Reviewerly/exHarmony huggingface dataset:
Tracking Hate in Social Media: Evaluation, Challenges and Approaches

This paper presents online hate speech as a societal and computational challenge. Offensive content detection in social media is considered as a multilingual, multi-level, multi-class classification problem for three Indo-European languages. This research problem is offered to the community through the HASOC shared task. HASOC intends to stimulate research and development in hate speech recognition across different languages. Three datasets (in English, German, and Hindi) were developed from Twitter and Facebook, and made available. This paper describes the creation of the multilingual datasets and the annotation method. We will present the numerous approaches based on traditional classifiers, deep neural models, and transfer learning models, along with features used for the classification. Results show that the best classifier for the binary classification might not perform best in the multi-class classification, and the performance of the same classifier varies across the languages. Overall, transfer learning models such as BERT, and deep neural models based on LSTMs and CNNs perform similar but better than traditional classifiers such as SVM. We will conclude the discussion with a list of issues that needs to be addressed for future datasets.
Blockchain-Based Healthcare Workflows in Federated Hospital Clouds

Nowadays, security is one of the biggest concerns against the wide adoption of on-demand Cloud services. Specifically, one of the major challenges in many application domains is the certification of exchanged data. For these reasons, since the advent of bitcoin and smart contracts respectively in 2009 and 2015, healthcare has been one of the major sectors in which Blockchain has been studied. In this paper, by exploiting the intrinsic security feature of the Blockchain technology, we propose a Software as a Service (SaaS) that enables a hospital Cloud to establish a federation with other ones in order to arrange a virtual healthcare team including doctors coming from different federated hospitals that cooperate in order to carry out a healthcare workflow. Experiments conducted in a prototype implemented by means of the Ethereum platform show that the overhead introduced by Blockchain is acceptable considering the obvious gained advantages in terms of security.
Object-Centric Image Generation with Factored Depths, Locations, and Appearances

We present a generative model of images that explicitly reasons over the set of objects they show. Our model learns a structured latent representation that separates objects from each other and from the background; unlike prior works, it explicitly represents the 2D position and depth of each object, as well as an embedding of its segmentation mask and appearance. The model can be trained from images alone in a purely unsupervised fashion without the need for object masks or depth information. Moreover, it always generates complete objects, even though a significant fraction of training images contain occlusions. Finally, we show that our model can infer decompositions of novel images into their constituent objects, including accurate prediction of depth ordering and segmentation of occluded parts.
High-Resolution Neural Network for Driver Visual Attention Prediction

Driving is a task that puts heavy demands on visual information, thereby the human visual system plays a critical role in making proper decisions for safe driving. Understanding a driver’s visual attention and relevant behavior information is a challenging but essential task in advanced driver-assistance systems (ADAS) and efficient autonomous vehicles (AV). Specifically, robust prediction of a driver’s attention from images could be a crucial key to assist intelligent vehicle systems where a self-driving car is required to move safely interacting with the surrounding environment. Thus, in this paper, we investigate a human driver’s visual behavior in terms of computer vision to estimate the driver’s attention locations in images. First, we show that feature representations at high resolution improves visual attention prediction accuracy and localization performance when being fused with features at low-resolution. To demonstrate this, we employ a deep convolutional neural network framework that learns and extracts feature representations at multiple resolutions. In particular, the network maintains the feature representation with the highest resolution at the original image resolution. Second, attention prediction tends to be biased toward centers of images when neural networks are trained using typical visual attention datasets. To avoid overfitting to the center-biased solution, the network is trained using diverse regions of images. Finally, the experimental results verify that our proposed framework improves the prediction accuracy of a driver’s attention locations.
Fixed point sets in digital topology, 2

We continue the work of [10], studying properties of digital images determined by fixed point invariants. We introduce pointed versions of invariants that were introduced in [10]. We introduce freezing sets and cold sets to show how the existence of a fixed point set for a continuous self-map restricts the map on the complement of the fixed point set.
E-Learning Management System Based on Reality Technology with AI

In the present time, the skill training for work is important to the industry and education.Practicing various skills are necessary to use technology to apply for many reasons: Safety reasons for resources, locations, time and limitations.AR VR MR or XR Technologies are increasingly used worldwide, because the equipment materials are more ready but the present learning management system Different from the skill training management system.The storage and using in video format that have been used for more than a decade.Therefore, this research is developed a system that will manage learning, practice skills with AR VR MR Technologies as well as use AI to be used in the management system as well.
On the EA-classes of known APN functions in small dimensions

