Reviewerly/exHarmony huggingface dataset:
Face Recognition in the Dark: A Unified Approach for NIR- VIS and VIS- NIR Face Matching

Visible face recognition security systems fail to recognize the faces in total darkness. Near Infrared (NIR) spectrum provides an affordable and effectual method to capture good quality images in poor lighting or complete darkness condition. In this paper, a unified model has been proposed for both NIRVIS and VIS- NIR heterogeneous face recognition to solve the illumination problem. The proposed model is based on transfer learning which is a modified version of the Resnet-34 model and it avoids training on large scale NIR face images. Two experiments have been conducted using the proposed model on publicly available datasets, namely Oulu- CASIA and HITSZ. NIR query images are matched with VIS gallery images whereas, in the second experiment, VIS query images are matched with NIR gallery images. The proposed model has given a rank-l accuracy of 98.54% on the Oulu- CASIA dataset which outperforms the existing state-of-the-art models.
GCN embedded with Polynomial Aggregation Function for Group Activity Recognition

Group activity detection is a crucial task for an automated analysis of human actions in many environments, such as surveillance or sport footage. Group activity recognition focuses on identifying the activities of one or more individuals through a sequence of observations on the conduct of the individuals. Numerous approaches have been proposed for the same using deep learning neural networks. In this paper, we focus on graphical convolutional neural network (GCN) which uses graphs as inputs, aggregate the vectors using aggregation functions and attempt to create models which can provide ample knowledge innate in human mobility. The paper aims to build a GCN method by adding the application of Polynomial Aggregation Function (PAF) to ensure the precision in investigating the activities is improved and further increases the accuracy of identification of human actions. The experiment is carried out using VGG16, GCN and GCN embedded with PAF on the Collective dataset. The experimental results obtained show that the whole group's actions as an entity is recognized and the proposed method provide better accuracy.
Distribution of Number of Levels in an $$[\varvec{s}]$$-Specified Random Permutation

Successions, Eulerian and Simon Newcomb numbers, and levels are the best-known patterns associated with $$[\varvec{s}]$$ -specified random permutations. The distribution of the number of rises was first studied in 1755 by Euler. However, the distribution of the number of levels in an $$[\varvec{s}]$$ -specified random permutation remained unknown. In this study, our main goal is to identify the distribution of the number of levels, which we achieve using the finite Markov chain imbedding technique and insertion procedure. An example is given to illustrate the theoretical result.
Rainfall modeling based on early predicted and season zone characteristic in the BMKG season zone over Lombok river basin

Abstract Planning water resources for long-term (annual, monthly) and operational (daily) requires a more accurate analysis of water availability calculations. The Meteorology, Climatology and Geophysics Agency (BMKG) publishes bulletin of forecasting every year in the form of the early wet season and the characteristics of the wet and dry season in each Season Zone (ZOM). BMKG conducts analysis and forecasting with complex climate parameters. Thomas Fiering analysis with the help of Excel Visual Basic Application (VBA) software is used to forecast rainfall at each rainfall station in BMKG Season Zone. The average error of forecasting rainfall during the wet season on the BMKG season zone in 2015/2016, 2016/2017 and 2017/2018 were 20%, 30% and 26%. While the average error for the dry season on the BMKG season zone is 44%, 34% and 52%. The existence of this deviation is caused by several factors including the uncertain season changes every year, as well as the influence of “El Nino.”.
Technologization of student behavior management in the modern system of secondary vocational education

The article studies the theoretical foundations of technologization of student behavior management in the modern system of secondary vocational education (SVE). The author systematizes scientific approaches to the study of technologization of human behavior management in the modern system of vocational education. Concepts such as “social behavior in a technologized society”, “technologization of student behavior management” and “educational behavior” have been elaborated and introduced into the conceptual apparatus of management sociology. The effectiveness of the educational process has been substantiated to primarily lie in the expansion of knowledge and the acquisition of the ability by students to apply it in new transformational situations, rather than depend on the simple assimilation of certain volumes of knowledge by students. Social technologization presupposes a convincing vision of the transformation of the educational program and a vision of modern education in the era of digitalization. The article presents the model of the process of social technologization and provides the classification of social technologies of management. The trends in the development and implementation of innovative social technologies for managing human behavior in modern educational institutions are identified and summarized.
Privacy Attack and Defense in Network Embedding

