Reviewerly/exHarmony huggingface dataset:
A Context-Enhanced De-identification System

Many modern entity recognition systems, including the current state-of-the-art de-identification systems, are based on bidirectional long short-term memory (biLSTM) units augmented by a conditional random field (CRF) sequence optimizer. These systems process the input sentence by sentence. This approach prevents the systems from capturing dependencies over sentence boundaries and makes accurate sentence boundary detection a prerequisite. Since sentence boundary detection can be problematic especially in clinical reports, where dependencies and co-references across sentence boundaries are abundant, these systems have clear limitations. In this study, we built a new system on the framework of one of the current state-of-the-art de-identification systems, NeuroNER, to overcome these limitations. This new system incorporates context embeddings through forward and backward n -grams without using sentence boundaries. Our context-enhanced de-identification (CEDI) system captures dependencies over sentence boundaries and bypasses the sentence boundary detection problem altogether. We enhanced this system with deep affix features and an attention mechanism to capture the pertinent parts of the input. The CEDI system outperforms NeuroNER on the 2006 i2b2 de-identification challenge dataset, the 2014 i2b2 shared task de-identification dataset, and the 2016 CEGS N-GRID de-identification dataset ( p &amp;#x003C; 0.01 ). All datasets comprise narrative clinical reports in English but contain different note types varying from discharge summaries to psychiatric notes. Enhancing CEDI with deep affix features and the attention mechanism further increased performance.
Multi-scale Sentiment Analysis of Location-Enriched COVID-19 Arabic Social Data

After the recent outbreak of COVID-19, researchers have risen working on several challenges related to the mining of social data to learn about people's reactions to the epidemic. Recent studies have largely focused on extracting current themes and inferring broad attitudes, with a particular emphasis on the English language. This study presents various perspective for Arabic social data mining to provide in-depth insights related to the COVID-19 pandemic. We initially devised a method for inferring geographical whereabouts from Arabic tweets not initially geotagged. Secondly, a sentiment analysis mechanism based on Arabic word embeddings is introduced, with several levels of geographical granularity (regions/countries) considered. Sentiment-based classifications of topics and subtopics related to COVID-19 will also be presented. According to our findings, the overall percentage of location-enabled tweets has increased from 2% to 46% (about 2.5M tweets). During the pandemic, Arab Twitter users' negative emotions about lockdown, restriction, and law enforcement were also widely expressed.
A wide-spectrum plug-and-play Fizeau interferometric system

In this paper, we present a wide-spectrum plug-and-play Fizeau interferometric system, which can complete precision interferometric measurement at any wavelength in the range of 600-1600 nm with a maximum measurement aperture of 150 mm. The system can be designed with multiple optical fiber input terminals, different wavelengths share only one set of interferometric system, and no components need to be adjusted when switching the working wavelength. The development of the system is helpful to accurately measure the surface profile error of coated optical elements at a specified wavelength.
TIMPANY—deTectIon of Model Poisoning Attacks usiNg accuracY

Nowadays, Federated Learning has widely been adopted for data security in the Industrial IoTs. With Federated Learning, local Industrial IoTs devices download the current machine learning model and update it on their own local Industrial IoTs devices. Then, local Industrial IoTs devices transmit these locally trained models back to the Industrial Server. The Industrial Server aggregates all the locally trained models into a single consolidated and enhanced global model. On one side, Federated Learning secures the data; on the other side, Federated Learning itself is vulnerable to one subtle yet severe attack: the model poisoning attack. Model poisoning attack is difficult to detect, especially in Industrial IoTs applications, for two reasons: a) neither the Industrial Server nor the local Industrial IoTs devices in Federated Learning is capable of identifying poisoned local models, and b) every iteration of Federated Learning consists of many Industrial IoTs devices, and therefore, verification of every single device is computationally expensive. Thus, this study proposes an effective and efficient framework for deTectIon of Model Poisoning Attacks usiNg AccuracY (TIMPANY). TIMPANY is the first detection framework for the model poisoning attack that utilizes accuracy as a detection measure. We performed theoretical analysis of TIMPANY with other detection solutions (for model poisoning attack) concerning communication and computational efficiency, security, and detection accuracy. Our thorough theoretical comparative analysis showed that TIMPANY efficiently addresses these open research challenges that previous studies failed to address. In our thorough experimental analysis, error analysis from the first iteration shows that TIMPANY results in 0% error, leading to a True Positive Rate and accuracy of 100% with 0% False Positive Rate. Thus, TIMPANY outperformed some of the existing detection solutions for model poisoning attacks against Federated Learning. We conclude that TIMPANY is effective and efficient against model poisoning attacks in Federated Learning, even for resource-constrained Industrial IoTs devices widely used in various industrial applications.
Lesion Detection in Breast Tomosynthesis Using Efficient Deep Learning and Data Augmentation Techniques

