Reviewerly/exHarmony huggingface dataset:
An Innovative Approach for the Evaluation of the Web Page Impact Combining User Experience and Neural Network Score

The proposed paper introduces an innovative methodology useful to assign intelligent scores to web pages. The approach is based on the simultaneous use of User eXperience (UX), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM) algorithms, providing the web page scoring and taking into account outlier conditions to construct the training dataset. Specifically, the UX tool analyses different parameters addressing the score, such as navigation time, number of clicks, and mouse movements for page, finding possible outliers, the ANN are able to predict outliers, and the LSTM processes the web pages tags together with UX and user scores. The final web page score is assigned by the LSTM model corrected by the UX output and improved by the navigation user score. This final score is useful for the designer by suggesting the tags typologies structuring a new web page layout of a specific topic. By using the proposed methodology, the web designer is addressed to allocate contents in the web page layout. The work has been developed within a framework of an industry project oriented on the formulation of an innovative AI interface for web designers.
Research of the Relations Among Cloud Computing, Internet of Things, Big Data, Artificial Intelligence, Block Chain and Their Application in Maritime Field

Abstract At the forefront of technological development, cloud computing, the Internet of Things (IoT), big data, artificial intelligence (AI), and blockchain (CTDAB) are not mutually exclusive, but complementary and integrated. The connotative characteristics and correlation of cloud computing, IoT, big data, AI, and blockchain are mainly introduced in this study, and their application in maritime field are summarized. Cloud computing, big data, and AI are the core technologies for realizing IoT technology. Blockchain technology supplements the functions of IoT. The cross-support and comprehensive application of these emerging technologies has started in the process of people’s exploration of the maritime field, which has played a key role in promoting the intelligent development of the maritime field.
End-to-End Hierarchical Relation Extraction for Generic Form Understanding

Form understanding is a challenging problem which aims to recognize semantic entities from the input document and their hierarchical relations. Previous approaches face significant difficulty dealing with the complexity of the task, thus treat these objectives separately. To this end, we present a novel deep neural network to jointly perform both entity detection and link prediction in an end-to-end fashion. Our model extends the Multistage Attentional U-Net architecture with the Part-Intensity Fields and Part-Association Fields for link prediction, enriching the spatial information flow with the additional supervision from entity linking. We demonstrate the effectiveness of the model on the Form Understanding in Noisy Scanned Documents (FUNSD) dataset, where our method substantially outperforms the original model and state-of-the-art baselines in both Entity Labeling and Entity Linking task.
Globally-Robust Neural Networks

The threat of adversarial examples has motivated work on training certifiably robust neural networks to facilitate efficient verification of local robustness at inference time. We formalize a notion of global robustness, which captures the operational properties of on-line local robustness certification while yielding a natural learning objective for robust training. We show that widely-used architectures can be easily adapted to this objective by incorporating efficient global Lipschitz bounds into the network, yielding certifiably-robust models by construction that achieve state-of-the-art verifiable accuracy. Notably, this approach requires significantly less time and memory than recent certifiable training methods, and leads to negligible costs when certifying points on-line; for example, our evaluation shows that it is possible to train a large robust Tiny-Imagenet model in a matter of hours. Our models effectively leverage inexpensive global Lipschitz bounds for real-time certification, despite prior suggestions that tighter local bounds are needed for good performance; we posit this is possible because our models are specifically trained to achieve tighter global bounds. Namely, we prove that the maximum achievable verifiable accuracy for a given dataset is not improved by using a local bound.
The Relevance of Inquiry-Based Learning in Basic Reading Skills Exercises for Improving Student Learning Outcomes in Madrasah Ibtidaiyah

This study discussed inquiry-based learning activities in reading skills training students in Madrasah Ibtidayah and elementary schools in Indonesia. In learning to understand according to Islam, reading skills receive special attention from other skills such as speaking, listening, and writing skills. Get an understanding and application of strengthening reading learning that can be started from the elementary school level, and this theme will be discussed with the literature review method from national and international journal publications. A descriptive qualitative design study will be a method of analyzing a series of data. The review process involves a phenomenological approach, coding systems, and critical evaluation to produce data findings to answer our research themes that meet the findings' validity and reliability. Finally, we can conclude our findings, among others, that the ten publications and literature have confirmed that the application of inquiry-based learning approaches is very relevant to be applied in the learning environment of elementary madrasah schools. Thus these findings will be useful to whichever party
Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models

