Reviewerly/exHarmony huggingface dataset:
Informatics and machine learning methods for health applications

The 2020 International Conference on Intelligent Biology and Medicine (ICIBM 2020) provided a multidisciplinary forum for computational scientists and experimental biologists to share recent advances on all aspects of intelligent computing, informatics and data science in biology and medicine. ICIBM 2020 was held as a virtual conference on August 9-10, 2020, including four live sessions with forty-one oral presentations over video conferencing. In this special issue, ten high-quality manuscripts were selected after peer-review from seventy-five submissions to represent the medical informatics and decision making aspect of the conference. In this editorial, we briefly summarize these ten selected manuscripts.
SQL Generation via Machine Reading Comprehension

Text-to-SQL systems offers natural language interfaces to databases, which can automatically generates SQL queries given natural language questions.On the WikiSQL benchmark, state-ofthe-art text-to-SQL systems typically take a slot-filling approach by building several specialized models for each type of slot.Despite being effective, such modularized systems are complex and also fall short in jointly learning for different slots.To solve these problems, this paper proposes a novel approach that formulates the task as a question answering problem, where different slots are predicted by a unified machine reading comprehension (MRC) model.For this purpose, we use a BERT-based MRC model, which can also benefit from intermediate training on other MRC datasets.The proposed method can achieve competitive results on WikiSQL, suggesting it being a promising direction for text-to-SQL.
LMVE at SemEval-2020 Task 4: Commonsense Validation and Explanation Using Pretraining Language Model

This paper describes our submission 1 to subtask a and b of SemEval-2020 Task 4. For subtask a, we use a ALBERT based model with improved input form to pick out the common sense statement from two statement candidates.For subtask b, we use a multiple choice model enhanced by hint sentence mechanism to select the reason from given options about why a statement is against common sense.Besides, we propose a novel transfer learning strategy between subtasks which help improve the performance.The accuracy scores of our system are 95.6 / 94.9 on official test set 2 and rank 7 th / 2 nd on Post-Evaluation leaderboard.
A Mobile Application for Offline Handwritten Character Recognition

The handwritten character recognition is a computerized system that is able to identify and recognize characters and words written by user. In this paper, we proposed offline handwritten character recognition using deep learning architecture. The Android application of the proposed system is also created by using OpenCV and TensorFlow Lite. The proposed system is aimed to use as a teaching aid in helping kindergarten to primary school level students, especially for practicing their writing and learning. The local handwritten dataset, which includes digit, English alphabet and mathematical symbols that are collected from students, is used for training and testing operations. According to the experimental results, the proposed system is very promising and it will be a useful application for educational environment.
Lattice‐based unidirectional Proxy Re‐Encryption and Proxy Re‐Encryption <sup>+</sup> schemes

The concept of Proxy Re-Encryption (PRE) was introduced by Blaze, Bleumer and Strauss at Eurocrypt in 1998. In this concept, a semi-trusted proxy user acquires a re-encryption key from the delegator. Then the proxy can easily convert the encrypted message under Alice's public key into an encrypted message under Bob's public key, without knowing the content of the message. For a unidirectional PRE, a master secret security (MSS) has been proposed as another security requirement by Ateniese et al. MSS provides security to the secret key of the delegator from computation by association with a malicious delegatee and dishonest proxy. In this study, first we have shown that Aono et al.'s scheme is not secure under the MSS model. Second, we have designed a unidirectional PRE scheme, based on Aono et al.'s study, and proved its security under the MSS model. In Table 2, we have shown that our scheme is more efficient in terms of computational and communication costs. Third, we have constructed a lattice-based unidirectional PRE+ scheme, similar to the scheme proposed by Wang et al. In this scheme the encryptor itself works as the delegator.
Team Solomon at SemEval-2020 Task 4: Be Reasonable: Exploiting Large-scale Language Models for Commonsense Reasoning

In this paper, we present our submission for SemEval 2020 Task 4 -Commonsense Validation and Explanation (ComVE).The objective of this task was to develop a system that can differentiate statements that make sense from the ones that don't.ComVE comprises of three subtasks to challenge and test a system's capability in understanding commonsense knowledge from various dimensions.Commonsense reasoning is a challenging task in the domain of natural language understanding and systems augmented with it can improve performance in various other tasks such as reading comprehension, and inferencing.We have developed a system that leverages commonsense knowledge from pretrained language models trained on huge corpus such as RoBERTa, GPT2, etc.Our proposed system validates the reasonability of a given statement against the backdrop of commonsense knowledge acquired by these models and generates a logical reason to support its decision.Our system ranked 2nd in subtask C with a BLEU score of 19.3, which by far is the most challenging subtask as it required systems to generate the rationale behind the choice of an unreasonable statement.In subtask A and B, we achieved 96% and 94% accuracy respectively standing at 4th position in both the subtasks.
Multilevel Thresholding based Image Segmentation using Optimization Algorithm

