Reviewerly/exHarmony huggingface dataset:
Identification of Extreme Guilt and Grave Fault in Bengali Language

Numerous study has been done on the Bengali Language for the extraction of information, but none of them deals with extreme guilt (বাংলা দোষ) and grave fault (গুরুচণ্ডালী দোষ) in the Bengali Language. In this study, we have described extreme guilt (বাংলা দোষ) and grave fault (গুরুচণ্ডালী দোষ). We have also used two machine learning methods, such as Logistic Regression (LR) and Support Vector Machine (SVM). We have used count vectorizer for feature extraction. Result analysis shows that SVM has better accuracy then LR. LR and SVM method has 80% and 87% of accuracy for extreme guilt (বাংলা দোষ) respectively and 60% and 85% accuracy for grave fault (গুরুচণ্ডালী দোষ) respectively.
Agricultural Sensor Captured Data Analysis for Optimum Storage

As we know that data captured from various agricultural sensors is in the infinite sequence. This data can also be called as Agricultural Unstructured data. It is necessary to find out the useful information from the big volume of unstructured data for scientific analysis, predictions to improve agricultural productivity. In the preceding paper we are providing a framework which can solve the problem of randomly stored big data volume. This paper basically deals with classification of agricultural sensor generated data by distinguishing the classes according to sources as sensors such as soil moisture sensor, soil temperature sensor, heat sensor and so on. Each sensor produces variety of data which can be classified by using the proposed Pro-Brokering algorithm.
Multivariate Anomaly Detection in Mixed Telemetry time-series Using A Sparse Decomposition

Spacecraft health monitoring from housekeeping telemetry data represents one of the main issues in space operations. Motivated by the success of machine learning or data driven-based methods in many signal and image processing applications, some of these methods have been applied to anomaly detection in housekeeping telemetry via a semi-supervised learning. This paper studies a new multivariate anomaly detection algorithm based on a sparse decomposition on a dictionary of nominal patterns. One originality of the proposed method is a multivariate framework allowing us to take into account possible relationships between different telemetry parameters, in particular through a joint processing of time-series described by mixed continuous and discrete parameters. The proposed method is tested with real satellite telemetry and evaluated on a representative anomaly dataset composed of actual anomalies that occurred on several operated satellites. The first results confirm the interest of the proposed method and demonstrate its competitiveness with respect to the state-of-the-art.
Integration of support vector machines for hydrothermal alteration mapping using ASTER data – case study: the northwestern part of the Kerman Cenozoic Magmatic Arc, Iran

This work applies support vector machine (SVM) algorithms in two versions of singular and general SVM classifiers to map hydrothermal alteration zones in the northwestern part of the Kerman Cenozoic Magmatic Arc (KCMA). Three visible bands and six SWIR bands of ASTER images were applied as inputs for SVM classifiers. The develosped algorithms were able to classify ASTER images into hydrothermal alteration or non-hydrothermal alteration classes. In singular SVM, nine classifiers were able to vote individually for every pixel in the image. Then, they were combined through integration rules to present a final decision about every pixel. The general SVM classifier integrated nine ASTER bands at the signal level to produce the final decision. The classification error rate showed that the general Gaussian RBF kernel-based SVM classifier had higher accuracy for the classification of hydrothermal alteration zones. The SVM results were then compared with other classified images based on band ratio and SAM methods. The main problem associated with these methods was that vegetation covering was highlighted as alteration zones while the SVM algorithm could solve this issue. Also, the verification of results, based on field and laboratory investigations, showed the SVM method to produce a more accurate map of alteration than that obtained from the band ratio and SAM.
Image Compression Using High Level Wavelet Transformer with Non-Uniform Quantizer and Different Levels Huffman Codes

Abstract Image compression solutions are a pivotal field that gain over a remarkable research interest and called upon to extend widely with respect to the increasing of its applications. The considerable challenge of this solution is related to the storage and bandwidth requirements. A highly compression rate with a high image resolution is considered to be the key factors for implementing a robust image compression technique. In this paper, the proposed method is deemed to be simple and effective due to using different levels of compression process which is starting from dividing the image into sub-images and using high level discrete wavelet transform (DWT). In addition, the Huffman code with non-uniform Quantizer has been used to minimize the compression data rate with high quality reconstructed image. The promising simulation results of the proposed method achieved the most two significant criteria which are denoted by high image quality and a high image compressed ratio. For instance, the proposed strategy simulation results of Lena image achieved PSNR equals to 42.4094 dB with a compression ratio (CR) equals to 47.5435. Moreover, the simulation results of Flower image accomplished PSNR equals to 42.8348 dB with a compression ratio equals to 55.3389 under the same simulation conditions.
BirdsEyeView: Aerial View Dataset for Object Classification and Detection