Network embedding aims to learn the low-dimensional latent representations of vertices in a network. The existing works have primarily focused on various embedding methods for network data in general and overlooked the privacy security issue of them. For example, when a vertex is deleted from the network, it is easy to achieve the deleted relations by remaining embedding vectors. To address these issues, we propose choosing the node degree with selectivity to study the problem of privacy attack and defense in network embedding. While some existing works are addressed the data protection problem, none of them has paid special attention to combine network embedding with privacy security in such a deep way. Our solution consists of two components. First, we propose a new method named SANE, short for Sampling Attack in Network Embedding to utilize remaining vertex information to obtain the deleted relations between vertices as privacy attack. Second, we propose a new privacy defense algorithm named DPNE, short for Differential Privacy in Network Embedding, to employ obfuscation function to defend against attacks to prevent deleted related relations being recovered. The two components are integrated in a principled way for considering the node degree to classify the vertices into different levels for sampling. We conduct extensive experiments on several real-world datasets and one synthetic dataset covering the task of link prediction. Both quantitative results and qualitative analysis verify the effectiveness and rationality of our methods.
Achieving Human Expert Level Time Performance for Atari Games – A Causal Learning Approach

In a previous paper, we have argued that what constitutes human-level performance for Atari games must include the measurements of both the score achieved as well as the time taken to learn to achieve it. When both of these measurements approach the level of humans, the method and system will also become practical as high score and short time performance for learning can lead to practical real-world applications. Other well-known approaches, such as DeepMind's Atari game player, achieve human-level scores but the time taken to learn to play the games is many orders of magnitudes slower than that of humans, leading to inapplicability in real-world scenarios beyond the games. In our previous paper, we demonstrated the ability of a causal learning framework to learn to play the Atari game, Space Invaders, at both the score and time performance levels of a human novice. In this paper, we show that an extension of this system reported earlier leads to the ability for the system to perform at both the score and time level of a human expert. A major innovation of this paper is a “changing goal tracking” system that is not only applicable to Atari games such as Space Invaders, it is also applicable to general AI systems.
The Research on Clustering Ensembles Selection Algorithm based on Semi-supervised K-means Clustering

Abstract Selective clustering ensemble algorithm can eliminate the inferior quality clustering member’s influence and can achieve a better clustering solution relative to the clustering ensemble algorithm. For high dimensional data clustering, in this paper, a novel selective ensemble algorithm based on semi-supervised K-means clustering is proposed. In this paper, through a large number of experiments to verify the validity of the proposed algorithm for dealing with high dimensional data clustering. The new algorithm can achieve statistically significant performance improvement over other clustering algorithms.
Improving Reconstructed Image Quality Via Hybrid Compression Techniques

Data compression is one of the core fields of study for applications of image and video processing.The raw data to be transmitted consumes large bandwidth and requires huge storage space as a result, it is desirable to represent the information in the data with considerably fewer bits by the mean of data compression techniques, the data must be reconstituted very similarly to the initial form.In this paper, a hybrid compression based on Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) is used to enhance the quality of the reconstructed image.These techniques are followed by entropy encoding such as Huffman coding to give additional compression.Huffman coding is optimal prefix code because of its implementation is more simple, faster, and easier than other codes.It needs less execution time and it is the shortest average length and the measurements for analysis are based upon Compression Ratio, Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR).We applied a hybrid algorithm on (DWT-DCT 2 × 2, 4 × 4, 8 × 8, 16 × 16, 32 × 32) blocks.Finally, we show that by using a hybrid (DWT-DCT) compression technique, the PSNR is reconstructed for the image by using the proposed hybrid algorithm (DWT-DCT 8 × 8 block) is quite high than DCT.
Towards Smart Behavior of Agents in Evacuation Planning Based on Local Cooperative Path Finding

We address engineering of smart behavior of agents in evacuation problems from the perspective of cooperative path finding (CPF) in this paper. We introduce an abstract version of evacuation problems we call multi-agent evacuation (MAE) that consists of an undirected graph representing the map of the environment and a set of agents moving in this graph. The task is to move agents from the endangered part of the graph into the safe part as quickly as possible. Although the abstract evacuation task can be solved using centralized algorithms based on network flows that are near-optimal with respect to various objectives, such algorithms would hardly be applicable in practice since real agents will not be able to follow the centrally created plan. Therefore we designed a decentralized evacuation planning algorithm called LC-MAE based on local rules derived from local cooperative path finding (CPF) algorithms. We compared LC-MAE with near-optimal centralized algorithm using agent-based simulations in multiple real-life scenarios. Our finding it that LC-MAE produces solutions that are only worse than the optimum by a small factor. Moreover our approach led to important observations about how many agents need to behave rationally to increase the speed of evacuation. A small fraction of rational agents can speed up the evacuation dramatically.
SIGNIFICANCE OF LANGUAGE IN PEACE EDUCATION