The process of image segmentation is very important in the context of image analysis. Segmentation makes it easier to analyze a portion of the image one has to deal with. The application of image segmentation is immense in the areas of machine vision, medical imaging, locating objects in satellite images, object detection, recognition tasks and various other fields of science and engineering. In recent times nature-inspired algorithms are being used for both bi-level and multilevel thresholding based image segmentation. In this paper the performances of the Grasshopper Optimization Algorithm and the Whale Optimization Algorithm are evaluated for image segmentation.The performances of the proposed methods are compared with Cuckoo Search Algorithm along with Kapur's entropy criterion.The quality of segmentation is measured by various image quality metrices which indicates Whale Optimization as the best performing algorithm.
Exploring the process of teacher development toward teaching English by using the Trajectory Equifinality Modelling approach: Elementary, middle and high school comparison

Within the education system in Japan, English classes have historically been conducted in Japanese. While the reasons for this are obvious, there are potential pitfalls, including the fact that such classes depend on translating English to Japanese and explaining grammar principles in Japanese. In 2008 The Japanese Ministry of Education, Culture, Sports, Science, and Technology introduced a major reform to the teaching of English. The policy reform specifically related to high school English classes, but in 2019 it was updated to include junior high school classrooms. This policy placed increasing pressure on Japanese school teachers to teach English in English. Despite this increased pressure to speak English exclusively during class time, a 2019 survey found that many teachers choose to ignore the guidelines. Professor Mitsuyo Toya, who is an expert in applied linguistics and TEFL and is based at the University of the Ryukyus in Japan, is part of a team exploring the process of teacher development for teaching English in Japan. By using the Trajectory Equifinality Modelling approach, the team hopes to suggest improvements that the education sector can put in place to benefit students and teachers alike.
MIDAS at SemEval-2020 Task 10: Emphasis Selection Using Label Distribution Learning and Contextual Embeddings

This paper presents our submission to the SemEval 2020 -Task 10 on emphasis selection in written text.We approach this emphasis selection problem as a sequence labeling task where we represent the underlying text with various contextual embedding models.We also employ label distribution learning to account for annotator disagreements.We experiment with the choice of model architectures, trainability of layers, and different contextual embeddings.Our best performing architecture is an ensemble of different models, which achieved an overall matching score of 0.783, placing us 15th out of 31 participating teams.Lastly, we analyze the results in terms of parts of speech tags, sentence lengths, and word ordering.
A Hybrid Method for Industrial Robot Navigation

Robot navigation in dynamic unknown environments is a challenging issue in the field of autonomous mobile robot control. This paper presents a hybrid robust method for navigating an industrial robot in an environment that contains dynamic obstacles. The objectives are to find the shortest path, to minimize the energy consumption of robot, to make the smoothness of the generated paths and to tackle dynamic obstacles. Robots employed in industrial environments demand considerable autonomy and require high level of accuracy and manoeuvrability at the same time. Besides, no collision is tolerable along the way. A single-objective optimization method based on path criteria fails to satisfy all of the requirements. This paper proposes a hybrid algorithm including the whale optimization algorithm (WOA) for path planning, a learnable function approximation network for making smoothness of the generated paths and a fuzzy logic controller to avoid obstacle collision. In this algorithm, WOA optimizes the best path to be taken from the start to goal position. Once a sequence of points is candidate and segments of path are merged, a radial basis function is trained to provide a smooth movement path in the dynamic environment while trying to maximize the safety margin. To further improve the safety of navigation, a fuzzy-based obstacle avoidance algorithm is executed when the robot is placed in the vicinity of an obstacle. Fuzzy decisions are made based on values of distance information. The proposed hybrid method for path planning and obstacle avoidance issues was implemented and evaluated in dynamic environments including specific shaped obstacles. A GUI-based simulation platform was designed in Matlab environment for testing the proposed algorithm. Implementation results indicate that the proposed algorithm has yielded in smooth non-marginal goal-directed navigation with acceptable performance metrics. Meanwhile, collisions to dynamic obstacles were adaptively and non-rigidly avoided. Such a model-free hybrid algorithm for path planning and obstacle avoidance can improve autonomy in industrial operation and decrease computational complexity.
How to Induce Trust in Medical AI Systems