In recent years, deep learning based computer vision technology has progressed rapidly thanks to the significant increases in computing power and high-quality datasets. In this article, we present an aerial view image and video dataset dedicated to facilitating vision applications on the UAV platform, such as object detection, classification and tracking. The dataset consists of 5,000 images, each of which is carefully annotated according to the guidelines of the PASCAL VOC. The dataset is designed to cover diverse real-life scenes with aerial view angles which is different from other datasets. Such kind of specific dataset will be of great importance in developing and testing deep learning algorithms for UAV applications. Moreover, the dataset can serve as a benchmark to evaluate UAV visual solutions.
STEM's Impact on Integral Learning. Exploratory and Predictive Study in Primary School Students in Lima Norte (Peru)

STEM (Science, Technology, Engineering and Mathematics) is a methodology that raises in the development of skills, such as: research, creativity, critical thinking, problem solving, communication and collaboration, that prepares children in the school stage. This research work aims to determine how the STEM methodology application affects integral learning. The sample consists of 27 primary education second grade students in a private school in Lima Norte -11 men (40.7%) and 16 women (59.3%), between 7 (88.9%) and 8 (11.1%) years old-. The results of the correlation and simple linear regressions allow us to conclude that the development of communication, collaboration, creativity and research (typical of STEM) predict the obtaining of integral learning in students.
A Framework for Evaluating the Quality of Academic Websites

The main goal of this paper is to design a tool for the evaluation of academic website, taking into account perspectives of different user groups. A literature review was conducted on the existing models, and a list of the factors affecting the quality of academic websites was identified. A framework was developed based on the identified quality factors, to evaluate the new framework, a questionnaire was devised, and a survey was conducted on the reliability of this questionnaire. To assess the effectiveness of the framework, an experiment was conducted, considering six academic websites and 6300 people from different user groups. The threats encountered during the study were also discussed with recommendations for future work.
Examination-Style Reading Comprehension with Neural augmented Retrieval

In this paper, we focus on an examination-style reading comprehension task which requires a multiple choice question solving but without a pre-given document that is supposed to contain direct evidences for answering the question. Unlike the common machine reading comprehension tasks, the concerned task requires a deep understanding into the detail-rich and semantically complex question. Such a reading comprehension task can be considered as a variant of early deep question-answering. We propose a hybrid solution to solve the problem. First, an attentive neural network to obtain the keywords in question. Then a retrieval based model is used to retrieve relative evidence in knowledge sources with the importance score of each word. The final choice is made by considering both question and evidence. Our experimental results show that our system gives state-of-the-art performance on Chinese benchmarks and shows its effectiveness on English dataset only using unstructured knowledge source.
Automatic Synthesis of Merging and Inserting Algorithms on Binary Trees Using Multisets in Theorema

We demonstrate the automatic proof–based synthesis of merging and inserting algorithms for [sorted] binary trees, using the notion of multisets, in the Theorema system. Each algorithm is extracted from the proof of the conjecture based on the specification of the desired function, in the form of a list of [conditional] equalities, which can be directly executed. The proofs are performed in natural style, using general techniques, but most importantly efficient inference rules and strategies specific for the domains involved. In particular we present specific techniques for the construction of arbitrarily nested recursive algorithms by general Noetherian induction, as well as a systematic method for the generation of the conjectures and consequently of the algorithms for the auxiliary functions needed in the main function.
Algorithm for Palindrome Detection by Suffix Heap

In this article, we present an efficient algorithm for linear-time suffix heap construction to use in detecting a pattern of palindrome, which can be applied in a field of bioinformatics for detecting a pattern that may denote a cancer cell. We used the concept of the Longest Previous reverse Factor (LPrF) table and suffix heap construction to develop the algorithm. From such, we obtained a new algorithm that demonstrates efficient time and space attributes for detecting all reverse substrings in a string. The palindrome detection algorithm runs with high performance in data processing.
Analyzing the sophistication of Chinese checkers

