Reviewerly/exHarmony huggingface dataset:
Debiased recommendation with neural stratification

Debiased recommender models have recently attracted increasing attention from the academic and industry communities. Existing models are mostly based on the technique of inverse propensity score (IPS). However, in the recommendation domain, IPS can be hard to estimate given the sparse and noisy nature of the observed user-item exposure data. To alleviate this problem, in this paper, we assume that the user preference can be dominated by a small amount of latent factors, and propose to cluster the users for computing more accurate IPS via increasing the exposure densities. Basically, such method is similar with the spirit of stratification models in applied statistics. However, unlike previous heuristic stratification strategy, we learn the cluster criterion by presenting the users with low ranking embeddings, which are future shared with the user representations in the recommender model. At last, we find that our model has strong connections with the previous two types of debiased recommender models. We conduct extensive experiments based on real-world datasets to demonstrate the effectiveness of the proposed method.
‘Sidekick’ Application: A Smart Mobile Application for Generation Z

Educational systems are based on students and professors who are linked in an organized manner to achieve learning outcomes. Recently, however, academic institutions are experiencing very few, yet ineffective, ways of communication among these parties. Therefore, a University ‘Sidekick’ Application has been developed in one of the educational institutions in the Gulf region to tackle the lack of communication among students and academics in a university community. The methodology followed to develop this application is the waterfall SDLC. Therefore, the application was planned for, the requirements were gathered, modeled and analyzed, the interfaces and databases were designed, and the coding and testing took place subsequently. Sidekick application is a friendly virtual assistant and intuitive to use. It provides booking for amenities, receiving alerts and notifications, a live chat which makes it easier to communicate on and off campus. Moreover, the virtual assistant can strengthen the relationships between “students and instructor”, “alumni students and current students” and “instructor and alumni students”. Furthermore, Sidekick is compatible on IOS and Android. The study concluded with implications and future development directions.
What should the university of the future look like?

Purpose In this paper, the author has tried to outline the main ideas in connection with what the author conceives to be the university of the future, a university that should not only educate people within the university system but also prepare them to fill specific job positions at both local and global levels, apart from necessarily providing them with the critical thinking and competences in autonomous learning that will make them flexible and capable of adapting to the job market and to a fast-changing world in general. Design/methodology/approach The author has revised some of the major issues that are going to determine the direction of the university of the future, i.e. the employment opportunities of tomorrow; the role of new technologies, especially the impact of artificial intelligence (AI); quality in higher education; and internationalization. Findings The author has also pointed out the importance of the technologies and the great role they indisputably play in present and future education at all levels, a fact that has been particularly and hugely enhanced and promoted by the COVID-19 pandemic situation, thereby facilitating and fostering distance learning. This is very much connected to the application of AI to higher education, another unavoidable issue of utmost importance for the university of the future. While these technological advances present a challenge to universities, which must determine which are necessary and desirable and how to implement them, it is, ultimately, our responsibility to use them, in an ethical way, to the benefit of our students. The university of the future also has to be of high quality, and this involves carrying out important and decisive action having to do with matters of inclusion, hiring policies and the expansion of international opportunities for all parties involved. Originality/value This paper outlines the main ideas in connection with what the author conceives to be the university of the future, a university that should not only educate people within the university system but also prepare them to fill specific job positions at both local and global levels, apart from necessarily providing them with the critical thinking and competences in autonomous learning that will make them flexible and capable of adapting to the job market and to a fast-changing world in general. Moreover, the role of new technologies (especially the impact of AI), quality and internationalization are also discussed as relevant factors in this view of the university of the future.
Edulinguistica ludica Facilitare l’apprendimento linguistico con il gioco e la ludicità

