Reviewerly/exHarmony huggingface dataset:
The Implementation of Telegram in Reading Class to Improve Students’ Skimming and Scanning Skills

Skimming and scanning are important reading strategies that allow individuals to quickly gather information from written materials. With the advance of digital communication platforms, like Telegram improving students' scanning and skimming skills has become increasingly relevant. While the utilization of technological resources in reading and teaching English has been examined in different settings, there is a need to investigate the potential of Telegram as a specific tool for enhancing skimming and scanning abilities. This research uses both quantitative and qualitative data collection methods. The respondents in the research are 40 English major learners. The protocols to gather the data were questionnaires and interviews. The study suggests that utilizing Telegram can help students to improve their motivation and engagement, which can further enhance the learning process. Exploring the future implications of employing Telegram requires more investigation, especially for skimming as well as scanning development, as well as its incorporation across different age groups and academic contexts. Nonetheless, the results of the research offer insightful information into the integration of digital communication platforms as effective tools for improving reading strategies, paving the way for innovative approaches to enhance literacy skills in the digital era.
A Deep Reinforcement Learning Framework for Optimizing Congestion Control in Data Centers

Various congestion control protocols have been designed to achieve high performance in different network environments. Modern online learning solutions that delegate the congestion control actions to a machine cannot properly converge in the stringent time scales of data centers. We leverage multi-agent reinforcement learning to design a system for dynamic tuning of congestion control parameters at end-hosts in a data center. The system includes agents at the end-hosts to monitor and report the network and traffic states, and agents to run the reinforcement learning algorithm given the states. Based on the state of the environment, the system generates congestion control parameters that optimize network performance metrics such as throughput and latency. As a case study, we examine BBR, an example of a prominent recently-developed congestion control protocol. Our experiments demonstrate that the proposed system has the potential to mitigate the problems of static parameters.
Designing Closed-Loop Models for Task Allocation

Automatically assigning tasks to people is challenging because human performance can vary across tasks for many reasons. This challenge is further compounded in real-life settings in which no oracle exists to assess the quality of human decisions and task assignments made. Instead, we find ourselves in a “closed” decision-making loop in which the same fallible human decisions we rely on in practice must also be used to guide task allocation. How can imperfect and potentially biased human decisions train an accurate allocation model? Our key insight is to exploit weak prior information on human-task similarity to bootstrap model training. We show that the use of such a weak prior can improve task allocation accuracy, even when human decision-makers are fallible and biased. We present both theoretical analysis and empirical evaluation over synthetic data and a social media toxicity detection task. Results demonstrate the efficacy of our approach.
ALLEGET Keynote Remarks: Emerging Topics in Sentiment Analysis

Sentiment analysis has become an indispensable tool in various domains, including e-business, e-commerce, e-tourism, e-mobility, e-governance, e-security, e-learning, and e-health. By analyzing public opinion and preferences, organizations and policymakers can make data-driven decisions to improve their products, services, and policies. The implementation of sentiment analysis across these domains not only enhances customer satisfaction and engagement but also contributes to the overall growth and development of these industries.
Neurosymbolic Narrative Generation for Cultural Heritage

Aim of my research is to exploit Linguistic Linked Open Data (LLOD) as base for advanced Cultural Heritage (CH) fruition by means of Automatic Story Generation (ASG) . Following the rationale that discovering and reviving already existing (yet latent) narratives is worthier than automatically generating them from anew in eliciting the user’s interest, the input-2-graph and the graph-2-sequence ASG-pipeline phases, heavily relying on LLOD, will be given a deeper focus, whereby the final Natural Language Generation (NLG) module will be constrained by the entities and relations established in the Knowledge Graph (KG) generation modules (a configuration typical of the neurosymbolic approach). In order to enhance possibilities of implementation in real-life contexts, the elaborated pipeline will be modular, i.e. self-sufficient in its constituent parts. Beyond the countless possible application scenarios ranging from education to entertainment, this solution detangles the user from his role of mere consumer, and empowers him not only to control the creation process [3.1], but also to find already within it, and not necessarily in the final outcome, a valuable source for intellectual growth. This work intends the addressing of a specific societal need as an avalanche to simultaneously fill knowledge gaps identified in and among the related scientific domains.
Representation Learning for Semantic Scene Understanding

