Reviewerly/exHarmony huggingface dataset:
Does Brandenburg v. Ohio still hold in the social media era? Racist (and other) online hate speech and the First Amendment

First Amendment protections for hate speech, before and after the advent of the Internet, have been the subject of various court cases. This study presents the results of a content analysis of tweets using racist, anti-Semitic, anti-Islamic and homophobic language. Tweet content is considered using a framework from prior court cases: does a tweet constitute political speech; contribute to the marketplace of ideas; encourage violence or include threats? The results show that messages of hate can be communicated without using overly offensive words; and that Twitter users discuss who should vs. should not use a word. Study findings are considered in the context of the U.S. having become more divided politically, and social media's role in amplifying racists' messaging.
Generalized Nets as a Tool for Modelling of Railway Networks. Part 2

In this paper a new (second) example is given that shows that generalized nets can be used as a tool for modelling of railway networks. In the first part of the research, a generalized net model of the railway network in Southern Bulgaria was given, while here, a generalized net model of the railway network in Northern Bulgaria is described.
Rule Learning Through Active Inductive Inference

We propose a grammar-based approach to active inference based on hypothesis-driven rule learning where new hypotheses are generated on the fly. This contrasts with traditional approaches based on fixed hypothesis spaces and Bayesian model reduction. We apply these two contrasting approaches to an established active inference task and show that grammar-based agents’ performance benefits from the explicit rule representation underpinning hypothesis generation. Our proposal is a synthesis of the active inference framework with language-of-thought models, which paves the way for computational-level descriptions of false inference based on an aberrant hypothesis-generating process.
Improving local symmetry estimations in RGB-D images by fitting superquadrics

Real-time manipulation tasks rely on finding good candidates for apprehension points which, in turn, usually requires the computation of local symmetries. When RGB-D images are used as input in- formation, these local symmetries can be deduced from segmenting these images and computing geometric moments for each cluster of points. This approach gives a rough approximation because it does not take into account that the considered points lie on a surface. In this paper, to improve the quality of the symmetry estimations, we propose a simple refinement process that takes as input the estimation obtained using moments and then fits a superquadric to the considered set of points. We evaluate our approach on data collected using a Microsoft's Kinect 2 sensor. The obtained experimental results demonstrate the efficacy of the proposed approach.
Corporate and project Master's degree program: a new format of interaction between business and university

There are given some reasons for organizational and structural changes in management systems and ways of positioning universities in the market of educational services. As there is a need to analyze new practices of interaction with enterprises and organize new formats of the educational process. The case of the flagship university in Siberia on the introduction of an innovative format for implementing the Master's degree program in the direction of "Management" in partnership with a production corporation is described. An overview of the existing formats for implementing project activities in universities is presented, and a description of the business request for corporate training programs is given. Organizational and managerial limitations and obstacles that the organizer had to face are highlighted and analyzed, and ways to overcome them are given. The conditions for increasing the practice of implementing corporate design Master's degree and replicating the experience are outlined.
STP-Net: Spatio-Temporal Polarization Network for action recognition using polarimetric videos

Deep learning has brought tremendous progress in computer vision and natural language processing, and is used in multiple non-critical applications. A major bottleneck for its use in many other areas is the black box nature of these algorithms, resulting in a lack of explainability in their decisions. One of the key problems identified is the confounding effect, which causes confusion between the desired causes and other irrelevant factors affecting an outcome. This is more pronounced in the spatio-temporal case, such as the bias on the static background in the classification of a video. A way to handle this is by making use of sensors that capture additional scene properties, to mitigate spurious associations. In this work, we integrate the polarimetric videos with deep learning and evaluate it on the popular action recognition problem. We construct a dataset of polarimetric videos for fine-grained actions and study the effect of various parameters, extracted from the polarimetric video frames, as inputs to a deep network. Using these observations, we design a spatio-temporal polarization network (STP-Net) to effectively extract polarimetric features. This is evaluated on the recent HumanAct12 dataset for human activity recognition. Extensive evaluation clearly shows that the polarimetric modality is able to localize the correct action regions, leading to better generalizability.
Distinguishing transformative from incremental clinical evidence: A classifier of clinical research using textual features from abstracts and citing sentences

