Reviewerly/exHarmony huggingface dataset:
Credible System for Industrial IoT Networks Using Block Chain Technology

The industrial Internet of things specifies the interconnected network of instruments, sensors, and devices used in industrial applications, such as manufacturing and energy management. One technology that has the potential to improve IIoT systems is block chain, which can provide redundancy, immutable storage, and encryption. Another way to enhance IIoT security is to use data science techniques to detect and prevent malicious activities. This study aims to address the problem of malicious devices in industrial settings that can degrade network performance and compromise the security of legitimate IoT devices. To establish trust between IoT devices and safeguard the network from malicious entities, the coordinator's IoT device is used. While some open-source ciphers may still be vulnerable to attack, organizations can benefit from the scrutiny of numerous users, making it difficult for centralized entities or third parties to make malicious modifications. Additionally, integrating a block chain-based data model can ensure data transparency. The proposed framework is experimentally evaluated using MATLAB to measure a variety of security metrics such as the likelihood of false authentication, message tampering, and attack intensity. Based on the simulation results, it is observed that the suggested approach effectively improves the security of industrial IoT networks by identifying and mitigating malicious attacks.
Joint Client Selection and CPU Frequency Control in Wireless Federated Learning Networks with Power Constraints

Federated learning (FL) represents a distributed machine learning approach that eliminates the necessity of transmitting privacy-sensitive local training samples. However, within wireless FL networks, resource heterogeneity introduces straggler clients, thereby decelerating the learning process. Additionally, the learning process is further slowed due to the non-independent and identically distributed (non-IID) nature of local training samples. Coupled with resource constraints during the learning process, there arises an imperative need for optimizing client selection and resource allocation strategies to mitigate these challenges. While numerous studies have made strides in this regard, few have considered the joint optimization of client selection and computational power (i.e., CPU frequency) for both clients and the edge server during each global iteration. In this paper, we initially define a cost function encompassing learning latency and non-IID characteristics. Subsequently, we pose a joint client selection and CPU frequency control problem that minimizes the time-averaged cost function subject to long-term power constraints. By utilizing Lyapunov optimization theory, the long-term optimization problem is transformed into a sequence of short-term problems. Finally, an algorithm is proposed to determine the optimal client selection decision and corresponding optimal CPU frequency for both the selected clients and the server. Theoretical analysis provides performance guarantees and our simulation results substantiate that our proposed algorithm outperforms comparative algorithms in terms of test accuracy while maintaining low power consumption.
A Fine-Grained Structural Partitioning Approach to Graph Compression

To compress a graph, some methods rely on finding highly compressible structures, such as very dense subgraphs, and encode a graph by listing these structures compressed. However, structures can overlap, leading to encoding the same information multiple times. The method we propose deals with this issue, by identifying overlaps and encoding them only once. We have tested our method on various real-world graphs. The obtained results show that our approach is efficient and outperforms state of the art methods. The source code of our algorithms, together with some sample input instances, are available at https://gitlab.liris.cnrs.fr/fpitois/fgsp.git .
Anomaly Detection in Financial Transactions Via Graph-Based Feature Aggregations

Anomaly detection in the financial domain aims to detect abnormal transactions such as fraudulent transactions that can lead to loss of revenues to financial institutions. Existing solutions utilize solely transaction attributes as feature representations without the consideration of direct/indirect interactions between users and transactions, leading to limited accuracy. We formulate anomaly detection in financial transactions as the problem of edge classification in an edge-attributed multigraph, where each transaction is regarded as an edge, and each user is represented by a node. Then, we propose an effective solution $$\texttt{DoubleFA}$$ , which contains two novel schemes: proximal feature aggregation and anomaly feature aggregation. The former is to aggregate features from neighborhoods into edges based on top-k Personalized PageRank (PPR). In anomaly feature aggregation, we employ a predict-and-aggregate strategy to accurately preserve anomaly information, thereby alleviating the over-smoothing issue incurred by proximal feature aggregation. Our experiments comparing $$\texttt{DoubleFA}$$ against 10 baselines on real transaction datasets from PayPal demonstrate that $$\texttt{DoubleFA}$$ consistently outperforms all baselines in terms of anomaly detection accuracy. In particular, on the full PayPal dataset with 160 million users and 470 million transactions, our method achieves a significant improvement of at least 23% in F1 score compared to the best competitors.
Utility-Oriented Gradual Itemsets Mining Using High Utility Itemsets Mining

