Reviewerly/exHarmony huggingface dataset:
A proposal for the supplementation of traditional Postmarket Surveillance systems based on Named Entity Recognition

Nowadays vaccines against COVID-19 have begun to be applied and some adverse events may arise. Postmarket surveillance studies are important to obtain information about rare adverse events (AE). Traditional methods of postmarket surveillance are dependent on spontaneous notifications and are therefore subject to underreporting. Named Entity Recognition (NER) has been used for the information extraction. We did a survey on recent papers that have used NER to supplement postmarket surveillance systems based on the information extraction of social media, with predominance of websites related to some health topics, electronic health records (EHR), official databases and reference databases. We present negative and positive endorses for each approach of the use of NER and propose a guide for the use of NER in the information extraction task to supplement traditional postmarket surveillance systems.
PEMBELAJARAN MENULIS TEKS FABEL MENGGUNAKAN METODE BROKEN SQUARE BERBASIS CERITA BERGAMBAR

Writing fable texts on the learning of junior high school students can be categorized into difficult lessons. Because students are required to be able to rew write stories according to their language structure and rules. Students find it hard to imagine pouring out their ideas. This causes students to become less excited about Learning Indonesian. In this study, researchers found that students' difficulties with learning to write fable texts were still difficult in pouring their imagination into writing the fable text. It is still rare for students to apply the language rules and structure of the fable text. The purpose of this study is to improve the writing ability of students in pouring their imagination into a writing. This research was conducted by applying broken square method assisted by pictorial story. This method directs students to play puzzles or compose fragments of images that have been shared by the teacher and then students are directed to arrange the fragments into a whole image medium. This method is used to build the spirit and creativity of students so as not to get bored in writing skills. As for the results of the study obtained, researchers conducted a pre-test whose results are still many students who do not yet understand the elements and language rules of the fable text. After the use of broken square method, the writing results of students are much better than before the applied broken square method.
SEDL: A Symmetric Encryption Method Based on Deep Learning

Recent years have seen an increasing emphasis on information security, and various encryption methods have been proposed. However, for symmetric encryption methods, the well-known encryption techniques still rely on the key space to guarantee security and suffer from frequent key updating. Aiming to solve those problems, this paper proposes a novel general symmetry-key encryption method based on deep learning called SEDL, where the secret key includes hyperparameters in deep learning model and the core step of encryption is processing input data with weights trained under hyperparameters. Firstly, both communication parties establish a weight vector table by training a deep learning model on the constructed synthetic training sets according to specified hyperparameters. Then, a self-update codebook is constructed on the weight vector table with the SHA-256 function and other tricks. When communication starts, encryption and decryption are equivalent to indexing the corresponding value on the codebook to obtain ciphertext or plaintext, respectively. Results of experiments and relevant analyses show that SEDL performs well for security, efficiency, generality, and has a lower demand for the frequency of key redistribution. Especially, as a supplement to current encryption methods, the time-consuming process of constructing a codebook increases the difficulty of brute-force attacks, meanwhile, it does not degrade the efficiency of communications.
Are emotional objects visually salient? The Emotional Maps Database

The visual system prioritizes emotional content in natural scenes, but it is unclear whether emotional objects are systematically more salient. We compare emotional maps - created by averaging multiple manual selections of the most meaningful regions in images of negative, positive, and neutral affective valence - with saliency maps generated by Graph-Based Visual Saliency, Proto-object, and SalGAN models. We found that similarity between emotional and saliency maps is modulated by the scenes’ arousal and valence ratings: the more negative and high-arousing content, the less it was salient. Simultaneously, the negative and high-arousing content was the easiest to identify by the participants, as shown by the highest inter-individual agreement in the selections. Our results support the “affective gap” hypothesis, i.e., decoupling of emotional meaning from image’s formal features. The Emotional Maps Database created for this study, proven useful in gaze fixation prediction, is available online for scientific use.
A Comparative Analyzing of SMS Spam Using Topic Models