Digital breast tomosynthesis (DBT) is one of the powerful breast cancer screening technologies. DBT can improve the ability of radiologists to detect breast cancer, especially in the case of dense breasts, where it beats mammography. Although many automated methods were proposed to detect breast lesions in mammographic images, very few methods were proposed for DBT due to the unavailability of enough annotated DBT images for training object detectors. In this paper, we present fully automated deep-learning breast lesion detection methods. Specifically, we study the effectiveness of two data augmentation techniques (channel replication and channel-concatenation) with five state-of-the-art deep learning detection models. Our preliminary results on a challenging publically available DBT dataset showed that the channel-concatenation data augmentation technique can significantly improve the breast lesion detection results for deep learning-based breast lesion detectors.
Summary Markov Models for Event Sequences

Datasets involving sequences of different types of events without meaningful time stamps are prevalent in many applications, for instance when extracted from textual corpora. We propose a family of models for such event sequences -- summary Markov models -- where the probability of observing an event type depends only on a summary of historical occurrences of its influencing set of event types. This Markov model family is motivated by Granger causal models for time series, with the important distinction that only one event can occur in a position in an event sequence. We show that a unique minimal influencing set exists for any set of event types of interest and choice of summary function, formulate two novel models from the general family that represent specific sequence dynamics, and propose a greedy search algorithm for learning them from event sequence data. We conduct an experimental investigation comparing the proposed models with relevant baselines, and illustrate their knowledge acquisition and discovery capabilities through case studies involving sequences from text.
Attention-driven Graph Clustering Network

The combination of the traditional convolutional network (i.e., an auto-encoder) and the graph convolutional network has attracted much attention in clustering, in which the auto-encoder extracts the node attribute feature and the graph convolutional network captures the topological graph feature. However, the existing works (i) lack a flexible combination mechanism to adaptively fuse those two kinds of features for learning the discriminative representation and (ii) overlook the multi-scale information embedded at different layers for subsequent cluster assignment, leading to inferior clustering results. To this end, we propose a novel deep clustering method named Attention-driven Graph Clustering Network (AGCN). Specifically, AGCN exploits a heterogeneity-wise fusion module to dynamically fuse the node attribute feature and the topological graph feature. Moreover, AGCN develops a scale-wise fusion module to adaptively aggregate the multi-scale features embedded at different layers. Based on a unified optimization framework, AGCN can jointly perform feature learning and cluster assignment in an unsupervised fashion. Compared with the existing deep clustering methods, our method is more flexible and effective since it comprehensively considers the numerous and discriminative information embedded in the network and directly produces the clustering results. Extensive quantitative and qualitative results on commonly used benchmark datasets validate that our AGCN consistently outperforms state-of-the-art methods.
Cybernotary as Support for Cybersecurity and Resilience

Cybernotary is intended to facilitate and accelerate the process of creating authentic deeds, including complying with all actions, agreements, or provisions required by law and ensuring all requested statements from interested parties are stated in the authentic deed. Strong, professional, and reli
Catch'em young! nurturing the enquiry among students in school library set up during Covid-19

The role and responsibilities of school libraries have always been an area of close observation. The concepts and activities related to curiosity and enquiry among the young students are on the initial levels while the beginning of their studies takes place. This study has endeavored to explore the current scenario in the school library reflecting the levels of involvement of students with the library resources and services, and their impact on their studies; to gain the real experiences related to enquiry and learning among students, along with literature review.
A Novel Trade Transaction Agreement Algorithm Using Blockchain Consensus Mechanism