Abstract Recently Budaghyan et al. (Cryptogr. Commun. 12 , 85–100, 2020) introduced a procedure for investigating if CCZ-equivalence can be more general than EA-equivalence together with inverse transformation (when applicable). In this paper, we show that it is possible to use this procedure for classifying, up to EA-equivalence, all known APN functions in dimension 6. We also give some discussion for dimension 7, 8 and 9. In particular, in these cases it is possible to give an upper bound on the EA-classes contained in the CCZ-classes of the known APN functions.
Designing and Developing of E-Commerce Website for Unused New Goods Shopping

According to the fast-changing business environment nowadays, we have to be more effective and faster in responding to customers' needs to make them able to access products instantly. This can be done by designing an E-commerce web website for unused goods, which sells various fashions and goods to the customers. To implement an online shopping website, a virtual store on the Internet is needed which allows customers to seek products and select them from a catalog. The customer needs to fill some fields to order a specific product. The purpose of this paper is designing and implementation of the website of unused goods, which sells various fashions and goods to the customers, the good that will be for sale on the website are new unused goods which the customer couldn’t return to the store they buy from to any reason. The proposed system was developed using the Unified Modeling Language (UML), ASP.NET and Access.
#Walangpasok on Twitter: Natural language processing as a method for analyzing tweets on class suspensions in the Philippines

In this study, we aim to prove that natural language processing (NLP) can be used as a method for analyzing qualitative data like tweets. To validate this, we examine various topics emerging from tweets about class suspensions in the Philippines using #walangpasok (no classes). By utilizing three NLP techniques, we presented various topics. Using Latent Dirichlet Allocation (LDA), the study identified the following: (1) information dissemination and public announcements from government offices, and (2) sentiments of students. Through Word2Vec, we generated (1) monitoring and dissemination of alerts and warnings, (2) local government accountability, and (3) sentiments of Twitter users. An intrinsic evaluation was conducted for word2vec model using cosine similarity. The word-groups have an average cosine similarity of 0.997811. Lastly, the topics that emerged using K-means are (1) weather advisory, (2) class suspension announcements, (3) role of local government, and (4) users' sentiments and outlook towards a situation. The number of clusters generated by the k-means clustering algorithm was decided based on the silhouette score of 0.0153423865462. It turns out that the study provided the same results using NLP techniques. We also performed open coding to analyze the data manually and to ensure that the obtained results using the techniques are accurate. Thus, the use of NLP as a method of qualitative data analysis can be considered reliable and may be recommended to use in other qualitative research, particularly in the field of social sciences.
A Framework to Quality Control Oceanographic Data

The ocean is an intrinsically challenging environment to collect data, which makes spurious measurements inevitable.Thus, the quality of oceanographic datasets is highly dependent on the ability to identify and remove bad samples.Quality control (QC) of oceanographic data has mostly relied on manual QC by experts, which, despite resulting in the best data quality, is not scalable and becomes impractical to handle large datasets or real-time data streams.To address this issue, automatic QC procedures have been proposed and widely used for decades (e.g., IOC/IODE, 1993; DATA-MEQ working group, 2010; GTSPP Real-Time Quality Control Manual, 2010;Morello et al., 2014;QARTOD group, 2016; Wong, Keeley, Carval, & Argo Data Management Team, 2015); however, these procedures are seldom organized and distributed as packages, so it is still common for new users to have to implement them from scratch.Additionally, different applications of the same dataset may require different QC procedures.For example, a particular user faced with the QC of a small dataset might be willing to apply a less conservative QC in order to preserve a larger number of data points, paying the price of having some false positives.CoTeDe is an Open Source Python package that provides a flexible way to automatic QC oceanographic data by combining multiple QC standards while allowing the users to fully control and tune the parameters according to their own needs.
e-SNLI-VE: Corrected Visual-Textual Entailment with Natural Language Explanations

The recently proposed SNLI-VE corpus for recognising visual-textual entailment is a large, real-world dataset for fine-grained multimodal reasoning. However, the automatic way in which SNLI-VE has been assembled (via combining parts of two related datasets) gives rise to a large number of errors in the labels of this corpus. In this paper, we first present a data collection effort to correct the class with the highest error rate in SNLI-VE. Secondly, we re-evaluate an existing model on the corrected corpus, which we call SNLI-VE-2.0, and provide a quantitative comparison with its performance on the non-corrected corpus. Thirdly, we introduce e-SNLI-VE, which appends human-written natural language explanations to SNLI-VE-2.0. Finally, we train models that learn from these explanations at training time, and output such explanations at testing time.
Guided Dialog Policy Learning without Adversarial Learning in the Loop