Game refinement theory has been used as a reliable tool for measuring the attractiveness and entertainment of games, which is derived from a game information progress model and has been applied in various games. This study aims to explore the entertainment of popular multiplayer game-Chinese checkers. The experiments in this study investigated the entertainment of Chinese checkers by creating different battle modes of Chinese checkers. The results show that the three players battle mode has the highest game refinement value among four battle modes, and the recommended number of players is three people. It is recommended that more master level players participate in the game will make the game more interesting. Consider the impact of board piece settings on game experience; the study compares the playability among three board pieces of Chinese checkers, which are six pieces, ten pieces, and 15 pieces. The results found the justification for the ten pieces to be the mainstream version, while six pieces version provides consistent play experience regardless of player number.
On Merging MobileNets for Efficient Multitask Inference

When deploying two or more well-trained deep-learningmodels on a system, we would hope to unify them into asingle deep model for the execution in the inference stage, sothat the computation time can be increased and the energyconsumption can be saved. This paper presents an effective method to build a single deep neural network that canexecute multiple tasks. Our approach can merge two well-trained feed-forward neural networks of the same architecture into a single one, where the required on-line storageis reduced and the inference speed is enhanced. We evalu-ate our approach by using MobileNets and show that ourapproach can improve both compression ratio and speedup. The experimental results demonstrate the satisfactory per-formance and verify the feasibility of the method.
Extracting Methodological Sentences from Unstructured Abstracts of Academic Articles

Methodological sentence is the smallest unit that depicts how the research method is used in one paper. Researchers can understand a method by reading these sentences. So, extracting methodological sentences automatically is meaningful for them to evaluate and select appropriate methods in their research process. However, previous studies rely too much on manually annotated corpus, in which the quantity is limited. Furthermore, some studies do not perform well when generalized to testing sets. In this paper, we use structured abstracts as training data to alleviate the burden of manually annotation. The label for each sentence is determined by its corresponding title in the abstract. Moreover, in order to extract methodological sentences more precisely, a rule-based method is applied for pruning the prediction result. In experimental results, the P, R, and F1 value after pruning are 65.14%, 57.00% and 60.80% respectively, which are all higher than those are not pruned.
A Data Driven PID Control System

Proportional, Integral, Derivative control systems are used in aviation, automotive industry, robotics, mechatronics and many other application areas. The proportional, integral, and derivative coefficients are defined deterministically in a generic way regardless of the process they control. In this research paper we compute these coefficients using mean square error analysis. The data used in the analysis, are produced by setting up proper experiments, using stratification, pre-sampling, experimental, design, sampling and finally computation of the coefficients. The method outlined in this research paper can be used in any control application where PID controllers are to be considered. The application emphasized in this paper is UAVs (Unmanned Air Vehicles), and cruise control in automobiles. Our Data driven PID system is modeled like a finite state machine. The PID coefficients are optimized separately for each state. A PID control system uses sensors to obtain the needed input from its environment, processors to process the input and decide which action is needed, and actuators to control the components of the system that is designed to control. The processors we use are FPGAs. The reason for that is because they make it easy to upgrade, debug, and improve the software driving the hardware.
Improving RNN-based Answer Selection for Morphologically Rich Languages

Question answering systems have improved greatly during thelast five years by employing architectures of deep neuralnetworks such as attentive recurrent networks ortransformer-based networks with pretrained con- textualinformation. In this paper, we present the results and detailedanalysis of experiments with the largest question answeringbenchmark dataset for the Czech language. The best resultsevaluated in the text reach the accuracy of 72 %, which is a 4% improvement to the previous best result. We also introducethe newest version of the Czech Question Answering benchmarkdataset SQAD 3.0, which was substantially extended to more than13,000 question-answer pairs, and we report the first answerselection results on this dataset which indicate that the sizeof the training data is important for the task.
Improved Siamese Network for Video-Based Person Re-identification

In the field of human-computer interaction and visual surveillance, person re-identification is undoubtedly a key task. In this paper, we propose improved Siamese network for video-based person re-identification, which combines the verification model and the classification model, in a way that all the supervision information of the samples is fully utilized. The key frame extraction is able to select informative frames over the sequence, which removes occluded frames according to optical flow vector and measures the similarity of two adjacent frames' contents in terms of the block mutual information entropy. A convolutional network and long short-term memory joint training is used as a feature extractor to re-identify using the improved Siamese network framework. Experimental data on the iLIDS-VID and PRID-2011 datasets show that this approach is superior to existing video-based re-identification methods.
Super-Resolution Reconstruction of Single Image based on Multiscale Recursive Residual Network