Blockchain, the underlying technology of Bitcoin, has been deeply studied in various fields after its development in recent years. As a typical decentralized distributed data storage system, consensus reached among all participants in a blockchain system requires a consensus mechanism to be realized. In order to make blockchain applicable to different application scenarios, different consensus mechanisms have been proposed. With the further development of blockchain applications, more and more studies have been conducted on the consensus mechanism. However, some existing consensus mechanisms still have some problems in various aspects. Therefore, this paper proposes a trade deal algorithm based on the blockchain mechanism of consensus. First of all, according to PBFT, the lack of a dynamic problem in the VPBFT voting mechanism was introduced. The node system is divided into four types with different responsibilities and gives the number of relations between nodes. When the number of nodes is changed, it can be calculated according to the quantity relation, ensuring dynamic. Second, a data anonymous transaction and authentication protocol is designed. In the protocol, when the seller sells data, the mapping relationship between the real identity and the false identity of the data owner is blinded and sent to the buyer. When the buyer wants to verify their identity, the seller’s identity can only be verified with the authentication of the blockchain. The proposed algorithm is superior to the current consensus in terms of time and energy consumption, throughput, and fault tolerance methods, which is proven through experimental tests and simulation analysis.
New Media Ecology and Theoretical Foundations for Nonfiction Digital Narrative Creative Practice

Digital storytelling techniques and persuasive communications are becoming increasingly intertwined and realized in cultural discourses such as cultural heritage, environmental activism, and politics.Rhetorical theory has grounded and influenced communication practice since the age of oration, and as society is increasingly undergoing new mediatization, digital rhetorical theory can be reexamined and applied to nonfiction digital narratives for improved practice.Narratology provides key theoretical foundations that are braided into digital rhetoric for application to digital nonfiction narratives.This article highlights how new media has changed the impacts of the modes of persuasion (ethos, pathos, logos, and kairos) on today's multimedia-consuming audiences and how the classical rhetorical canons (invention, arrangement, style, delivery, and memory) can be reframed and updated by incorporating narrative theory to aid creators of new nonfiction digital narratives across different genres.
Extending 6-DoF VR Experience Via Multi-Sphere Images Interpolation

Three-degrees-of-freedom (3-DoF) omnidirectional imaging has been widely used in various applications ranging from street maps to 3-DoF VR live broadcasting. Although allowing for navigating viewpoints rotationally inside a virtual world, it does not provide motion parallax key for human 3D perception. Recent research mitigates this problem by introducing 3 transitional degrees of freedom (6-DoF) using multi-sphere images (MSI) which is beginning to show promises in handling occlusions and reflective objects. However, the design of MSI naturally limits the range of authentic 6-DoF experiences, as existing mechanisms for MSI rendering cannot fully utilize multi-layer information when synthesizing novel views between multiple MSIs. To tackle this problem and extend the 6-DoF range, we propose an MSI interpolation pipeline that utilizes adjacent MSIs' 3D information embedded inside their layers. In this work, we describe an MSI projection scheme along with an MSI interpolation network to predict intermediate MSIs in order to facilitate the need for extended range. We demonstrate that our system significantly improves the range of 6-DoF experience compared with other MSI-based methods. With extensive experiments, we show our algorithm outperforms state-of-the-art methods both qualitatively and quantitatively in synthesizing novel view panoramas.
An Efficacious Text Summarization Process Using Triple Encoding-Decoding Mechanism

As the big data and internet growing huge day by day, there is in need to overwhelm people by the information in large. These kind of issues makes researchers to create a technique that can generate automatic text. This research is very great interesting topic and the summarization of text is the process which undergoes finding important information within a document. Automatic text summarization is the process where a subset is created which represents the relevant content using the original text trained. So, text summarization is an intimidating challenge in Natural Language Processing because getting a good summary is a very big challenge as it is based on various aspects like metrics, non-redundancy, readability etc., At the outset, the text is trained into the system, next the trained text is fed into encoder decoder process where additional techniques are employed between the result in best coherent summary. The aim of automatic text summarization is to the produce finest and top quality text summary and our model also produces supreme quality summary.
Technical Elements of Machine Learning for Intellectual Property Law

Recent advances in artificial intelligence (AI) technologies have transformed our lives in profound ways. Indeed, AI has not only enabled machines to see (e.g., face recognition), hear (e.g., music retrieval), speak (e.g., speech synthesis), and read (e.g., text processing), but also, so it seems, given machines the ability to think (e.g., board game-playing) and create (e.g., artwork generation). This chapter introduces the key technical elements of machine learning (ML), which is a rapidly growing sub-field in AI and drives many of the aforementioned applications. The goal is to elucidate the ways human efforts are involved in the development of ML solutions, so as to facilitate legal discussions on intellectual property issues.
Idealized Influence and Inspirational Motivation in a Microfinance Context: Review of Literature

