Reviewerly/exHarmony huggingface dataset:
Online Learning Support Service System Architecture Based on Location Service Architecture

With the development of network, location-based service architecture is gradually applied to the learning support service system. The purpose of this study is to develop an online learning support system using allocation information service architecture. In this study, the 2019 year of this study was selected as a research material through an online learning support service system within six months. The system architecture of this study is the Browser/Server architecture. Based on this, we analyze the learning result data using the weight setting of the feature attribute and the learning evaluation algorithm. The results show that the improved algorithm based on location-based service architecture can get the best algorithm accuracy when the weight of the whole algorithm is about α = 0.4. For the degree of demand for learning guidance, 16.2% thought it was absolutely necessary and 50.7% thought it was more necessary. And 70.6% of the learners thought that the learning system was not very accurate or generally accurate. It is concluded that the learning support service system of this study has a certain role. It provides a new path to improve the quality of online education learning support service and promotes the development of online education.
Secure Messaging Platform Based on Blockchain

The boundaries between personal and business communications is a key issue faced by most organizations. Use of unsecured and unsafe applications in workplaces pose enormous security risks. Companies are not adequately aware about the applications that are being used in their employees' devices. When it comes to critical business communication involving exchanging trade secrets, making business referrals and strategic business decisions, protecting of messages and shared files becomes a challenge. Most publicly available communication platforms do not empower organizations to regulate, track and scale their communication and does not provide compliance with data protection frameworks, which can result in cross industry system risks. As a result, both individuals and organizations express deep concern about data security and protection of privacy when using Instant Messaging applications. Non-repudiation in communications not only conveys to the user, recognition of the communication process, but it is also a crucial way to establish a relationship of trust and to overcome trust disputes. Our primary objective, through this research, is to develop a chat application with more secure channels of enterprise level communication. Using new technologies such as blockchain, which operate on a decentralized model, we can surmount the drawbacks of traditional messaging applications, thereby ensuring confidentiality, integrity and availability of official data, along with advanced auditing features.
Towards electronic-photonic-converged thermo-optic feedback tuning

Abstract As Moore’s law approaching its end, electronics is hitting its power, bandwidth, and capacity limits. Photonics is able to overcome the performance limits of electronics but lacks practical photonic register and flexible control. Combining electronics and photonics provides the best of both worlds and is widely regarded as an important post-Moore’s direction. For stability and dynamic operations considerations, feedback tuning of photonic devices is required. For silicon photonics, the thermo-optic effect is the most frequently used tuning mechanism due to the advantages of high efficiency and low loss. However, it brings new design requirements, creating new design challenges. Emerging applications, such as optical phased array, optical switches, and optical neural networks, employ a large number of photonic devices, making PCB tuning solutions no longer suitable. Electronic-photonic-converged solutions with compact footprints will play an important role in system scalability. In this paper, we present a unified model for thermo-optic feedback tuning that can be specialized to different applications, review its recent advances, and discuss its future trends.
THE MODAL LOGIC OF STEPWISE REMOVAL

Abstract We investigate the modal logic of stepwise removal of objects, both for its intrinsic interest as a logic of quantification without replacement, and as a pilot study to better understand the complexity jumps between dynamic epistemic logics of model transformations and logics of freely chosen graph changes that get registered in a growing memory. After introducing this logic ( MLSR ) and its corresponding removal modality, we analyze its expressive power and prove a bisimulation characterization theorem. We then provide a complete Hilbert-style axiomatization for the logic of stepwise removal in a hybrid language enriched with nominals and public announcement operators. Next, we show that model-checking for MLSR is PSPACE-complete, while its satisfiability problem is undecidable. Lastly, we consider an issue of fine-structure: the expressive power gained by adding the stepwise removal modality to fragments of first-order logic.
ClEnDAE: A classifier based on ensembles with built-in dimensionality reduction through denoising autoencoders