Peace is another faction of human psychology. When one expresses peaceful behavior, then it signifies that he/she is in a state of spiritual peace and has sufficient knowledge of peaceful qualities with proper endurance of staying clam at any situation.Well it may be defined as the absence of violence and also the presence of states of mind and of society such as harmony, security accord, and mutual understanding. In order to better understand how to achieve peace on the global level, it can be marked as the absence of violent conflicts or wars between nations, states, and communities. It is equally important for us to examine the personal attitudes, values, and knowledge that provide the basis for working toward peace making and keeping it.While almost all of us want peace and prefer to live and work in a peaceful situation, there are some who wants to lead an anarchical life, since they are ignorant about the values of peace in their lives. The elements of peace must be explored and incorporated in the language we use in our discussions. Tolerance, understanding, empathy, cooperation, respect and gratitude are at the core of our peaceful coexistence on earth, but each of us may interpret these concepts somewhat differently based on our social and cultural values, our life experiences, and our beliefs about the world.Students and the peers are the building blocks of each and every generation and hence they must be equipped with various peaceful qualities such that they can carry this exclusive thought forward in the near future. For reflecting ideas, beliefs and concepts, children are encouraged to cooperate with peers to share information. Students are left to discuss, comment or decide about the solution to the problems regarding unethical activities. Students are also invited to ask questions throughout the peace education classes.We are growing up in a world where the remedy of every problem; may it be political or racial or religious or otherwise, tends to be solved through means of violence or unethical means. We have fallen prey to numerous World Wars, countless rebellions and mass massacres which ultimately leads to mass murders or genocide. These phenomenon have increased generation wise and now we are standing on the grounds of the 21st century. To maintain peace all over the world, several nations have indulged in making peace treaties to maintain international relations among them. Well, a better solution to maintain peace at high grounds is peace education which is a bright and innovative step to equip the countrymen with sufficient knowledge and different values of peace. It is a fact that peace education is one of the most planned processes to raise the learners ‘critical and psychic consciousness. Mostly peace educators believe that it provides students with a better understanding of their social, cultural as well as political contexts while at the same time considering the possibilities for action and change.Therefore, in our paper we would like to discuss on why and how language is the core requirement for peace education at both academic and international level.
Factors Involved in Artificial Intelligence-based Automated HTML Code Generation Tool

Automated code generation or automatic programming is a widely researched topic these days. The auto code generation fascinates the young researchers towards this field more than ever. The Automatic code generation is the process to generate the valid programming code in various languages and makes programming easy for the next generations. Automatic coding is to build a machine which can write code. The code writing process involves several wearisome steps tearing down a process into small instructions, allotting specific memory locations or to specify the system interrupts. It can also be able to manage the input-output buffers and many other things. After observing these steps as mentioned above, we implement mathematical routines, a subroutine library, and sorting programs. In this paper, we look closely towards the factors involved in Automatic programming. We studied more extensive programming and code writing process to understand how these factors affect the process and which can help anyone build a better machine for auto programming. As we programmed manually first we observed the process and tried to invent ways to abstract these steps to combine them into a higher-level programming language. This includes the development of interpreters, assemblers, compilers, and finally, the automatic code generator programs designed to operate with the process as mentioned earlier, that is, automatic programming.
One-Step Modal Logics, Intuitionistic and Classical, Part 1

This paper and its sequel “look under the hood” of the usual sorts of proof-theoretic systems for certain well-known intuitionistic and classical propositional modal logics. Section 1 is preliminary. Of most importance: a marked formula will be the result of prefixing a formula in a propositional modal language with a step-marker, for this paper either 0 or 1. Think of 1 as indicating the taking of “one step away from 0.” Deductions will be constructed using marked formulas. Section 2 presents the model-theoretic concepts, based on those in [7], that guide the rest of this paper. Section 3 presents Natural Deduction systems IK and CK, formalizations of intuitionistic and classical one-step versions of K. In these systems, occurrences of step-markers allow deductions to display deductive structure that is covered over in familiar “no step” proof-theoretic systems for such logics. $\square $ and ♢ are governed by Introduction and Elimination rules; the familiar K rule and Necessitation are derived (i.e. admissible) rules. CK will be the result of adding the 0-version of the Rule of Excluded Middle to the rules which generate IK. Note: IK is the result of merely dropping that rule from those generating CK, without addition of further rules or axioms (as was needed in [7]). These proof-theoretic systems yield intuitionistic and classical consequence relations by the obvious definition. Section 4 provides some examples of what can be deduced in IK. Section 5 defines some proof-theoretic concepts that are used in Section 6 to prove the soundness of the consequence relation for IK (relative to the class of models defined in Section 2.) Section 7 proves its completeness (relative to that class). Section 8 extends these results to the consequence relation for CK. (Looking ahead: Part 2 will investigate one-step proof-theoretic systems formalizing intuitionistic and classical one-step versions of some familiar logics stronger than K.)
Effects of ABRC module utilization in improving the reading comprehension of grade 5 pupils in Tangway Loob elementary school

