knowing the teachers’ strategies in teaching listening comprehension , the problems are found by the teachers and how they overcome the problems. The research was conducted by using qualitative method. Techniques used to collect the data are observation, taking note and interview . The researcher selected two participants which is the English teacher’s in grade 12th at MAN Serdang Bedagai . The results of the study are The participant one use note-taking strategies and summarizing strategies and the participant two, use the pay attention strategies when deliver the listening theory and when do the listening practice. Based on the research finding, the conclusion is that there are several strategies which used by the English teachers in teaching listening, the strategies are taking note, summarizing and paying attention.
macJNet: weakly-supervised multimodal image deformable registration using joint learning framework and multi-sampling cascaded MIND

Abstract Deformable multimodal image registration plays a key role in medical image analysis. It remains a challenge to find accurate dense correspondences between multimodal images due to the significant intensity distortion and the large deformation. macJNet is proposed to align the multimodal medical images, which is a weakly-supervised multimodal image deformable registration method using a joint learning framework and multi-sampling cascaded modality independent neighborhood descriptor (macMIND). The joint learning framework consists of a multimodal image registration network and two segmentation networks. The proposed macMIND is a modality-independent image structure descriptor to provide dense correspondence for registration, which incorporates multi-orientation and multi-scale sampling patterns to build self-similarity context. It greatly enhances the representation ability of cross-modal features in the registration network. The semi-supervised segmentation networks generate anatomical labels to provide semantics correspondence for registration, and the registration network helps to improve the performance of multimodal image segmentation by providing the consistency of anatomical labels. 3D CT-MR liver image dataset with 118 samples is built for evaluation, and comprehensive experiments have been conducted to demonstrate that macJNet achieves superior performance over state-of-the-art multi-modality medical image registration methods.
PENGEMBANGAN APLIKASI ANALISIS KORELASI BERBASIS EXCEL (STUDI KASUS: PENGARUH PERTUMBUHAN PENDUDUK TERHADAP TINGKAT PENGANGGURAN)

The purpose of this research is to develop an Excel-based Correlation Analysis application that can be used by students, lecturers, and researchers as an alternative that can be used to carry out tests by making it easier for users to analyze and draw conclusions. This research was conducted using the Research and Development (R&amp;D) method to produce products through a development process with a 4D development model which includes Define, Design, Develop, Disseminate. The instruments used were expert validation sheets and student questionnaires. Based on the assessment of the application developed, the results of the expert validation obtained an average value of 3.80 or a percentage of 94.44% which is included in the very valid category. The results of a limited scale trial of 5 final year students showed results with an average percentage of 93.5% which was included in the very practical category and deserved to be disseminated. Furthermore, the application is implemented directly to real problems with a case study of the effect of population growth on the unemployment rate in the city of Medan and btained the result that the correlation between the effect of population growth (X) and the number of open unemployment (Y) in the city of Medan is very strong, where the rxy value is 0.82 and the effect between the X variable on the Y variable is 66.7377% with a ttable value of 2.78 and tcount of 3 so that the correlation is significant.
Frustratingly Simple Memory Efficiency for Pre-trained Language Models via Dynamic Embedding Pruning

The extensive memory footprint of pre-trained language models (PLMs) can hinder deployment in memory-constrained settings, such as cloud environments or on-device. PLMs use embedding matrices to represent extensive vocabularies, forming a large proportion of the model parameters. While previous work towards parameter-efficient PLM development has considered pruning parameters within the transformer layers, pruning the embedding matrix as part of fine-tuning or inference has yet to be explored. We first demonstrate that a significant proportion of the vocabulary remains unused in these scenarios. We then propose a simple yet effective approach that leverages this finding to minimize the memory footprint of the embedding matrix. We show that this approach provides substantial reductions in memory usage across a wide range of models and tasks. Notably, our approach maintains equivalent downstream task performance while allowing a more efficient use of compute resources.
CDDM: Channel Denoising Diffusion Models for Wireless Semantic Communications

Diffusion models (DM) can gradually learn to remove noise, which have been widely used in artificial intelligence generated content (AIGC) in recent years. The property of DM for eliminating noise leads us to wonder whether DM can be applied to wireless communications to help the receiver mitigate the channel noise. To address this, we propose channel denoising diffusion models (CDDM) for semantic communications over wireless channels in this paper. CDDM can be applied as a new physical layer module after the channel equalization to learn the distribution of the channel input signal, and then utilizes this learned knowledge to remove the channel noise. We derive corresponding training and sampling algorithms of CDDM according to the forward diffusion process specially designed to adapt the channel models and theoretically prove that the well-trained CDDM can effectively reduce the conditional entropy of the received signal under small sampling steps. Moreover, we apply CDDM to a semantic communications system based on joint source-channel coding (JSCC) for image transmission. Extensive experimental results demonstrate that CDDM can further reduce the mean square error (MSE) after minimum mean square error (MMSE) equalizer, and the joint CDDM and JSCC system achieves better performance than the JSCC system and the traditional JPEG2000 with low-density parity-check (LDPC) code approach.