Reviewerly/exHarmony huggingface dataset:
Comparation Analysis of Ensemble Technique With Boosting(Xgboost) and Bagging (Randomforest) For Classify Splice Junction DNA Sequence Category

Bioinformatics research currently supported by rapid growth of computation technology and algorithm. Ensemble decision tree is common method for classifying large and complex dataset such as DNA sequence. By implementing two classification methods with ensemble technique like xgboost and random Forest might improve the accuracy result on classifying DNA Sequence splice junction type. With 96,24% of xgboost accuracy and 95,11% of Random Forest accuracy, our conclusions the xgboost and random forest methods using right parameter setting are highly effective tool for classifying small example dataset. Analyzing both methods with their characteristics will give an overview on how they work to meet the needs in DNA splicing.
Hybrid Load Balancing Approach based on the Integration of QoS and Power Consumption in Cloud Computing

Over the past few years, there has been keen research interest in load balancing and task scheduling in the cloud as the extensive amount of data that is stored in the server leads to significantly increased load. This can be resolved by using a hybrid algorithm in which the honeybee behavior algorithm’s advantages are integrated with fuzzy logic to conduct task scheduling and as well as balancing in the cloud. The design of this hybrid algorithm aims to enhance prior approaches. It is developed as per ABC and merges the important QoS factors along with power consumption so that the power that virtual machines (VMs) consume on the host can be precisely assessed, thereby ensuring efficient load balancing algorithm. The present study aims to evaluate the VMs’ power consumption by taking into account crucial QoS factors for selecting which host and virtual machine will be best suited for receiving the task. CloudSim was used to simulate the ILBA_HB algorithm. In terms of makespan, average response time, and degree of imbalance, the performance of the ILBA HB algorithm is compared to that of the LBA HB and HBB-LB algorithms. According to the results, the proposed algorithm outperformed LBA_HB and HBB-LB.
Recurrent Attention for Neural Machine Translation

Recent research questions the importance of the dot-product self-attention in Transformer models and shows that most attention heads learn simple positional patterns.In this paper, we push further in this research line and propose a novel substitute mechanism for self-attention: Recurrent AtteNtion (RAN).RAN directly learns attention weights without any token-to-token interaction and further improves their capacity by layer-to-layer interaction.Across an extensive set of experiments on 10 machine translation tasks, we find that RAN models are competitive and outperform their Transformer counterpart in certain scenarios, with fewer parameters and inference time.Particularly, when apply RAN to the decoder of Transformer, there brings consistent improvements by about +0.5 BLEU on 6 translation tasks and +1.0 BLEU on Turkish-English translation task.In addition, we conduct extensive analysis on the attention weights of RAN to confirm their reasonableness.Our RAN is a promising alternative to build more effective and efficient NMT models.
Legal Aspects of Digital Asset Market in the Republic of Serbia

Over the last decade, virtual currencies have captured an increasingly significant role in financial markets, introducing a new class of assets and revolutionizing market notions of issuing, trading, storing and transferring value.Hence, the author considers the legal consequences of financial growth and success of digital assets in the contemporary economy.The wave of general transition on the Internet in 2020 also affected cryptocurrencies, i.e. digital assets, which existed somewhere between two extreme positions: legal regulation and grey area.The unresolved legal status of cryptocurrencies has created vast problems for both legislators and investors.As digital assets and other blockchain applications mature, the global regulatory authorities work hard to keep pace and to adopt legal frameworks pertinent to regulating this new method of exchange.At the end of 2020, Serbia adopted the Law on Digital Assets, thus becoming one of the first countries that legally regulate the field of digital assets.The paper analyses specific features of theoretical comprehension and legal regulation of digital assets in the Republic of Serbia.Using the normative and comparative method, the author will explore the legal status of cryptocurrencies, issuance and trade of cryptocurrencies, emphasizing the similarities and differences with the issuance and trade of securities.Therefore, the main objectives of this article are the regulation of digital asset and the application of securities law to issuance and sale of digital assets.Accordingly, it is of substantial importance to develop a holistic and coherent understanding of digital assets and related market activities rooted in empirical evidence and deeper knowledge of the underlying mechanisms.
Deep Residual Learning With Dilated Causal Convolution Extreme Learning Machine