Recent advances in semantic scene understanding have underscored its growing significance in the field of computer vision. Enhanced representations can be achieved by incorporating semantic information derived from textual data and applying it to generative models for scene modeling. Nevertheless, the features extracted from text prompts may not seamlessly model a scene. Scene graphs offer a robust solution to address this challenge, serving as a powerful representation for semantic image generation and manipulation. In this study, we delve into the utilization of scene graphs for this purpose and propose novel methodologies to augment both the representation and learning processes involved in image generation and manipulation. For image generation, we examine meta-learning for producing images in unprecedented scenes and refine the generated images using an autoregressive scene graph generation model. In terms of image manipulation, we put forth a novel self-supervised method that eliminates the need for paired before-and-after data. Additionally, we boost image manipulation performance by disentangling latent and graph representations in a self-supervised manner. By evaluating the efficacy of our proposed approaches on a diverse range of publicly available benchmarks, we demonstrate their superiority, ultimately achieving state-of-the-art performance in the domain of semantic image generation and manipulation.
Evaluating the effects of task design on unfamiliar Francophone listener and automatic speaker identification performance

Many questions remain with regards to how context affects perceptual and automatic speaker identification performance. To examine the effects of task design on perceptual speaker identification performance, three tasks were developed, including lineup and binary tasks, as well as a novel clustering task. Speech recordings of native French speakers were compared similarly across tasks evaluated by unfamiliar Francophone listeners. True positive (sensitivity) and true negative (specificity) response rates across tasks were measured. Our results showed participants had similar sensitivity and specificity for binary (88%) and clustering (84%) tasks, but random selection rates for the lineup task. Pearson correlation procedures were used to evaluate the efficiency of scores produced by a state-of-the-art automatic speaker verification to model perceptual responses (equal error rate = 89%). Automatic scores modelled lineup (r2 = 0.6) and clustering (r2 = 0.5) task accuracy quite well, however, they were less robust when modelling binary task responses (r2 = -0.2). The results underscore the role task design plays in shaping perceptual responses, which, in turn, affects the modelling effectiveness of automatic scores. As evidence points to humans and algorithms modelling speakers differently, our findings suggest automatic speaker identification performance might be improved with a greater understanding on how context shapes perceptual responses.
Face recognition for occluded face with mask region convolutional neural network and fully convolutional network: a literature review

&lt;p&gt;&lt;span lang="EN-US"&gt;Face recognition technology has been used in many ways, such as in the authentication and identification process. The object raised is a piece of face image that does not have complete facial information (occluded face), it can be due to acquisition from a different point of view or shooting a face from a different angle. This object was raised because the object can affect the detection and identification performance of the face image as a whole. Deep leaning method can be used to solve face recognition problems. In previous research, more focused on face detection and recognition based on resolution, and detection of face. Mask region convolutional neural network (mask&lt;br /&gt; R-CNN) method still has deficiency in the segmentation section which results in a decrease in the accuracy of face identification with incomplete face information objects. The segmentation used in mask R-CNN is fully convolutional network (FCN). In this research, exploration and modification of many FCN parameters will be carried out using the CNN backbone pooling layer, and modification of mask R-CNN for face identification, besides that, modifications will be made to the bounding box regressor. it is expected that the modification results can provide the best recommendations based on accuracy.&lt;/span&gt;&lt;/p&gt;
A Feature Extraction Approach for the Detection of Phishing Websites Using Machine Learning

In this growing world of the internet, most of our daily routine tasks are somehow connected to the internet, from smartphones to internet of things (IoT) devices to cloud networks. Internet users are growing rapidly, and the internet is accessible to everyone from anywhere. Data phishing is a cyber security attack that uses deception to trick internet users to get their content and information. In this attack, malicious users try to steal personal data such as login credentials, credit card details, health care information, etc., of the users on the internet. They exploit users’ sensitive information using vulnerabilities. Information stealers are known as phishers. Phishers use different techniques for phishing. One of the most common methods is to direct the users to a false website to enter their login credentials and their details on these phishing sites. Phishing websites look like the original websites. Phishers use these details to get access to the user’s accounts and hijack them for monetary purposes. Many internet users fall for this trap of phishing sites and share their personal and sensitive details. In this paper, we will analyze and implement machine learning (ML) techniques to detect phishing attacks. There are different methods to identify phishing attacks, one of them is by checking the uniform resource locator (URL) address using ML. ML is used to teach a machine to differentiate between phishing and original site URLs. There are many different techniques to overcome this attack. This research paper aims to provide accurate and true phishing detection with less time complexity.
Performance analysis of perturbation-based privacy preserving techniques: an experimental perspective