Mobile phones or smart phones have changed or revolutionized the way we live. These days the short message service (SMS) is becoming fashionable. For spammers, the success of the mobile messaging channel has become a very attractive target to attack. To impose an additional level of security in the pervasive environment, we will create a system which is more authenticated for SMS. This system will have impact on user’s usability from the point of view of user’s safety. In modern era, the financial industries and other related agencies are seeing the SMS as an important aspect to communicate with their customers which somehow opens the easy flap for spammers, and customer’s safety measures is at hazard. The digital encryption methodologies are useful to support the SMS formation which needs two nodes to swap over digital signed SMS message. These two nodes are protected by the public key cryptography and authentication is done with the help of the ECDSA signature scheme. These two nodes are recognized as sender and receiver, and when a sender sends an SMS to any receiver, the unencrypted text is sent means that there is possibility of loss of information. In this paper, we propose the technique called Gaussian Naive Bayes Classification (GNBC) for the filtering of spam by SMS that solves the message topic model (MTM) problems. It is believed that some pre-processing rules and background terms make it the most appropriate model to completely represent spam by SMS. Finally, we have concluded that GNBC is more accurate for the SMS spam filtering activity.
Detecting Facial Images In Public With And Without Masks Using VGG And FR-TSVM Models

Since 2019, Covid-19 has become a common problem affecting all mankind. The disease has successfully spread all over the world. Wearing a mask can practically protect the infection. Thus, detecting people wearing and not wearing masks in public is essential. However, there is still some room to improve detection accuracy of the present methods. In this paper, the transfer learning model and FR-TSVM model are used to study the latest data of pneumonia epidemic situation in Covid-19. First, a data set of 12,000 facial images wearing masks and not wearing masks in public was collected for training, testing, and validation. The pictures will be put into the improved VGG model. Then the structure of VGG model was used to extract the features of images. These features were trained by FR-TSVM with fuzzy concept included. This approach can achieve 95.5% accuracy, and it is also higher than the detection results of other methods.
A Robust Saliency Integrated Method for Monocular Motion Estimation

The moving objects in the background bring the challenges for motion estimation in the visual odometer applications for autonomous driving. This paper proposed a robust motion estimation method with saliency based outlier removal with a single camera in visual odometer's applications. First, an efficient saliency calculation method is proposed to detect the moving objects from the noisy monocular image sequences. Then the robust motion estimation is conducted with weighted saliency map while considering of textures area and smooth area. Experiments on KITTI dataset are conducted and the experimental results show that our proposed method with a single camera is comparable with the start-of-art stereo motion estimation method and outperform other compared stereo motion estimation methods.
Modelling the development of counting with memory-augmented neural networks

Author(s): Dulberg, Zack; Webb, Taylor; Cohen, Jonathan | Abstract: Learning to count is an important example of the broader human capacity for systematic generalization, and the development of counting is often characterized by an inflection point when children rapidly acquire proficiency with the procedures that support this ability. We aimed to model this process by training a reinforcement learning agent to select N items from a binary vector when instructed (known as the give-$N$ task). We found that a memory-augmented modular network architecture based on the recently proposed Emergent Symbol Binding Network (ESBN) exhibited an inflection during learning that resembled human development. This model was also capable of systematic extrapolation outside the range of its training set - for example, trained only to select between 1 and 10 items, it could succeed at selecting 11 to 15 items as long as it could make use of an arbitrary count sequence of at least that length. The close parallels to child development and the capacity for extrapolation suggest that our model could shed light on the emergence of systematicity in humans.
일본인 대상 한국 언어문화 지도 및 평가 방안

In this study, I presented a specific class case for the Korean language culture education method in Korean language education for Japanese learners and a language culture evaluation method in the TOPIK. First, in the Korean class application, 1) OX quiz to prevent misuse by literal translation, 2) a new year greeting education using a scenario, 3) a new word education using a speed quiz game, 4) Busan dialect education using the movie 'Friends', 5) demonstration it suggested teaching methods such as non-verbal behavior education when making promises, and comparison of non-verbal behavior between Korea and Japan through capturing TV scenes. In addition, to reflect the TOPIK, questions related to Korean language culture corresponding to the TOPIK II were developed, and in addition to the existing reading fingerprint format, various forms such as conversation format and blog format were proposed.
Usability: Can Mobile Applications Be Used Easily?

This chapter discusses the usability of a mobile application as one of the main qualitative attributes. The chapter focuses on the users’ expectations about usability and the redundancy of content or appearance of the product. It discusses how fast and easy it to learn the use of the mobile application is by memorizing what has been learned from the application. Moreover, it emphasizes the application&#039;s capabilities to recover the required information. It discusses how experienced mobile users can perform common tasks very fast and the possibilities of interacting with other applications. This chapter emphasizes protection against making mistakes and meeting the accessibility standards, which apply to the mobile application.
(M+1)-Sequence Generators with Concurrent Error Detection