A feedforward neural network with random weights (RW-FFNN) uses a randomized feature map layer.This randomization enables the optimization problem to be replaced by a standard linear leastsquares problem, which offers a major advantage in terms of training speed.An extreme learning machine (ELM) is a well-known RW-FFNN that can be implemented as a single-hidden-layer feedforward neural network.However, for a large dataset, owing to the shallow architecture, such an ELM typically requires a very large number of nodes in a single hidden layer to achieve a sufficient level of accuracy.In this paper, we propose a deep residual learning method with a dilated causal convolution ELM (DRLDCC-ELM).The baseline layer performs feature mapping to predict the target features based on the input features.The subsequent residual-compensation layers then iteratively remodel the uncaptured prediction errors in the previous layer.The proposed network architecture also adopts dilated causal convolution based on the ELM in each layer to effectively expand the receptive field of the multilayer network.The results of experiments involving acoustic scene classification of daily activities in a home environment confirmed that the proposed DRLDCC-ELM outperforms the previously proposed residual-compensation ELM and deep-residualcompensation ELM methods.We also confirmed that the generalization capability of the proposed DRLDCC-ELM tends to be superior to that of convolutional neural network-based models, especially for a large number of parameters.
IGA: An Intent-Guided Authoring Assistant

While large-scale pretrained language models have significantly improved writing assistance functionalities such as autocomplete, more complex and controllable writing assistants have yet to be explored.We leverage advances in language modeling to build an interactive writing assistant that generates and rephrases text according to fine-grained author specifications.Users provide input to our Intent-Guided Assistant (IGA) in the form of text interspersed with tags that correspond to specific rhetorical directives (e.g., adding description or contrast, or rephrasing a particular sentence).We fine-tune a language model on a dataset heuristically-labeled with author intent, which allows IGA to fill in these tags with generated text that users can subsequently edit to their liking.A series of automatic and crowdsourced evaluations confirm the quality of IGA's generated outputs, while a smallscale user study demonstrates author preference for IGA over baseline methods in a creative writing task.We release our dataset, code, and demo to spur further research into AI-assisted writing.* * Most of the work done during an internship at Adobe. 1 As these systems are not open-sourced, it is unclear how exactly they are implemented.
Image Quality Evaluation of Light Field Image Based on Macro-Pixels and Focus Stack

Due to the complex angular-spatial structure, light field (LF) image processing faces more opportunities and challenges than ordinary image processing. The angular-spatial structure loss of LF images can be reflected from their various representations. The angular and spatial information penetrate each other, so it is necessary to extract appropriate features to analyze the angular-spatial structure loss of distorted LF images. In this paper, a LF image quality evaluation model, namely MPFS, is proposed based on the prediction of global angular-spatial distortion of macro-pixels and the evaluation of local angular-spatial quality of the focus stack. Specifically, the angular distortion of the LF image is first evaluated through the luminance and chrominance of macro-pixels. Then, we use the saliency of spatial texture structure to pool an array of predicted values of angular distortion to obtain the predicted value of global distortion. Secondly, the local angular-spatial quality of the LF image is analyzed through the principal components of the focus stack. The focalizing structure damage caused by the angular-spatial distortion is calculated using the features of corner and texture structures. Finally, the global and local angular-spatial quality evaluation models are combined to realize the evaluation of the overall quality of the LF image. Extensive comparative experiments show that the proposed method has high efficiency and precision.
REST API Composition for Effective Testing the Cloud

Cloud offers many ready-made REST services for the end users. This offer realizes the service composition through implementation somewhere on internet based on Service Level Agreement (SLA). For ensuring this SLA, a software testing is a useful means for attesting a non-functional requirement that guarantees quality assurance from end user's perspective. However, test engineer experiences only what goes in and out through an interface that contains a high level behaviors separated from its underlying details. Testing with these behaviors become an issue for classical testing procedures. So, REST API through composition is an alternative new promising approach for modeling behaviors with parameters against the cloud. This new approach helps to devise test effectiveness in terms of REST based behavior-driven implementation. It aims to understand functional behaviors through API methods based on input domain modeling (IDM) on the standard keyboard pattern. By making an effective REST design the test engineer sends complete test inputs to its API directly on application, and gets test responses from the infrastructure. We consider NEMo mobility API specification to design an IDM, which represents pattern match of mobility search URL API path scope. With this scope, sample mobility REST API service compositions are used. Then, the test assertions are implemented to validate each path resource to test the components and the end-to-end integration on the specified service.
Students' Writing Self-Efficacy, Writers' Block, and Academic Writing Performance: An Empirical Study in Eastern Indonesian Students