High dimensionality is an issue that affects most classification algorithms. This factor implies that the predictive performance of many traditional classifiers decreases considerably as the number of features increases. Therefore, there are numerous proposals that try to mitigate the effects of this issue. This study proposes ClEnDAE, a new classifier based on ensembles whose components incorporate denoising autoencoders (DAEs) to reduce the dimensionality of the input space. On the one hand, the use of ensembles improves the predictive performance by using several components that work jointly. On the other hand, the use of DAEs allows a new higher-level, smaller-sized feature space to be generated, reducing high dimensionality effects. Finally, an experimentation is conducted with the goal of evaluating the behavior of ClEnDAE. The first part of the test compares the performance of ClEnDAE to a model based on basic DAE and to the original untreated data. The second part analyzes the results of ClEnDAE and other traditional methods of dimensionality reduction in order to determine the improvement achieved with the proposed algorithm. In both parts of the experimentation, conclusions show that ClEnDAE offers better predictive performance than the other analyzed models. The main advantage of the ClEnDAE method is the combination of the potential of the ensemble-based methodology, where several components work in parallel, and DAEs, which generate new low-dimensional features that provide more relevant information. Therefore, the classification performance is better than with other classic proposals.
Tough but Effective

Accessibility is a topic that is recognized as increasingly important in both industry and academia. In industry, there is a growing awareness of the importance of designing technologies that provide an optimal user experience regardless of the user's ability or impairment. At the university level, this awareness of the need for accessibility minded future technologists has encouraged the incorporation of accessibility and inclusive design content into a wide range of computer science and related courses. Despite this growing awareness, the actual process of teaching students about the needs and perspectives of persons with disabilities remains challenging. Within this paper we report on our experience teaching a graduate course on inclusive design with a specific focus on our experience incorporating a participatory design (PD) activity involving older adults as the course's concluding design activity. While students responded favorably to much of the course's content, learning was enhanced through the design activity which mirrored a real-world project and included three older adult (80+) co-designers. Despite the benefits of PD, students were at times overwhelmed by the workload and the logistical difficulties of working within a remote participatory design process. We believe that a review of our experience and student reflections may prove beneficial to those similarly teaching inclusive design and accessibility.
Quotebank

We present Quotebank, an open corpus of 178 million quotations attributed to the speakers who uttered them, extracted from 162 million English news articles published between 2008 and 2020. In order to produce this Web-scale corpus, while at the same time benefiting from the performance of modern neural models, we introduce Quobert, a minimally supervised framework for extracting and attributing quotations from massive corpora. Quobert avoids the necessity of manually labeled input and instead exploits the redundancy of the corpus by bootstrapping from a single seed pattern to extract training data for fine-tuning a BERT-based model. Quobert is language- and corpus agnostic and correctly attributes 86.9% of quotations in our experiments. Quotebank and Quobert are publicly available at https://doi.org/10.5281/zenodo.4277311.
Genre annotation for the Web

Abstract This paper describes a digital curation study aimed at comparing the composition of large Web corpora, such as enTenTen, ukWac or ruWac, by means of automatic text classification. First, the paper presents a Deep Learning model suitable for classifying texts from large Web corpora using a small number of communicative functions, such as Argumentation or Reporting. Second, it describes the results of applying the automatic classification model to these corpora and compares their composition. Finally, the paper introduces a framework for interpreting the results of automatic genre classification using linguistic features. The framework can help in comparing general reference corpora obtained from the Web and in comparing corpora across languages.
FANet: Feature Aggregation Network for Semantic Segmentation

Due to the rapid development in robotics and autonomous industries, optimization and accuracy have become an important factor in the field of computer vision. It becomes a challenging task for the researchers to design an efficient, optimized model with high accuracy in the field of object detection and semantic segmentation. Some existing off-line scene segmentation methods have shown an outstanding result on different datasets at the cost of a large number of parameters and operations, whereas some well-known real-time semantic segmentation techniques have reduced the number of parameters and operations in demand for resource-constrained applications, but model accuracy is compromised. We propose a novel approach for scene segmentation suitable for resource-constrained embedded devices by keeping a right balance between model architecture and model performance. Exploiting the multi-scale feature fusion technique with accurate localization augmentation, we introduce a fast feature aggregation network, a real-time scene segmentation model capable of handling high-resolution input image (1024 × 2048 px). Relying on an efficient embedded vision backbone network, our feature pyramid network outperforms many existing off-line and real-time pixel-wise deep convolution neural networks (CNNs) and produces 89.7% pixel accuracy and 65.9% mean intersection over union (mIoU) on the Cityscapes benchmark validation dataset whilst having only 1.1M parameters and 5.8B FLOPS.
Network Visualization and Pyramidal Feature Comparison for Ablative Treatability Classification Using Digitized Cervix Images