The paper proposes a method for designing self-checking pseudorandom number generators based on shift registers with linear and nonlinear feedbacks (LFSR and NLFSR, respectively). The concept of an (M + 1)-sequence generator is introduced for NLFSRs, which form sequences of the maximum possible length for a given generator bit width. The circuits of generators of (M – 1)- and (M – 3)-sequences have been modified in order to increase the length of the period of the generated sequences. Logical schemes of NLFSR self-control based on parity checking of PRNG states are presented.
Heuristic Evaluation of Vulnerability Risk Management Leaders’ Presentations of Cyber Threat and Cyber Risk

This work is an initial investigation into the way cybersecurity companies convey the concept of cyber-related threat and/or cyber-related risk to their clients. We survey the current cybersecurity business landscape and examine product outputs from a select group of companies identified by the analyst firm Forrester [24] as leading providers of vulnerability risk management services. Of specific interest are those tools/products that reflect a cybersecurity company’s efforts to combine data related to vulnerability information, threat intelligence, asset criticality, and/or network exposure in order to distill and quantify the complex ideas of cyber threat and cyber risk into relatively simple outputs like a single value or chart. We conduct a heuristic evaluation [9, 11] of static views of the vendors’ offerings and introduce the concept of the mythical average, reasonable IT professional (MARIP) to inspect the product outputs with respect to the key HCI principles of familiarity and consistency as they pertain to use of colors, numbers, and charts.
A Multi-Valued Simplified Halpern–Shoham–Moszkowski Logic for Gradable Verifiability in Reasoning about Digital Circuits

In 1983, B. Moszkowski introduced a first interval-interpreted temporal logic system, the so-called Interval Temporal Logic (ITL), as a system suitable to express mutual relations inside intervals for reasonings about digital circuits. In 1991, Halpern and Shoham proposed a new temporal system (HS) to describe external relations between intervals. This paper is aimed at proposing a basis-type combination of HS and a simplified ITL end extends it towards a multi-valued system—also capable of rendering a gradable justification of agents in a similar contexts of reasoning about digital circuits. This newly introduced system is semantically interpreted in the so-called fibred semantics.
Procedural and Software Development of a Jury-based Stability Analysis Method for Control Systems

This paper presents an application that uses the Jury stability criterion to determine if a system is stable or unstable. The software application described, in this paper, is based on a MATLAB script. With this application it can easily establish the fulfillment of stability conditions from Jury criterion.
Estimation of monthly average daily of the global solar radiation using the linear regression algorithm

Abstract Solar radiation is the backbone for the existence of life on the earth. Its measurement is too expensive. Therefore, it has been great importance to propose an efficient method to use as a solar predictor based on other more readily available meteorological data. In this study, Linear regression method has been applied in two locations that have two different climates in Egypt, location A that has a coastal climate on the Mediterranean coast and location B that has a dry desert climate, using three weather vectors (minimum, maximum and average) of temperature values. The performance of the linear regression models provides better predictions for global solar radiation (GSRa) at different locations. The Root Mean Square Error (RMSE) = (2.7716 and 2.9392) MJ m −2 with Relative Root Mean Square Error (rRMSE) = (14.5962 and 14.0861) % in location A and B respectively. The accurate prediction results of the GSRa using this approach can be employed in the various purposes of the solar applications.
Gothic VR Game Scene Automatic Generation Design

Computer-aided technology and automatic programs are critical to improve the efficiency of game design. The goal of the paper is to research the automated design method of the Gothic architectural scene in VR games as to facilitating the existing design procedure. Firstly, we analyzed the aesthetic and structural characteristics of Gothic architecture, summarized the spatial and topological features of Gothic buildings. Secondly, we discussed the connection probability between Gothic building modules based on the principle of space topology. Then based on the Wave Function Collapse algorithm, an automatic generation model of Gothic VR game scenes was established. Finally, this model was tested on the U3D virtual simulation platform and proved its availability. The contribution of this work is to directly improve the efficiency and scale of Gothic building scene construction in VR games with an automatic generated program, but also present a reference to the design and generation of the other game architectural art scenes.
Persuasive Technology in Games: A Brief Review and Reappraisal

Persuasive technology is a new field of research that has attracted considerable attention from game designers since there is a growing interest in games promoting positive behavioral changes. Persuasive games have been exploited to tremendous effect with applications ranging from mobile healthcare, which persuade users to exercise more often and adopt a healthy lifestyle, to government programs encouraging civic engagement. Therefore, persuasive technologies have become an indispensable part of the modern game designer’s toolkit, and their importance is only set to grow with time. In this paper, we begin by reviewing the existing body of work in this field while also explaining the pros and cons of emerging design models and theoretical frameworks. We then uncover major pitfalls in the current work and suggest directions for future research. Hopefully, this article will prove instructive to game designers and leave them with a better understanding of the central concepts in the field of persuasive technology.
Education: innovative approaches and sustainable development in modern world