In federated learning, models are learned from users’ data that are held private in their edge devices, by aggregating them in the service provider’s “cloud” to obtain a global model. Such global model is of great commercial value in, e.g., improving the customers’ experience. In this paper we focus on two possible areas of improvement of the state of the art. First, we take the difference between user habits into account and propose a quadratic penalty-based formulation, for efficient learning of the global model that allows to personalize local models. Second, we address the latency issue associated with the heterogeneous training time on edge devices, by exploiting a hierarchical structure modeling communication not only between the cloud and edge devices, but also within the cloud. Specifically, we devise a tailored block coordinate descent-based computation scheme, accompanied with communication protocols for both the synchronous and asynchronous cloud settings. We characterize the theoretical convergence rate of the algorithm, and provide a variant that performs empirically better. We also prove that the asynchronous protocol, inspired by multi-agent consensus technique, has the potential for large gains in latency compared to a synchronous setting when the edge-device updates are intermittent. Finally, experimental results are provided that corroborate not only the theory, but also show that the system leads to faster convergence for personalized models on the edge devices, compared to the state of the art.
Survey of Target Detection Based on Neural Network

Abstract Target detection is a hot topic in the field of artificial intelligence, which is widely used in robot, UAV, aerospace and other fields. In this paper, the research background and significance of target detection are summarized, and two categories of target detection algorithms based on deep learning, i.e., candidate region based and regression based, are described. For the first category, a series of region with convolutional neural network (r-cnn) algorithms are introduced, this paper introduces the researchers’ research on the basis of r-cnn algorithm: the improvement of feature extraction network, pooling layer of region of interest and non-maximum suppression algorithm. The second algorithm is divided into Yolo (you only look once) series, SSD (single shot multibox detector) algorithm and its improvement. According to the current target detection algorithm in the development of more efficient and reasonable development trend, the research hotspot of target detection algorithm in the future is prospected, including unsupervised and unknown class object detection.
The relationship between the use of digital devices and cognitive development in preschool children: Evidence from scholarly literature

Introduction. Preschool children are active users of digital devices, which affects their cognitive development. The nature of these impacts has not been sufficiently studied, and there are different opinions of experts on this issue. Thus, the purpose of this article is to analyze the findings of research investigations devoted to the impact of digital devices on preschool children’s cognitive development. Materials and Methods. The authors conducted a systematic literature review of 46 full-text research articles describing the relationship between the use of digital devices (screen time and media content features) and the development of speech and mathematical skills, as well as the development of executive functions in 3-7 year-old children. The review is based on the cultural-historical approach. Results. The analysis of studies has shown that the impact of using digital devices on preschool children’s cognitive development depends on screen time, the exposure type (background TV or targeted use of digital devices), the content (educational or entertaining content), age appropriateness (age-appropriate or inappropriate content), the extent of how realistic the content is (fantasy or realistic content), the stimulus material complexity (visual or audial, black and white or color). There are also conclusions about the most relevant prospects for further research on the impact of the use of digital devices on the cognitive development of children. Conclusions. The review has indicated that long screen time negatively affects the development of speech and mathematical skills and executive functions in preschool children. The study has revealed that the content impact on cognitive development depends on the characteristics of this content. In particular, children-oriented educational content can contribute to cognitive development. Adult-oriented content can negatively affect cognitive development.
UserAdapter: Few-Shot User Learning in Sentiment Analysis

Adapting a model to a handful of personalized data is challenging, especially when it has gigantic parameters, such as a Transformerbased pretrained model.The standard way of fine-tuning all the parameters necessitates storing a huge model for each user.In this work, we introduce a lightweight approach dubbed UserAdapter, which clamps hundred millions of parameters of the Transformer model and optimizes a tiny user-specific vector.We take sentiment analysis as a test bed, and collect datasets of reviews from Yelp and IMDB respectively.Results show that, on both datasets, UserAdapter achieves better accuracy than the standard fine-tuned Transformerbased pre-trained model.More importantly, UserAdapter offers an efficient way to produce a personalized Transformer model with less than 0.5% parameters added for each user.
Uncertainty-Based Semantics for Multi-Agent Knowing How Logics

We introduce a new semantics for a multi-agent epistemic operator of knowing how, based on an indistinguishability relation between plans.Our proposal is, arguably, closer to the standard presentation of knowing that modalities in classical epistemic logic.We study the relationship between this semantics and previous approaches, showing that our setting is general enough to capture them.We also define a sound and complete axiomatization, and investigate the computational complexity of its model checking and satisfiability problems.
Updated Lithological Map in the Forest Zone of the Centre, South and East Regions of Cameroon Using Multilayer Perceptron Neural Network and Landsat Images