Abstract In order to solve the problems of single image super-resolution algorithm based on convolutional neural network, such as shallow network structure, single feature extraction scale and fuzzy texture of reconstructed image, a single image super-resolution reconstruction method based on multi-scale convolutional neural network is proposed. The convolution of several different scales is used to check the original low-resolution image for feature detail extraction, and the recurrent residual network is used to gradually restore the high-frequency information of the image with fewer parameters. Finally, the final reconstructed image is obtained by fusion of the features extracted by different convolutional kernels. The experimental results show that the algorithm proposed in this paper has obtained better image super-resolution result, can obtain more detailed information, and get better visual effect. The classical reconstruction methods such as bicubic, SRCNN, FSRCNN, ESPCN, VDSR are compared, and the peak signal-to-noise ratio (PSNR) and the structure similarity (SSIM) are higher than that of the existing algorithms.
Security implications of digital business

Digital transformation of business and cyber security have recently caused great interest, not only in business and political circles, but also among scientists and researchers. In recent years digital technology has dramatically changed, redesigned and accelerated the way of doing business, education, entertainment, public sector, and other segments of society. Configuration and scope of changes, along with inevitable disruption, take place at three levels: individual, organizational and social level. With its direct impact on the consciousness, ideas, behavior and change of paradigm, focus and the manner of business, digital transformation has undoubtedly led to the creation and expansion of digital economy, as a brand new and unknown model of business. With the development of new business models and modes of business, disruptive technologies have become a great challenge and a kind of threat to the digital business model itself in the complex global environment. This is due to the unlimited possibilities of abuse of technologies and their weaknesses. More precisely, there is an obvious imbalance between the very rapid development of technological innovations and their implementation, on the one hand, and accompanying protection and security challenges, on the other.
Efficient Predictions on Asymmetrical Financial Data Using Ensemble Random Forests

The technological advances in the areas of Big Data and machine learning have led to many useful applications in the financial industry. However, the success of these technologies depends on the analysis of useful information. The financial data is often asymmetrical in nature. It is the nature of information that is crucial in making financial decisions. It is often used to detect the financial frauds, predict the market trends, marketing financial products, and various other use cases. In this work, we are proposing that the ensemble random forests will be able to make better predictions on the asymmetrical financial data. We are taking two cases for making the predictions—one, predicting the customers who will buy the term deposit and two, credit card fraud detection. In both cases, the ensemble random forests were compared with the logistic regression and demonstrated with the results where the random forests performed better than the logistic regression.
Semi-automatic processing and annotation of meeting audio recordings

The main theme of this article is creating a test database consisting of meeting audio recordings. The article describes creation and experimental evaluation of proposed database. The principle of automatic speech recognition is briefly described in the introduction. The basic scheme of the functioning of the Kaldi Speech Recognition System is described below. The main part of the thesis is the presentation of the results of the experimental evaluation of the Kaldi system by the proposed testing database. In the end the possibility of improving the results of the Kaldi system by adding automatically recognized data to the system training process is described.
A Genetic Algorithm Model for Financial Asset Diversification

Machine learning models can produce balanced financial portfolios through a variety of methods. Genetic algorithms are one such method that can optimally combine different funds that may occupy a portfolio. This study introduces a genetic algorithm model that finds optimal combinations of funds for a portfolio through a new approach to fitness formula calculation. Each fund in a given population has a base fitness score consisting of the sum of several technical analysis indicators. Each indicator chosen measures a different performance aspect of a fund, allowing for a balanced fitness score. Additionally, each fund has multiple category variables that determine diversity when combined into a portfolio. The base fitness score for each portfolio is the sum of its funds' individual fitness scores. Portfolio fitness scores adjust based on the included funds' category variable diversity. Portfolios that consist of funds with largely similar categories receive lower adjusted fitness scores and do not cross over. This process encourages strong and diversified portfolios to reproduce. This model creates diverse portfolios that outperform market benchmarks and demonstrates future potential as a diversification-aware investment strategy.