Playful methodology for language education still finds some prejudice at school, especially when it targets students of the secondary school or adults. The book aims at discrediting such prejudices, considered the most recent neuroscientific and psychopedagogical theories, and at being a contribution for researchers of educational linguistics and for language teachers. The book has a theoretical approach but it offers operational indications for those who wish to use this methodology, which goes far beyond the simple presentation of games. The book is composed by two parts: the first part focuses on the concept of linguistic facilitation (and on the role of the teacher as a facilitator), the second one presents the playful methodology for language learning. The epistemological framework is Humanistic Language Teaching, which refers to the psychological and educational ‘revolution’ of the 1960s and 1970s, i.e., to the works of Chomsky, Arnold, Maslow, Bruner, Rogers, Neisser and many other researchers who claimed that knowledge, experience and education were the result of the way the human brain works and of the personal characteristics of each mind. The slogan used for this edulinguistic revolution was Focus on the Learner . In fact, the communicative approach used today includes a humanistic component as far as the role and nature of the learner are concerned. Yet, this debt to the humanistic revolution is more and more an implicit one, as the awareness of the role played by Humanistic Language Teaching is taken for granted. Within this framework, the book introduces the concept of ‘meaningful learning’, the difference between game and play; the difference between free play and didactic play; the use of play to enhance intercultural communication (fundamental in non-native language teaching) and intercultural education (which involves all humanistic parameters). The book also presents five methodologies that can be used to put the playful methodology into practice: Asher’s Total Physical Response , Gattegno’s Silent Way , Lozanov’s Suggestopedia , Di Pietro’s Scenario , and the use of drama.
Generation of a GHZ-type optical entangled coherent state without measurements

Typically, multipartite entangled coherent states are difficult to be extended and produced without measurement. We here propose a way to deterministically generate a GHZ (Greenberger–Horne–Zeilinger)-type entangled coherent state of cavities, utilizing a system consisting of a single superconducting qutrit (i.e., a three-level quantum system) and multiple microwave cavities. Due to the use of only a coupler qutrit, the architecture of the circuit system is quite simple. More importantly, our proposal does not require measurement on the state of qutrit compared with the previous proposals. Since the qutrit's third energy level is not populated during the operation, decoherence from the higher energy level is greatly minimized. Furthermore, the entire operation time is independent of the number of microwave cavities. As an example, our numerical simulations show that high-fidelity generation of a three-cavity GHZ-type entangled coherent state is feasible with present circuit quantum electrodynamics technology. This proposal is universal and can be applied to other physical systems, such as microwave or optical cavities, which are coupled to a single natural or artificial three-level atom.
A Fast Large-Scale Path Planning Method on Lunar DEM Using Distributed Tile Pyramid Strategy

In lunar exploration missions, path planning for lunar rovers using digital elevation models (DEMs) is currently a hot topic in academic research. However, research on path planning using large-scale DEMs has rarely been discussed, owing to the low time efficiency of existing algorithms. Therefore, in this article, we propose a fast path-planning method using a distributed tile pyramid strategy and an improved A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm. The proposed method consists of three main steps. First, the tile pyramid is generated for the large lunar DEM and stored in Hadoop distributed file system. Second, a distributed path-planning strategy based on tile pyramid (DPPS-TP) is used to accelerate path-planning tasks on large-scale lunar DEMs using Spark and Hadoop. Finally, an improved A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm was proposed to improve the speed of the path-planning task in each tile. The method was tested using lunar DEM images. Experimental results demonstrate that: in a single-machine serial strategy using source DEM generated by the Chang'e-2 CCD stereo camera, the proposed A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm for open list and closed list with random access feature (OC-RA-A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm) is 3.59 times faster than the traditional A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm in long-distance path planning tasks and compared to the distributed parallel computation strategy using source DEM generated by the Chang'e-2 CCD stereo camera, the proposed DPPS-TP based on tile pyramid DEM is 113.66 times faster in the long-range path planning task.
Hybrid Approach for Phishing Website Detection Using Classification Algorithms

The internet has significantly altered how we work and interact with one another.Statistics show 63.1 % of the present world population are internet users. This clearly indicates how heavily man is dependent on digital media. Digital media users are on the rise and so is the incidence of cyber crimes. People who lack experience and knowledge are more vulnerable and susceptible to phishing scams.The victims experience severe consequences as their personal credentials are at stake. Phishers use publicly available sources to acquire details about the victim's professional and personal history.Countermeasures must be implemented with the highest priority. Detection of malicious websites can significantly reduce the risk of phishing attempts.In this research, a highly accurate website phishing detection method based on URL features is proposed. We investigated eight existing machine learning classification techniques for this, including extreme gradient boosting (XGBoost), random forest (RF), adaptive boosting (AdaBoost), decision trees (DT), K-nearest neighbors (KNN), support vector machines (SVM), logistic regression and naïve bayes (NB) to detect malicious websites.The results show that XGboost had the best accuracy with a score of 96.71%, followed by random forest and AdaBoost.We further experimented with various hybrid combinations of the top three classifiers and observed that XGboost-Random Forest hybrid algorithms produced the best results.The hybrid model classified the websites as legitimate or phishing with an accuracy of 97.07%.
Short term photovoltaic power prediction based on transfer learning and considering sequence uncertainty