Uterine cervical cancer is a leading cause of women’s mortality worldwide. Cervical tissue ablation is an effective surgical excision of high grade lesions that are determined to be precancerous. Our prior work on the Automated Visual Examination (AVE) method demonstrated a highly effective technique to analyze digital images of the cervix for identifying precancer. Next step would be to determine if she is treatable using ablation. However, not all women are eligible for the therapy due to cervical characteristics. We present a machine learning algorithm that uses a deep learning object detection architecture to determine if a cervix is eligible for ablative treatment based on visual characteristics presented in the image. The algorithm builds on the well-known RetinaNet architecture to derive a simpler and novel architecture in which the last convolutional layer is constructed by upsampling and concatenating specific RetinaNet pretrained layers, followed by an output module consisting of a Global Average Pooling (GAP) layer and a fully connected layer. To explain the recommendation of the deep learning algorithm and determine if it is consistent with lesion presentation on the cervical anatomy, we visualize classification results using two techniques: our (i) Class-selective Relevance Map (CRM), which has been reported earlier, and (ii) Class Activation Map (CAM). The class prediction heatmaps are evaluated by a gynecologic oncologist with more than 20 years of experience. Based on our observation and the expert’s opinion, the customized architecture not only outperforms the baseline RetinaNet network in treatability classification, but also provides insights about the features and regions considered significant by the network toward explaining reasons for treatment recommendation. Furthermore, by investigating the heatmaps on Gaussian-blurred images that serve as surrogates for out-of-focus cervical pictures we demonstrate the effect of image quality degradation on cervical treatability classification and underscoring the need for using images with good visual quality.
Situated impoliteness revisited: Blunt anti-epidemic slogans and conflicting comments during the coronavirus outbreak in China

In this paper, blunt slogans used in China's health campaign against coronavirus are closely examined and the public's conflicting comments on them are analyzed. These slogans, due to their extreme effectiveness in making the public comply with the health preventive measures suggested by the government, are called Yinghe or "hardcore" slogans by the Chinese people. Containing harsh and taboo language, they convey threats of death and disease, insults or negative evaluation, and harsh demands. Despite their impolite nature, "hardcore" slogans have received significantly more positive judgment than negative judgment, especially when they made their debut in rural areas in Henan, an agricultural province in China. Criticism towards these blunt slogans then gradually increased after their initial appearance. Plausible factors contributing to the change of judgment are analyzed. The public's conflicting judgment regarding the impoliteness of the slogans may be related to the public's different positioning across time and space. This study shows that impoliteness is a practice situated not only in discourse, genres, and institutions, but also in social, cultural, and political contexts. More attention should be paid to impoliteness in special social configurations (e.g., rural areas) and social emergencies, which not only contextualize a discourse event but also define it.
Staged data delivery protocol: A blockchain‐based two‐stage protocol for non‐repudiation data delivery

Abstract For many years, non‐repudiation in data delivery has not been satisfactorily achieved due to the difficulty of supervising delivery behavior. Existing fair protocols are still not entirely free of third parties who are usually not as trustworthy as expected. Moreover, most of them are built on complex encryption schemes, resulting in inefficiencies when handling large chunks of data and non‐exchanged data sharing. To be able to deliver data without third parties and repudiation, we propose the staged data delivery protocol (SDDP) that integrates fair data delivery into Blockchain consensus processes. SDDP confirms transactions of data delivery in a cascading manner where nodes sign on each transaction one after another. It consists of two stages, in which neither party will have a considerable advantage over the other. In this way, SDDP enables non‐repudiation of directly delivering large chunks of data through an understandable communication strategy; and the Blockchain network replaces third parties to preserve an immutable history of data delivery activities. The results of extensive evaluations show that SDDP is admissible for dealing with fair data delivery on the basis of its good qualities and performance.
Interruptions in Agile Software Development Teams

Agile approaches help software development project teams to better meet user needs and ensure flexibility in uncertain environments. But using agile approaches invites changes to the project and increases interactions between team members, which both cause interruptions in the workplace. While interruptions can help in task completion and increase process flexibility, they can also hinder employee productivity. We conducted an exploratory study of four agile software development teams. Our analysis identified (1) programming-related work impediments, (2) interaction-related interruptions, and (3) interruptions imposed by the external environment, which were managed by improved information retrieval and reduced team dependencies.
A comparative survey on data encryption Techniques: Big data perspective