The Multilayer Perceptron Neural Network (MLPNN) induction technique has been successfully applied to a variety of machine learning tasks, including the extraction and classification of image features.However, not much has been done in the application of MLPNN on images obtained by remote sensing.In this article, two automatic classification systems used in image feature extraction and classification from remote sensing data are presented.The first is a combination of two models: a MLPNN induction technique, integrated under ENVI (Environment for Visualizing Images) platform for classification, and a pre-processing model including dark subtraction for the calibration of the image, the Principal Components Analysis (PCA) for band selections and Independent Components Analysis (ICA) as blind source separator for feature extraction of the Landsat image.The second classification system is a MLPNN induction technique based on the Keras platform.In this case, there was no need for pre-processing model.Experimental results show the two classification systems to outperform other typical feature extraction and classification methods in terms of accuracy for some lithological classes including Granite1 class with the highest class accuracies of 96.69% and 92.69% for the first and second classification system respectively.Meanwhile, the two How to cite this paper:
General Purpose Atomic Crosschain Transactions

The General Purpose Atomic Crosschain Transaction (GPACT) protocol allows composable programming across multiple Ethereum blockchains. It allows for inter-contract and inter-blockchain function calls that are both synchronous and atomic: if one part fails, the whole call graph of function calls is rolled back. The protocol operates on existing Ethereum blockchains without modification. It works for both public and consortium blockchains, and is expected to work across heterogeneous blockchains other than Ethereum. The protocol has been analysed in terms of Gas usage and Finalised Block Periods for writing a value from one blockchain to another.
Virtual Reality for Hazard Mitigation and Community Resilience: An Interdisciplinary Collaboration with Community Engagement to Enhance Risk Awareness

To achieve community resilience and mitigate the consequences of natural hazards, community officials must balance competing priorities for local resources and funding.Besides the challenge of dealing with multiple competing priorities, community officials face another challenge: low risk awareness of natural hazards by the public and other stakeholders.Considering that virtual reality (VR) has been used to enhance learning and to change attitudes and behaviors, animating natural hazards in VR has the potential to enhance stakeholders' (e.g., the public, local/state/federal governments, insurance agencies, and property owners) risk awareness.Informed stakeholders make better decisions related to protective action.Therefore, we propose using VR to create a sense of presence and immersion that can provide stakeholders with hazard exposure, demonstrate a hazard's personalized consequences, and simulate the consequences of protective action, which, in turn, can influence attitudes and behavioral intentions of the general public to take protective action.Researchers could also apply VR to other hazardous or life-threatening situations and use interdisciplinary research to identify best methods to develop realistic and credible VR that all citizens can access to help mitigate hazards and enhance community resilience.
Efficient Model Monitoring for Quality Control in Cardiac Image Segmentation

Deep learning methods have reached state-of-the-art performance in cardiac image segmentation. Currently, the main bottleneck towards their effective translation into clinics requires assuring continuous high model performance and segmentation results. In this work, we present a novel learning framework to monitor the performance of heart segmentation models in the absence of ground truth. Formulated as an anomaly detection problem, the monitoring framework allows deriving surrogate quality measures for a segmentation and allows flagging suspicious results. We propose two different types of quality measures, a global score and a pixel-wise map. We demonstrate their use by reproducing the final rankings of a cardiac segmentation challenge in the absence of ground truth. Results show that our framework is accurate, fast, and scalable, confirming it is a viable option for quality control monitoring in clinical practice and large population studies.
Sensor Integrated Load-Bearing Structures: Measuring Axis Extension with DIC-Based Transducers

In this paper we present a novel, cost-effective camera-based multi-axis force/torque sensor concept for integration into metallic load-bearing structures. A two-part pattern consisting of a directly incident and mirrored light beam is projected onto the imaging sensor surface. This allows the capturing of 3D displacements, occurring due to structure deformation under load in a single image. The displacement of defined features in size and position can be accurately analyzed and determined through digital image correlation (DIC). Validation on a prototype shows good accuracy of the measurement and a unique identification of all in- and out-of-plane displacement components under multiaxial load. Measurements show a maximum deviation related to the maximum measured values between 2.5% and 4.8% for uniaxial loads (Fx, Fy,Fz,Mz) and between 2.5% and 10.43% for combined bending, torsion and axial load. In the course of the investigations, the measurement inaccuracy was partly attributed to the joint used between the sensor parts and the structure as well as to eccentric load.
Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions?

