Reviewerly/exHarmony huggingface dataset:
Conference Paper Acceptance Prediction: Using Machine Learning

The paper presents a model that will predict the acceptance of the paper for a particular conference. The model is designed for the conferences, which accept researches done researches in the Machine Learning domain. The dataset that is used to develop this model is the ICLR 2017 (International Conference Of Learning Representation). The model gives its prediction based on extracted features. The features that most of the conferences consider are Number of References, Number of Figures, Number of Tables, Bag of words for ML related terms, etc. Some more features are taken into consideration to give better results such as Length of Title, Frequency of ML related words, Number of ML algorithms, Average length of sentences. The model is trained on the above-mentioned dataset, which contains 70 accepted and 100 rejected papers. For the prediction, different Machine Learning algorithms are used. The model is trained by applying algorithms such as Logistic Regression, Decision Tree, Random Forest, KNN, and SVM. The comparative study of different algorithms on the dataset gives the result that Decision Tress works effectively by providing 85% accuracy.
Evaluation of recent advances in recommender systems on Arabic content

Abstract Various recommender systems (RSs) have been developed over recent years, and many of them have concentrated on English content. Thus, the majority of RSs from the literature were compared on English content. However, the research investigations about RSs when using contents in other languages such as Arabic are minimal. The researchers still neglect the field of Arabic RSs. Therefore, we aim through this study to fill this research gap by leveraging the benefit of recent advances in the English RSs field. Our main goal is to investigate recent RSs in an Arabic context. For that, we firstly selected five state-of-the-art RSs devoted originally to English content, and then we empirically evaluated their performance on Arabic content. As a result of this work, we first build four publicly available large-scale Arabic datasets for recommendation purposes. Second, various text preprocessing techniques have been provided for preparing the constructed datasets. Third, our investigation derived well-argued conclusions about the usage of modern RSs in the Arabic context. The experimental results proved that these systems ensure high performance when applied to Arabic content.
Motivation Enhancement Methods for Community Building in Extended Reality

Extended Reality (XR) is an umbrella term that embraces several immersive technologies such as Augmented (AR), Mixed (MR) and Virtual Reality (VR). The application of XR for teaching and learning purposes is called Immersive Education. Immersive Education has the potential to enhance student learning motivation, engagement and performance. Game-based motivation enhancement methods include playful design, gamification and serious games. The current manuscript presents playful blended learning experiences, gamified distance education courses and serious game designs in XR community settings. Its main contribution is the discussion of results from multiple studies and the formulation of recommendations for practitioners for building communities of inquiry and practice in XR environments.
Education online in lockdown: limits and possibilities. The vision of families in Spain

Purpose March 14, 2020, marked the beginning of an unexpected state of emergency in Spain due to the impact of the coronavirus disease 2019 (COVID-19). From that moment on, the educational system had to adapt so that millions of students could continue their education at home. Through a descriptive study, the reality and perceptions that Spanish families have about the educational actions that took place during the compulsory lockdown of the Spanish population is presented. Design/methodology/approach 3,400 representatives of family units from 17 autonomous communities answered a survey, the data were analyzed using descriptive and frequency statistics. Findings Relevant conclusions were drawn from the results. Despite the efforts of the authorities, the economic, cultural, social and digital divides leave many households without access to the fundamental right of education; families value the support of technologies but consider that they should not replace the face-to-face education that is necessary for the development process of minors; it is necessary to adapt the school content for a future postpandemic, discriminating the expendable from the essential in the school curriculum; priority must be given to the integral well-being of people in educational policies and also to the most vulnerable ones. Originality/value The study allows progress in the analysis of educational policy proposals in the face of future crisis.
Symmetric and asymmetric cyclic controlled quantum teleportation via nine-qubit entangled state

In this paper, by utilizing a nine-qubit entangled state as a quantum channel, we propose new schemes for symmetric and asymmetric cyclic controlled quantum teleportation (CYCQT). In our proposed schemes, four participants Alice, Bob, Charlie and David teleport their unknown quantum states cyclically among themselves with the help of a controller Eve. No participants can reconstruct the original states sent from the respective senders without the permission of the controller. Also, by considering same nine-qubit entangled state as a quantum channel, we propose a generalized scheme for CYCQT of multi-qubit states. In contrast to the previous CYCQT schemes involving three communicators and a controller, there are four communicators and a controller in the proposed schemes. Also, compared with previous CYCQT schemes, our proposed CYCQT schemes require less consumption of quantum resource and the intrinsic efficiency of the generalized scheme increases with the increase of number of qubits in the information states.
Is non-fungible token pricing driven by cryptocurrencies?

