Reviewerly/exHarmony huggingface dataset:
A Dataset for Linguistic Understanding, Visual Evaluation, and Recognition of Sign Languages: The K-RSL

The paper presents the first dataset that aims to serve interdisciplinary purposes for the utility of computer vision community and sign language linguistics.To date, a majority of Sign Language Recognition (SLR) approaches focus on recognising sign language as a manual gesture recognition problem.However, signers use other articulators: facial expressions, head and body position and movement to convey linguistic information.Given the important role of non-manual markers, this paper proposes a dataset and presents a use case to stress the importance of including non-manual features to improve the recognition accuracy of signs.To the best of our knowledge no prior publicly available dataset exists that explicitly focuses on non-manual components responsible for the grammar of sign languages.To this end, the proposed dataset contains 28250 videos of signs of high resolution and quality, with annotation of manual and nonmanual components.We conducted a series of evaluations in order to investigate whether non-manual components would improve signs' recognition accuracy.We release the dataset to encourage SLR researchers and help advance current progress in this area toward realtime sign language interpretation.
DualTKB: A Dual Learning Bridge between Text and Knowledge Base

In this work, we present a dual learning approach for unsupervised text to path and path to text transfers in Commonsense Knowledge Bases (KBs).We investigate the impact of weak supervision by creating a weakly supervised dataset and show that even a slight amount of supervision can significantly improve the model performance and enable better-quality transfers.We examine different model architectures, and evaluation metrics, proposing a novel Commonsense KB completion metric tailored for generative models.Extensive experimental results show that the proposed method compares very favorably to the existing baselines.This approach is a viable step towards a more advanced system for automatic KB construction/expansion and the reverse operation of KB conversion to coherent textual descriptions.
EvoMSA: A Multilingual Evolutionary Approach for Sentiment Analysis [Application Notes]

Sentiment analysis (SA) is a task related to understanding people's feelings in written text; the starting point would be to identify the polarity level (positive, neutral or negative) of a given text, moving on to identify emotions or whether a text is humorous or not. This task has been the subject of several research competitions in a number of languages, e.g., English, Spanish, and Arabic, among others. In this contribution, we propose an SA system, namely EvoMSA, that unifies our participating systems in various SA competitions, making it domain-independent and multilingual by processing text using only language-independent techniques. EvoMSA is a classifier, based on Genetic Programming that works by combining the output of different text classifiers to produce the final prediction. We analyzed EvoMSA on different SA competitions to provide a global overview of its performance. The results indicated that EvoMSA is competitive obtaining top rankings in several SA competitions. Furthermore, we performed an analysis of EvoMSA's components to measure their contribution to the performance; the aim was to facilitate a practitioner or newcomer to implement a competitive SA classifier. Finally, it is worth to mention that EvoMSA is available as open-source software.
Two-phase protein folding optimization on a three-dimensional AB off-lattice model

This paper presents a two-phase protein folding optimization on a three-dimensional AB off-lattice model. The first phase is responsible for forming conformations with a good hydrophobic core or a set of compact hydrophobic amino acid positions. These conformations are forwarded to the second phase, where an accurate search is performed with the aim of locating conformations with the best energy value. The optimization process switches between these two phases until the stopping condition is satisfied. An auxiliary fitness function was designed for the first phase, while the original fitness function is used in the second phase. The auxiliary fitness function includes an expression about the quality of the hydrophobic core. This expression is crucial for leading the search process to the promising solutions that have a good hydrophobic core and, consequently, improves the efficiency of the whole optimization process. Our differential evolution algorithm was used for demonstrating the efficiency of two-phase optimization. It was analyzed on well-known amino acid sequences that are used frequently in the literature. The obtained experimental results show that the employed two-phase optimization improves the efficiency of our algorithm significantly and that the proposed algorithm is superior to other state-of-the-art algorithms.
EXAMS: A Multi-subject High School Examinations Dataset for Cross-lingual and Multilingual Question Answering

We propose Eχαµs -a new benchmark dataset for cross-lingual and multilingual question answering for high school examinations.We collected more than 24,000 highquality high school exam questions in 16 languages, covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others.Eχαµs offers a fine-grained evaluation framework across multiple languages and subjects, which allows precise analysis and comparison of various models.We perform various experiments with existing top-performing multilingual pre-trained models and we show that Eχαµs offers multiple challenges that require multilingual knowledge and reasoning in multiple domains.We hope that Eχαµs will enable researchers to explore challenging reasoning and knowledge transfer methods and pretrained models for school question answering in various languages which was not possible before.The data, code, pre-trained models, and evaluation are available at http:// github.com/mhardalov/exams-qa.
Causal testing: understanding defects' root causes

