Reviewerly/exHarmony huggingface dataset:
A Fast Evolutionary adaptation for MCTS in Pommerman

Artificial Intelligence, when amalgamated with games makes the ideal structure for research and advancing the field. Multi-agent games have multiple controls for each agent which generates huge amounts of data while increasing search complexity. Thus, we need advanced search methods to find a solution and create an artificially intelligent agent. In this paper, we propose our novel Evolutionary Monte Carlo Tree Search (FEMCTS) agent which borrows ideas from Evolutionary Algorthims (EA) and Monte Carlo Tree Search (MCTS) to play the game of Pommerman. It outperforms Rolling Horizon Evolutionary Algorithm (RHEA) significantly in high observability settings and performs almost as well as MCTS for most game seeds, outperforming it in some cases.
On the rate of convergence of a classifier based on a Transformer encoder

Pattern recognition based on a high-dimensional predictor is considered. A classifier is defined which is based on a Transformer encoder. The rate of convergence of the misclassification probability of the classifier towards the optimal misclassification probability is analyzed. It is shown that this classifier is able to circumvent the curse of dimensionality provided the aposteriori probability satisfies a suitable hierarchical composition model. Furthermore, the difference between Transformer classifiers analyzed theoretically in this paper and Transformer classifiers used nowadays in practice are illustrated by considering classification problems in natural language processing.
Management of Social and Economic Development of Municipalities

The paper discusses the process of social and economic development of municipalities. A conclusion is made that developing an adequate model of social and economic development using conventional approaches presents a considerable challenge. It is proposed to use semantic modeling to represent the social and economic development of municipalities, and cognitive mapping to identify the set of connections that occur among indicators and that have a direct impact on social and economic development.
The 5th Recognizing Families in the Wild Data Challenge: Predicting Kinship from Faces

Recognizing Families In the Wild (RFIW), held as a data challenge in conjunction with the 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG), is a large-scale, multi-track visual kinship recognition evaluation. For the fifth edition of RFIW, we continue to attract scholars, bring together professionals, publish new work, and discuss prospects. In this paper, we summarize submissions for the three tasks of this year's RFIW: specifically, we review the results for kinship verification, tri-subject verification, and family member search and retrieval. We look at the RFIW problem, share current efforts, and make recommendations for promising future directions.
Answer Generation for Questions With Multiple Information Sources in E-Commerce

Automatic question answering is an important yet challenging task in E-commerce given the millions of questions posted by users about the product that they are interested in purchasing. Hence, there is a great demand for automatic answer generation systems that provide quick responses using related information about the product. There are three sources of knowledge available for answering a user posted query, they are reviews, duplicate or similar questions, and specifications. Effectively utilizing these information sources will greatly aid us in answering complex questions. However, there are two main challenges present in exploiting these sources: (i) The presence of irrelevant information and (ii) the presence of ambiguity of sentiment present in reviews and similar questions. Through this work we propose a novel pipeline (MSQAP) that utilizes the rich information present in the aforementioned sources by separately performing relevancy and ambiguity prediction before generating a response. Experimental results show that our relevancy prediction model (BERT-QA) outperforms all other variants and has an improvement of 12.36% in F1 score compared to the BERT-base baseline. Our generation model (T5-QA) outperforms the baselines in all content preservation metrics such as BLEU, ROUGE and has an average improvement of 35.02% in ROUGE and 198.75% in BLEU compared to the highest performing baseline (HSSC-q). Human evaluation of our pipeline shows us that our method has an overall improvement in accuracy of 30.7% over the generation model (T5-QA), resulting in our full pipeline-based approach (MSQAP) providing more accurate answers. To the best of our knowledge, this is the first work in the e-commerce domain that automatically generates natural language answers combining the information present in diverse sources such as specifications, similar questions, and reviews data.
Code Clone Detection based on Event Embedding and Event Dependency

The code clone detection method based on semantic similarity has important value in software engineering tasks (e.g., software evolution, software reuse). Traditional code clone detection technologies pay more attention to the similarity of code at the syntax level, and less attention to the semantic similarity of the code. As a result, candidate codes similar in semantics are ignored. To address this issue, we propose a code clone detection method based on semantic similarity. By treating code as a series of interdependent events that occur continuously, we design a model namely EDAM to encode code semantic information based on event embedding and event dependency. The EDAM model uses the event embedding method to model the execution characteristics of program statements and the data dependence information between all statements. In this way, we can embed the program semantic information into a vector and use the vector to detect codes similar in semantics. Experimental results show that the performance of our EDAM model is superior to state of-the-art open source models for code clone detection.
Urban Radiance Fields