In the article, the authors consider problems related to the professional mobility of students, outline and analyze innovative teaching methods used in the educational process. Features of modern technologies used in the educational process are identified and presented; active methods of training and modeling are determined and discussed, efficiency of training technologies is investigated. Special attention is paid to the study of interactive forms of students’ training. The authors present the results of deep analysis of approaches used in the educational process, within the framework of which, classifications of interactive teaching methods are given. The main stages of the business game with a discussion approach are developed and justified; an experimental test of the application of a business game with a discussion approach the purpose of which is to control knowledge and motivate students is presented. The authors prove that usage of debating approach in the framework of active forms of education has a positive impact on educational process and increases students' motivation to gain knowledge.
A Method of Search Space Adaptation in the False Pseudonyms Detection Procedure

The paper deals with the problem of the false pseudonyms detection in the Internet. This problem relates to the victim behavior of individuals, so, it is quite relevant under the conditions of the lack of physical representation and deanonymizing. The stylomentry approach has showed good results in the authorship analysis in the field of the doppelgangers finding, yet, in case of big data analysis the methods are needed to improve the capabilities of the false pseudonyms search procedure. In this paper fuzzy cognitive map and some elements of the distributed ledger technology are proposed as a facility to enhance the search space according to the assumption about the audiences intersection. The method proposed aims two goals: to improve the search results by means of cognitive analysis of the WEB-resources relations and to decrease the search time, eliminating the weakly-integrated resources.
Snakes and Ladders Media in Social Studies learning for Elementary School Students

This studi aims to analyze how the application of snake and ladder leraning media in social studies learning of elementary school students. This research uses a qualitative approach. The type of research used is a literature review. The data source in this research is secondary data theory obtained from the results of research that has been conducted by previous researchers. Methods of data collection using documentation. The data analysis technique in this study used descriptive analysis. The results of this study indicate that the application of snake and ladder learning media can increase students’ interest and social studies learning outcomes.
Online and Offline Blended Teaching of College English in the Internet Environment

With the continuous deepening of education reform, more and more scientific teaching concepts and novel teaching models have emerged, which have become an effective way to improve the efficiency and quality of modern education and teaching. The effective use of mixed teaching mode in college English teaching can meet the needs of reform and innovation of vocational education teaching, provide students with rich teaching resources, diversified teaching methods, and advanced educational technology services, thereby effectively stimulating college students’ interest in learning English. Students learn English knowledge and correctly use English ability to solve more problems to lay a solid foundation and improve the level of talent training in college English education. This article studies the online and offline English teaching in colleges and universities under the Internet environment.
Learning (k,l)-context-sensitive probabilistic grammars with nonparametric Bayesian approach

Inferring formal grammars with nonparametric Bayesian approach is one of the most powerful approach for achieving high accuracy from unsupervised data. In this paper, mildly-context-sensitive probabilities, called (k, l)-context-sensitive probabilities, are defined on context-free grammars (CFGs). Inferring CFGs where the probabilities of rules are identified from contexts can be seen as a kind of dual approaches for distributional learning, in which the contexts characterize the substrings. We can handle the data sparsity for the context-sensitive probabilities by the smoothing effect of the hierarchical nonparametric Bayesian models such as Pitman–Yor processes (PYPs). We define the hierarchy of PYPs naturally by augmenting the infinite PCFGs. The blocked Gibbs sampling is known to be effective for inferring PCFGs. We show that, by modifying the inside probabilities, the blocked Gibbs sampling is able to be applied to the (k, l)-context-sensitive probabilistic grammars. At the same time, we show that the time complexity for (k, l)-context-sensitive probabilities of a CFG is $$O(|V|^{l+3}|w|^3)$$ for each sentence w, where V is a set of nonterminals. Since it is computationally too expensive to iterate sufficient times especially when |V| is not small, some alternative sampling algorithms are required. Therefore, we propose a new sampling method called composite sampling, with which the sampling procedure is separated into sub-procedures for nonterminals and for derivation trees. Finally, we demonstrate that the inferred (k, 0)-context-sensitive probabilistic grammars can achieve lower perplexities than other probabilistic language models such as PCFGs, n-grams, and HMMs.