With the increasing proportion of solar grid-connected, the establishment of an accurate photovoltaic (PV) power prediction model is very important for safe operation and efficient dispatching of a power grid. Considering the multi-level periodicity of PV power caused by many factors, such as seasons and weather, a short-term PV power prediction model based on transfer component analysis is designed by introducing the idea of transfer learning. In order to measure the uncertainty of numerical weather prediction (NWP) and power sequence, a novel algorithm considering weather similarity and power trend similarity is proposed. First, the intrinsic trend is measured by extracting permutation entropy, variance, and mean from the historical PV power sequence. Second, weighting of NWP is accomplished based on the Pearson correlation coefficient. PV power data are divided into different clusters by K-medoids clustering. At the same time, the transfer component analysis alleviates the time-varying problem of data distribution caused by multi-level time periodicity and effectively improves the prediction accuracy of the model. Finally, simulation experiments are carried out on the PV power output dataset (PVOD). The results show that the prediction accuracy of the proposed method is better than the traditional methods, and the accuracy and applicability of the proposed method are verified.
Geometric algebra based recurrent neural network for multi-dimensional time-series prediction

Recent RNN models deal with various dimensions of MTS as independent channels, which may lead to the loss of dependencies between different dimensions or the loss of associated information between each dimension and the global. To process MTS in a holistic way without losing the inter-relationship among dimensions, this paper proposes a novel Long-and Short-term Time-series network based on geometric algebra (GA), dubbed GA-LSTNet. Specifically, taking advantage of GA, multi-dimensional data at each time point of MTS is represented as GA multi-vectors to capture the inherent structures and preserve the correlation of those dimensions. In particular, traditional real-valued RNN, real-valued LSTM, and the back-propagation through time are extended to the GA domain. We evaluate the performance of the proposed GA-LSTNet model in prediction tasks on four well-known MTS datasets, and compared the prediction performance with other six methods. The experimental results indicate that our GA-LSTNet model outperforms traditional real-valued LSTNet with higher prediction accuracy, providing a more accurate solution for the existing shortcomings of MTS prediction models.
Symptoms are known by their companies: towards association guided disease diagnosis assistant

Abstract Over the last few years, dozens of healthcare surveys have shown a shortage of doctors and an alarming doctor-population ratio. With the motivation of assisting doctors and utilizing their time efficiently, automatic disease diagnosis using artificial intelligence is experiencing an ever-growing demand and popularity. Humans are known by the company they keep; similarly, symptoms also exhibit the association property, i.e., one symptom may strongly suggest another symptom’s existence/non-existence, and their association provides crucial information about the suffering condition. The work investigates the role of symptom association in symptom investigation and disease diagnosis process. We propose and build a virtual assistant called Association guided Symptom Investigation and Diagnosis Assistant (A-SIDA) using hierarchical reinforcement learning. The proposed A-SIDDA converses with patients and extracts signs and symptoms as per patients’ chief complaints and ongoing dialogue context. We infused association-based recommendations and critic into the assistant, which reinforces the assistant for conducting context-aware, symptom-association guided symptom investigation. Following the symptom investigation, the assistant diagnoses a disease based on the extracted signs and symptoms. The assistant then diagnoses a disease based on the extracted signs and symptoms. In addition to diagnosis accuracy, the relevance of inspected symptoms is critical to the usefulness of a diagnosis framework. We also propose a novel evaluation metric called Investigation Relevance Score (IReS), which measures the relevance of symptoms inspected during symptom investigation. The obtained improvements (Diagnosis success rate-5.36%, Dialogue length-1.16, Match rate-2.19%, Disease classifier-6.36%, IReS-0.3501, and Human score-0.66) over state-of-the-art methods firmly establish the crucial role of symptom association that gets uncovered by the virtual agent. Furthermore, we found that the association guided symptom investigation greatly increases human satisfaction, owing to its seamless topic (symptom) transition.
AN LSTM model-based Prediction of Chaotic System: Analyzing the Impact of Training Dataset Precision on the Performance

