Reviewerly/exHarmony huggingface dataset:
Blockchain enabled security mechanism for preventing data forgery in IoT-based smart homes

Conventional homes have grown to be smart homes (SM) with the assistance of the Internet of Things (IoT). A unified gateway connects all Internet-enabled devices. Those gateways need to be secured by the usage of a sturdy safety method due to the fact safety is very vital. In order to conquer those problems, this observation shows employing Blockchain Technology (BC) for secured houses to make certain information private and safe. To cope with the safety problem with SH gateways, this paper proposes a solution. A reliable Q-learning algorithm with Blockchain assistance is proposed for the SH gateway to save data. three sections make up the protocol Q Learning Algorithm with Blockchain Support (QLABS). First, a Lattice cryptosystem based on Cipolla’s extended Euclidean distance algorithm is used to record and store user data (CEED-LC). Second, while preserving data secrecy and integrity, the proposed cryptosystem offers a small Key Generation (KG) with excellent security. Third, with the assistance of QLABS protocol agreement, the blocks are created with a specific gateway enrolment-ID. The testing results demonstrate how highly safe the suggested framework is against the vulnerabilities by obtaining a higher throughput along with a higher Packet Delivery Ratio (PDR) value. The results show that the recommended approach tops other existing algorithms in terms of PDR and throughput.
Machine Learning (ML)–centric resource security in cloud computing using authenticated key

The computing is the on-request transportation of registering contributions over the web to give speedier advancement, bendy assets, and economies of scale. Administrations incorporate servers, stockpiling, data sets, systems administration, programming, and examination. The routine security tasks that the providers carry out include protecting the basic system, reaching, maintaining, and setting up the actual has and organization, on which the strategy illustrations run, as well as the capacity and unique possessions.
Secure Cloud Storage Privacy-Preserving In Public Auditing

Cloud computing is a technology that is both efficient and cost-effective, thanks to its low maintenance requirements. This paper implemented a approach is used to share group resources across cloud users. Regrettably, as a result of the frequent differences in participation, the issue of sharing information in a multi-owner manner while also preserving privacy and information from a trusted cloud continues to be a challenging one. During the course we put into place a safe method of data sharing among multiple owners using the Diffe- Hellman algorithm that are cloud-based and applicable to rotating groups. By leveraging techniques like as group signature and dynamic broadcast encryption, any user of the cloud has the ability to anonymously share data with other users. In the meanwhile, the number of users whose access has been revoked does not have any impact on the storage overhead or encryption computation costs associated with our system. In addition, employ rigorous proofs to investigate the scheme's level of safety, and conduct tests to verify the scheme's usefulness.
Study on MVC Framework for Web Development in PHP

The Model–View–Controller (MVC) design pattern is widely used in web development and ERP to separate data layer, presentation layer, and user interaction concerns. In recent years there have been many PHP frameworks available for web developers to choose from right one. Developers face difficulty in choosing the relevant frameworks and appropriate support functions with libraries to include in their projects. For this reason, a clear understanding of the various frameworks is now becoming an important requirement for web developers. In this paper, we studied several research papers talking about different PHP frameworks, and we compared them with each other for result. In PHP, there are several MVC frameworks available that provide a structured approach to developing web applications. The purpose of this research paper is to explore and review some of the popular MVC frameworks for web development in PHP.
AutoML for NLP

Automated Machine Learning (AutoML) is an emerging field that has potential to impact how we build models in NLP. As an umbrella term that includes topics like hyperparameter optimization and neural architecture search, AutoML has recently become mainstream at major conferences such as NeurIPS, ICML, and ICLR. What does this mean to NLP? Currently, models are often built in an ad hoc process: we might borrow default hyperparameters from previous work and try a few variant architectures, but it is never guaranteed that final trained model is optimal. Automation can introduce rigor in this model-building process. This tutorial will summarize the main AutoML techniques and illustrate how to apply them to improve the NLP model-building process.
Going beyond research datasets: Novel intent discovery in the industry setting