Is it possible to use natural language to intervene in a model's behavior and alter its prediction in a desired way?We investigate the effectiveness of natural language interventions for reading-comprehension systems, studying this in the context of social stereotypes.Specifically, we propose a new language understanding task, Linguistic Ethical Interventions (LEI), where the goal is to amend a questionanswering (QA) model's unethical behavior by communicating context-specific principles of ethics and equity to it.To this end, we build upon recent methods for quantifying a system's social stereotypes, augmenting them with different kinds of ethical interventions and the desired model behavior under such interventions.Our zero-shot evaluation finds that even today's powerful neural language models are extremely poor ethical-advice takers, that is, they respond surprisingly little to ethical interventions even though these interventions are stated as simple sentences.Fewshot learning improves model behavior but remains far from the desired outcome, especially when evaluated for various types of generalization.Our new task thus poses a novel language understanding challenge for the community. 1
Scalable Affinity Propagation for Massive Datasets

Affinity Propagation (AP) is a fundamental algorithm to identify clusters included in data objects. Given a similarities among objects, it iteratively performs message updates between all data object pairs until convergence. Although AP yields a higher clustering quality compared with other methods, it is computationally expensive. Hence, it has difficulty handling massive datasets that include numerous data objects. This is because the message updates require a quadratic cost of the number of data objects. Here, we propose a novel fast algorithm, ScaleAP, which outputs the same clusters as AP but within a shorter computation time. ScaleAP dynamically excludes unnecessary message updates without sacrificing its clustering accuracy. Our extensive evaluations demonstrate that ScaleAP outperforms existing AP algorithms in terms of running time by up to two orders of magnitude.
A Few Brief Notes on DeepImpact, COIL, and a Conceptual Framework for Information Retrieval Techniques

Recent developments in representational learning for information retrieval can be organized in a conceptual framework that establishes two pairs of contrasts: sparse vs. dense representations and unsupervised vs. learned representations. Sparse learned representations can further be decomposed into expansion and term weighting components. This framework allows us to understand the relationship between recently proposed techniques such as DPR, ANCE, DeepCT, DeepImpact, and COIL, and furthermore, gaps revealed by our analysis point to "low hanging fruit" in terms of techniques that have yet to be explored. We present a novel technique dubbed "uniCOIL", a simple extension of COIL that achieves to our knowledge the current state-of-the-art in sparse retrieval on the popular MS MARCO passage ranking dataset. Our implementation using the Anserini IR toolkit is built on the Lucene search library and thus fully compatible with standard inverted indexes.
Subsequence Based Deep Active Learning for Named Entity Recognition

Active Learning (AL) has been successfully applied to Deep Learning in order to drastically reduce the amount of data required to achieve high performance.Previous works have shown that lightweight architectures for Named Entity Recognition (NER) can achieve optimal performance with only 25% of the original training data.However, these methods do not exploit the sequential nature of language and the heterogeneity of uncertainty within each instance, requiring the labelling of whole sentences.Additionally, this standard method requires that the annotator has access to the full sentence when labelling.In this work, we overcome these limitations by allowing the AL algorithm to query subsequences within sentences, and propagate their labels to other sentences.We achieve highly efficient results on OntoNotes 5.0, only requiring 13% of the original training data, and CoNLL 2003, requiring only 27%.This is an improvement of 39% and 37% compared to querying full sentences.k , k > 1 groups the raw labels c 2k-2 and c 2k-1 .
Detection of Face Mask in Real-time using Convolutional Neural Networks and Open-CV

The world has witnessed a major uproar in the year 2020 with the widespread transmission of COVID-19. The propensity for wearing a face mask has become essential as a countermeasure against the transmission of the infection particularly in open settings where keeping up with social distancing is not functional more often than not. As a result, wearing a face mask is crucial to curb the spread of the pandemic. This paper is concerned with a simplified approach for the detection of face mask in real-time using convolutional neural networks (CNN) and Open-CV. The proposed CNN model trains on a dataset of 12000 images of faces with and without mask using two convolutional layers and predicts on real-time video streams using the Haar cascade classifier of Open-CV. The model reported an accuracy score of 98.8% on the training set and 99.37% on the validation set using the CNN architecture without undergoing any problems of overfitting.