This study was conducted to explore the students' perception related to the variables of writing self-efficacy, writers' block, and perceived academic writing performance. The researcher collected data from a University located in Papua Province. This research used an online questionnaire by employing simple random sampling. This study enrolled 280 students as respondents. The researchers used SPSS 23 to conduct a descriptive statistical analysis to determine the proportion of respondents based on the frequency of responses. The findings of this study revealed that the respondents of this study have low perceived levels of writing self-efficacy and academic writing performance. Additionally, students believe they face hurdles that contribute to the issue of writers' block. Further action needs to be taken regarding the findings to enhance the variables related to academic writing performance. The empirical findings from this study can be utilized as the foundation for future research to generate policy to solve issues relating to students' academic writing abilities.
Machine Learning Algorithms in Fraud Detection: Case Study on Retail Consumer Financing Company

This study aims to implement a machine learning algorithm in detecting fraud based on historical data set in a retail consumer financing company. The outcome of machine learning is used as samples for the fraud detection team. Data analysis is performed through data processing, feature selection, hold-on methods, and accuracy testing. There are five machine learning methods applied in this study: Logistic Regression, K-Nearest Neighbor (KNN), Decision Tree, Random Forest, and Support Vector Machine (SVM). Historical data are divided into two groups: training data and test data. The results show that the Random Forest algorithm has the highest accuracy with a training score of 0.994999 and a test score of 0.745437. This means that the Random Forest algorithm is the most accurate method for detecting fraud. Further research is suggested to add more predictor variables to increase the accuracy value and apply this method to different financial institutions and different industries.
GraphMR: Graph Neural Network for Mathematical Reasoning

Mathematical reasoning aims to infer satisfiable solutions based on the given mathematics questions.Previous natural language processing researches have proven the effectiveness of sequence-to-sequence (Seq2Seq) or related variants on mathematics solving.However, few works have been able to explore structural or syntactic information hidden in expressions (e.g., precedence and associativity).This dissertation set out to investigate the usefulness of such untapped information for neural architectures.Firstly, mathematical questions are represented in the format of graphs within syntax analysis.The structured nature of graphs allows them to represent relations of variables or operators while preserving the semantics of the expressions.Having transformed to the new representations, we proposed a graph-to-sequence neural network GraphMR, which can effectively learn the hierarchical information of graphs inputs to solve mathematics and speculate answers.A complete experimental scenario with four classes of mathematical tasks and three Seq2Seq baselines is built to conduct a comprehensive analysis, and results show that GraphMR outperforms others in hidden information learning and mathematics resolving.
Stance Detection in German News Articles

The widespread use of the Internet and the rapid dissemination of information poses the challenge of identifying the veracity of its content.Stance detection, which is the task of predicting the position of a text in regard to a specific target (e.g.claim or debate question), has been used to determine the veracity of information in tasks such as rumor classification and fake news detection (Küçük and Can, 2020).While most of the work and available datasets for stance detection address short texts snippets extracted from textual dialogues, social media platforms, or news headlines with a strong focus on the English language, there is a lack of resources targeting long texts in other languages.Our contribution in this paper is twofold.First, we present a German dataset of debate questions and news articles that is manually annotated for stance and emotion detection.Second, we leverage the dataset to tackle the supervised task of classifying the stance of a news article with regards to a debate question and provide baseline models as a reference for future work on stance detection in German news articles.
The Implementation of E-Learning to Support Learning and Teaching Foreign Language: A Case Study in Ukrainian Higher Education

The research paper examines current tendencies in implementing e-learning as an integral part of the educational process, particularly in foreign language learning in higher education. The scholar attempts to answer the following question: How can e-learning contribute to effective foreign language learning? We have identified how e-learning is applied, the different existing e-learning strategies students prefer to learn with, and the extent to which learners are satisfied with the support offered by e-learning to assist the process of foreign language learning. The study is significant because understanding the learners’ preferences and support of these up-dated methods would help to find effective teaching practices within e-learning, fostering student-centered approaches. The contribution of this research is to estimate the new experiences of university students due to e-learning implementation. We used a quantitative method to collect research data by online tools (Google forms). It was achieved due to analyzing responses to the survey-based questionnaire of 35 learners of Vinnytsia Institute of Trade and Economics of Kyiv National University of Trade and Economics, Ukraine. Within our research, we have found out that students usually use e-learning as it helps them to develop lots of soft skills besides the four basic language skills. It has also been proved that students are delighted with the e-learning application. We concluded that e-learning is an effective and innovative educational technology that can improve both the teaching and learning process in educational institutions.
Putting Data Science Pipelines on the Edge

This paper proposes a composable “Just in Time Architecture” for Data Science (DS) Pipelines named JITA-4DS and associated resource management techniques for configuring disaggregated data centers (DCs). DCs under our approach are composable based on vertical integration of the application, middleware/operating system, and hardware layers customized dynamically to meet application Service Level Objectives (SLO - application-aware management). Thereby, pipelines utilize a set of flexible building blocks that can be dynamically and automatically assembled and re-assembled to meet the dynamic changes in the workload’s SLOs. To assess disaggregated DC’s, we study how to model and validate their performance in large-scale settings.
Autonomous navigation in unknown environment using sliding mode SLAM and genetic algorithm