Reinforcement Learning (RL) methods have emerged as a popular choice for training an efficient and effective dialogue policy. However, these methods suffer from sparse and unstable reward signals returned by a user simulator only when a dialogue finishes. Besides, the reward signal is manually designed by human experts, which requires domain knowledge. Recently, a number of adversarial learning methods have been proposed to learn the reward function together with the dialogue policy. However, to alternatively update the dialogue policy and the reward model on the fly, we are limited to policy-gradient-based algorithms, such as REINFORCE and PPO. Moreover, the alternating training of a dialogue agent and the reward model can easily get stuck in local optima or result in mode collapse. To overcome the listed issues, we propose to decompose the adversarial training into two steps. First, we train the discriminator with an auxiliary dialogue generator and then incorporate a derived reward model into a common RL method to guide the dialogue policy learning. This approach is applicable to both on-policy and off-policy RL methods. Based on our extensive experimentation, we can conclude the proposed method: (1) achieves a remarkable task success rate using both on-policy and off-policy RL methods; and (2) has the potential to transfer knowledge from existing domains to a new domain.
Extractive Text Summarization - An effective approach to extract information from Text

Everyday large volume of data is gathered from different sources and are stored since they contain valuable piece of information. The storage of data must be done in efficient manner since it leads in difficulty during retrieval. Text data are available in the form of large documents. Understanding large text documents and extracting meaningful information out of it is time-consuming tasks. To overcome these challenges, information in the form of text are summarized in with an objective to get relevant knowledge with the help of text mining tools. Summarized text will have reduced size as compared to original one. In this paper, we have tried to highlight major techniques for extracting important information from a given text with the help of topic modeling, key phrase extraction and summary generation. For topic modelling LSI and NMF method is used, weighted TF-IDF method is used for key phrase extraction while text summary is generated by using LSA and Text Rank method.
Engineering cross resonance interaction in multi-modal quantum circuits

Existing scalable superconducting quantum processors have only nearest-neighbor coupling. This leads to reduced circuit depth, requiring large series of gates to perform an arbitrary unitary operation in such systems. Recently, multi-modal devices have been demonstrated as a promising candidate for small quantum processor units. Always on longitudinal coupling in such circuits leads to implementation of native high fidelity multi-qubit gates. We propose an architecture using such devices as building blocks for a highly connected larger quantum circuit. To demonstrate a quantum operation between such blocks, a standard transmon is coupled to the multi-modal circuit using a 3D bus cavity giving rise to small exchange interaction between the transmon and one of the modes. We study the cross resonance interaction in such systems and characterize the entangling operation as well as the unitary imperfections and cross-talk as a function of device parameters. Finally, we tune up the cross resonance drive to implement multi-qubit gates in this architecture.
Violation of Svetlichny inequality in Triple Jaynes-Cummings Models

Abstract We study the genuine tripartite nonlocality of some qubit states in a triple JCM. In this model, each atom state ( A , B or C ) was initially prepared with an independent cavity ( a , b or c ). By using two kinds of GHZ-like states as the atomic initial states, we investigate the genuine tripartite nonlocality as the time evolutions for the non-interaction three-qubit subsystems. We also study the genuine tripartite nonlocality of the subsystems by using the Svetlichny inequality. For the subsystems of three atoms ABC and three cavity modes abc , we show that they are genuinely nonlocal at certain period intervals of time. The states of all the other inequivalent subsystems satisfy the Svetlichny inequality for two types of GHZ-like states.
A Mathematical Model of the Covid-19 Cases in Indonesia (Under and Without Lockdown Enforcement)

COVID-19 stands for Corona (CO), Virus (VI), Disease (D) and year 2019 (19), which is COVID-19 first appeared in 2019. Mathematical model of covid deployment in Indonesia under and without lockdown case uses the SIRV model, such as Susceptible, Infected, Recovery, and Virus. The results of this model state that under lockdown the spread of COVID-19 could be stopped. If it were not under lockdown it can multiply 1,276 times higher over two months.
Neural Opacity Point Cloud

Fuzzy objects composed of hair, fur, or feather are impossible to scan even with the latest active or passive 3D scanners. We present a novel and practical neural rendering (NR) technique called neural opacity point cloud (NOPC) to allow high quality rendering of such fuzzy objects at any viewpoint. NOPC employs a learning-based scheme to extract geometric and appearance features on 3D point clouds including their opacity. It then maps the 3D features onto virtual viewpoints where a new U-Net based NR manages to handle noisy and incomplete geometry while maintaining translation equivariance. Comprehensive experiments on existing and new datasets show our NOPC can produce photorealistic rendering on inputs from multi-view setups such as a turntable system for hair and furry toy captures.
Micro-Blog Sentiment Classification Method Based on the Personality and Bagging Algorithm