The chaotic systems are crucial due to their spread applications in different fields. The modeling and prediction of chaotic time series improved significantly with the recent advances in artificial intelligence algorithms, especially deep learning models. In this research, we use a deep learning-based long short-term memory (LSTM) model for the prediction of chaotic time series of the Lorenz attractor. This paper uses three datasets of (25k) samples with three precisions (the step size$\triangle$ t=0.01, 0.005, and 0.001) for training the LSTM model. The mean square error MSE and root mean square error RMSE metrics are used to measure the training performance. The best performance was obtained by increasing the precisions of the training data, where the values of metrics were 5. 3033e - 5 and 0.0073 for MSE and RMSE respectively. It is found that the training performance can be improved by increasing the precision of the training data i.e., reducing the step size. This provides useful knowledge towards reducing the number of data samples and corresponding acquisition time for a prediction.
Inspection of Ship Hulls with Multiple UAVs: Exploiting Prior Information for Online Path Planning

This paper addresses a path planning problem for a fleet of Unmanned Aerial Vehicles (UAVs) that uses both prior information and online gathered data to efficiently inspect large surfaces such as ship hulls and water tanks. UAVs can detect corrosion patches and other defects on the surface from low-resolution images. If defects are detected, they get closer to the surface for a high-resolution inspection. The prior information provides expected defects locations and is affected by both false positives and false negatives. The mission objective is to prioritize the close-up inspection of defected areas while keeping a reasonable time for the coverage of the entire surface. We propose two solutions to this problem: a coverage algorithm that divides the problem into a set of Traveling Salesman Problems (Part-TSP) and a cooperative frontier approach that introduces frontier utilities to incorporate the prior information (Coop-Frontier). We finally provide extensive simulation results to analyze the performance of these approaches and compare them with alternative solutions. These results suggest that both Part-Tspand Coop-Frontier perform better than the baseline solution. Part-Tsphas the best performance in most cases. However, coop-Frontier is preferable in extreme cases because more robust to inhomogeneous corrosion distribution and imperfect information.
HPA* Enhancements

In video games, pathfinding must be done quickly and accurately. Not much computational time is allowed for pathfinding, but realistic looking paths are required. One approach to pathfinding which attempts to satisfy both of these constraints is to perform pathfinding on abstractions of the map. Botea et al.'s Hierarchical Pathfinding A* (HPA*) does this by dividing the map into square sectors and defining entrances between them. Although HPA* performs quick pathfinding which produces near-optimal paths, some improvements can be introduced. Here we discuss a faster path smoothing method, an alternative way to compute the weights of abstract edges, and lazy edge weight computations.
Efficient Hierarchical State Vector Simulation of Quantum Circuits via Acyclic Graph Partitioning

Early but promising results in quantum computing have been enabled by the concurrent development of quan-tum algorithms, devices, and materials. Classical simulation of quantum programs has enabled the design and analysis of algorithms and implementation strategies targeting current and anticipated quantum device architectures. In this paper, we present a graph-based approach to achieving efficient quantum circuit simulation. Our approach involves partitioning the graph representation of a given quantum circuit into acyclic sub-graphs/circuits that exhibit better data locality. Simulation of each sub-circuit is organized hierarchically, with the iterative construction and simulation of smaller state vectors, improving overall performance. Also, this partitioning reduces the number of passes through data, improving the total computation time. We present three partitioning strategies and observe that acyclic graph partitioning typically results in the best time-to-solution. In contrast, other strategies reduce the partitioning time at the expense of potentially increased simulation times. Experimental evaluation demonstrates the effectiveness of our approach.
Adversarial VAE with Normalizing Flows for Multi-Dimensional Classification

Exploiting correlations among class variables and using them to facilitate the learning process are a key challenge of Multi-Dimensional Classification (MDC) problems. Label embedding is an efficient strategy towards MDC problems. However, previous methods for MDC only use this technique as a way of feature augmentation and train a separate model for each class variable in MDC problems. Such two-stage approaches may cause unstable results and achieve suboptimal performance. In this paper, we propose an end-to-end model called Adversarial Variational AutoEncoder with Normalizing Flow (ADVAE-Flow), which encodes both features and class variables to probabilistic latent spaces. Specifically, considering the heterogeneity of class spaces, we introduce a normalizing flows module to increase the capacity of probabilistic latent spaces. Then adversarial training is adopted to help align transformed latent spaces obtained by normalizing flows. Extensive experiments on eight MDC datasets demonstrate the superiority of the proposed ADVAE-Flow model over state-of-the-art MDC models.