In clinical research and clinical decision-making, it is important to know if a study changes or only supports the current standards of care for specific disease management. We define such a change as transformative and a support as incremental research. It usually requires a huge amount of domain expertise and time for humans to finish such tasks. Faculty Opinions provides us with a well-annotated corpus on whether a research challenges or only confirms established research. In this study, a machine learning approach is proposed to distinguishing transformative from incremental clinical evidence. The texts from both abstract and a 2-year window of citing sentences are collected for a training set of clinical studies recommended and labeled by Faculty Opinions experts. We achieve the best performance with an average AUC of 0.755 (0.705–0.875) using Random Forest as the classifier and citing sentences as the feature. The results showed that transformative research has more typical language patterns in citing sentences than abstract sentences. We provide an efficient tool for identifying those clinical evidence challenging or only confirming established claims for clinicians and researchers.
Android-based household electronic maintenance reminder system

Abstract Along with the development of electronic devices used in households such as fans, rice cookers, and others do not always function appropriately. There are times when the device is damaged, and the user will try to fix it. One way to fix this is to find an electronic equipment repair place. Therefore, the electronic user will lose time and energy to find a service place. So we need an online ordering service that specifically handles the maintenance and repair of household electronic equipment. With this system, it is also beneficial for business actors of electronic equipment repair and maintenance services. They will effortlessly get information about customers who need repair or maintenance services for electronic equipment. Thus this system is expected to facilitate service users and increase the income of service users.
Securing Healthcare Data With Blockchain

This chapter shows that blockchain has a lot of potential for revolutionizing the traditional healthcare industry. When attempting to completely integrate blockchain technology with existing EHR systems, however, a number of research and operational hurdles remain. The authors evaluated and discussed some of these issues in this chapter. After that, they discovered a variety of possible research topics, such as IoT, big data, machine learning, and edge computing. They offer a methodology for implementing blockchain technology in the healthcare industry for electronic health records (EHR). The goal of the proposed structure is to first integrate blockchain technology for EHR and then to enable safe storage of electronic data for users of the framework by setting access controls. They hope that this review will help us gain a better understanding of the development and deployment of future generation EHR systems that will benefit humankind.
Towards Solution of Integral Equations in Electromagnetics on Quantum Computers

We present some of the fundamental methods in quantum computing for matrix equations, framed in terms of their applicability to solution of integral equations in electromagnetics. In particular, we describe the HHL algorithm for matrix inversion and matrix vector products, as well as techniques of Hamiltonian simulation for general matrices.
Political Cartoon Representations of Freedom of Information in Canada

Background: This article examines political cartoon depictions of access to information and freedom of information appearing in Canadian newspapers published between 2005 and 2019. Analysis: It focuses on three dimensions of these cartoons. First, many of these cartoons mirror the categories of political cartoons devised by Ray Morris. Second, several spatial metaphors regarding blockages, walls, and locked doors are used to represent government information control. Third, many of the cartoons suggest secrecy is germane to government across Canada and that transparency and secrecy are imbricated. Conclusion and implications: The implication of the findings for literatures on freedom of information, transparency, and political cartoons is assessed.
An Attention-Based Medical NER in the Bengali Language

Medical Named Entity Recognition is a process where medical entities are identified for extracting keywords in particular tasks in the medical sector such as summarizing prescriptions, identifying diseases. NER can make a context more comfortable to understand by identifying entities in the context. In the Bengali language, there is no artificial work that can identify automatically which kind of medical specialist a patient needs to consult based on patients' problems and symptoms. In this paper, NER has been selected and proposed an attention-based BiLSTM-CRF model for the task of telemedicine consultancy where the patient tells their problems, symptoms, and diseases at the first attempt, both the consultant and patient needs to understand which specialist the patient requires according to the problems or symptoms. This task has been implemented based on a self-made medical dataset in the Bengali language which gives an F1 score of 95.6% accuracy level and performs more efficiently in this task.
Distributed Local Two-Time-Scale Stochastic Approximation

In this paper, we consider a distributed variant of the popular two-time-scale stochastic approximation, where there are a group of agents communicating with a centralized coordinator. The goal of the agents is to find the roots of two coupling operators composed of the local operators at the agents. Such a framework models many practical problems in different areas, including those in federated learning and reinforcement learning. Over a series of time epoch, each agent runs a number of local two-time-scale stochastic approximation steps based on its own data, whose results are then aggregated at the centralized coordinator. Our main contribution is to characterize the finite-time performance of the local two-time-scale stochastic approximation, where we provide explicit formulas for the rate of this method.
A dynamic object filtering approach based on object detection and geometric constraint between frames