Besides the enormous attention paid to transformational leadership construct for the last four decades, much of the literature does not adequately delve deeper into the respective dimensions of the construct, thus limiting the clarity of how the dimensions impact organizations. This paper reviews the extant conceptual, theoretical, and empirical literature on the idealized influence and inspirational motivation dimensions of transformational leadership style focusing on microfinance context. This paper presents a comprehensive and integrative theoretical framework for knowledge advancement in the field of leadership. The methodology used for the review integrates the desktop and critical analysis of 56 journal articles in these dimensions. The key databases used to extract the relevant literature were JSTOR, Emerald, Google Scholar, DOAJ, and Wiley Interscience, with 69.09% being articles published in 2017 – 2021. Aspects used to qualify articles for review consideration were transformational leadership, idealized influence, inspirational motivation, and leadership in microfinance or any combination. The emerging gaps in the theorization and conceptualization of idealized influence and inspirational motivation were identified, presenting a case for further research on the transformational leadership construct. The review establishes that the four “Is” of transformational leadership (Idealized influence, Inspirational motivation, Intellectual stimulation, and Individualized consideration) are ambiguously interrelated, presenting a challenge of incoherence in the conceptual models used by researchers to conduct empirical research. The study also seeks to fill the contextual gap on the scanty research done to date on the outcomes of idealized influence and inspirational motivation in microfinance context such as staff retention, organizational commitment, self-efficacy, and organizational performance. A comprehensive conceptual framework for guiding further research on the constructs is formulated, including supportive propositions that can be empirically tested.
Voice memos por WhatsApp en la locución verbal del inglés en estudiantes de educación secundaria

The objective of the research is to determine the relationship of the implementation of the Voice memos by WhatsApp in the verbal locution of English in high school students of the school N ° 88044 Coishco, 2021. A correlational descriptive typology was implemented with a non-experimental design. Given that the significance is .623 being less than 0.05 (0.000 <0.05), the H1 or affirmative hypothesis is accepted and H0 or null is rejected, which indicates that using Voice memos by WhatsApp has a favorable effect on the generation of the verbal locution of English by the surveyed students. It is found that WhatsApp contributes to academic training from a group work perspective, allowing to contribute to assertive communication between teachers and students in order to build learning contextualized to the social realities of the participants.
YOUTUBE DATA ANALYSIS USING HADOOP FRAMEWORK

— Analysis of consistent and structured data has seen huge success in past decades. Where the analysis of unstructured data in the form of multimedia format remains a challenging task. YouTube is one of the most used and popular social media tool. The main aim of this paper is to analyze the data that is generated from YouTube that can be mined and utilized. API (Application Programming Interface) and going to be stored in Hadoop Distributed File System (HDFS). Dataset can be analyzed using MapReduce. Which is used to identify the video categories in which most number of videos are uploaded. The objective of this paper is to demonstrate Hadoop framework, to process and handle big data there are many components. In the existing method, big data can be analyzed and processed in multiple stages by using MapReduce. Due to huge space consumption of each job, Implementing iterative map reduce jobs is expensive. A Hive method is used to analyze the big data to overcome the drawbacks of existing methods, which is the state-ofthe-art method. The hive works by extracting the YouTube information by generating API (Application Programming Interface) key and uses the SQL queries.
Shopping in a Pandemic: A Persuasive Game for COVID-19

Dino-Store is a persuasive game that was designed to use gamification way to communicate with people and raise awareness on COVID-19. The game's setting is grocery shopping and the mechanic indicates that how different protection strategies, such as wearing mask, keeping social distance can affect people's infection chances in the COVID-19 pandemic. This paper will break down how the game designed by merging concepts from persuasive game models and uncomfortable interaction theory to create an engaging, but stressful experience for the user.
Personnel training in Russia: response to modern challenges and threats in the context of digitalization of the economy

The article discusses the results of a study conducted at the Research Institute “New Economics and Business” of the Plekhanov Russian University of Economics. University science, the system of university research is a modern form of integration of science and digital technologies. The development of the research methodology and the transformation of personnel training for the Russian economy, taking into account the emerging risks and threats to business, society and the state, is one of the priority areas of university research. The rapid development of digital technologies not only opens up opportunities for achieving the goals of sustainable socio-economic development, but also creates threats and risks, including of an ethical nature. It allows us to link positive trends and the growth of sustainable development from the widespread use of digital resources, tools and technologies, as well as possible risks and threats associated with it. According to the authors, the answer to the risks and threats that have arisen can only be a deep complex transformation of the entire domestic personnel training system. Only a digital educational ecosystem is able to form a modern specialist with high adaptability and self-learning. The circumstances considered in the article determine the relevance of such studies and indicate the need for digital transformation of personnel training for the Russian economy in the current socio-economic conditions.