In early 2021, non-fungible tokens (NFT) became the first application of blockchain technology to achieve clear public prominence. NFTs are tradeable rights to digital assets (images, music, videos, virtual creations) where ownership is recorded in smart contracts on a blockchain. Given the NFT market emerged out of cryptocurrencies, we explore if NFT pricing is related to cryptocurrency pricing. A spillover index shows only limited volatility transmission effects between cryptocurrencies and NFTs. But wavelet coherence analysis indicates co-movement between the two sets of markets. This suggests that cryptocurrency pricing behaviours might be of some benefit in understanding NFT pricing patterns. However, the low volatility transmissions also indicate that NFTs can potentially be considered as a low-correlation asset class distinct from cryptocurrencies.
Real Time Face-mask Detection with Arduino to Prevent COVID-19 Spreading

The rise of COVID-19 pandemic has had a lasting impact in many countries worldwide since 2019. Face-mask detection had been significant progress in the Image processing and deep learning fields studies. Many face detection models have been designed using different algorithms and techniques. The proposed approach in this paper developed to avoid mask-less people from entering to a desired places (i.e. Mall, University, Office, …etc.) by detecting face mask using deep learning, TensorFlow, Keras, and OpenCV and sending a signal to Arduino device that connected to the gate to be open. it detect a face in a real-time and identifies whether the person wear mask or not. The method attains accuracy up to 97.80%. The dataset provided in this paper, was collected from various sources.
RGBT Tracking via Noise-Robust Cross-Modal Ranking

Existing RGBT tracking methods usually localize a target object with a bounding box, in which the trackers are often affected by the inclusion of background clutter. To address this issue, this article presents a novel algorithm, called noise-robust cross-modal ranking, to suppress background effects in target bounding boxes for RGBT tracking. In particular, we handle the noise interference in cross-modal fusion and seed labels from the following two aspects. First, the soft cross-modality consistency is proposed to allow the sparse inconsistency in fusing different modalities, aiming to take both collaboration and heterogeneity of different modalities into account for more effective fusion. Second, the optimal seed learning is designed to handle label noises of ranking seeds caused by some problems, such as irregular object shape and occlusion. In addition, to deploy the complementarity and maintain the structural information of different features within each modality, we perform an individual ranking for each feature and employ a cross-feature consistency to pursue their collaboration. A unified optimization framework with an efficient convergence speed is developed to solve the proposed model. Extensive experiments demonstrate the effectiveness and efficiency of the proposed approach comparing with state-of-the-art tracking methods on GTOT and RGBT234 benchmark data sets.
On the Effect of Auxiliary Tasks on Representation Dynamics

While auxiliary tasks play a key role in shaping the representations learnt by reinforcement learning agents, much is still unknown about the mechanisms through which this is achieved. This work develops our understanding of the relationship between auxiliary tasks, environment structure, and representations by analysing the dynamics of temporal difference algorithms. Through this approach, we establish a connection between the spectral decomposition of the transition operator and the representations induced by a variety of auxiliary tasks. We then leverage insights from these theoretical results to inform the selection of auxiliary tasks for deep reinforcement learning agents in sparse-reward environments.
Transition Flight Control and Simulation of a Novel Tail-Sitter UAV With Varying Fuselage Shape

This article studies the model, control and simulation on transition flight of a novel tail-sitter with vertical takeoff and landing capability. The proposed tail-sitter adopts an innovative varying fuselage shape design to break the technical bottleneck in the balance of efficient horizontal flight and agile vertical flight. Overset grids and computational fluid dynamic methods are used to explore the vehicle’s prestall aerodynamics, which are relative to not only the angle of attack and sideslip angle but also to the varying angle between two rear fuselage’s parts. High angle of attack aerodynamics based on the improved Viterna and Corrigan stall model is also established for this novel tail-sitter. Meanwhile, an accurate model of the propeller is tested in a wind tunnel. Combining the forces and moments generated by propellers, aerodynamics and gravity, a 6DoF nonlinear time-varying dynamic model is built. A robust controller based on incremental dynamic inversion method is designed for this tail-sitter, which is good at dealing with uncertainties and external forces. Nominal and model mismatch conditions are simulated to verify the controller’s performance. Different varying strategies for the mechanism are analyzed during the transition flight. Simulation results show that this novel tail-sitter can transform between vertical and horizontal flight mode easily and the varying strategy related to pitch angle is a prior choice for transition flight.
A Secure and Decentralized DLaaS Platform for Edge Resource Scheduling against Adversarial Attacks

