Reviewerly/exHarmony huggingface dataset:
YouTube Videos in the Virtual Flipped Classroom Model Using Brain Signals and Facial Expressions

As a result of the confinement due to the COVID-19 pandemic, various educational institutions migrated their face-to-face teaching modality to a virtual modality. This article presents the implementation of the Flipped Classroom model in a completely virtual format to develop grammatical competency in Spanish. The model used videos from YouTube, one of the leading global social network platforms, and the videoconferencing system Zoom, the tool selected by the studied educational institution to continue academic operations during the health confinement. The model was enriched with the Index for Learning Style test to provide more differentiated teaching. This study showed considerable improvement in the academic performance of high school students taking a Spanish course at the Mexico City campus of Tecnologico de Monterrey. Of the total sample, 98% increased their score by between 2 and 46 points, from a total of 100, in their grammatical competency in Spanish. Additionally, the student satisfaction survey showed that more than 90% considered the course methodology beneficial for developing their grammatical competency in Spanish. This study demonstrates the potential of the Flipped Classroom model in a virtual format. This teaching structure using the Flipped Classroom model could be replicated in various educational settings and for different areas of knowledge.
Adaptive Information Seeking for Open-Domain Question Answering

Information seeking is an essential step for open-domain question answering to efficiently gather evidence from a large corpus.Recently, iterative approaches have been proven to be effective for complex questions, by recursively retrieving new evidence at each step.However, almost all existing iterative approaches use predefined strategies, either applying the same retrieval function multiple times or fixing the order of different retrieval functions, which cannot fulfill the diverse requirements of various questions.In this paper, we propose a novel adaptive information-seeking strategy for open-domain question answering, namely AISO.Specifically, the whole retrieval and answer process is modeled as a partially observed Markov decision process, where three types of retrieval operations (e.g., BM25, DPR, and hyperlink) and one answer operation are defined as actions.According to the learned policy, AISO could adaptively select a proper retrieval action to seek the missing evidence at each step, based on the collected evidence and the reformulated query, or directly output the answer when the evidence set is sufficient for the question.Experiments on SQuAD Open and HotpotQA fullwiki, which serve as single-hop and multi-hop open-domain QA benchmarks, show that AISO outperforms all baseline methods with predefined strategies in terms of both retrieval and answer evaluations.
An Artificial Intelligence URL Parser for Safer Web Browsing and Detection of Suspicious Links

With more than seven billion people actively using the Internet, the number of cyber attacks has increased, and personal data breaches have become a concern among the general public. The COVID-19 pandemic has only increased the use of online platforms and services for work and leisure activities, which opens the door to more scams, viruses, and other cyber security breaches. Guided by SEO techniques and research regarding dangerous website and domain patterns, we have designed and implemented a visual system that tracks suspicious links on an active webpage and marks them in order to alert users to proceed with caution. Our AI utilizes linear regression to best detect trends in URL parsing, comparing them with registered unsafe links to see if they pose similar threats. The results reveal that AI isn’t entirely accurate since some trends are hard to decipher; however, it can reliably flag certain redirects and out-of-domain links that would otherwise remain hidden to users.
Kalman Filter Photovoltaic Power Prediction Model Based on Forecasting Experience

A Kalman filter photovoltaic (PV) power prediction model based on forecasting experience is proposed to solve the problem that the accuracy of the prediction method based on historical experience is reduced under anomalous situations. This study uses the hourly solar irradiance forecasting model, numerical weather prediction (NWP) data, and the photoelectric conversion model to calculate the ground irradiance and PV power generation, which are used as the forecasting experience data. The dynamic equation of the Kalman filter model is obtained by fitting the forecasting data to make the prediction model with the future situation information properties while solving the modeling difficulties caused by the transcendental equation characteristic of the photoelectric conversion model. In the iterative process of the Kalman filter algorithm, the measured power is used to correct the prediction error and significantly limit the error variability so as to realize the ultra-short-term accurate prediction of PV power and ultimately improve the management of PV energy storage power stations. The comparative analysis through DKASC data simulation verifies that the results show that the proposed model is effective and can achieve better results in predictive accuracy.
Improving Arabic Sentiment Analysis Using CNN-Based Architectures and Text Preprocessing