Understanding the root cause of a defect is critical to isolating and repairing buggy behavior. We present Causal Testing, a new method of root-cause analysis that relies on the theory of counterfactual causality to identify a set of executions that likely hold key causal information necessary to understand and repair buggy behavior. Using the Defects4J benchmark, we find that Causal Testing could be applied to 71% of real-world defects, and for 77% of those, it can help developers identify the root cause of the defect. A controlled experiment with 37 developers shows that Causal Testing improves participants' ability to identify the cause of the defect from 80% of the time with standard testing tools to 86% of the time with Causal Testing. The participants report that Causal Testing provides useful information they cannot get using tools such as JUnit. Holmes, our prototype, open-source Eclipse plugin implementation of Causal Testing, is available at http://holmes.cs.umass.edu/.
Analysis of key components of the “Digital University” Model

One of the priorities of the government program “Digital economy of the Russian Federation” is the progressive development of the existing education system, which should provide the economy with competent personnel. In addition, this Program declares the need to create a system of motivation for the development of the necessary competencies and participation of personnel in the development of the digital economy of Russia. On the other hand, the current educational trends both in Russia and abroad require the creation of a fundamentally new, open education system aimed not only at creating future competencies, but also at expanding the availability of education. Access to education in this case implies the possibility of obtaining educational services for all categories of citizens, including residents of remote regions, as well as for people with disabilities. Currently actively developing online course platforms (Massive Open Online Courses) do not yet provide a full range of educational services in addition online students do not have the opportunity to obtain a higher education diploma, which would be recognized by employers. Accordingly, the task of providing modern, accessible and open educational services still falls on classical universities. All this requires transformation of the classical educational system, which ultimately implies the creation of a “Digital University”. The author of the article presents her own view on the formation of the “Digital University” model and explains her point of view about the key elements of this model. The article substantiates and describes the five main components of the “Digital University” model and also concludes that further detailed description of these proposed components in their relationship with the processes of digital transformation of the University is necessary.
Online Xenophobic Behavior Amid the COVID-19 Pandemic

The COVID-19 pandemic has developed to be more than a bio-crisis as global news has reported a sharp rise in xenophobia and discrimination in online and offline communities. This commentary discusses the deterrence policies and automated content moderation technology to curb the spread of online xenophobic behaviors. We suggested some potential exploration areas where multiple stakeholders can come together to address the online xenophobic issues amid the COVID-19 pandemic.
Action Recognition Based on Fusion Skeleton of Two Kinect Sensors

Along with the development of human-computer interaction, action recognition has become an aspect of computer vision. In recent years, skeleton-based action recognition has become a research hotspot in the field of computer vision. The human skeleton can be obtained through the Kinect sensor, but the single Kinect sensor is often affected by self-occlusion so that it is impossible to accurately obtain information on all skeleton joints of the human. In this paper, a data fusion method is proposed. The two Kinect sensors are placed in a fixed space, and they are orthogonal to each other. They extract the human skeleton from different perspectives. Then, the two skeletons were fused to obtain accurately skeleton and eliminate the self-occlusion effect. Finally, the Spatial Temporal Graph Convolutional Networks (ST-GCN) be able to process skeleton-based action recognition by learning the spatial and temporal patterns of data automatically, then to achieve the goal of accurate recognition of human body posture. It is used to verify the validity of the data fusion method in this paper. Particularly, the skeleton under two cameras of the large dataset NTU-RGB+D is fused to be used as the dataset of this experiment. Experimental results show that the data fusion method is reasonable and effective.
An online conjugate gradient algorithm for large-scale data analysis in machine learning

In recent years, the amount of available data is growing exponentially, and large-scale data is becoming ubiquitous. Machine learning is a key to deriving insight from this deluge of data. In this paper, we focus on the large-scale data analysis, especially classification data, and propose an online conjugate gradient (CG) descent algorithm. Our algorithm draws from a recent improved Fletcher-Reeves (IFR) CG method proposed in Jiang and Jian[13] as well as a recent approach to reduce variance for stochastic gradient descent from Johnson and Zhang [15]. In theory, we prove that the proposed online algorithm achieves a linear convergence rate under strong Wolfe line search when the objective function is smooth and strongly convex. Comparison results on several benchmark classification datasets demonstrate that our approach is promising in solving large-scale machine learning problems, viewed from the points of area under curve (AUC) value and convergence behavior.
Analysis of the possibilities of the MOODLE learning management system for organization of distance learning in the conditions of the university