In order to eliminate the influence of moving targets in visual positioning, a dynamic object filtering approach based on object detection and inter-frame geometric constraints is proposed to filter the dynamic objects in monocular images. The object detection algorithm is firstly used to identify and locate objects in the single-frame image, and the object matching between frames is performed. Then, the depth estimation network and pose recovery network are trained jointly to output estimated depth along with transformation matrix of object centroids between frames respectively. Finally, the mapping centroid is obtained from the estimated depth and inter-frame transformation matrix. The joint constraint function is performed to complete the detection and filtering of dynamic objects. The experimental results show that the proposed dynamic object filtering approach not only can filter moving objects in sequence images accurately but also allows to reserve the dynamic objects that are temporarily in the stop state. The generalization ability of this approach is also verified in a real urban road scene and meets the requirements of follow-up research in visual positioning.
COPOMBOCY: A COVID-19 Pandemic Ontology Model of Bogor City

Coronavirus Disease of 2019 (COVID-19) has become a global health problem along with a declaration by the World Health Organization (WHO) on March 11, 2020, which declared it a pandemic. COVID-19 has spread to almost all countries, including Indonesia. Data related to the COVID-19 pandemic is complex and heterogeneous. To generate and maintain knowledge that is semantically stored in it, knowledge modeling is necessary to be done. This study aims to develop a model of knowledge about the COVID-19 pandemic in Bogor City in the form of ontology. The scope of knowledge includes the distribution of agents, close contact tracing, COVID-19 transmission, diagnosis, disease, type of illness, location, Large-Scale Social Restrictions (Pembatasan Sosial Berskala Besar [PSBB]) implementation, PSBB sanctions, statistics, status, symptoms, test processes, test results, and color zones. Data were obtained from the Bogor City COVID-19 Task Force, COVID-19 Information & Coordination Center of West Java Province (Pusat Informasi & Koordinasi COVID-19 Provinsi Jawa Barat [PIKOBAR]), Covid19.go.id, literature review, interview with epidemiologist, and reuse of knowledge from existing ontologies. The result of this study is an ontology in the Web Ontology Language (OWL) format consisting of 88 classes, 29 object properties, 48 property data, 2103 axioms, and 229 individuals. The test results on the built ontology were successful in answering 85.7% of non-expert questions and 71.4% of expert questions. Overall, the ontology built successfully answered 78.6% of the questions about the COVID-19 pandemic in Bogor City. The ontology has been published so that it is publicly available and is still being developed to accommodate the latest data.
Joint entity structural and attribute information for knowledge graph completion

Knowledge Graph Completion (KGC) aims at predicting missing information for knowledge graphs. Most methods concentrate on learning entities’ representations with structural information indicating the relations between entities, while the utilization of entity attribute information is not sufficient for KGC. How to use the complex and diverse entity attribute information for KGC is still a challenging problem. In this paper, we propose a novel joint model Entity Structural and Attribute Embedding (ESAE) for KGC, which takes advantage of the entity structural and attribute information. Specifically, we first design a novel encoder Attribute Encoder (AE), which encodes both entity attribute types and values to generate the entities’ attribute-based representations. Based on AE, we use the structure-based and attribute-based representations in ESAE. We evaluate our method on the KGC task. Experimental results on real-world datasets show that our method outperforms other baselines on KGC.
Preliminary studies on Dual-energy CT image super-resolution based on dual-dictionary learning

This paper proposes a dual-energy computed tomography (DECT) image super-resolution (SR) reconstruction scheme. The SR reconstruction scheme is based on sparse representation theory and dictionary learning of low-resolution and high-resolution image block pairs to improve the poor quality of low-resolution dual-energy CT images obtained in clinical practice. The proposed strategy is based on the idea of sparse representation, that is, image blocks can be well represented by sparse coding elements from over-complete dictionaries. We have jointly trained two pairs of dictionaries for high spectral CT images and low spectral CT images, and each pair of dictionaries contains dictionaries of low-resolution and high-resolution image blocks. Low-resolution dual-energy CT images can be represented by a low-resolution dictionary trained on high spectral CT images and a dictionary trained on low spectral CT images multiplied by sparse representation coefficients. And the sparse representation coefficient is multiplied by the corresponding high-resolution dictionary to reconstruct the high-resolution CT images. With an appropriate amount of iterative operations, the reconstructed high-resolution image can obtain better image quality and clearer visual effects. Experiments prove that the dual-energy CT image reconstructed by the sparse representation method has improved peak signal-to-noise ratio and structural similarity, and the image details and textures are clearer.
An Overview of Recommender Systems and Its Next Generation: Context-Aware Recommender Systems

