Reviewerly/exHarmony huggingface dataset:
The Use of Artificial Intelligence Combined with Cloud Computing in the Design of Education Information Management Platform

Based on cloud computing theory and service-oriented architecture (SOA) design pattern, a smart education management platform is designed by using cloud computing and artificial intelligence technology. The platform is deployed in the server cluster environment, with Hadpoop managed storage cluster as the data storage center. For the education management, education portal and remote classroom, the corresponding services are launched, which has the advantages of intelligent and efficient, massive data access and intelligent collaborative management. The test results show that the platform can be used normally in the ports of computer, mobile phone and tablet computer, and can successfully complete the basic operations such as user registration and login, educational administration, and content storage. The results of database stress test show that the total time consumption of sequential read, sequential write, random read and random write is 752s, 312s, 968s and 211s, respectively when millions of simulated data are inserted into the database, which indicates that the database can support large-scale data access. The stress test results of content storage service show that when the number of clients is adjusted to 500, 1000 and 3000, the output quantity of each read-write interface can be maintained at about 3500 pages/min in 60s, indicating that the system can still run stably under the condition of high concurrency. The management platform discussed has practical significance to promote the development of intelligent and information-based education management.
Survival in the Intensive Care Unit: A prognosis model based on Bayesian classifiers

We develop a predictive prognosis model to support medical experts in their clinical decision-making process in Intensive Care Units (ICUs) (a) to enhance early mortality prediction, (b) to make more efficient medical decisions about patients at higher risk, and (c) to evaluate the effectiveness of new treatments or detect changes in clinical practice. It is a machine learning hierarchical model based on Bayesian classifiers built from some recorded features of a real-world ICU cohort, to bring about the assessment of the risk of mortality, also predicting destination at ICU discharge if the patient survives, or the cause of death otherwise, constructed as an ensemble of five base Bayesian classifiers by using the average ensemble criterion with weights, and we name it the Ensemble Weighted Average (EWA). We compare EWA against other state-of-the-art machine learning predictive models. Our results show that EWA outperforms its competitors, presenting in addition the advantage over the ensemble using the majority vote criterion of allowing to associate a confidence level to the provided predictions. We also prove the convenience of locally recalibrate from data the standard model used to predict the mortality risk based on the APACHE II score, although as a predictive model it is weaker than the other.
Overview of the HASOC track at FIRE 2020: Hate Speech and Offensive Content Identification in Indo-European Languages.

With the growth of social media, the spread of hate speech is also increasing rapidly. Social media are widely used in many countries. Also Hate Speech is spreading in these countries. This brings a need for multilingual Hate Speech detection algorithms. Much research in this area is dedicated to English at the moment. The HASOC track intends to provide a platform to develop and optimize Hate Speech detection algorithms for Hindi, German and English. The dataset is collected from a Twitter archive and pre-classified by a machine learning system. HASOC has two sub-task for all three languages: task A is a binary classification problem (Hate and Not Offensive) while task B is a fine-grained classification problem for three classes (HATE) Hate speech, OFFENSIVE and PROFANITY. Overall, 252 runs were submitted by 40 teams. The performance of the best classification algorithms for task A are F1 measures of 0.51, 0.53 and 0.52 for English, Hindi, and German, respectively. For task B, the best classification algorithms achieved F1 measures of 0.26, 0.33 and 0.29 for English, Hindi, and German, respectively. This article presents the tasks and the data development as well as the results. The best performing algorithms were mainly variants of the transformer architecture BERT. However, also other systems were applied with good success.
Quadrature Coherence Scale Driven Fast Decoherence of Bosonic Quantum Field States

We introduce, for each state of a bosonic quantum field, its quadrature coherence scale (QCS), a measure of the range of its quadrature coherences. Under coupling to a thermal bath, the purity and QCS are shown to decrease on a timescale inversely proportional to the QCS squared. The states most fragile to decoherence are therefore those with quadrature coherences far from the diagonal. We further show a large QCS is difficult to measure since it induces small scale variations in the state's Wigner function. These two observations imply a large QCS constitutes a mark of ``macroscopic coherence.'' Finally, we link the QCS to optical classicality: optical classical states have a small QCS and a large QCS implies strong optical nonclassicality.
Efficient estimation of Hindi WSD with distributed word representation in vector space