The prime aspect in data handling is the invulnerability of Data, therefore, Encryption is the most conventionally used technique for data security. for instance, Data Encryption Standard (DES), Rivest–Shamir-Adleman (RSA), Advanced Encryption Standard (AES) to attain it moreover, with the exponentially growing of data over time leads to the concept of Big Data which involves not only the structured but also unstructured data such as images, audio, video, and other data formats. Encrypting such varied and immense data is a challenge in itself. In this paper, we delve into some of the major algorithms and techniques for efficient encryption and decryption of the data. Moreover, we have also done a comparative analysis of the different encryption and decryption techniques to predict the use of these techniques based on application requirements. The main objective of this work is to convey a better focus on data encryption approaches and to bring out the major issues and application areas where these encryption algorithms best suit.
Hybrid Machine Learning Model for Forecasting Solar Power Generation

Solar power generation through photovoltaic technology is one of the most popular renewable energy sources. But solar energy is a non-dispatchable source and it is dynamic in nature. Hence from the power system operation point of view, solar power forecasting becomes imperative for a stable grid operation. In this paper, a novel hybrid machine learning approach is proposed for forecasting solar power generation through a hybrid Ensemble Averager technique which exploits the advantages of different machine learning approaches and incorporates them into a single model. Missing values in insolation have been dealt with using a univariate regression-based imputation technique. The ensemble averager is a weighted average model of five individual models, namely - a non-linear autoregressive neural network (NAR-NN), a non-linear autoregressive neural network with exogenous signal (NARX-NN), a least square boosted decision tree model, a support vector regressor with RBF kernel and an Extreme Learning Machine (ELM). The proposed model is tested on a real-world dataset of a 1 MW solar park situated in Gujarat, India (23°09'15.1”N 72°40'00.8”E). Proposed model shows better performance as compared to other models.
Convolutional Network based Animal Recognition using YOLO and Darknet

In general, the manual detection of animals with their names is a very tedious task. To overcome this challenge, this research work has developed a YOLOV3 model to identify the animal present in the image given by user. The algorithm used in YOLOV3 model is darknet, which has a pre-trained dataset. The overall performance of the model is based on different training images and testing images of the dataset. The main goal of this research work is to build an animal recognition methodology using YOLOV3 model. The image of animal will be given as input, then it will display the name of the animal as output by using YOLOV3 model. The detection is done by using a pre-trained coco dataset from darknet.
Why do builds fail?—A conceptual replication study

Previous studies have investigated a wide range of factors potentially explaining software build breakages, focusing primarily on build-triggering code changes or previous CI outcomes. However, code quality factors such as the presence of code/test smells have not been yet evaluated in the context of CI, even though such factors have been linked to problems of comprehension and technical debt, and hence might introduce bugs and build breakages. This paper performs a conceptual replication study on 27,675 Travis CI builds of 15 GitHub projects, considering the features reported by Rausch et al. and Zolfagharinia et al., as well as those related to code/test smells. Using a multivariate model constructed from nine dimensions of features, results indicate a precision (recall) ranging between 58.3% and 79.0% (52.4% and 69.6%) in balanced project datasets, and between 2.5% and 37.5% (2.5% and 12.4%) in imbalanced project datasets. Models trained on our balanced project datasets were later used to perform cross-project prediction on the imbalanced projects, achieving an average improvement of 9.3% (16.2%) in precision (recall). Statistically, the results confirm that features from the build history, author, code complexity, and code/test smell dimensions are the most important predictors of build failures.
Method of Encoding Binary Structures of Stationary Component of Video Stream

It is shown that the existing technical characteristics of video information systems do not provide the required information delivery time. Possibilities for reducing the amount of service data generated during processing of differentially presented frames are considered. To reduce the volume of the matrix of signs, it is proposed to take into account the structural similarity with the matrix of the binary mask. On the basis of a series of single elements of the matrix of a binary mask, the formation of a binary vector of indicators of signs of the dynamic component is justified. The main stages of the development of a method for compressing a matrix of signs, implemented on the basis of coding by the power of the alphabet, are described, which makes it possible to reduce the number of bits for the presentation of service information.
Virtual reality and sustainable behavior in business

In this short communication we propose virtual reality (VR) as an experimental approach to study sustainable behavior in business, taking advantage of its characteristics that provide objective assessment methods with high validity. This approach exploits VR capabilities to generate immersive environments that recreate situations under which theorized relationships of sustainable behavior reveal themselves. We conjecture that high-immersive virtual environment (HIVE) research on corporate responsiveness to green demand will improve our understanding of the conditions and contexts under which a theory-driven approach to study this sustainable behavior applies.