Reviewerly/exHarmony huggingface dataset:
Illegal access to scholarly information: considerations regarding the use of sci-hub and its constraints to academic libraries

This work intends to study the influence that the use of sci-hub has on academic libraries, aiming to fill the gap of the risks that sci-hub might develop in academic libraries. Its purpose is to have a view between the interconnection of the library structure and the vulnerability that this undergoes when affected by an external factor, perceiving the long-term implications that the use of this platform has on academic library services. To reach these goals, it was conducted a narrative literature review on the academic library structure, the cause of the appearance of sci-hub and the increase of its popularity. Sci-hub has a direct influence on library circulation services. So, the conclusion is, since that academic libraries are living organisms, where all of their parts communicate with one another when one of their parts is damaged, the others will be resent as well, especially in the long term.
Social Media based Hate Speech Detection using Machine Learning

Abstract: Hate speech is a crime that has been increasing in recent years, not only in person but also online. There are several causes for this. There is tremendous growth in social media that promotes full freedom of expression through anonymity features. Freedom of expression is a human right, but hate speech directed at individuals or groups on the basis of race, caste, religion, ethnicity or nationality, gender, disability, gender identity, etc. is a violation of that sovereignty. Freedom of expression is a human right, but hate speech directed at individuals or groups on the basis of race, caste, religion, ethnicity or nationality, gender, disability, gender identity, etc. is a violation of that sovereignty. It promotes violence and hate crimes, creates social imbalances, and undermines peace, trust and human rights. Revealing hate speech in social media discourse is a very important but complex task.
Deep metric learning for visual servoing: when pose and image meet in latent space

We propose a new visual servoing method that controls a robot's motion in a latent space. We aim to extract the best properties of two previously proposed servoing methods: we seek to obtain the accuracy of photometric methods such as Direct Visual Servoing (DVS), as well as the behavior and convergence of pose-based visual servoing (PBVS). Photometric methods suffer from limited convergence area due to a highly non-linear cost function, while PBVS requires estimating the pose of the camera which may introduce some noise and incurs a loss of accuracy. Our approach relies on shaping (with metric learning) a latent space, in which the representations of camera poses and the embeddings of their respective images are tied together. By leveraging the multimodal aspect of this shared space, our control law minimizes the difference between latent image representations thanks to information obtained from a set of pose embeddings. Experiments in simulation and on a robot validate the strength of our approach, showing that the sought out benefits are effectively found.
Evaluation of drug information services in self-medication services with the patient simulation method at community pharmacies

Background: Globally the number of medicinal products that can be obtained through pharmacies without a prescription continues to increase. Community pharmacists are the health professionals most accessible to patients seeking advice on the use of drugs in self-medication practice. Patient counselling is one of the most important services that pharmacists can provide to patients. Studies have shown that counselling provided by pharmacists can prevent drug-related problems and improve adherence to drug therapy. Objective: To explore the practice of drug information services at community pharmacies and assess the quality of drug information services in self-medication services according to applicable procedures. Methods: The patient simulation method was carried out at 51 community pharmacies in the City of Lamongan, involving scenarios related to self-medication services by purchasing drugs without a prescription, with 90 simulation visits in total. Assessment forms were filled out immediately after the visits by the simulated patients. The assessment of drug information services was based on the completeness of pharmacists in providing drug information, comprised of drug names, doses, quantities, indications, times of use, interactions, and storage methods. Results: For the suitability of community pharmacists in drug information services, 90.0% of pharmacists mentioned drug names, 65.5% explained drug doses, 64.5% stated the amount of obtained drugs, 65.6% explained drug indications, 70.0% explained the times of drug use, 22.2% explained drug interactions, and 25.5% explained drug storage. Conclusion: The practice of drug information services by community pharmacists for self-medication services is very important to help patients avoid the dangers of irrational self-medication practices.
Enriched Model of Case Based Reasoning and Neutrosophic Intelligent System for DDoS Attack Defence in Software Defined Network based Cloud