&lt;span lang="EN-US"&gt;Nowadays, enormous amounts of data are produced every second. These data also contain private information from sources including media platforms, the banking sector, finance, healthcare, and criminal histories. Data mining is a method for looking through and analyzing massive volumes of data to find usable information. Preserving personal data during data mining has become difficult, thus privacy-preserving data mining (PPDM) is used to do so. Data perturbation is one of the several tactics used by the PPDM data privacy protection mechanism. In perturbation, datasets are perturbed in order to preserve personal information. Both data accuracy and data privacy are addressed by it. This paper will explore and compare several hybrid perturbation strategies that may be used to protect data privacy. For this, two perturbation-based techniques named improved random projection perturbation (IRPP) and enhanced principal component analysis-based technique (EPCAT) were used. These methods are employed to assess the precision, run time, and accuracy of the experimental results. This paper provides the impacts of perturbation-based privacy preserving techniques. It is observed that hybrid approaches are more efficient than the traditional approach.&lt;/span&gt;
Analysis and prediction of seed quality using machine learning

&lt;span lang="EN-US"&gt;The mainstay of the economy has always been agriculture, and the majority of tasks are still carried out without the use of modern technology. Currently, the ability of human intelligence to forecast seed quality is used. Because it lacks a validation method, the existing seed prediction analysis is ineffective. Here, we have tried to create a prediction model that uses machine learning algorithms to forecast seed quality, leading to high crop yield and high-quality harvests. For precise seed categorization, this model was created using convolutional neural networks and trained using the seed dataset. Using data that can be used to forecast the future, this model is used to learn about whether the seeds are of premium quality, standard quality, or regular quality. While testing data are employed in the algorithm’s predictive analytics, training data and validation data are used for categorization reasons. Thus, by examining the training accuracy of the convolution neural network (CNN) model and the prediction accuracy of the algorithm, the project’s primary goal is to develop the best method for the more accurate prediction of seed quality.&lt;/span&gt;
Defining Quantum Games

&lt;p&gt;In this article, we explore the concept of quantum games. We define quantum games as any type of playable games that are related to or reference quantum physics through any of three proposed dimensions: the perceivable dimension of quantum physics, the dimension of quantum technologies, and the dimension of scientific purposes. The rise of quantum computers has made it possible to think about a new wave of computer games, namely, quantum computer games, games on quantum computers. At the same time, there are various games exploring quantum mechanics and related topics through digital, analogue, and hybrid means. In this article we go through the emerging body of quantum games, the history of quantum games, and the different ways a game may be considered a quantum game. We propose that three dimensions of quantum games identified in this article are used for analysing and defining the phenomenon of quantum games. &lt;/p&gt;
Multi-label text classification of Indonesian customer reviews using bidirectional encoder representations from transformers language model

&lt;p&gt;&lt;span lang="EN-US"&gt;Customer review is a critical resource to support the decision-making process in various industries. To understand how customers perceived each aspect of the product, we can first identify all aspects discussed in the customer reviews by performing multi-label text classification. In this work, we want to know the effectiveness of our two proposed strategies using bidirectional encoder representations from transformers (BERT) language model that was&lt;br /&gt; pre-trained on the Indonesian language, referred to as IndoBERT, to perform multi-label text classification. First, IndoBERT is used as feature representation to be combined with convolutional neural network-extreme gradient boosting (CNN-XGBoost). Second, IndoBERT is used both as the feature representation as well as the classifier to directly solve the classification task. Additional analysis is performed to compare our results with those using multilingual BERT model. According to our experimental results, our first model using IndoBERT as feature representation shows significant performance over some baselines. Our second model using IndoBERT as both feature representation and classifier can significantly enhance the effectiveness of our first model. In summary, our proposed models can improve the effectiveness of the baseline using Word2Vec-CNN-XGBoost by 19.19% and 6.17%, in terms of accuracy and F-1 score, respectively.&lt;/span&gt;&lt;/p&gt;
Cascade Aggregation Network for Ship Instance Segmentation

Instance segmentation technology has great application in the field of intelligent ships. However, existing methods still have many problems when used directly for ship instance segmentation. For the problems of imprecise bounding box and poor segmentation of detailed contour of ships. We propose a new Cascade Aggregation Network (CAN) for ship instance segmentation. The GIoU loss function is used to optimize the bounding box. And we propose an aggregation segmentation net-work with multi-scale edge aggregation information can improve mask. Finally, CAN can predict more accurate bounding boxes and generates higher quality masks. Experimental comparison and visual analysis show that our CAN outperforms Cascade Mask R-CNN on the ship instance segmentation dataset.
AdaptEdge: Targeted Universal Adversarial Attacks on Time Series Data in Smart Grids