Gradual itemsets capture frequent covariations of attributes of the form “more/less A, more/less B” from a quantitative database. These patterns have gained considerable interest during these years and have been applied in several domains. Various algorithms have been proposed to extract those itemsets efficiently. However, an inherent limitation of the proposed algorithms is that they only evaluate items in terms of increase and decrease. Therefore, all the covariations of items have an equal importance/signification in evaluating the frequency of a gradual itemset. Those algorithms are not appropriate for certain real-world applications where strong covariations which are scarce may be useful. This paper proposes a solution to cope with this limitation with the task of high utility gradual itemsets mining, whose goal is to extract covariations of attributes which generate a high profit for the user. Two algorithms are proposed to mine these patterns efficiently called HUGI (High Utility Gradual Itemsets mining), and HUGI-Merging, which extracts these patterns from both a negative and positive quantitative data separately and merges the obtained results. Experimental results show that the proposed algorithms are efficient and can filter many gradual itemsets to focus only on desired high-utility gradual itemsets.
A QUASI-EXPERIMENTAL STUDY ON THE USE OF THE BERLITZ METHOD TOWARDS THE STUDENTS' PREPOSITION MASTERY

This research was conducted to investigate the effect of applying the Berlitz Method on Students' Preposition Mastery to eleventh-grade students of MA Raudhatul Ulum Kubu Raya. The sample of this research was the students of grade eleventh of MA Raudhatul Ulum Kubu Raya in the academic year of 2022/2023, which consisted of two classes. The total number of students was 30, divided into 15 students for the experimental class and another 15 students for the control class. The researcher used a multiple-choice test to collect the data. The test consisted of two types, namely pre-test and post-test. The data were analyzed by using the t-test formula. To know the normality by utilizing the Chi-Square formula, homogeneity by measuring F-formula, validity and reliability by exerting Kuder Richardson Formula-20 or KR 20. The experimental class was taught by using Berlitz Method while the control class was taught using the method used by the English teacher at that school. The result of the research showed that the value of t-observed was higher than the value of t-table (293&gt;2,16) at the level significance of α = 0,05 and the degree of freedom (df)= 13. The calculation concluded that applying Berlitz Method has a significant effect on students' preposition mastery or in other words the alternative hypothesis (Ha) was accepted.
DESIGNED-IN SCAFFOLDING: SUPPORTING EFL YOUNG LEARNERS IN CREATING MULTIMODAL TEXTS

The research explored the designed-in scaffolding features implemented by the teacher in the English as a foreign language (EFL) young learner classroom to support young learners in creating multimodal texts. The research used scaffolding theory proposed by Hammond and Gibbons (2005) which categorized scaffolding into two fragments: designed-in and interactional scaffolding. The research was conducted in one of the elementary schools in Bandung. It involved one teacher who taught 18 students in 4th grade. The data were gained from classroom observation and teacher’s lesson plans. The findings revealed that the features of designed-in scaffolding were employed by the teacher in planning and implementing the lesson in the classroom. This research also found that following the designed-in scaffolding features could lead the teacher to structure and provide activities, tasks, and multimodal exposures for the students to be familiar with the multimodal texts. Hence, this research resulted in the pedagogical implication to encourage the teacher to be aware of scaffolding. If the teacher could provide an appropriate scaffolding, the students could gain the assistances, supports, and multimodal exposure for young learners to learn and create multimodal texts.
Card and Table Visual Styles

To extend your knowledge of JSON formatting, it is time to move on to a few of the core data-driven visuals. A useful starter set to look at is
Optimal predictive neuro-navigator design for mobile robot navigation with moving obstacles

Introduction: The challenge of navigating a Mobile robot in dynamic environments has grasped significant attention in recent years. Despite the available techniques, there is still a need for efficient and reliable approaches that can address the challenges of real-time near optimal navigation and collision avoidance. Methods: This paper proposes a novel Log-concave Model Predictive Controller (MPC) algorithm that addresses these challenges by utilizing a unique formulation of cost functions and dynamic constraints, as well as a convergence criterion based on Lyapunov stability theory. The proposed approach is mapped onto a novel recurrent neural network (RNN) structure and compared with the CVXOPT optimization tool. The key contribution of this study is the combination of neural networks with model predictive controller to solve optimal control problems locally near the robot, which offers several advantages, including computational efficiency and the ability to handle nonlinear and complex systems. Results: The major findings of this study include the successful implementation and evaluation of the proposed algorithm, which outperforms other methods such as RRT, A-Star, and LQ-MPC in terms of reliability and speed. This approach has the potential to facilitate real-time navigation of mobile robots in dynamic environments and ensure a feasible solution for the proposed constrained-optimization problem.
Improved SRGAN model