Software Defined Networking in Cloud paradigm is most suitable for dynamic functionality and reduces the computation complexity. The routers and switches located at the network's boundaries are managed by software-defined netwrking (SDN) using open protocols and specialised open programmable interfaces. But the security threats often degrade the performance of SDN due to its constraints of resource usage. The most sensitive components which are vulnerable to DDoS attacks are controller and control plane bandwidth. The existing conventional classification algorithms lacks in detection of new or unknown traffic packets which are malicious and results in degradation of SDN performance in cloud resources. Hence, in this paper double filtering methodology is devised to detect both known and unknown pattern of malicious packets which affects the bandwidth of the control panel and the controller. The case-based reasoning is adapted for determining the known incoming traffic patterns before entering the SDN system. It classifies the packets are normal or abnormal based on the previous information gathered. The traffic patterns which is not matched from the previous patterns is treated as indeterministic packet and it is defined more precisely using the triplet representation of Neutrosophic intelligent system. The grade of belongingness, non-belongingness and indeterminacyis used as the main factors to detect the new pattern of attacking packets more effectively. From the experimental outcomes it is proved that DDoS attack detection in SDN based cloud environment is improved by adopting CBR-NIS compared to the existing classification model.
Duolingo Application in English Teaching Practice: Teacher’s Perception

Unique learning media will produce good learning. Unique in this case means a teacher who creates a fun and innovative learning atmosphere. So that the process of learning English in class inspires them to keep going. Duolingo is a gamified software for language learning. The heavy-duty of this popular language learning software, incentive benefits, logical levels, and user ratings based on success are just a few examples of aspects that demonstrate the strong elements of gamification. As an outcome, a teacher must utilize creative and innovative learning media. Many English teachers struggle with designing and implementing English learning media that piques students’ interest in learning English. As a result, the research focuses on introducing and developing the effectiveness of using duolingo application in English learning for students. This research utilizes a qualitative descriptive design to evaluate how English teachers perceive the Duolingo application in learning English in the classroom. The participants in this study were 15 English teachers from various schools in Medan City. The finding showed that the teacher’s perception of the Duolingo application was very influential and effective while using in the classroom to learn English.&#x0D; Keywords: duolingo, teacher’s perception, English learning
Digital Hash Data Encryption for IoT Financial Transactions using Blockchain Security in the Cloud

Blockchain security via the Internet of Things (IoT) will reshape the decision-making function of the data-driven incumbent smart enterprise, providing the vision of the connected world of things. Enterprise IoT development of devices, personnel, and systems in such a way that they may connect and communicate with each other through the Internet. Blockchain is an enterprise financial transaction, and its digital network is distributed transaction ledger. Today, enterprises need the massive global data management and rapid trading volume to keep things going and growing. It creates enterprise business challenges of different types of security, transparency, and complexity of the problem. Enterprise architecture offers several advantages for the thief to obtain a specific user account, application, and access to the device. This is, will doesn't be to provide the necessities of security. The proposed Digital Hash Data Encryption (DHDE) is used to secure the transaction data-based embedded system people and blockchain. Blockchain and IoT technology integration may bring numerous benefits to mention. Therefore, the proposed DHDE algorithm comprehensively discusses the blockchain technology integration system. The proposed DHDE algorithm encrypts the transaction data for an unauthorized person who cannot access the enterprise transaction data based on embedded system people and blockchain.
Secure Digital Information Forward Using Highly Developed AES Techniques in Cloud Computing

Nowadays, in communications, the main criteria are ensuring the digital information and communication in the network. The normal two users' communication exchanges confidential data and files via the web. Secure data communication is the most crucial problem for message transmission networks. To resolve this problem, cryptography uses mathematical encryption and decryption data on adaptation by converting data from a key into an unreadable format. Cryptography provides a method for performing the transmission of confidential or secure communication. The proposed AES (Advanced Encryption Standard)-based Padding Key Encryption (PKE) algorithm encrypts the Data; it generates the secret key in an unreadable format. The receiver decrypts the data using the private key in a readable format. In the proposed PKE algorithm, the sender sends data into plain Text to cypher-text using a secret key to the authorized person; the unauthorized person cannot access the data through the Internet; only an authorized person can view the data through the private key. A method for identifying user groups was developed. Support vector machines (SVM) were used in user behaviour analysis to estimate probability densities so that each user could be predicted to launch applications and sessions independently. The results of the proposed simulation offer a high level of security for transmitting sensitive data or files to recipients compared to other previous methods and user behaviour analysis.
Online Task Scheduling of Big Data Applications in the Cloud Environment