Word Sense Disambiguation (WSD) is significant for improving the accuracy of the interpretation of a Natural language text. Various supervised learning-based models and knowledge-based models have been developed in the literature for WSD of the language text. However, these models do not provide good results for low-resource languages, due to the lack of labelled and tagged data. Therefore, in this work, we have examined different word embedding techniques for word sense disambiguation of the Hindi language texts. Several studies in the literature show that these embeddings have been utilized for different foreign languages in the field of word sense disambiguation. However, to the best of our knowledge, no such work exists for the Hindi language. Therefore, in this paper, we utilize various existing word embeddings for WSD of Hindi text. Moreover, we have created Hindi word embeddings on articles taken from Wikipedia and test the quality of the created word embeddings using Pearson correlation. In this direction, we perform different experiments and observe that Word2Vec model gives best performance among all the considered embeddings on the used Hindi dataset. In our method, the proposed model directly takes input that is trained with word embedding methods and helps to develop a sense inventory using clustering that has been employed for performing disambiguation. Experimental observations indicate that the performance of the proposed approach is moderate and competent in terms of accuracy. The paper, thus, presents how WSD can leverage these representations to encode rich semantic information.
Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learning

Generalized Zero-Shot Learning (GZSL) targets recognizing new categories by learning transferable image representations. Existing methods find that, by aligning image representations with corresponding semantic labels, the semantic-aligned representations can be transferred to unseen categories. However, supervised by only seen category labels, the learned semantic knowledge is highly task-specific, which makes image representations biased towards seen categories. In this paper, we propose a novel Dual-Contrastive Embedding Network (DCEN) that simultaneously learns task-specific and task-independent knowledge via semantic alignment and instance discrimination. First, DCEN leverages task labels to cluster representations of the same semantic category by cross-modal contrastive learning and exploring semantic-visual complementarity. Besides task-specific knowledge, DCEN then introduces task-independent knowledge by attracting representations of different views of the same image and repelling representations of different images. Compared to high-level seen category supervision, this instance discrimination supervision encourages DCEN to capture low-level visual knowledge, which is less biased toward seen categories and alleviates the representation bias. Consequently, the task-specific and task-independent knowledge jointly make for transferable representations of DCEN, which obtains averaged 4.1% improvement on four public benchmarks.
A novel MR image denoising via LRMA and NLSS

Nonlocal self-similarity has been proven to be a useful tool for image denoising. For MR image denoising, the method combining the nonlocal self-similarity with the low-rank approximation has been recently attracting considerable attentions, due to its favorable performance. Since the original low-rank approximation problem is difficult to be solved, the frequently used method is to use the nuclear norm minimization for the matrix low-rank approximation. However, the solution obtained by nuclear norm minimization generally deviates from the solution of the original problem. In this paper, an approach for MR image denoising is proposed by combining a novel nonlocal self-similarity scheme with a novel low-rank approximation scheme. In proposed approach, a similarity evaluation with respect to the noise is proposed in the patch matching stage. To approximate the original low-rank minimization problem, the propose approach minimizes trace-based operator at each step. Every minimization is solvable and used to approximate the original low-rank minimization. An algorithm is established for this approximation, as well. Experimental results show that the proposed approach has a superior performance, comparing with some of the low-rank approximation methods, in both the objective quality metrics and visual inspections.
Leap Motion based Myanmar Sign Language Recognition using Machine Learning

Sign language is the main communication language for deaf and dumb people. There have many difficulties for deaf and dumb people when they communicate with normal people. Only a few people can use Sign Language proficiently and most of the people don't know how to communicate the deaf people. From the point of view of computer vision, we can develop the sign language recognition for deaf and dumb people. Most of the sign language recognition systems are based on the static sign. In motion-based sign language recognition system, we proposed feature extraction method and recognition using machine learning. Every nation has its own sign language and our proposed system is based on Myanmar National Sign Language.
Prerequisites defining the trajectory of creative thinking