The modern world has changed dramatically with the widespread introduction of information and communication technologies. Today, an effective learning process without the use of modern technologies is unthinkable. Learning management systems are a bright representative of modern technologies, on the basis of which it is possible to organize the interaction of all participants in the learning process in the global Internet. The search for new approaches to the use of learning management systems can further enhance the effect of the use of modern information and communication technologies in the organization of the educational process.The article gives a general description of the MOODLE training management system and the main elements of the course: questionnaire database; external tool; glossary; task; lecture; feedback; survey; SCORM package; seminar; test; forum; chat. Among the resources were identified: hyperlink; book; IMS content package; explanations; page; file. The practical use of the listed elements of the course of the MOODLE learning management system allows to fully implement distance learning and effective interaction between student and teacher.The structure of the course includes the necessary set of structural elements (lectures, practical work, independent work, control of knowledge on the basis of tests) for the organization of distance learning in the conditions of higher educational institution.A significant advantage of the introduction of MOODLE in the traditional educational process is that, without violating its course and principles, it greatly enriches it due to its rich information, communication and pedagogical capabilities. Distance learning with the help of MOODLE allows you to organize productive independent work of the student to master the discipline. Through the means of access to interactive learning elements and MOODLE resources, students have the opportunity to optimize the assimilation of educational material in preparation for classes, and with the help of control elements - to improve the quality of self-control
Processing Speaker-Specific Information in Two Stages During the Interpretation of Referential Precedents

To reduce ambiguity across a conversation, interlocutors reach temporary conventions or referential precedents on how to refer to an entity. Despite their central role in communication, the cognitive underpinnings of the interpretation of precedents remain unclear, specifically the role and mechanisms by which information related to the speaker is integrated. We contrast predictions of one-stage, original two-stage, and extended two-stage models for the processing of speaker information and provide evidence favoring the latter: we show that both stages are sensitive to speaker-specific information. Using an experimental paradigm based on visual-world eye tracking in the context of a referential communication task, we look at the moment-by-moment interpretation of precedents and focus on the temporal profile of the influence of the speaker and linguistic information when facing ambiguity. We find two clearly identifiable moments where speaker-specific information has its effects on reference resolution. We conclude that these two stages reflect two distinct cognitive mechanisms, with different timings, and rely on different representational formats for encoding and accessing information about the speaker: a cue-driven memory retrieval process that mediates language processing and an inferential mechanism based on perspective-taking abilities.
Fed-SCNN: A Federated Shallow-CNN Recognition Framework for Distracted Driving

Although distracted driving recognition is of great significance to traffic safety, drivers are reluctant to provide their own personalized driving data to machine learning because of privacy protection. How to improve the accuracy of distracted driving recognition on the basis of ensuring privacy protection? To address the issue, we proposed the federated shallow-CNN recognition framework (Fed-SCNN). Firstly, a hybrid model is established on the user-side through DNN and shallow-CNN, which recognizes the data of the in-vehicle images and uploads the encrypted parameters to the cloud. Secondly, the cloud server performs federated learning on major parameters through DNN to build a global cloud model. Finally, The DNN is updated in the user-side to further optimize the hybrid model. The above three steps are cycled to iterate the local hybrid model continuously. The Fed-SCNN framework is a dynamic learning process that addresses the two major issues of data isolation and privacy protection. Compared with the existing machine learning method, Fed-SCNN has great advantages in accuracy, safety, and efficiency and has important application value in the field of safe driving.
Credibility-limited Base Revision: New Classes and Their Characterizations

In this paper we study a kind of operator —known as credibility-limited base revisions— which addresses two of the main issues that have been pointed out to the AGM model of belief change. Indeed, on the one hand, these operators are defined on belief bases (rather than belief sets) and, on the other hand, they are constructed with the underlying idea that not all new information is accepted. We propose twenty different classes of credibility-limited base revision operators and obtain axiomatic characterizations for each of them. Additionally we thoroughly investigate the interrelations (in the sense of inclusion) among all those classes. More precisely, we analyse whether each one of those classes is or is not (strictly) contained in each of the remaining ones.
A Survey on Phishing Website Detection Using Hadoop