The development of big data has generated data-intensive tasks that are usually time-consuming, with a high demand on cloud data centers for hosting big data applications. It becomes necessary to consider both data and task management to find the optimal resource allocation scheme, which is a challenging research issue. In this paper, we address the problem of online task scheduling combined with data migration and replication in order to reduce the overall response time as well as ensure that the available resources are efficiently used. We introduce a new scheduling technique, named Online Task Scheduling algorithm based on Data Migration and Data Replication (OTS-DMDR). The main objective is to efficiently assign online incoming tasks to the available servers while considering the access time of the required datasets and their replicas, the execution time of the task in different machines, and the computational power of each machine. The core idea is to achieve better data locality by performing an effective data migration while handling replicas. As a result, the overall response time of the online tasks is reduced, and the throughput is improved with enhanced machine resource utilization. To validate the performance of the proposed scheduling method, we run in-depth simulations with various scenarios and the results show that our proposed strategy performs better than the other existing approaches. In fact, it reduces the response time by 78% when compared to the First Come First Served scheduler (FCFS), by 58% compared to the Delay Scheduling, and by 46% compared to the technique of Li et al. Consequently, the present OTS-DMDR method is very effective and convenient for the problem of online task scheduling.
Comparing Statistical Models for Retrieval based Question-answering Dialogue: BERT vs Relevance Models

In this paper, we compare the performance of four models in a retrieval based question answering dialogue task on two moderately sized corpora (~ 10,000 utterances). One model is a statistical model and uses cross language relevance while the others are deep neural networks utilizing the BERT architecture along with different retrieval methods. The statistical model has previously outperformed LSTM based neural networks in a similar task whereas BERT has been proven to perform well on a variety of NLP tasks, achieving state-of-the-art results in many of them. Results show that the statistical cross language relevance model outperforms the BERT based architectures in learning question-answer mappings. BERT achieves better results by mapping new questions to existing questions.
Towards a multi-modal Deep Learning Architecture for User Modeling

Deep learning has succeeded in various applications, including image classification and feature learning. However, there needs to be more research on its use in Intelligent Tutoring Systems or Serious Games, particularly in modeling user behavior during learning or gaming sessions using multi-modal data. Creating an effective user model is crucial for developing a highly adaptive system. To achieve this, it is necessary to consider all available data sources to inform the user’s current state. This study proposes a user-sensitive deep multi-modal architecture that leverages deep learning and user data to extract a rich latent representation of the user. The architecture combines a Long Short-Term Memory, a Convolutional Neural Network, and multiple Deep Neu-ral Networks to handle the multi-modality of data. The resulting model was evaluated on a public multi-modal dataset, achieving better results than state-of-the-art algorithms for a similar task: opinion polarity detection. These findings suggest that the latent representation learned from the data is useful in discriminating behaviors. This proposed solution can be applied in various contexts where user modeling using multi-modal data is critical for improving the user experience.
Graph Neural Networks for Link Prediction

Graph Neural Networks (GNNs) belong to a class of deep learning methods that are specialized for extracting critical information and making accurate predictions on graph representations. Researchers have been striving to adapt neural networks to process graph data for over a decade. GNNs have found practical applications in various fields, including physics simulations, object detection, and recommendation systems. Predicting missing links in graphs is a crucial problem in various scientific fields because real-world graphs are frequently incompletely observed. This task, also known as link prediction, aims to predict the existence or absence of links in a graph. This tutorial is designed for researchers who have no prior experience with GNNs and will provide an overview of the link prediction task. In addition, we will discuss further reading, applications, and the most commonly used software packages and frameworks.
EFFC-Net: lightweight fully convolutional neural networks in remote sensing disaster images

Continuous development of remote sensing technology can rapidly and accurately extract secondary disaster information, such as the area of various disasters. However, in the extraction process, some disasters should be initially classified and identified. In view of this concept, a lightweight fully Convolutional Neural Network (CNN) model Earthquake – Flood–Fire – Cyclone (EFFC)-Net is proposed. Two modules, EFFC_Block and EFFC_Tran_Block, which are used for feature extraction and feature transformation, respectively, are introduced. The EFFC-Net network model is reconstructed through the EFFC_Block and EFFC_Tran_Block modules. Subsequently, EFFC-Net is compared with CNN and transformer models. Results show that the EFFC-Net network model performs effectively in precision, recall, F1_score, and parameters, outperforming the more advanced CNN and transformer models. Moreover, the test time of the Cifar_10 and Cifar_100 datasets was compared, and the results indicate that the EFFC-Net algorithm has the shortest running time and achieves the lightweight goal. Therefore, the EFFC-Net lightweight fully CNN has high disaster classification application value and good portability.
Rainbow Colormaps Are Not All Bad