Edge Computing is promising for latency-sensitive applications. However, current edge resource scheduling is inefficient. Deep Learning as a Service (DLaaS) provides deep learning methods to optimize the resource scheduling problem, but faces great challenges of security and reliability. On one hand, DLaaS training agents and raw data are exposed to various adversarial attacks. On the other hand, dishonest DLaaS trainers can generate poisoned models to attack the DLaaS system. In this paper, we propose SAPE, a Secure and decentralized DLAaS Platform in Edge computing. SAPE allows users to submit their tasks, which will be scheduled to the appropriate edge clusters to minimize the task execution time. We formulate the resource scheduling problem and develop the federated deep reinforcement learning (DRL) method to optimize the problem and resist the adversarial attacks of DLaaS. We utilize blockchain and propose a consortium-based verification scheme to improve the reliability of the federated training process, protecting the DLaaS models from being poisoned and compromised. We conduct experiments to evaluate the latency and security performance of SAPE and the federated DRL scheduling policy. The results show that SAPE outperforms the traditional schemes when defending against adversarial attacks towards the DLaaS platform in edge computing.
Communication-Efficient Randomized Algorithm for Multi-Kernel Online Federated Learning

Online federated learning (OFL) is a promising framework to learn a sequence of global functions from distributed sequential data at local devices. In this framework, we first introduce a single kernel-based OFL (termed S-KOFL) by incorporating random-feature (RF) approximation, online gradient descent (OGD), and federated averaging (FedAvg). As manifested in the centralized counterpart, an extension to multi-kernel method is necessary. Harnessing the extension principle in the centralized method, we construct a vanilla multi-kernel algorithm (termed vM-KOFL) and prove its asymptotic optimality. However, it is not practical as the communication overhead grows linearly with the size of a kernel dictionary. Moreover, this problem cannot be addressed via the existing communication-efficient techniques (e.g., quantization and sparsification) in the conventional federated learning. Our major contribution is to propose a novel randomized algorithm (named eM-KOFL), which exhibits similar performance to vM-KOFL while maintaining low communication cost. We theoretically prove that eM-KOFL achieves an optimal sublinear regret bound. Mimicking the key concept of eM-KOFL in an efficient way, we propose a more practical pM-KOFL having the same communication overhead as S-KOFL. Via numerical tests with real datasets, we demonstrate that pM-KOFL yields the almost same performance as vM-KOFL (or eM-KOFL) on various online learning tasks.
Blockchain Integrated Framework for Resolving Privacy Issues in Smart City

Smart City is the concept for improvising the urban cities operating and services efficiency by making use of IoT (Internet of things) which is a modular approach that operates by deploying sensors with significant qualities and integrating them with ICT (Information and Communication Technologies) solutions. The application area for the IoT platform has been in smart building and office management, transportation, environmental degradation surveillance, and smart grid management, etc. However, maintaining an efficient architecture for its operation in a complex environment has been a challenge for several years. This will increase the security and privacy concerns with the increase in smart applications within smart cities. This chapter aims to study the issues related to data integrity and security and the approach used to resolve these issues using blockchain analytics algorithms and architecture. This chapter also gives the future direction towards achieving low-cost architectural management for smart cities. This chapter is mainly focused to analyze such challenges and to identify limitations of the existing secure smart cities framework and to proposes an effective blockchain-based smart city interaction framework.
Multi-level embeddings for processing Arabic social media contents

Embeddings are very popular representations that allow computing semantic and syntactic similarities between linguistic units from text co-occurrence matrix. Units can vary from character n-grams to words, including more coarse-grained units such as sentences and documents. Recently, multi-level embeddings combining representations from different units have been proposed as an alternative to single-level embeddings to account for the internal structure of words (i.e., morphology) and help systems to generalise well over out of vocabulary words. These representations, either pre-trained or learned, have shown to be quite effective, outperforming word-level baselines in several NLP tasks such as machine translation, part of speech tagging and named entity recognition. Our aim here is to contribute to this line of research proposing for the first time in Arabic NLP an in-depth study of the impact of various subwords configurations ranging from character to character n-grams (including word) for social media text classification. We propose several neural architectures to learn character, subword and word embeddings, as well as a combination of these three levels, exploring different composition functions to obtain the final representation of a given text. To evaluate the effectiveness of these representations, we perform extrinsic evaluations on three text classification tasks (sentiment analysis, emotion detection and irony detection) while accounting for different Arabic varieties (Modern Standard Arabic, dialects (Levantine and Maghrebi)). For each task, we experiment with well-known dialect-agnostic and dialect-specific datasets, including those that have been recently used in shared tasks to better compare our results with those reported in previous studies on the same datasets. The results show that the multi-level embeddings we propose outperform current static and contextualised embeddings as well as best performing state of the art models in sentiment and emotion detection. In addition, we achieve competitive results in irony detection. Our models are also the most productive across dialects observing that different dialects require different composition configurations. We finally show that these performances tend to increase when coupling the multi-level representations with task-specific features.
A Clinical Prediction Model to Predict Heparin Treatment Outcomes and Provide Dosage Recommendations: Development and Validation Study