In this paper, sliding mode control is combined with the classical simultaneous localization and mapping (SLAM) method. This combination can overcome the problem of bounded uncertainties in SLAM. With the help of genetic algorithm, our novel path planning method shows many advantages compared with other popular methods.
Modeling of a hardware and software complex “Poligraf” based on freely distributable microcontroller platforms

This article is devoted to the modeling of the software and hardware complex (SHC) “Polygraph” on the basis of freely distributed microcontroller platforms. In the work, the analysis of primary sources was carried out and the most promising microcontroller platform for the purposes of visual modeling and training to work on such devices was chosen. Within the framework of modeling tasks, on the basis of a number of criteria, the most optimal automated system for the design of devices has been determined. Using the chosen toolkit, the authors modeled the first educational test version of “Polygraph” with the ability to display some parameters measured by real SHC. The article considered the connection of the following sensors of the SHC “Polygraph”: pulse, body temperature and respiratory rate. Based on the work done, it was concluded that this development is promising and relevant for design purposes and training in work on devices such as “Polygraph”.
CMC2R: Cross‐modal collaborative contextual representation for RGBT tracking

The key challenge in RBGT tracking is how to fuse dual-modality information to build a robust RGB-T tracker. Motivated by CNN structure for local features, and visual transformer structure for global representations, the authors propose a two-stream hybrid structure, termed CMC2R, to take advantage of convolutional operations and self-attention mechanisms to lean the enhanced representation. CMC2R fuses local features and global representations under different resolutions through the transformer layer of the encoder block, and the two modalities are collaborated to get contextual information by the spatial and channel self-attention. The temporal association is performed with the track query, each track query models the entire track of an object, and updated frame-by-frame to build the long-range temporal relation. Experimental results show the effectiveness of the proposed method, and achieve the SOTAs performance.
SEMANTIC GRAPH KNOWLEDGE REPRESENTATION FOR AL-QURAN VERSES BASED ON WORD DEPENDENCIES

Semantic approaches present an efficient, detailed and easily understandable representation of knowledge from documents. Al-Quran contains a vast amount of knowledge that needs appropriate knowledge extraction. A semantic based approach can help in designing an efficient and explainable knowledge representation model for Al-Quran. This research aims to propose a semantic-graph knowledge representation model for verses of Al-Quran based on word dependencies. These features are used in the proposed knowledge representation model allowing the semantic graph matching to improve Al-Quran search applications' accuracy. The proposed knowledge representation model is essentially a formalism for generating a semantic graph representation of Quranic verses, which can be applied for knowledge base construction for other applications such as information retrieval system. A set of rules called Semantic Dependency Triple Rules are defined to be mapped into the semantic graph representing the verse's logic. The rules translate word dependencies and other NLP metadata into a triple form that holds logical information. The proposed model has been tested with English translation of Al-Quran on a document retrieval prototype The basic system has been enhanced with anaphoric pronouns correction, which has shown improvement in retrieval performance. The results have been compared with a closely related system and evaluated on the accuracy of the document retrieval in Precision, Recall and F-score measurements. The proposed model has achieved 65%, 60% and 62.4% for the measurements, respectively. It has also improved the overall accuracy of previous system by 43.8%.
A Fast Fourier Transform with Brute Force Algorithm for Detection and Localization of White Points on 3D Film Pattern Images

The procedures of white points detection and localization are practically complex on noisy images.In this paper, we propose an algorithm that detects and localizes white points on 3D film images.The proposed algorithm uses the fast Fourier transform to convert the binarized image into real and imaginary parts to obtain the number of white points along the horizontal and vertical.We determine the sorted coordinates of the white points by adding a brute-force solution to the coordinates obtained from the real part of the image.These sorted coordinates are obtained by subtracting the error between the Euclidean distances of the normalized coordinates along the vertical and horizontal direction.The proposed algorithm with and without brute-force achieved an average detection ratio of 0.98 and 0.88 respectively, while the others underperformed.We perform various experiments using the existing algorithms such as template matching, thresholding, and an iterative method to validate the performance of our algorithm.We also compare the rule-based algorithms that detect and localize objects in noisy images with the proposed one to determine the reliability of our algorithm.The experimental results indicate that the proposed algorithm performs better than the template matching, thresholding, and iterative algorithm.