Some 15 years ago, Visualization Viewpoints published an influential article titled Rainbow Color Map (Still) Considered Harmful (Borland and Taylor, 2007). The paper argued that the “rainbow colormap’s characteristics of confusing the viewer, obscuring the data and actively misleading interpretation make it a poor choice for visualization.” Subsequent articles often repeat and extend these arguments, so much so that avoiding rainbow colormaps, along with their derivatives, has become dogma in the visualization community. Despite this loud and persistent recommendation, scientists continue to use rainbow colormaps. Have we failed to communicate our message, or do rainbow colormaps offer advantages that have not been fully appreciated? We argue that rainbow colormaps have properties that are underappreciated by existing design conventions. We explore key critiques of the rainbow in the context of recent research to understand where and how rainbows might be misunderstood. Choosing a colormap is a complex task, and rainbow colormaps can be useful for selected applications.
Deep Photonic Reservoir Computer Based on Frequency Multiplexing with Fully Analog Connection Between Layers

Reservoir computers (RC) are randomized recurrent neural networks well adapted to process time series, performing tasks such as nonlinear distortion compensation or prediction of chaotic dynamics. Deep reservoir computers (deep-RC), in which the output of one reservoir is used as the input for another one, can lead to improved performance because, as in other deep artificial neural networks, the successive layers represent the data in more and more abstract ways. We present a fiber-based photonic implementation of a two-layer deep-RC based on frequency multiplexing. The two RC layers are encoded in two frequency combs propagating in the same experimental setup. The connection between the layers is fully analog and does not require any digital processing. We find that the deep-RC outperforms a traditional RC by up to two orders of magnitude on two benchmark tasks. This work paves the way towards using fully analog photonic neuromorphic computing for complex processing of time series, while avoiding costly analog-to-digital and digital-to-analog conversions.
Probabilistic forecast of nonlinear dynamical systems with uncertainty quantification

Data-driven modeling is useful for reconstructing nonlinear dynamical systems when the underlying process is unknown or too expensive to compute. Having reliable uncertainty assessment of the forecast enables tools to be deployed to predict new scenarios unobserved before. In this work, we first extend parallel partial Gaussian processes for predicting the vector-valued transition function that links the observations between the current and next time points, and quantify the uncertainty of predictions by posterior sampling. Second, we show the equivalence between the dynamic mode decomposition and the maximum likelihood estimator of the linear mapping matrix in the linear state space model. The connection provides a {probabilistic generative} model of dynamic mode decomposition and thus, uncertainty of predictions can be obtained. Furthermore, we draw close connections between different data-driven models for approximating nonlinear dynamics, through a unified view of generative models. We study two numerical examples, where the inputs of the dynamics are assumed to be known in the first example and the inputs are unknown in the second example. The examples indicate that uncertainty of forecast can be properly quantified, whereas model or input misspecification can degrade the accuracy of uncertainty quantification.
Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation (WSSS) models relying on class activation maps (CAMs) have achieved desirable performance comparing to the non-CAMs-based counterparts. However, to guarantee WSSS task feasible, we need to generate pseudo labels by expanding the seeds from CAMs which is complex and time-consuming, thus hindering the design of efficient end-to-end (single-stage) WSSS approaches. To tackle the above dilemma, we resort to the off-the-shelf and readily accessible saliency maps for directly obtaining pseudo labels given the image-level class labels. Nevertheless, the salient regions may contain noisy labels and cannot seamlessly fit the target objects, and saliency maps can only be approximated as pseudo labels for simple images containing single-class objects. As such, the achieved segmentation model with these simple images cannot generalize well to the complex images containing multi-class objects. To this end, we propose an end-to-end multi-granularity denoising and bidirectional alignment (MDBA) model, to alleviate the noisy label and multi-class generalization issues. Specifically, we propose the online noise filtering and progressive noise detection modules to tackle image-level and pixel-level noise, respectively. Moreover, a bidirectional alignment mechanism is proposed to reduce the data distribution gap at both input and output space with simple-to-complex image synthesis and complex-to-simple adversarial learning. MDBA can reach the mIoU of 69.5% and 70.2% on validation and test sets for the PASCAL VOC 2012 dataset. The source codes and models have been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/MDBA.