Novel intent discovery automates the process of grouping similar messages (questions) to identify previously unknown intents. However, current research focuses on publicly available datasets which have only the question field and significantly differ from real-life datasets. This paper proposes methods to improve the intent discovery pipeline deployed in a large e-commerce platform. We show the benefit of pre-training language models on in-domain data: both self-supervised and with weak supervision. We also devise the best method to utilize the conversational structure (i.e., question and answer) of real-life datasets during fine-tuning for clustering tasks, which we call Conv. All our methods combined to fully utilize real-life datasets give up to 33pp performance boost over state-of-the-art Constrained Deep Adaptive Clustering (CDAC) model for question only. By comparison CDAC model for the question data only gives only up to 13pp performance boost over the naive baseline.
Incorporating Dropped Pronouns into Coreference Resolution: The case for Turkish

Representation of coreferential relations is a challenging and actively studied topic for pro-drop and morphologically rich languages (PD-MRLs) due to dropped pronouns (e.g., null subjects and omitted possessive pronouns). These phenomena require a representation scheme at the morphology level and enhanced evaluation methods.In this paper, we propose a representation & evaluation scheme to incorporate dropped pronouns into coreference resolution and validate it on the Turkish language.Using the scheme, we extend the annotations on the only existing Turkish coreference dataset, which originally did not contain annotations for dropped pronouns. We provide publicly available pre and post processors to enhance the prominent CoNLL coreference scorer also to cover coreferential relations arising from dropped pronouns.As a final step, the paper reports the first neural Turkish coreference resolution results in the literature.Although validated on Turkish, the proposed scheme is language-independent and may be used for other PD-MRLs.
Deep Learning Approaches for Twitter User Classification

Twitter, a popular social media platform, has become a rich source of user-generated content. The classification of Twitter users based on their characteristics and behavior has gained significant attention. Deep learning techniques, with their ability to capture complex patterns and representations, have emerged as powerful tools for Twitter user classification. This research article presents a comprehensive review of deep learning approaches for Twitter user classification. We discuss various deep learning architectures, pretraining techniques, and transfer learning strategies used in the classification task. Through a thorough analysis of existing studies, we highlight the strengths and limitations of deep learning approaches and provide recommendations for future research in this field.
Self-supervised anomaly detection based on foreground enhancement and autoencoder reconstruction

Abstract Anomaly detection algorithms typically utilize generative models to reconstruct anomaly regions. Post-processing is used to pinpoint the anomalies. However, the paucity of real-world anomaly samples and the complex image backgrounds pose significant challenges for anomaly detection. The work innovatively proposed a self-supervised anomaly detection method. An efficient channel attention mechanism in the autoencoder was introduced to improve the reconstruction performance. Besides, a foreground enhancement strategy was designed to distinguish the foreground from the background by maximizing the inter-class variance. The strategy reduced the effect of background noises and simulated various anomalies that were rare in real samples. The MVTecAD and BTAD datasets were used to experiment with anomaly detection and location. Experimental results demonstrated that our method achieved higher AUC and AP scores at both the image level and pixel level compared to other advanced methods. In particular, the average AP score increased by 12.5% at the pixel level.
The lines of the head: An analysis of the headlines in a State University newspaper

Headlines are important parts of any news and newspaper. They serve as an overview of the story or article. This study aims to describe and analyze the news headlines of the past 5 issues of the school publication in one of the state universities in Region III, Philippines. Specifically, it focuses on the types of grammatical structures are used in the headline writing; patterns that the headlines observe; semantic categories of the headwords of the headlines; reasons why the headlines framed the way they appear in the newspaper; and implications of the results to future journalism training and campus paper publication. The study is a sequential explanatory type of research that used the Fairclough theory to analyze the previous issues of the newspaper. After which, the results were presented to the two (2) advisers of the newspaper who served as the participants during the interview. It was found out that the last five issues of the student publication of a university in the Philippines commonly used clausal headlines, Subject-Verb Phrase-Direct Object clausal pattern, Headword-Postmodifier phrasal headlines, and proper names for the headwords of the headlines. The participants of the study explained that the patterns were adopted for clarity, comprehensiveness, getting the reader’s attention, conciseness, and identification. Therefore, it is recommended that continuous training must be given to the student writers to be able to be abreast of other styles and techniques so that they can present news in a comprehensive, clear, concise, and attractive way.
THE EFFECTIVENESS OF SPEED READING TECHNIQUE TOWARDS STUDENT' READING COMPREHENTION IN FABLE TEXTS