The goal of this work is to perform 3D reconstruction and novel view synthesis from data captured by scanning platforms commonly deployed for world mapping in urban outdoor environments (e.g., Street View). Given a sequence of posed RGB images and lidar sweeps acquired by cameras and scanners moving through an outdoor scene, we produce a model from which 3D surfaces can be extracted and novel RGB images can be synthesized. Our approach extends Neural Radiance Fields, which has been demonstrated to synthesize realistic novel images for small scenes in controlled settings, with new methods for leveraging asynchronously captured lidar data, for addressing exposure variation between captured images, and for leveraging predicted image segmentations to supervise densities on rays pointing at the sky. Each of these three extensions provides significant performance improvements in experiments on Street View data. Our system produces state-of-the-art 3D surface reconstructions and synthesizes higher quality novel views in comparison to both traditional methods (e.g.~COLMAP) and recent neural representations (e.g.~Mip-NeRF).
Fast algorithms for solving the Hamilton Cycle problem with high probability

We study the Hamilton cycle problem with input a random graph G=G(n,p) in two settings. In the first one, G is given to us in the form of randomly ordered adjacency lists while in the second one we are given the adjacency matrix of G. In each of the settings we give a deterministic algorithm that w.h.p. either it finds a Hamilton cycle or it returns a certificate that such a cycle does not exists, for p > 0. The running times of our algorithms are w.h.p. O(n) and O(n/p) respectively each being best possible in its own setting.
Research on English-Chinese Subtitle Translation Strategies Based on the Perspective of Translation Communication Studies—Take the Subtitle Translation of Soul as an Example

From the perspective of translation communication, this paper focuses on translation strategies adopted in English-Chinese subtitle translation to achieve communication effect, according to the “5Ws” of communication mode, and to provide a useful reference and a new research perspective for subtitle translation research.
Conceptually Diverse Base Model Selection for Meta-Learners in Concept Drifting Data Streams

Meta-learners and ensembles aim to combine a set of relevant yet diverse base models to improve predictive performance. However, determining an appropriate set of base models is challenging, especially in online environments where the underlying distribution of data can change over time. In this paper, we present a novel approach for estimating the conceptual similarity of base models, which is calculated using the Principal Angles (PAs) between their underlying subspaces. We propose two methods that use conceptual similarity as a metric to obtain a relevant yet diverse subset of base models: (i) parameterised threshold culling and (ii) parameterless conceptual clustering. We evaluate these methods against thresholding using common ensemble pruning metrics, namely predictive performance and Mutual Information (MI), in the context of online Transfer Learning (TL), using both synthetic and real-world data. Our results show that conceptual similarity thresholding has a reduced computational overhead, and yet yields comparable predictive performance to thresholding using predictive performance and MI. Furthermore, conceptual clustering achieves similar predictive performances without requiring parameterisation, and achieves this with lower computational overhead than thresholding using predictive performance and MI when the number of base models becomes large.
Topic Classification of Quranic Verses in English Translation Using Word Centrality Measurement

Every Muslim in the world believes that the Quran is a miracle and the words of God (Kalamullah) revealed to the Prophet Muhammad SAW to be conveyed to humans. The Quran is used by humans as a guide in dealing with all problems in every aspect of life. To study the Quran, it is necessary to know what topic is being discussed in every single verse. With the help of technology, the verses of the Quran can be given topics automatically. This task is called multilabel classification where input data can be classified into one or more categories. This research aims to apply the multilabel classification to classify the topics of the Quranic verses in English translation into 10 topics using the Word Centrality measurement as the word weighting value. Then a comparison is made to the 4 classification methods, namely SVM, Naïve Bayes, KNN, and Decision Tree. The result of the centrality measurement shows that the word ‘Allah’ is the most important or the most central word of the whole document of the Quran with the scenario using stopword removal. Furthermore, the use of word centrality value as term weighting in feature extraction can improve the performance of the classification system.
On the Potential of Randomization-based Neural Networks for Driving Behavior Classification

Naturalistic Driving has recently garnered the attention from the community working on Deep Learning models, producing a plethora of modeling proposals on account of reaching an increasingly better predictive performance. Little attention has been paid to the computational implications of adopting such models, particularly when used for inferring the behavior of the driver from naturalistic driving data. This work enters this uncharted research area by probing the balance between complexity and performance of randomization-based neural networks for driving behavior classification. To this end, results from an extensive experimental benchmark comparing these networks to diverse Deep Learning and ensemble learning models are discussed, unveiling a significantly better-balanced trade-off between performance and complexity of randomization-based neural networks, and suggesting more concern with the efficiency of models in prospective studies.
Social Distance Monitoring System Using YOLO and Pixel-to-real-world Distance Mapping