Background Unfractionated heparin is widely used in the intensive care unit as an anticoagulant. However, weight-based heparin dosing has been shown to be suboptimal and may place patients at unnecessary risk during their intensive care unit stay. Objective In this study, we intended to develop and validate a machine learning–based model to predict heparin treatment outcomes and to provide dosage recommendations to clinicians. Methods A shallow neural network model was adopted in a retrospective cohort of patients from the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC III) database and patients admitted to the Peking Union Medical College Hospital (PUMCH). We modeled the subtherapeutic, normal, and supratherapeutic activated partial thromboplastin time (aPTT) as the outcomes of heparin treatment and used a group of clinical features for modeling. Our model classifies patients into 3 different therapeutic states. We tested the prediction ability of our model and evaluated its performance by using accuracy, the kappa coefficient, precision, recall, and the F1 score. Furthermore, a dosage recommendation module was designed and evaluated for clinical decision support. Results A total of 3607 patients selected from MIMIC III and 1549 patients admitted to the PUMCH who met our criteria were included in this study. The shallow neural network model showed results of F1 scores 0.887 (MIMIC III) and 0.925 (PUMCH). When compared with the actual dosage prescribed, our model recommended increasing the dosage for 72.2% (MIMIC III, 1240/1718) and 64.7% (PUMCH, 281/434) of the subtherapeutic patients and decreasing the dosage for 80.9% (MIMIC III, 504/623) and 76.7% (PUMCH, 277/361) of the supratherapeutic patients, suggesting that the recommendations can contribute to clinical improvements and that they may effectively reduce the time to optimal dosage in the clinical setting. Conclusions The evaluation of our model for predicting heparin treatment outcomes demonstrated that the developed model is potentially applicable for reducing the misdosage of heparin and for providing appropriate decision recommendations to clinicians.
Role of Artificial Intelligence in Detection of Hateful Speech for Hinglish Data on Social Media

Social networking platforms provide a conduit to disseminate our ideas, views, and thoughts and proliferate information. This has led to the amalgamation of English with natively spoken languages. Prevalence of Hindi-English code-mixed data (Hinglish) is on the rise with most of the urban population all over the world. Hate speech detection algorithms deployed by most social networking platforms are unable to filter out offensive and abusive content posted in these code-mixed languages. Thus, the worldwide hate speech detection rate of around 44% drops even more considering the content in Indian colloquial languages and slangs. In this paper, we propose a methodology for efficient detection of unstructured code-mix Hinglish language. Fine-tuning-based approaches for Hindi-English code-mixed language are employed by utilizing contextual-based embeddings such as embeddings for language models (ELMo), FLAIR, and transformer-based bidirectional encoder representations from transformers (BERT). Our proposed approach is compared against the pre-existing methods and results are compared for various datasets. Our model outperforms the other methods and frameworks.
Vis Ex Machina: An Analysis of Trust in Human versus Algorithmically Generated Visualization Recommendations

More visualization systems are simplifying the data analysis process by automatically suggesting relevant visualizations. However, little work has been done to understand if users trust these automated recommendations. In this paper, we present the results of a crowd-sourced study exploring preferences and perceived quality of recommendations that have been positioned as either human-curated or algorithmically generated. We observe that while participants initially prefer human recommenders, their actions suggest an indifference for recommendation source when evaluating visualization recommendations. The relevance of presented information (e.g., the presence of certain data fields) was the most critical factor, followed by a belief in the recommender’s ability to create accurate visualizations. Our findings suggest a general indifference towards the provenance of recommendations, and point to idiosyncratic definitions of visualization quality and trustworthiness that may not be captured by simple measures. We suggest that recommendation systems should be tailored to the information-foraging strategies of specific users.