&lt;p&gt;In recent years, deep learning has become an indispensable tool for various applications, including the analysis and operations of smart grids. These models have shown remarkable success in tasks like power quality disturbance (PQD) classification, enhancing the expansion and efficiency of power infrastructure. However, the susceptibility of deep learning models to adversarial attacks has raised concerns about their reliability and security in critical environments. In this paper, we address this vulnerability by investigating targeted universal adversarial perturbations, specifically in the context of time series data. For the first time, we introduce a novel algorithm called Adaptive Edge (AdaptEdge), which effectively deceives deep learning models working with time series data while balancing the trade-off between fooling rate and imperceptibility of the attack to human observers. Our research represents a significant advancement in adversarial attacks on deep learning models for time series data and emphasizes the need for robust and resilient deep learning models in PQD classification for smart grids. Our results demonstrate a fooling rate of up to 90.78%—the highest achieved thus far—while maintaining an optimal signal-to-noise ratio of 3dB and ensuring signal integrity. This underscores the effectiveness and reliability of our proposed method. &lt;/p&gt;
AdaptEdge: Targeted Universal Adversarial Attacks on Time Series Data in Smart Grids

&lt;p&gt; Deep learning (DL) has emerged as a key technique in smart grid operations for task classification of power quality disturbances (PQDs). Even though these models have considerably improved the efficiency of power infrastructure, their susceptibility to adversarial attacks presents potential difficulties. For the first time, we introduce a novel algorithm called Adaptive Edge (AdaptEdge), which effectively employs targeted universal adversarial attacks to deceive DL models working with time series data. The unique contribution of this algorithm is its ability to maintain a delicate balance between the fooling rate and the imperceptibility of perturbations to human observers. Our results demonstrate a fooling rate of up to 90.78% in the ResNet50 model—the highest achieved thus far—while maintaining an optimal signal-to-noise ratio (SNR) of 3dB and ensuring signal integrity. We implemented our algorithm across various advanced DL models and found considerable efficacy, demonstrating its adaptability and versatility across diverse architectures. The results of our study highlight the pressing need for developing more robust DL model implementations in the context of the smart grid. Additionally, our proposed approach demonstrates its effectiveness in addressing this need. &lt;/p&gt;
Examination of the impact of cyber security on networks operations: A case of Vodafone Ghana

Computer network operations of organizations are always prone to cyber-attacks from different people, including misguided employees within the organization, due to the errors they make which lead to exploitation by cyber criminals. These people normally have destructive tendencies better known to themselves, as they would go to every extent to make sure that their nefarious intentions are achieved. It therefore, behooves on network operation technologists to innovate concrete and tested security measures to secure organizations’ network systems against hackers. The destruction could impact the activities of the organization to come to complete halt, as important information, a resource of the enterprise, may be damaged, stolen or lost. Some of these hackers may then turn around and demand huge sums of money from the organization before the stolen information may be released; likely the restored software may be tainted and therefore, different from the original state. Consequently, management of organizations should make adequate security preparations to forestall these destructive propensities of hackers. Vodafone Ghana therefore, should reinforce its security system to make sure the firewall and password systems, and access to its servers from the outside world are not compromised.
USE OF READING CORNER AND WALL MAGAZINE IN LITERACY ACTIVITIES OF CLASS IV STUDENTS OF SD NEGERI 1 BAYEMHARJO

This article aims to examine the use of reading corners and bulletin boards in literacy activities.
Recent Development of Parameter Estimation Methods in Diagnostic Classification Models

Diagnostic classiﬁcation models (DCMs) or cognitive diagnostic models (CDMs) are a family of models that consider elements of cognitive abilities to solve test items named "attributes" and classify test takers into attribute mastery patterns. DCMs are a useful tool to analyze educational tests and have been applied to various tests. However, estimation methods of DCMs have not been assessed in Japan. This study aimed to review the current state of development of estimation methods of DCMs and to contribute to improving the application and theoretical development of DCMs. As a result, estimation methods were developed according to the maximum likelihood estimation, Bayesian estimation, and non-parametric estimation methods. In particular, regularization methods in the maximum likelihood estimation and variational Bayes, which are relatively new methods, were employed. Finally, the remaining estimation problems and future research topics in this area were discussed.