Introduction . The modern, rapidly changing world needs specialists with creative thinking skills, who are able to show rapidity, flexibility and originality in solving complex and extraordinary problems. When a specialist finds himself in such a situation where he faces extraordinary problems, he does not know how to solve them and is at a loss. It is impossible to acquire creative thinking skills without preparation. Therefore, a student, a future specialist, must be taught these skills. However, the difficulty lies in the fact that without developing students' skills such as analysis, synthesis, abstract, associative and combinatorial thinking, selective comparison, generalisation of information, system vision, assessment of ideas, the ability to ask right questions, to visualise and to draw conclusions, it will be difficult to further develop creative thinking in students. Aim . The aim of the study is to discover the relationship between various types of thinking and creative thinking, to determine the types of thought operations and the list of prerequisites that precede creative thinking, which will contribute to the formation of creative thinking in students. Methodology and research methods . General logical reasoning methods and some scientific research methods were applied. The phenomenological method was employed to understand the process of creative thinking. Based on the determination of the main parameters and properties of each type of thinking, it became possible to model the process of creative thinking activity, to investigate this mental process and draw certain conclusions. The use of the explanatory method also makes it possible to substantiate the need for the following prerequisites for the formation of creative thinking. Results and scientific novelty . Based on the analysis, it was discovered that various types of thinking are related to creative thinking, the types of thought operations were determined and a list of prerequisites was proposed. The list of prerequisites includes the following types of thinking: positive, synergetic, associative, abstract, visual, algorithmic, divergent, lateral, Janusian, questioning style, combinatorial, intuitive, systemic, and critical. The research results show the importance and necessity of prerequisites for creative thinking and also determine the trajectory of creative thinking activity. Practical significance . The research results can be useful for teachers of higher educational institutions with the aim of applying them in the learning process.
Traceable Multiauthority Attribute-Based Encryption with Outsourced Decryption and Hidden Policy for CIoT

Cloud-assisted Internet of Things (IoT) significantly facilitate IoT devices to outsource their data for high efficient management. Unfortunately, some unsettled security issues dramatically impact the popularity of IoT, such as illegal access and key escrow problem. Traditional public-key encryption can be used to guarantees data confidentiality, while it cannot achieve efficient data sharing. The attribute-based encryption (ABE) is the most promising way to ensure data security and to realize one-to-many fine-grained data sharing simultaneously. However, it cannot be well applied in the cloud-assisted IoT due to the complexity of its decryption and the decryption key leakage problem. To prevent the abuse of decryption rights, we propose a multiauthority ABE scheme with white-box traceability in this paper. Moreover, our scheme greatly lightens the overhead on devices by outsourcing the most decryption work to the cloud server. Besides, fully hidden policy is implemented to protect the privacy of the access policy. Our scheme is proved to be selectively secure against replayable chosen ciphertext attack (RCCA) under the random oracle model. Some theory analysis and simulation are described in the end.
A Solution for the Remote Care of Frail Elderly Individuals via Exergames

Internet of Things (IoT) solutions are a concrete answer to many needs in the healthcare framework since they enable remote support for patients and foster continuity of care. Currently, frail elderly people are among end users who most need and would benefit from IoT solutions from both a social and a healthcare point of view. Indeed, IoT technologies can provide a set of services to monitor the healthcare of the elderly or support them in order to reduce the risk of injuries, and preserve their motor and cognitive abilities. The main feature of IoT solutions for the elderly population is ease of use. Indeed, to fully exploit the potential of an IoT solution, patients should be able to autonomously deal with it. The remote-monitoring validation engineering system (ReMoVES) described here is an IoT solution that caters to the specific needs of frail elderly individuals. Its architecture was designed for use at rehabilitation centers and at patients’ homes. The system is user-friendly and comfortably usable by persons who are not familiar with technology. In addition, exergames enhance patient engagement in order to curb therapy abandonment. Along with the technical presentation of the solution, a real-life scenario application is described referring to sit-to-stand activity.
Leveraging technology to enhance STEM Education Amidst the Covid-19 pandemic: An overview of pertinent concerns

Although technology in its various forms had already permeated peoples’ lives, the closure of educational institutions worldwide due to the outbreak of the Covid-19 pandemic thrust education sectors in many countries into fraught experimentation with online learning. Many educators had to adopt pedagogical practices that were in tandem with online instruction. The pandemic had a silver lining as it opened doors to new ideas and technologies that could be leveraged to enhance STEM education. This paper adopts the desk top research approach to establish the technologies that were leverageable for STEM education during the ongoing pandemic, and to determine pertinent concerns about moving STEM education online. The study found that most institutions leveraged basic synchronous and asynchronous technologies for STEM education during the pandemic. The study also established that a few institutions were embracing sophisticated technology like Virtual Reality and Augmented Reality in enhancing STEM education. There were also institutions already training VR and VR experts to meet anticipated STEM education manpower needs. It was established that while the use of technology enhanced STEM education during the Covid-19 pandemic, it raised concerns about access, equity, quality and student engagement. To address these concerns, the study recommends that governments should invest in the provision of power in rural settings and subsidize the cost of hardware for students. For equity considerations, the disadvantaged should be prioritized in the provision of technology enablers for online STEM education and measures that safeguard them should be inbuilt in the online transition plans. Finally, where possible remote labs should be established but this is not possible, students should be allowed to take series of scheduled physical lab sessions in turns and rules of social distancing enforced. Countries should look to superior technologies for online STEM education so that any similar future occurrences do not stall practical STEM education sessions.
End-to-End Argument Mining as Biaffine Dependency Parsing