Sentiment analysis is an essential process which is important to many natural language applications. In this paper, we apply two models for Arabic sentiment analysis to the ASTD and ATDFS datasets, in both 2-class and multiclass forms. Model MC1 is a 2-layer CNN with global average pooling, followed by a dense layer. MC2 is a 2-layer CNN with max pooling, followed by a BiGRU and a dense layer. On the difficult ASTD 4-class task, we achieve 73.17%, compared to 65.58% reported by Attia et al., 2018. For the easier 2-class task, we achieve 90.06% with MC1 compared to 85.58% reported by Kwaik et al., 2019. We carry out experiments on various data splits, to match those used by other researchers. We also pay close attention to Arabic preprocessing and include novel steps not reported in other works. In an ablation study, we investigate the effect of two steps in particular, the processing of emoticons and the use of a custom stoplist. On the 4-class task, these can make a difference of up to 4.27% and 5.48%, respectively. On the 2-class task, the maximum improvements are 2.95% and 3.87%.
A conceptual framework for externally-influenced agents: an assisted reinforcement learning review

A long-term goal of reinforcement learning agents is to be able to perform tasks in complex real-world scenarios. The use of external information is one way of scaling agents to more complex problems. However, there is a general lack of collaboration or interoperability between different approaches using external information. In this work, while reviewing externally-influenced methods, we propose a conceptual framework and taxonomy for assisted reinforcement learning, aimed at fostering collaboration by classifying and comparing various methods that use external information in the learning process. The proposed taxonomy details the relationship between the external information source and the learner agent, highlighting the process of information decomposition, structure, retention, and how it can be used to influence agent learning. As well as reviewing state-of-the-art methods, we identify current streams of reinforcement learning that use external information in order to improve the agent's performance and its decision-making process. These include heuristic reinforcement learning, interactive reinforcement learning, learning from demonstration, transfer learning, and learning from multiple sources, among others. These streams of reinforcement learning operate with the shared objective of scaffolding the learner agent. Lastly, we discuss further possibilities for future work in the field of assisted reinforcement learning systems.
Continuous detection of concept drift in industrial cyber-physical systems using closed loop incremental machine learning

Abstract The embedded, computational and cloud elements of industrial cyber physical systems (CPS) generate large volumes of data at high velocity to support the operations and functions of corresponding time-critical and mission-critical physical entities. Given the non-deterministic nature of these entities, the generated data streams are susceptible to dynamic and abrupt changes. Such changes, which are formally defined as concept drifts, leads to a decline in the accuracy and robustness of predicted CPS behaviors. Most existing work in concept drift detection are classifier dependent and require labeled data. However, CPS data streams are unlabeled, unstructured and change over time. In this paper, we propose an unsupervised machine learning algorithm for continuous concept drift detection in industrial CPS. This algorithm demonstrates three types of unsupervised learning, online, incremental and decremental. Furthermore, it distinguishes between abrupt and reoccurring drifts. We conducted experiments on SEA, a widely cited synthetic dataset of concept drift detection, and two industrial applications of CPS, task tracking in factory settings and smart energy consumption. The results of these experiments successfully validate the key features of the proposed algorithm and its utility of detecting change in non-deterministic CPS environments.
Mitigating Bias in Metadata

Controlled vocabularies used in cultural heritage organizations (galleries, libraries, archives, and museums) are a helpful way to standardize terminology but can also result in misrepresentation or exclusion of systemically marginalized groups. Library of Congress Subject Headings (LCSH) is one example of a widely used yet problematic controlled vocabulary for subject headings. In some cases, systemically marginalized groups are creating controlled vocabularies that better reflect their terminology. When a widely used vocabulary like LCSH and a controlled vocabulary from a marginalized community are both available as linked data, it is possible to incorporate the terminology from the marginalized community as an overlay or replacement for outdated or absent terms from more widely used vocabularies. This paper provides a use case for examining how the Homosaurus, an LGBTQ+ linked data controlled vocabulary, can provide an augmented and updated search experience to mitigate bias within a system that only uses LCSH for subject headings.
Crude oil prices and volatility prediction by a hybrid model based on kernel extreme learning machine

In view of the important position of crude oil in the national economy and its contribution to various economic sectors, crude oil price and volatility prediction have become an increasingly hot issue that is concerned by practitioners and researchers. In this paper, a new hybrid forecasting model based on variational mode decomposition (VMD) and kernel extreme learning machine (KELM) is proposed to forecast the daily prices and 7-day volatility of Brent and WTI crude oil. The KELM has the advantage of less time consuming and lower parameter-sensitivity, thus showing fine prediction ability. The effectiveness of VMD-KELM model is verified by a comparative study with other hybrid models and their single models. Except various commonly used evaluation criteria, a recently-developed multi-scale composite complexity synchronization (MCCS) statistic is also utilized to evaluate the synchrony degree between the predictive and the actual values. The empirical results verify that 1) KELM model holds better performance than ELM and BP in crude oil and volatility forecasting; 2) VMD-based model outperforms the EEMD-based model; 3) The developed VMD-KELM model exhibits great superiority compared with other popular models not only for crude oil price, but also for volatility prediction.
MRE: A Military Relation Extraction Model Based on BiGRU and Multi-Head Attention