Trust is an important prerequisite for the acceptance of an Artificial Intelligence (AI) system, in particular in the medical domain. Explainability is currently discussed as the key approach to induce trust. Since a medical AI system is considered a medical device, it also has to be formally certified by an officially recognised agency. The paper argues that neither explainability nor certification suffice to tackle the trust problem. Instead, we propose an alternative approach aimed at showing the physician how well a patient is represented in the original training data set. We operationalize this approach by developing formal indicators and illustrate their usefulness with a real-world medical data set.
A hybrid friend-based recommendation system using the combination of Meta-heuristic Invasive weed and genetic algorithms

One of the most important goals of researchers in designing recommender systems is to increase the accuracy of recommender models. The main purpose of this study is to combine weed algorithm and genetics to increase the efficiency of clustering, which increases the accuracy of clustering for analyzing suggestions in the recommender system. Clustering is based on three similarity criteria KMEAN, JACCARD and MINKOWSKI and in addition, it has been implemented with the mentioned algorithms. Using clustering method considering the mutation of genetic algorithm and using weed algorithm has led to the emergence of an efficient system that for this purpose, genetic generators have been used to sample data in the problem space instead of random generators, This selection has led to an increase in the accuracy of the algorithm due to the more uniform coverage of the problem space and the increase in the variety of problem searches. The Recommender system on the standard MovieLense data set is tested and its error is 0.02, which has a more minimal error than other algorithms (genetics and weeds).
Arduino Based Authenticated Voting Machine (AVM) using RFID and Fingerprint for the Student Elections

Abstract The guiding principles of democracy are free elections. Through elections, people reaffirm their voice, their beliefs, and select someone whose ideas support them most. Voters will elect their representatives via elections. The purpose of the elections is not only to decide the outcome but also to give the winners credence, also for those voters who did not bother to vote for them. The need to hold free, equal, and secret elections is emphasized here. It involves the supervision of elections by free, accountable, impartial and autonomous electoral bodies. As the phrase is ‘today teens, tomorrow’s citizens,’ the student community needs the process of elections to be involved, trained, and acquainted with. Under this respect, the institutions of higher education hold elections in the form of general elections to the Office of the student councils. By this student will receive first-hand information about filing nomination, scrutiny of papers, withdrawing, canvassing, addressing, and most importantly voting. The use of technology in voting will make it easier, more effective, and less prone to breaches of security. Technology will boost and speed up the safety of all votes and make the counting and automated verification much more effective. The design of an advanced voting system is a difficult task as many main requirements have to be met. The secrecy of a poll should be maintained. No proof of which candidate gets particular voting shall be provided by the voting system. The authors implemented the Authenticated Voting Machine in the College elections in this paper to ease the process and improve transparency. The concept is still in its infancy and requires more research to keep it stable and theoretically strong. To ensure protection, the model uses radiofrequency and fingerprint recognition.
An approach towards development of automated attendance system using face detection and recognition

Conventionally, the process of taking attendance of students in a classroom is quite a laborious task, wherein either the teacher has to call out names of each individual student, or the student has to sign an attendance sheet. In recent times, due to the Covid-19 pandemic, special importance has been laid on facial recognition techniques, which are contact-free (unlike fingerprint scanners), and are in accordance with social distancing norms. In this paper, a software system automating the attendance-taking scheme is presented. This software integrates face detection, image processing and face recognition approaches to come up with a consolidated attendance system capable of overcoming the disadvantages of manual attendance. In the system, an end user has to first log in and subsequently, an IP camera (which is to be installed in the classroom) gets turned on, and the camera starts taking photographs of the classroom. The user can also manually upload images into the system, in case calculation of attendance is not immediately required. The Histogram of Oriented Gradients (HOG) approach is employed for the face detection mechanism in the proposed system. After a comparative performance analysis of different facial recognition techniques, the Local Binary Patterns Histograms (LBPH) method is chosen as the facial recognition procedure for the system. Once all the individual students have been recognised by comparison with the model built from the extracted faces, the final results are sorted according to date (similar to taking attendance of a class on a particular day) and stored in the database.
An Importance Assessment Model of Open-Source Community Java Projects Based on Domain Knowledge Graph