Recommender Systems have been generally utilized in different areas including motion pictures, news, music with an intend to give the most important recommendations to clients from an assortment of accessible alternatives. Recommender Systems are planned utilizing procedures from numerous fields, some of which are: AI, data recovery, information mining, direct variable based math and man-made consciousness. However, in typical commodity applications, due to the huge user and project library and just few evaluations (Sparsity issue), and at the point when the client is new to Recommender Frameworks, the framework can’t prescribe things that are applicable to clients in light of absence of past data about the client as well as the client thing rating history that assists with deciding the clients’ preferences (cold start). What’s more, presently there’s an innovation called Context-aware Recommender Systems (CARS), which utilizing setting information (location, time, peer, etc.) during the time spent proposal. In this work, we present an outline of some of noticeable customary RS and the high level CARS. We discuss the advantages and disadvantages of them. Furthermore, we reveal some inherent problems in RS. At last, we make a conclusion and give some challenges in current works.
Blockchain in Smart Healthcare Systems

With the technological sector booming combined with the internet revolutionizing the 21st century, along with the current coronavirus pandemic, more and more hands are getting virtually connected. Today, cyber criminals are not only behind money but are also designed to access, delete, or extort a user's sensitive data. These attacks range from DoS (denial of service) attacks and phishing to eavesdropping attacks. Blockchain is a technology that aims to keep data safe from being corrupted or accidentally deleted while maintaining its history. This technology has an impact on various industries, but the healthcare industry is impacted significantly. This chapter presents a systematic review of different use cases and applications of blockchain in healthcare. This chapter further discusses issues, solutions, and the future scope in blockchain applications in healthcare industries. An idea is to give a complete state of the art and challenges to better understand its utility in the healthcare sector.
Anomaly Detection Using Classification CNN Models: A Video Analytic Approach

Video anomaly detection has gained much attention in the computer vision community due to its wide applications in security. Specifically, the focus has been on feature extraction and the design of inference algorithms. The extraction of features to model the normality is challenging due to the scarcity of data and supervision. To this end, current computer vision technologies use reconstruction based methods that relied on auto-encoders to reconstruct normal events in an unsupervised manner. Higher reconstruction errors are often used to detect anomalies. However, the use of multiple auto-encoders to extract features (temporal and appearance) is redundant and expensive for videos. In this context, the present study proposes a novel feature extractor that uses a single CNN architecture to extract both temporal and appearance features. Also, this model is trained for classification tasks which are adapted as feature extractors in anomaly detection. The training of this model is easy and can be deployed efficiently due to its lightweight architecture. Further, the proposed model has been quantitatively evaluated on the UCSD ped 2 dataset and found to perform competitively with an AUC of 0.958.
Development of a Collaborative Platform for Education in Virtual Reality

The aim of the work is to study the possibility of creating and using virtual reality to develop a collaborative platform for virtual meetings in a pandemic. The aim of the study is to create an affordable solution that would provide access to the platform to as many people as possible, without complicating the development process. As a solution, an application was developed using the Unity Engine game engine for the family of Windows Mixed Reality and Google Cardboard platforms. The resulting prototype has the necessary set of functions for creating group meetings and training sessions. Users using Windows Mixed Reality devices have the ability to navigate with six degrees of freedom and interact with physical objects on the playing field, while users of Cardboard-compatible devices have only three degrees of freedom and no controllers to interact directly with the virtual environment. To solve this problem, the playing field has special interaction markers using the screen focus on the object or trigger.
A Method of Micro Pixel Similarity for Lung Cancer Diagnosis Using Adaboost

Today lung cancer is one of the difficult diseases to diagnose and is responsible for the higher number of deaths in the country, which is estimated to be 1.869 million by 2026 as compared to 1.192 million in 2011 for both sexes as per the statistical data of Indian Council for Medical Research. The responsible factors for this rise are the increase in population, pollution and drastic changes in living lifestyle. Researchers have been making efforts on radiological images such as X-ray, CT, and HRCT, but still, there is a scope of improvement in case of microscopic lung images due to less work. In this paper, we have proposed a new method of Micro pixel Similarity Technique for lung cancer analysis and diagnosis. The proposed method utilizes the identified statistical and mathematical parameters such as Structural Similarity Indices Matrix (SSIM), Mean Absolute Error (MAE), Absolute Difference (AD), Mean Square Error (MSE), and Skewness. Adaboost function of ANN is used as an image classifier. Each of the statistical and mathematical parameter utilized plays a decisive role in lung cancer analysis and diagnosis. The method is validated through a standard diagnostic test in terms of Specificity (66.34%), Sensitivity (94.95%), and Accuracy (85.71%). This paper will be useful for researchers, academicians, and physicians. The researchers and academicians will understand research scope in microscopic lung images, and it will be helpful for the physicians to take a final decision on lung cancer.