A great deal of operational information exists in the form of text. Therefore, extracting operational information from unstructured military text is of great significance for assisting command decision making and operations. Military relation extraction is one of the main tasks of military information extraction, which aims at identifying the relation between two named entities from unstructured military texts. However, the traditional methods of extracting military relations cannot easily resolve problems such as inadequate manual features and inaccurate Chinese word segmentation in military fields, failing to make full use of symmetrical entity relations in military texts. With our approach, based on the pre-trained language model, we present a Chinese military relation extraction method, which combines the bi-directional gate recurrent unit (BiGRU) and multi-head attention mechanism (MHATT). More specifically, the conceptual foundation of our method lies in constructing an embedding layer and combining word embedding with position embedding, based on the pre-trained language model; the output vectors of BiGRU neural networks are symmetrically spliced to learn the semantic features of context, and they fuse the multi-head attention mechanism to improve the ability of expressing semantic information. On the military text corpus that we have built, we conduct extensive experiments. We demonstrate the superiority of our method over the traditional non-attention model, attention model, and improved attention model, and the comprehensive evaluation value F1-score of the model is improved by about 4%.
Variational quantum algorithms to estimate rank, quantum entropies, fidelity, and Fisher information via purity minimization

Variational quantum algorithms (VQAs) that estimate values of widely used physical quantities such as the rank, the quantum entropies, the Bures fidelity, and the quantum Fisher information of mixed quantum states are developed. In addition, variations of these VQAs are also adapted to perform other useful functions such as quantum state learning and approximate fractional inverses. The common theme shared by the proposed algorithms is that their cost functions are all based on minimizing the quantum purity of a quantum state. Strategies to mitigate or avoid the problem of exponentially vanishing cost function gradients are also discussed.
Machine learning amalgamation of Mathematics, Statistics and Electronics

Interdisciplinary research is a manner of research carried out by an individual or group of persons.The knowledge, data, techniques, concepts are incorporated from two or more disciplines.In this paper we tried to throw light on this concept.Machine learning is a branch of computer science which uses the information, tools for collection of data, methods for analysis from the subjects like Electronics, Mathematics and Statistics.Why we use machine learning?Because it plays an influential role in prediction of data.Machine learning is used to find hidden patterns and essential ideas from data as well as it solve complex problems.In today's world, many applications have large volume of data like structured, unstructured and semi structured.This unexploited resource of knowledge can be used to improve business decisions.As data diversifies many are adapting to machine learning tool for analysis of data, so that, they can exploit intelligence and benefit from that data at most.Machine learning adopts different algorithms and each algorithm performs different functionality.In this paper, we tried to explain through example, how Electronics is used for collection of data while Mathematics and Statistics are used for analysis and finally using Machine learning results can be predicted.
MeLT: Message-Level Transformer with Masked Document Representations as Pre-Training for Stance Detection

Much of natural language processing is focused on leveraging large capacity language models, typically trained over single messages with a task of predicting one or more tokens.However, modeling human language at higher-levels of context (i.e., sequences of messages) is underexplored.In stance detection and other social media tasks where the goal is to predict an attribute of a message, we have contextual data that is loosely semantically connected by authorship.Here, we introduce Message-Level Transformer (MeLT) -a hierarchical messageencoder pre-trained over Twitter and applied to the task of stance prediction.We focus on stance prediction as a task benefiting from knowing the context of the message (i.e., the sequence of previous messages).The model is trained using a variant of masked-language modeling; where instead of predicting tokens, it seeks to generate an entire masked (aggregated) message vector via reconstruction loss.We find that applying this pre-trained masked messagelevel transformer to the downstream task of stance detection achieves F1 performance of 67%.
Shape Similarity Intersection-Over-Union Loss Hybrid Model for Detection of Synthetic Aperture Radar Small Ship Objects in Complex Scenes