Non-neural approaches to argument mining (AM) are often pipelined and require heavy feature-engineering.In this paper, we propose a neural end-to-end approach to AM which is based on dependency parsing, in contrast to the current state-of-the-art which relies on relation extraction.Our biaffine AM dependency parser significantly outperforms the state-ofthe-art, performing at F 1 = 73.5% for component identification and F 1 = 46.4% for relation identification.One of the advantages of treating AM as biaffine dependency parsing is the simple neural architecture that results.The idea of treating AM as dependency parsing is not new, but has previously been abandoned as it was lagging far behind the state-of-the-art.In a thorough analysis, we investigate the factors that contribute to the success of our model: the biaffine model itself, our representation for the dependency structure of arguments, different encoders in the biaffine model, and syntactic information additionally fed to the model.Our work demonstrates that dependency parsing for AM, an overlooked idea from the past, deserves more attention in the future.
IoT-school guidance: A holistic approach to vocational self-awareness &amp; career path

Schools are responsible for helping students in their vocational career development in the sense that they should sustain students in developing skills required by the job market. Pre-tertiary school guidance is an indispensable aspect, since poor scholar guidance is always associated with failure at school. In the context of career paths, we propose an innovative approach, a solution to guide pre-tertiary students to choose the optimum vocational path. The purpose of the current study is to develop a school guidance system called "IoT-School Guidance". The latter is supposed to create a smart and conducive environment for the successful adoption of school guidance using the Internet of things (IoT), given the fact that the IoT is the next revolutionary technology in the world where anything can transmit and receive information in real time. It is a new approach that includes implementing an adaptive orientation process using smart technologies to guide pre-tertiary students choose and follow the best professional career generated automatically by the IoT-school guidance system.
Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings

The aim of this paper is to provide an extended analysis of the outlier detection, using probabilistic and AI techniques, applied in a demo pilot demand response in blocks of buildings project, based on real experiments and energy data collection with detected anomalies. A numerical algorithm was created to differentiate between natural energy peaks and outliers, so as to first apply a data cleaning. Then, a calculation of the impact in the energy baseline for the demand response computation was implemented, with improved precision, as related to other referenced methods and to the original data processing. For the demo pilot project implemented in the Technical University of Cluj-Napoca block of buildings, without the energy baseline data cleaning, in some cases it was impossible to compute the established key performance indicators (peak power reduction, energy savings, cost savings, CO2 emissions reduction) or the resulted values were far much higher (&gt;50%) and not realistic. Therefore, in real case business models, it is crucial to use outlier’s removal. In the past years, both companies and academic communities pulled their efforts in generating input that consist in new abstractions, interfaces, approaches for scalability, and crowdsourcing techniques. Quantitative and qualitative methods were created with the scope of error reduction and were covered in multiple surveys and overviews to cope with outlier detection.
Witnesses of coherence and dimension from multiphoton indistinguishability tests

Quantum coherence marks a deviation from classical physics, and has been studied as a resource for metrology and quantum computation. Finding reliable and effective methods for assessing its presence is then highly desirable. Coherence witnesses rely on measuring observables whose outcomes can guarantee that a state is not diagonal in a known reference basis. Here we experimentally measure a novel type of coherence witness that uses pairwise state comparisons to identify superpositions in a basis-independent way. Our experiment uses a single interferometric set-up to simultaneously measure the three pairwise overlaps among three single-photon states via Hong-Ou-Mandel tests. Besides coherence witnesses, we show the measurements also serve as a Hilbert-space dimension witness. Our results attest to the effectiveness of pooling many two-state comparison tests to ascertain various relational properties of a set of quantum states.
Filtered BERT: Similarity Filter-Based Augmentation with Bidirectional Transfer Learning for Protected Health Information Prediction in Clinical Documents

For the secondary use of clinical documents, it is necessary to de-identify protected health information (PHI) in documents. However, the difficulty lies in the fact that there are few publicly annotated PHI documents. To solve this problem, in this study, we propose a filtered bidirectional encoder representation from transformers (BERT)-based method that predicts a masked word and validates the word again through a similarity filter to construct augmented sentences. The proposed method effectively performs data augmentation. The results show that the augmentation method based on filtered BERT improved the performance of the model. This suggests that our method can effectively improve the performance of the model in the limited data environment.