The purpose of the study is to know the effectiveness of speed reading technique toward students’ reading comprehension in fable text. A researcher discovered numerous reading errors made by students. True experimental research was used in the design of this study. Students from the experimental group and students from the control group were divided into Effective Reading Rate (ERR) groups for the pre-test and post-test. Students in the experimental group improve their effective reading rate at a faster rate than students in the control group. In the post-test, 2 of the experimental group's students (20%) and 8 of the experimental group's pupils (80%) were categorised as above average readers. 10 kids (100%) in the control group were identified as talkers. The researcher draws the conclusion that the use of speed reading techniques can help students understand fable texts better based on the results.
Multimodal Cyberbullying Meme Detection From Social Media Using Deep Learning Approach

Cyberbullying includes the repeated and intentional use of digital technology to target another person with threats, harassment, or public humiliation. One of the techniques of Cyberbullying is sharing bullying memes on social media, which has increased enormously in recent years. Memes are images and texts overlapped and sometimes together they present concepts that become dubious if one of them is absent. Here, we propose a unified deep neural model for detecting bullying memes on social media. In the proposed unified architecture, VGG16-BiLSTM, consists of a VGG16 convolutional neural network for predicting the visual bullying content and a BiLSTM with one-dimensional convolution for predicting the textual bullying content. The meme is discretized by extracting the text from the image using OCR. The perceptron-based feature-level strategy for multimodal learning is used to dynamically combine the features of discrete modalities and output the final category as bullying or nonbullying type. We also create a “bullying Bengali memes dataset” for experimental evaluation. Our proposed model attained an accuracy of 87% with an F1 score of 88%. The proposed model demonstrates the capability to detect instances of Cyberbullying involving Bengali memes on various social media platforms. Consequently, it can be utilized to implement effective filtering mechanisms aimed at mitigating the prevalence of Cyberbullying.
Reservoir-based 1D convolution: low-training-cost AI

In this study, we introduce a reservoir-based one-dimensional (1D) convolutional neural network that processes time-series data at a low computational cost, and investigate its performance and training time. Experimental results show that the proposed network consumes lower training computational costs and that it outperforms the conventional reservoir computing in a sound-classification task.
Routing

The last chapter discussed AJAX and how it changed web development forever. Another essential technique that drastically changed how users interact with web applications are Single Page Applications (SPAs).
PENGEMBANGAN E-MODUL INTERAKTIF DALAM MENINGKATKAN HASIL BELAJAR SISTEM EKSKRESI PADA MANUSIA

This research aimed to develop the interactive e-modules in improving learning outcomes of the excretory system in humans, furthermore it will be carried out effectively on the implementation of the products that have been developed. The method used in this research was Research and Development (R&amp;D) by applying the ADDIE development model which consisted of five stages, namely Analysis, Design, Development, Implementation, and Evaluation with class VIII students of SMP Negeri 1 Hulu Gurung as research subjects. Interactive E-Modules were developed using the Flip Pdf application Corporate with an html application file format, html software can be accessed using a laptop/computer or Android. The course material was equipped with learning videos made using the Camtasia application and interactive quizzes made directly using the Filip pdf Corporate application. At the end of the learning activity there was a formative test that was made using a Google form whose link is linked in the Flip pdf Corporate application. Based on the results of the analysis, it showed that there was a significant difference in student learning outcomes before and after the use of interactive e-modules and based on the effect size test, a value of 1.10 is obtained, then compared with the comparison table, 1.10 is included in the Strong Effect criteria or gives effect or the interactive e-module have been developed give the strong effect.
Recent Advancements in the Field of Deepfake Detection

A deepfake is a photo or video of a person whose image has been digitally altered or partially replaced with an image of someone else. Deepfakes have the potential to cause a variety of problems and are often used maliciously. A common usage is altering videos of prominent political figures and celebrities. These deepfakes can portray them making offensive, problematic, and/or untrue statements. Current deepfakes can be very realistic, and when used in this way, can spread panic and even influence elections and political opinions. There are many deepfake detection strategies currently in use but finding the most comprehensive and universal method is critical. So, in this survey we will address the problems of malicious deepfake creation and the lack of universal deepfake detection methods. Our objective is to survey and analyze a variety of current methods and advances in the field of deepfake detection.
The Influence of Role-Playing Methods on the Social Skills and Self-Confidence of Grade V Elementary School Students