Image super-resolution reconstruction is an ill-posed problem, as a low-resolution image can correspond to multiple high-resolution images. The models SRCNN and SRDenseNet produce high-resolution images using the mean square error (MSE) loss function, which results in blurry images that are the average of multiple high-quality images. However, the GAN model is capable of reconstructing a more realistic distribution of high-quality images. In this paper, we propose modifications to the SRGAN model by utilizing L1 norm loss for the discriminator's loss function, resulting in a more stable model. We also use VGG16 features for perceptual loss instead of VGG19, which produces better results. The content loss is calculated by weighting both the VGG loss and MSE loss, achieving a better balance between PSNR and human perception.
The Use of Green, Yellow and Red Question Cards Strategy to Improve Students’ Reading Ability at the Second Grade Students of SMK National Makassar

The objective of this research aimed at finding out whether or not the improvement of the students' reading ability by using green, yellow and red question cards strategy at the second grade students of SMK National Makassar.This research used pre-experimental method which one class was given pre-test, treatment and post-test.The population of this research was the second grade students of SMK National Makassar.The sample was 19 students and it used total sampling.The instrument was reading comprehension text in order to measure the students reading ability in reading process by using green, yellow and red question cards strategy.In other words, the finding indicated that by using green, yellow and red question cards strategy in teaching reading comprehension was able to improve the reading ability of the SMK National students.Based on results of the data indicated there was a significant difference between the posttest and the pretest of the students' mean score (8.48>4.53).The result of the data analysis showed green, yellow and red question cards strategy could improve the students' reading ability and it is an affective activity to make the students active in learning reading comprehension.Based on the finding of the research it could be concluded that the use of green yellow and red question cards strategy could improve the students' reading comprehension at the second grade of SMK National Makassar.
Exploration and Improvement of the Stable Diffusion Model in the Field of Image Generation

Image generation is an important research direction in the field of computer vision, which covers many tasks such as image synthesis, image conversion, image editing and other tasks.In recent years, the rapid development of deep learning technology has provided powerful tools for image generation, where the stable diffusion model has attracted much attention in the image generation field as a kind of image generation model.Stable diffusion model (Stable Diffusion Model) is a generative model based on the diffusion process.The basic principle is to gradually generate the target image by conducting the diffusion process on the noise image.Different from the traditional generation models such as generative adversarial networks (GANs) and variation auto-encoders (VAEs), the stable diffusion model can gradually control the details and quality of the image during the generation process, with good generation stability and sample quality.With the continuous exploration and application of stable diffusion model in the field of image generation, researchers have proposed many improvement methods, including the improvement of generation network structure, loss function and sample optimization method, to further improve the generation effect and generation speed of stable diffusion model.This paper aims to explore and summarize the application status and improvement methods of stable diffusion model in the field of image generation.Through the study and improvement of stable diffusion model, it can provide new methods and ideas to achieve more realistic, diversified and controllable image generation effects.
Inter-item Time Intervals in Sequential Patterns

In sequential pattern mining, the time intervals between each pair of items are usually ignored or mined using additional constraints such as gaps. However, a large part of time-related knowledge is lost during the original proposition of sequential pattern mining. In many applications, time-related information is valuable and its preservation is necessary. In this paper, we present two methods for exploiting time intervals between items and evaluate them in a classification task using a real-world customer intent dataset.
Searching by Keyword

Welcome to the third feature of our journey, where we dive into the implementation of search functionality by book name. This feature plays a crucial role in enhancing user experience, particularly when dealing with extensive book lists. As the list grows beyond a single screen or page, it becomes increasingly challenging for users to locate specific books. Therefore, enabling users to effortlessly search for books by name becomes an invaluable addition to our application.
Educational Technology Implications for Enhancing Reading Comprehension

Reading comprehension has always been a requirement for success in many facets of the educational system and in the majority of adult spheres of participation, since writing systems have been invented. In both the educational and therapeutic fields, the greater accessibility of technologies and web-based resources can be a truly valuable assistance for creating training exercises that can also be completed remotely. Studies that have looked at the effectiveness of technology for children with reading comprehension issues can be found in recent literature. The current study presents information on a technological tool for improving reading comprehension on 40 secondary school pupils. The students were trained to read, to spell with proper stress, idioms and phrases and also comprehension for understanding. These all were taught with the help of technology. The findings have both clinical and educational implications as they suggest the possibility to promote reading comprehension with a suitable technology. Keywords: Reading comprehension, Educational technology
Public Auditing with Secure Team Management for Shared Data in the Cloud with Maintaining the Confidentiality