With the continuous development and utilization of marine environments, the demand for accurate identification of ship targets at sea is increasing in both military and civilian fields.Synthetic aperture radar (SAR) is used to detect ship targets at sea and can provide 24-h detection under any weather conditions.Deep-learning models enable the effective detection of ship targets using SAR images; however, the recognition accuracy may be low or false positives may occur in complex scenarios wherein it is difficult to detect the ship targets.Current target-detection tasks include target classification and positioning through boundingbox regression.Herein, a regression loss function is derived to calculate the position of the bounding box, and intersection over union (IoU) is applied to estimate the positioning accuracy.As a result, a gap exists between the commonly used positioning losses for regressing the parameters of a bounding box and the optimization of these metric values.Therefore, the proposed hybrid model combines classification, localization, and segmentation with a novel multi-task loss function for boundarybox localization based on the improved IoU.This solves the problem of inconsistency between training and evaluation and improves the positioning accuracy.Experiments were conducted using the SAR dataset for ship detection; the dataset was labeled by SAR experts and included multi-scale ship chips with a resolution of 256 pixels in both range and azimuth.In summary, the experimental results indicate that the proposed hybrid model could improve the detection accuracy in complex scenarios, and its false positive rate is significantly lower than those of the other models. Index Terms-SAR image
Efficient topology control of blockchain peer to peer network based on SDN paradigm

Blockchain allows to securely store, using cryptography functions, validated transactions and other data across its peer to peer (P2P) network. This P2P network is generated and maintained by the participating peers in a distributed manner through peer discovery, neighbor selection and managing inbound/outbound connections. As these tasks require extra consumption of network bandwidth, energy, memory and CPU usage, resource-limited devices may not support inherent blockchain applications. Further, changes in the topology control functions or reconfiguration should be considered by all peers to fully benefit from the new performance, which can take time. The aim of this paper is to build a blockchain network in a manner that greatly reduce topology control overhead while guaranteeing the properties such as high flexibility, fast reconfigurability, connectivity, small diameter and clustering. For this, we propose to use the Software-Defined Networking (SDN) paradigm to manage the blockchain P2P network. This way, the topology control tasks are moved off the peers to a secure overlay layer composed of multiple servers having synchronized databases. This new layer is responsible for building and managing the topological structure of the P2P network layer based on random r-out digraphs. Next, we mathematically discuss r-out digraphs generation using binomial distribution and preferential attachment models. Then, and in order to reduce the number of connections per peer, we establish lower and minimum upper bounds on outbound and inbound connections respectively that still guarantee the P2P network feasibility and connectivity. Further, we investigate topological properties of blockchain P2P network such as connectivity, diameter and clustering. Finally, we provide extensive simulation and numerical results to verify the efficiency of our approach and illustrate the effects of centralized topology control on network performance.
Characterizing and Predicting Good First Issues

Background. Where to start contributing to a project is a critical challenge for newcomers of open source projects. To support newcomers, GitHub utilizes the Good First Issue (GFI) label, with which project members can manually tag issues in an open source project that are suitable for the newcomers. However, manually labeling GFIs is time- and effort-consuming given the large number of candidate issues. In addition, project members need to have a close understanding of the project to label GFIs accurately.
Integration of IoT and Blockchain

present, we are in the world of digital revolution. Use of smartphones and internet accelerated due to the impact of the novel Covid-19 virus. The whole world started to turn digital. The industrialization of Internet of Things (IoT) enables more devices to connect and communicate which leads to many data transfer transactions. The architecture of IoT is centralized. The distributed and decentralized architecture of Blockchain can be used to provide secure and scalable transactions of IoT devices. Blockchain is a distributed ledger technology, which provides secure data transactions that cannot be tampered and altered. In this paper, we provide advantages and challenges of integrating IoT and Blockchain. We also provide different architectures and algorithms proposed by researchers to provide secure data transactions. We shall also shed light on the future research directions of integrating Blockchain and IoT.
A Quaternion Two‐Stream R‐CNN Network for Pixel‐Level Color Image Splicing Localization

Recently, Zhou et al. designed a two-stream faster Region-Convolutional neural networks (R-CNN) model RGB-N for color image splicing localization in CVPR2018. However, the RGB-N locates spliced regions only at block-level and ignores the entirety and inherent correlation of three channels. Therefore, an improved quaternion two-stream R-CNN model is proposed to solve these drawbacks: a mask branch combining fully convolutional network and condition random field is added for locating spliced regions at pixel-level; quaternion representation of color images is used to process color spliced images in a holistical way. In addition, feature pyramid network based on quaternion residual network is considered to extract multi-scale features for color spliced images; attention region proposal network is combined with attention mechanism and is designed to pay more attention to the spliced regions; a high-pass filter designed for image splicing detection specifically is adopted to replace steganalysis rich model filter in the RGB-N to obtain noise input for the noise stream. Experimental results on a new synthetic dataset and three standard forgery datasets demonstrate that the proposed method is superior to the existing methods in the abilities of localization, generalization, and robustness.