This study aims to find out: (1) the effect of role-playing on students' social skills; (2) the influence of role-playing on student confidence; and (3) the effect of role-playing methods on students' social skills and self-confidence. This research is a pseudo-experimental research with a non-equivalent control group design. The population of this study was all grade V students of SDN Masbagik Selatan, Masbagik District, East Lombok District. The sample of this study was 52 students. Sample determination using cluster random sampling technique. Of the population to be sampled are 2 schools randomly. One school was determined as an experimental class using the role-playing method and one school was used as a control class using the lecture method. The classes sampled were class V SDN 2 Masbagik Selatan (experiment) and SDN 4 Masbagik Selatan (control). Research instruments in the form of questionnaires and observations. The data normality test was carried out using Kolmogrov- Smirnov, while the homogeneity test used Box's M test. The results showed that: (1) The role-playing method had a significant positive effect on students' social skills with a significance value of 0.000 &lt;0.05; (2) The role-playing method had a significant positive effect on students' confidence with a significance level of 0.000 &lt;.05; (3) Roleplaying methods have a significant positive effect on students' social skills and confidence. The results of data analysis in this study show that there is a significant influence of role-playing methods on social skills and student confidence in social studies learning content of grade V elementary school students.
Dynamic Pricing for Client Recruitment in Federated Learning

Though federated learning (FL) well preserves clients’ data privacy, many clients are still reluctant to join FL given the communication cost and energy consumption in their mobile devices. It is important to design pricing compensations to motivate enough clients to join FL and distributively train the global model. Prior pricing mechanisms for FL are static and cannot adapt to clients’ random arrival pattern over time. We propose a new dynamic pricing solution in closed-form by constructing the Hamiltonian function to optimally balance the client recruitment time and the model training time, without knowing clients’ actual arrivals or training costs. During the client recruitment phase, we offer time-dependent monetary rewards per client arrival to trade off between the total payment and the FL model’s accuracy loss. Such reward gradually increases when we approach to the recruitment deadline or have greater data aging, and we also extend the deadline if the clients’ training time per iteration becomes shorter. Further, we extend to consider heterogeneous client types in training data size and training time per iteration. We successfully extend our dynamic pricing solution and develop an optimal algorithm of linear complexity to monotonically select client types for FL. Finally, we also show robustness of our solution against estimation error of clients’ data sizes, and run numerical experiments to validate our results.
A General-Purpose Self-Supervised Model for Computational Pathology.

Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the large-scale image resolutions of WSIs and the enormous diversity of morphological phenotypes preclude large-scale data annotation. Current efforts have proposed using pretrained image encoders with either transfer learning from natural image datasets or self-supervised pretraining on publicly-available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using over 100 million tissue patches from over 100,000 diagnostic haematoxylin and eosin-stained WSIs across 20 major tissue types, and evaluated on 33 representative CPath clinical tasks in CPath of varying diagnostic difficulties. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree code classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient AI models that can generalize and transfer to a gamut of diagnostically-challenging tasks and clinical workflows in anatomic pathology.
[Spatial Distribution of Soil Heavy Metals and Regional Control Strategies in China at Province Level].

It is important to understand the spatial distribution characteristics and health risks of soil heavy metals for the implementation of soil pollution control measures in different levels and regions. Based on the data of 706 core studies in the last 20 years, the spatial distribution characteristics, accumulation degree, and health risks of soil heavy metals in China were analyzed at the provincial level. The results showed that the soil heavy metals had obvious spatial differences on the provincial scale, with an overall trend of "high in the south and low in the north and high in the east and low in the west." The content of heavy metals in the soil of agricultural land and construction land was high, and the rate of exceeding the standard was higher than that of other land types. Soil heavy metal concentrations in most areas of China were higher than the regional background values and were highly cumulative. The accumulation indices were:Cd(1.80)>Pb(0.23)>Cu(0.17)>Zn(-0.05)>As(-0.56)>Cr(-0.69), with more than 85% of the provincial soils reaching moderate levels of Cd pollution. Non-ferrous metal resource-based provinces such as Yunnan, Guizhou, Guangxi, Hunan, and Jiangxi generally had higher soil heavy metal levels than those in other provinces, and local children faced higher cancer risks. Soil pollution in coastal areas such as Fujian, Zhejiang, Jiangsu, and Tianjin mainly originated from industrial production and urbanization construction. High intensity agricultural utilization was an important cause of soil heavy metal accumulation in Henan, Shandong, and Anhui.