With the rise of open-source software, the social development paradigm occupies an indispensable position in the current software development process. This paper puts forward a variant of the PageRank algorithm to build the importance assessment model, which provides quantifiable importance assessment metrics for new Java projects based on Java open-source projects or components. The critical point of the model is to use crawlers to obtain relevant information about Java open-source projects in the GitHub open-source community to build a domain knowledge graph. According to the three dimensions of the Java opensource project’s project influence, project activity and project popularity, the project is measured. A modified PageRank algorithm is proposed to construct the importance evaluation model. Thereby providing quantifiable importance evaluation indicators for new Java projects based on or components of Java opensource projects. This article evaluates the importance of 4512 Java open-source projects obtained on GitHub and has a good effect.
Integration of Digital Technologies into Education

The number of universities using digital technologies is growing year by year. Digital technology in the modern world is not only a tool, but also a living environment that opens up new opportunities: learning at any convenient time, continuing education, etc. This article aims to describe the specificity of digital education, the current state of its implementation, the expected results and concerns in this respect. Having shown the core of the digital education and the state of its implementation in modern society, this type of education must be critically analyzed in terms of advantages and risks with reference to contemporary students and the effectiveness of the teaching – learning process, in which they participate. In the study pros and cons of digital learning are revealed. The current study presents information about advantages of using electronic educational resources in teaching a foreign language based on the experimental work which was done in Naberezhnye Chelny Institute of Kazan Federal University. The success of the experiment presented in this paper is demonstrated by comparing the results of the test group who were taught using electronic educational resources with the reference group who were taught in a common traditional way. The results of the questionnaire suggest that the majority of students believe they have acquired food knowledge using digital technology in education. The statistical analysis shows that the test group students had better achievements compared to the reference group since students from test group have the digital skills.
Features of smart learning at high school

Infocommunication technologies are increasingly intruding into the learning process, including higher education. The widespread access to omnifarious information resources from various telecommunication devices — computers, tablets, smartphones — taking into account the relatively high coverage of the territory with wireless communications, provides the necessary conditions for creating individual learning opportunities based on objective indicators of learning material and mastering the required skills and abilities. You must take into account already existing experience and established tradition, the ability to perceive new and prepared to use a variety of modern ICT solutions to educational institutions, not only students, but also their teachers as well as the willingness of the relevant learning and teaching materials. To implement this, it is necessary to form not only educational-methodical materials, but also an adequate control and measuring environment, which allows obtaining objective assessments characterizing the process of studying the discipline and the quality of mastering the material. For this purpose, the authors propose to create a model of an intellectual lecture-presentation with a built-in testing subsystem, in which there is a possibility of a step-by-step study of the educational material with the fixation of the quality of mastering the material in the form of certain indicators. Such indicators may include: the time spent on each element (slide) of the course, taking into account the number of reference to this element; the dynamics of answers to test questions — an indicator reflecting the speed of mastering the educational material; the number of correct and incorrect answers, as well as the number of missed questions when performing tests — an indicator reflecting the volume of assimilation of educational material, etc. The analysis of the collected quantitative indicators allows you to determine the dynamics of the study of the course and build an individual trajectory of the student’s learning, as well as develop appropriate recommendations for studying the course, which may appear after any slide, which is determined by the result of the analysis of the time and test results. Moreover, these recommendations can be both advisory and directive, compulsory. Thus, the created model of intellectual lecture-presentation allows you to form an objective “electronic educational portrait” of a student, taking into account his individual characteristics, interests and abilities, which makes it possible to study and assimilate not only formal knowledge, but also support the desire to learn new things.
Correction to: Dynamic Data Driven Applications Systems

The book was inadvertently published with a typo in the main title on the cover and in the frontmatter as “Dynamic Data Driven Application Systems” whereas it should be “Dynamic Data Driven Applications Systems”. The main title has been corrected in the book.
Single Image De-Raining Using Spinning Detail Perceptual Generative Adversarial Networks

In this paper, Spinning Detail Perceptual Generative Adversarial Networks (SDP-GAN) is proposed for single image de-raining. The proposed method adopts the Generative Adversarial Network (GAN) framework and consists of two following networks: the rain streaks generative network G and the discriminative network D. To reduce the background interference, we propose a rain streaks generative network which not only focuses on the high frequency detail map of rainy image, but also directly reduces the mapping range from input to output. To further improve the perceptual quality of generated images, we modify the perceptual loss by extracting high-level features from discriminative network D, rather than pre-trained networks. Furthermore, we introduce a new training procedure based on the notion of self spinning to improve the final de-raining performance. Extensive experiments on the synthetic and real-world datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art methods.