Integrated learning can be used to combine weak classifiers in order to improve the effect of emotional classification. Existing methods of emotional classification on micro-blogs seldom consider utilizing integrated learning. Personality can significantly influence user expressions but is seldom accounted for in emotional classification. In this study, a micro-blog emotion classification method is proposed based on a personality and bagging algorithm (PBAL). Introduce text personality analysis and use rule-based personality classification methods to divide five personality types. The micro-blog text is first classified using five personality basic emotion classifiers and a general emotion classifier. A long short-term memory language model is then used to train an emotion classifier for each set, which are then integrated together. Experimental results show that compared with traditional sentiment classifiers, PBAL has higher accuracy and recall. The F value has increased by 9%.
GeoLink Data Set: A Complex Alignment Benchmark from Real-world Ontology

Ontology alignment has been studied for over a decade, and over that time many alignment systems and methods have been developed by researchers in order to find simple 1-to-1 equivalence matches between two ontologies. However, very few alignment systems focus on finding complex correspondences. One reason for this limitation may be that there are no widely accepted alignment benchmarks that contain such complex relationships. In this paper, we propose a real-world data set from the GeoLink project as a potential complex ontology alignment benchmark. The data set consists of two ontologies, the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO), as well as a manually created reference alignment that was developed in consultation with domain experts from different institutions. The alignment includes 1:1, 1:n, and m:n equivalence and subsumption correspondences, and is available in both Expressive and Declarative Ontology Alignment Language (EDOAL) and rule syntax. The benchmark has been expanded from its original version to contain real-world instance data from seven geoscience data providers that has been published according to both ontologies. This allows it to be used by extensional alignment systems or those that require training data. This benchmark has been incorporated into the Ontology Alignment Evaluation Initiative (OAEI) complex track to help researchers test their automated alignment systems and algorithms. This paper also analyzes the challenges inherent in effectively generating, detecting, and evaluating complex ontology alignments and provides a road map for future work on this topic.
Kaplan-Meier V- and U-statistics

In this paper, we study Kaplan-Meier V-and U-statistics respectively defined as θ( Fnwhere Fn is the Kaplan-Meier estimator, {W 1 , . . ., Wn} are the Kaplan-Meier weights and K : (0, ∞) 2 → R is a symmetric kernel.As in the canonical setting of uncensored data, we differentiate between two asymptotic behaviours for θ( Fn) and θ U ( Fn).Additionally, we derive an asymptotic canonical V-statistic representation of the Kaplan-Meier V-and U-statistics.By using this representation we study properties of the asymptotic distribution.Applications to hypothesis testing are given.
Facial Recognition Based on DWT – HOG – PCA Features with MLP Classifier

Facial Recognition System has been widely used in various applications. Nevertheless, their efficiency rate fell dramatically when they were applied under unrestrained environments like the position of face, expression or illumination change. Because of these factors, it is essential to measure and calculate the performance rate of the dissimilar feature extraction techniques robust to such transformations in order to further integrate to a Facial Recognition System. This paper studies and evaluates the Histogram of Oriented Gradients method as a feature extraction method in order to deal with the abovementioned transformations. The study consists of four main phases: face detection, preprocessing, features extraction, and classification. Preprocessing is used to enhance the images by using the techniques of digital image processing. Feature extraction is used to get features from facial images based on the concept of Histogram Oriented Gradient feature that is applied to the facial image after conversion by means of the Discrete Wavelet Transform and vector reduction with the help of the Principle Component Analysis technique. Artificial neural network with the Back Propagation algorithm is used in training and testing as a classifier of the facial images to help recognize the face. To measure the performance of the method under consideration, some experiments were implemented using two datasets: ORL containing 400 facial images of 40 individuals that achieved the accuracy rate about 99.1%, and FERET containing 912 facial images of 152 individuals, which helped achieve accuracy rate about 94.5% at the multilayer perceptron neural network classifier.
Intellectual Analysis For Educational Path’ Cognitive Modeling

Regulative models based on the human cognitive systems and the ethics are embodied in the educational processes and social institutions for training and socialization. The intellectual potential of an organization, region and country forms the general ground for the competitiveness in the context of the global knowledge economy. The intellectual analysis is directed to solving the essential uncertainties of the knowledge – anticipation for the future (basic uncertainty) and the personal character of knowledge and competence (relating the personality, individual conscience and acting capacity). The widespread interest towards the intellectual systems and intelligence for any sphere of the social life is based, first of all, on the practice of the professional and educational path, the self-realization trajectory and strategic choice of the specialists in different industries. The paper presents the longitude results obtained in 2005-2018 of the values that determine the choice of the educational trajectory on the level of the second degree of higher education – Master programs, which are not necessary for the majority of the corporate positions, that allows scholars to analyze this choice as reflecting the free interest of the potential students for their personal cognitive growth.