Abstract: The cloud computing based public auditing data preserving refers to the process of conducting audits on data stored in the cloud to ensure its integrity, authenticity, and confidentiality are maintained. It involves allowing external entities, such as auditors or regulators, to verify the correctness of data without compromising its security. In this paper, we proposed a scheme for shared data that supports privacy, identity traceability and group dynamics. Cloud storage auditing is an extremely important technique for resolving the problem of ensuring the integrity of stored data in cloud storage. This scheme is secure against collusion attacks between CSPs and revoked users. This concept is for public auditing with secure group management. This scheme is useful for the people auditing purpose in this scheme the auditing details the auditor will upload the files to the cloud and the one group management will maintain a detail in the secure management. This group contains Third party auditor one who uploads the auditing details, a Group manager one who manages the group, Group members who work in that group and Cloud Service Provider that is providing the cloud service to the group management and will maintain the data. In this, the security will be provided to the group manager and group members so that the information will be secure before as by OTP process and after the data will be secure through attribute-based encryption of the process
RiBAC: Strengthening Access Control Systems for Pandemic Risk Reduction while Preserving Privacy

Traditional (physical) access control systems are well-established mechanisms, allowing organizations to determine who should be able to access which physical space. This can either be a facility such as a critical infrastructure with a well-defined set of individuals, e.g., employees, or public spaces where everyone can be subject to access control. During the Covid-19 pandemic, additional features to reduce the risks of individuals when entering spaces became popular or even mandatory, including automatic scanning for protective wear (e.g., whether an individual wears a mask), body temperature checks, or digital health certificates, certifying that one has been negatively tested for, or vaccinated against, Covid-19. We refer to this as risk-based access control (RiBAC).
Robust Integration of Contextual Information for Cross-Target Stance Detection

Stance detection deals with identifying an author's stance towards a target.Most existing stance detection models are limited because they do not consider relevant contextual information which allows for inferring the stance correctly.Complementary context can be found in knowledge bases but integrating the context into pretrained language models is non-trivial due to the graph structure of standard knowledge bases.To overcome this, we explore an approach to integrate contextual information as text which allows for integrating contextual information from heterogeneous sources, such as structured knowledge sources and by prompting large language models.Our approach can outperform competitive baselines on a large and diverse stance detection benchmark in a cross-target setup, i.e. for targets unseen during training.We demonstrate that it is more robust to noisy context and can regularize for unwanted correlations between labels and targetspecific vocabulary.Finally, it is independent of the pretrained language model in use.
PrecogIIITH@WASSA2023: Emotion Detection for Urdu-English Code-mixed Text

Code-mixing refers to the phenomenon of using two or more languages interchangeably within a speech or discourse context.This practice is particularly prevalent on social media platforms, and determining the embedded affects in a code-mixed sentence remains as a challenging problem.In this submission we describe our system for WASSA 2023 Shared Task on Emotion Detection in English-Urdu code-mixed text.In our system we implement a multiclass emotion detection model with label space of 11 emotions.Samples are code-mixed English-Urdu text, where Urdu is written in romanised form.Our submission is limited to one of the subtasks -Multi Class classification and we leverage transformer-based Multilingual Large Language Models (MLLMs), XLM-RoBERTa and Indic-BERT.We fine-tune MLLMs on the released data splits, with and without pre-processing steps (translation to english), for classifying texts into the appropriate emotion category.Our methods did not surpass the baseline, and our submission is ranked sixth overall.
Can Pretrained Language Models Derive Correct Semantics from Corrupt Subwords under Noise?

For Pretrained Language Models (PLMs), their susceptibility to noise has recently been linked to subword segmentation.However, it is unclear which aspects of segmentation affect their understanding.This study assesses the robustness of PLMs against various disrupted segmentation caused by noise.An evaluation framework for subword segmentation, named Contrastive Lexical Semantic (CoLeS) probe, is proposed.It provides a systematic categorization of segmentation corruption under noise and evaluation protocols by generating contrastive datasets with canonical-noisy word pairs.Experimental results indicate that PLMs are unable to accurately compute word meanings if the noise introduces completely different subwords, small subword fragments, or a large number of additional subwords, particularly when they are inserted within other subwords.