Reading comprehension is an essential skill for learners of English.For most of learners it is the most important skill to master in order to ensure success in learning.With strengthened reading skills, learners of English tend to make greater progress in other areas of learning.Reading should be an active, fluent process that involves the reader and the reading material in building meaning.Thus, the researcher uses the ABRC Module to increase the reading comprehension of Grade 5.This study aimed to improve the reading comprehension of Grade 5 through the use of ABRC Module.This includes thirty seven pupils.The descriptive method of research was used in this study with the Phil-IRI pre and post test results as the primary data gathering tools.There will be one group: the experimental.The data to be gathered will be statistically treated with the use of weighted mean and standard deviation.The results will be analyzed and tabulated through tables.The results revealed that 100% of the pupils increased their level comprehension after using the ABRC Module.
A Neural Expert System to Identify Major Risk Factors of Breast Cancer

Now a days Breast Cancer (BC) is a very common cancer among women and rarely man. BC incidence rate keeps increasing since 2000. Many intelligent systems have been proposed using different Machine Learning (ML) Techniques for early diagnosis of BC. Neural Networks (NN) is considered as one of the best ML classification techniques. In this paper we propose a medical expert system which named Transparent Neural Expert System for Breast Cancer (TNESBC) to manage BC by identifying Major Risk factors. The proposed TNESBC adopts the white-box NN model which named “Rule Extraction from Artificial Neural Network(ANN) applying Classified and Misclassified data” (RxNCM) for rule extraction from BC database. The rules are generated in this system which are most transparent to verify the Major Risk factors of BC. The proposed TNESBC algorithm is comparable the RxREN algorithm. The experimental analysis, it is noticed that TNESBC algorithm performs better than RxREN. The comparison is formed using 10-fold Cross-Validation(CV) accuracy, average recall rate, average number of antecedents per rules and average false positive rate.
TMUOU Submission for WMT20 Quality Estimation Shared Task.

We introduce the TMUOU submission for the WMT20 Quality Estimation Shared Task 1: Sentence-Level Direct Assessment. Our system is an ensemble model of four regression models based on XLM-RoBERTa with language tags. We ranked 4th in Pearson and 2nd in MAE and RMSE on a multilingual track.
Increasing Accuracy of Software Defect Prediction using 1-dimensional CNN with SVM

Precise imperfection expectation computations help to find more deformities with limited effort. To improve the exactness of deformity forecast, in this paper, we propose a significant learning-based strategy for effort careful without a second to save imperfection expectation. The key idea of the proposed approach is that neural system and significant learning could be mishandled to pick important features for deformity expectation since they have been shown amazing at picking accommodating features for plan and backslide. In this paper, we execute a proficient for programming deformity expectation in ventures utilizing 1-Dimentional convolution neural network (CNN) with support vector machine (SVM) and improve exactness in the model.
Predicting Bug in a Software using ANN Based Machine Learning Techniques

To achieve a successful software, bug prediction mainly place an important role. Nowadays, bug prediction has become an essential argument in the maintenance and development phase. So, it's necessary to predict bugs in earlier stages of software development life cycle. The challenge here is to develop a model that helps in predicting bugs leading to good quality, reliable, efficient and cost-effective software. For complex software projects, bugs are major issues. The proposed model provides the comparative analysis on various machine learning algorithms that are developed to predict bugs namely Random Forest, Logistic regression, Decision Tree, Artificial Neural Network and Naïve Bayes. Here Artificial Neural Network is used along with other algorithms such that the model can be trained for large datasets, in order to get more accurate results working effectively for various scenarios. The performance of each model is evaluated, and cross validation is performed followed by visualizing the results. Finally, when all the models are compared Artificial Neural Network appears to be the best model by providing 82.77%.
Random Subspace Combined LDA Based Machine Learning Model for OSCC Classifier

Oral squamous cell carcinoma (OSCC) remains a major death causing oral cancer in developing countries. In recent years, tremendous development in medical imaging devices made microscopic colour images of biopsy samples available to the researchers. Image processing and machine learning techniques can be used to develop automatic cancer grading mechanism. In this work, automatic OSCC classifier using Linear Discriminant Analysis combined with Random Subspace is developed and analyzed. The proposed classifiers automatically classifies the input image in one of the four categories, namely: Normal, Grade-I, II or III. Total 83 colour and texture features are computed from the 100 Haemotoxylin and Eosin (H&E) stained images of oral mucosa. The overall accuracy of the proposed classifier is 93.5% with sensitivity and specificity of 0.89 and 0.95 respectively.