Phishing is an activity carried out by phishers with the aim of stealing personal data of internet users such as user IDs, password, and banking account, that data will be used for their personal interests. Average internet user will be easily trapped by phishers due to the similarity of the websites they visit to the original websites. Because there are several attributes that must be considered, most of internet user finds it difficult to distinguish between an authentic website or not. There are many ways to detecting a phishing website, but the existing phishing website detection system is too time-consuming and very dependent on the database it has. In this research, the focus of Hadoop MapReduce is to quickly retrieve some of the attributes of a phishing website that has an important role in identifying a phishing website, and then informing to users whether the website is a phishing website or not.
Does Globalization improve Health in Sub-Saharan African countries?

This paper empirically examines the effects of globalization on health in 32 Sub-Saharan African countries. It also investigates whether the effect of globalization on health depends on the income level of countries. In particular, the paper focus on three health indicators namely infant mortality rate, child mortality rate and life expectancy. The fixed effects regression models with Driscoll-Kraay standard errors is used to control cross-sectional and temporal dependence. The empirical results reveal that globalization reduces infant mortality rate and child mortality rate in SSA countries. Our findings also show that the effect of globalization depends on the country's level of income. The health improvement impact of globalization appears stronger in SSA low-income countries than in SSA middle-income countries.
How to Apply Fuzzy MISO PID in the Industry? An Empirical Study Case on Simulation of Crane Relocating Containers

The proportional-integral-derivative (PID) algorithm automatically adjusts the control output based on the difference between a set point and a measured process variable. The classical approach is broadly used in the majority of control systems. However, in complex problems, this approach is not efficient, especially when the exact mathematical formula is difficult to specify. Besides, it was already proven that highly nonlinear situations are also significantly limiting the usage of the PID algorithm, in contrast to the fuzzy algorithms, which often work correctly under such conditions. In the case of multidimensional objects, where many independently operating PID algorithms are currently used, it is worth considering the use of one fuzzy algorithm with many-input single-output (MISO) or many-input many-output (MIMO) structure. In this work, a MISO type chip is investigated in the study case on simulation of crane relocating container with the external distribution. It is an example of control objects that due to badly conditioned dynamic features (strong non-linearities) require the operator’s intervention in manual or semi-automatic mode. The possibility of fuzzy algorithm synthesis is analyzed with two linguistic variable inputs (distance from −100 to 500 mm and angle from −45° to 45°). The output signal is the speed which is modelled as a linguistic power variable (in the domain from −100% to 100%). Based on 36 fuzzy rules, we present the main contribution, the control system with external disturbance, to show the effectiveness of the identified fuzzy PID approach with different gain values. The fuzzy control system and PID control are implemented and compared concerning the time taken for the container to reach the set point. The results show that fuzzy MISO PID is more effective than the classical one because fuzzy set theory helps to deal with the environmental uncertainty. The container’s angle deviations are taken into consideration, as mitigating them and simultaneously maintaining the fastest speed possible is an essential factor of this challenge.
FusionTalk: An IoT-Based Reconfigurable Object Identification System

Multisensor data fusion combines various information sources to produce a more accurate or complete description of the environment. This article studies an object identification (OID) system using multiple distributed cameras and Internet-of-Things (IoT) devices for better visualizability and reconfigurability. We first propose a data processing and fusing method to merge the detection results of different IoT devices and video cameras, in order to locate, identify, and track target objects in the monitored area. Then, we develop the FusionTalk system by integrating the data fusion techniques with IoTtalk, an IoT device management platform. FusionTalk is designed with flexibility, modularity, and expansibility, where cameras, IoT devices, and network applications are modularized and can be conveniently plugged in/out, reconfigured, and reused through graphical user interfaces. In FusionTalk, the scope and the target of surveillance can be flexibly configured and associated, and administrators can be warned and easily visualize the movement and behavior of specific objects. Our experimental evaluation of the data fusion algorithm in various scenarios shows an identification accuracy above 95%. Finally, theoretical and numerical analyses on the failure probability of pairing IoT devices with video objects by FusionTalk are presented. Extensive experiments are performed to demonstrate the pairing effectiveness in real-world scenarios with failure probability less than 0.01%.