Covid-19 is an infectious disease caused by the recently discovered coronavirus. This virus spreads through droplets produced when an infected person coughs, sneezes, or exhales. A person can be infected by breathing air containing the virus if a person is too close to someone who is already infected with Covid-19. A person can also be infected by touching contaminated surfaces and then touching their eyes, nose, or mouth. Social distancing is to reduce the spread of viruses, other than washing hands and wearing a mask. With the help of Computer Vision Technology, it can monitor the safe distance of human activities in a particular area or environment. The problem is to determine the safe distance in a pixel-based digital image. The difference in the same pixel distance does not always mean the actual distance between adjacent objects is the same. It must consider the actual distance to the camera. Since social distancing requires someone to keep a certain distance from another, a web-based application with a Convolutional Neural Network (CNN) algorithm is employed using the You Only Look Once (YOLO) and Pixel-to-real-world distance mapping technique. In testing, there are several test scenarios with the accuracy results obtained of 95% with a recall of 0.95 and a precision of 0.92, and MAE $5.9\mathrm{~cm}$.
Transfer Learning in Fall Detection Using Visual Data

Humans’ fall detection is critical in the prevention of life-threatening conditions. This is especially important for elderly people who are living alone. In this paper, we propose an approach based on a deep neural network for fall detection using data from the video camera and transfer learning techniques. We have chosen AlexNet as the baseline convolutional neural network architecture. The model was fine-tuned so it could solve a binary classification task (fall/non-fall). We used Le2i fall detection dataset, which was gathered in four locations with nine volunteers. 58 videos were used for model training and 47 videos were used for validation. The classifier has an accuracy and a Cohen’s kappa of 0.96 and 0.90, respectively, for the dataset where locations were familiar to the convolutional neural network. For the test dataset with previously unknown conditions, the accuracy and Cohen’s kappa were 0.96 and 0.80, respectively. Additionally, the heuristic application allowed us to increase specificity, that is, to decrease the rate of false alarms. This result may be potentially used as a part of a sensor-fusion fall detection system.
Study Of Super-resolution Methods Based On Fitted Dual Quadratic Polynomials

As an important index to measure infrared images, spatial resolution plays a key role in infrared remote sensing imaging, navigation guidance of aircraft and recognition of military targets. However, the pixel density of infrared imaging detectors is much lower than that of visible light detectors, resulting in low resolution of infrared images obtained. Infrared images also have shortcomings such as high noise, fuzzy interference and loss of high-frequency information, which affect the detection and recognition of targets. In this thesis, an infrared image super-resolution degradation model is established based on the degradation factors that occur in the infrared imaging process. The influence of noise and blur on improving the resolution of infrared images is analyzed, and in this way, a full-flow reconstruction model of infrared image super-resolution is established. On the basis of noise and blur removal,a super-resolution method based on fitted dual quadratic polynomials is proposed for the low resolution of infrared images. This method makes full use of the pixel information of the original image, and uses the fitted interpolation polynomial to expand the pixels of the low-resolution image to obtain a high-resolution image. On the basis of improving the resolution, the infrared image noise and blur are better suppressed, the detailed features are reflected and the subjective quality is improved.
Continuous-Time Fitted Value Iteration for Robust Policies

Solving the Hamilton-Jacobi-Bellman equation is important in many domains including control, robotics and economics. Especially for continuous control, solving this differential equation and its extension the Hamilton-Jacobi-Isaacs equation, is important as it yields the optimal policy that achieves the maximum reward on a give task. In the case of the Hamilton-Jacobi-Isaacs equation, which includes an adversary controlling the environment and minimizing the reward, the obtained policy is also robust to perturbations of the dynamics. In this paper we propose continuous fitted value iteration (cFVI) and robust fitted value iteration (rFVI). These algorithms leverage the non-linear control-affine dynamics and separable state and action reward of many continuous control problems to derive the optimal policy and optimal adversary in closed form. This analytic expression simplifies the differential equations and enables us to solve for the optimal value function using value iteration for continuous actions and states as well as the adversarial case. Notably, the resulting algorithms do not require discretization of states or actions. We apply the resulting algorithms to the Furuta pendulum and cartpole. We show that both algorithms obtain the optimal policy. The robustness Sim2Real experiments on the physical systems show that the policies successfully achieve the task in the real-world. When changing the masses of the pendulum, we observe that robust value iteration is more robust compared to deep reinforcement learning algorithm and the non-robust version of the algorithm. Videos of the experiments are shown at https://sites.google.com/view/rfvi
Online Stochastic DCA with applications to Principal Component Analysis

Stochastic algorithms are well-known for their performance in the era of big data. In convex optimization, stochastic algorithms have been studied in depth and breadth. However, the current body of research on stochastic algorithms for nonsmooth, nonconvex optimization is relatively limited. In this paper, we propose new stochastic algorithms based on DC (Difference of Convex functions) programming and DCA (DC Algorithm) - the backbone of nonconvex, nonsmooth optimization. Since most real-world nonconvex programs fall into the framework of DC programming, our proposed methods can be employed in various situations, in which they confront stochastic nature and nonconvexity simultaneously. The convergence analysis of the proposed algorithms is studied intensively with the help of tools from modern convex analysis and martingale theory. Finally, we study several aspects of the proposed algorithms on an important problem in machine learning: the expected problem in Principal Component Analysis.
Work-in-Progress: Ultra-fast yet Accurate Performance Prediction for Deep Neural Network Accelerators

We present an automatic methodology to accurately predict the performance of Deep Neural Network (DNN) accelerators using abstract descriptions of accelerator architectures and DNNs with a high degree of flexibility. By mapping partially unrolled neural network layers onto accelerator architectures, we automatically construct an analytical performance model, exploiting the dataflow-driven nature of DNNs that allows us to evaluate only a few loop iterations to determine the performance of a whole DNN layer.
A Dual-Population Cooperative Water Wave Optimization Algorithm with Knowledge-Driven Learning Mechanism and Its Performance Analysis

Abstract The water wave optimization algorithm (WWO), which is inspired by the shallow water wave theory, is a promising meta-heuristic in continuous optimization problems. However, the canonical WWO algorithm has proved to fall into the local optimum as lacking the guidance of the search direction and iteration step sizes in the search process. In this paper, a dual-population cooperative WWO with a knowledge-driven learning mechanism (DCLWWO) is proposed to relieve the drawback. The improved learning mechanism of WWO with the searching operations of different types of the population is combined to strengthen the self-learning and self-adapting capabilities of the algorithm. The wavelength [[EQUATION]] was redefined with a fixed external archive to further control the size of the search step during the iterations. DCLWWO is compared with the existing variants of WWO and several well-established algorithms to substantiate the performance of the algorithm. Meanwhile, DCLWWO is applied to solve the blocking flow shop scheduling problem (BFSP), which has been widely studied in artificial intelligence manufacturing. Experimental results and systematic analysis indicate the effectiveness of DCLWWO in solving complex optimization problems.
The Students’ Speaking Anxiety during Pandemic Covid-19

The new paradigm on the position of English in the world has shifted as now English was no longer the property of native speakers. This shifting paradigm challenge the traditional assumption of English teaching world on whether English teachers should incorporate local or target culture in their teaching materials. This is a qualitative research using critical literature review method. The topic being discussed in this article is how to incorporate local value in English language teaching materials. The issue is analysed from various perspectives, they are: world Englishes, English as International Language, culture and identity, materials development and how they shape the idea of how English teaching materials in Indonesia should be developed. In order to do that, the researchers implemented four criteria of a good ELT materials proposed by McDonough, Shaw, Masuhara, (2013). As the result of the research, we provide two suggestions. They are creating a team of book developers and providing alternatives ELT materials based on different learning styles, text genres, or even viewpoint towards an issue.
Komparasi Sistem Pembelajaran Offline, Semi Offline, dan Online Pada Hasil Belajar Mata Kuliah Metode Numerik

The impact of Covid-19 has been felt in the world of education. The students have finally attended lectures with offline, semi-offline, and online learning systems for the last three years so that it has an impact on learning outcomes. Therefore, one of the objectives of this study is 1) to test whether there are differences in the average learning outcomes of students who receive offline, semi-offline, and online learning systems; 2) test which student learning outcomes are better between students who get an offline learning system, a semi-offline learning system, and an online learning system. The sample in this study was taken through purposive sampling technique. The first sample is the 2016 mathematics education study program students who carry out offline lectures as experimental class 1, the second sample is the 2017 mathematics education study program students who carry out semi-offline lectures as experimental class 2, while the third sample is the 2018 mathematics education study program students who carry out online lectures as an experimental class 3. Data obtained through the test method. Data analysis of student learning outcomes included normality test with the Kolmogorov-Smirnov test, homogeneity test with lavender test, proportion test, One Way Anova test, and Scheffe advanced test. The results showed that lectures using an offline system were better than lectures using an online system. Keywords: offline, semi-offline, on line, learning outcomes, numerical method