Reviewerly/exHarmony huggingface dataset:
A Multi-strategy Improved Outpost and Differential Evolution Mutation Marine Predators Algorithm for Global Optimization

Marine Predators Algorithm (MPA) is a recent efficient metaheuristic algorithm that is enlightened by the biological behavior of ocean predators and prey. This algorithm simulates the Levy and Brownian movements of prevalent foraging strategy and has been applied to many complex optimization problems. However, the algorithm has defects such as a low diversity of the solutions, ease into the local optimal solutions, and decreasing convergence speed in dealing with complex problems. A modified version of this algorithm called ODMPA is proposed based on the tent map, the outpost mechanism, and the differential evolution mutation with simulated annealing (DE-SA) mechanism. The tent map and DE-SA mechanism are added to enhance the exploration capability of MPA by increasing the diversity of the search agents, and the outpost mechanism is mainly used to improve the convergence speed of MPA. To validate the outstanding performance of the ODMPA, a series of global optimization problems are selected as the test sets, including the standard IEEE CEC2014 benchmark functions, which are the authoritative test set, three well-known engineering problems, and photovoltaic model parameters tasks. Compared with some famous algorithms, the results reveal that ODMPA has achieved better performance than its counterparts in CEC2014 benchmark functions. And in solving real-world optimization problems, ODMPA could get higher accuracy than other metaheuristic algorithms. These practical results demonstrate that the mechanisms introduced positively affect the original MPA, and the proposed ODMPA can be a widely effective tool in tackling many optimization problems.
How to Teach a Persuasive -Text According to the Stages of The Pilot Model for Teaching the Written Presentation*

Functional texts, which include a persuasive text, are among the long texts whose teaching requires various complex skills such as understanding the meanings of words, paraphrasing sentences, rewriting statements, defining frank and logical relationships between the parts of the text, finding explanations, and interpretation of terms and new sentences in order to understand the purpose of the text. Thus, teaching this type of text requires simplification and graduation from simple stages into more sophisticated ones. his study focuses on teaching a persuasive text and enabling the pupils to write the text through the stages of the pioneering project of the Pilot Model for Teaching the Written Presentation, which consists of reading comprehension, lexical/vocabulary knowledge, writing, and evaluation. The study concludes that teaching the pupils written presentation skills, including persuasive text, through the stages of this pilot model, is likely to enable the pupils to acquire writing skills faster, more effectively, and more accurately.
Divide-and-Conquer in Automated Negotiations Through Utility Decomposition

The success of a negotiation depends largely on the actors and the negotiation do- main. It is common that negotiators rely on an agenda to simplify the process and reach better deals. This is particularly the case when the preferences of the nego- tiators are complex and when multiple issues are at stake. Using an agenda to ex- plore and decompose the interdependence relationships between the issues is one way to address this problem. In this paper, we propose to address this challenge by applying the classical divide-and-conquer approach to automated negotiations through means of utility decomposition and bottom-up agenda construction. The approach does not impose an agenda from the top level of the negotiations, but builds it bottom-up given the individual utility functions of the agents and the relationships between the issues. Our approach reduces the cost of exploring the utility spaces of the agents and the resulting bidding processes. We implement the approach in a novel protocol called the Decomposable Alternating Offers Proto- col (DAOP). The experimental results show that our divide-and-conquer algorithm makes a positive influence on the global performance of an automated negotiation system.
Self-assessment of Psychological Issues of Bangladeshi and Russian Students on the Online Learning During the Pandemic

Currently the educational systems of many countries have a growing influence of the changing educational paradigm due to global changes in global politics, economics’ transition to the information society and the challenges of pandemics. COVID-19 has significantly affected tertiary learning systems in Bangladesh, Russia and all around the world, and has forced curricula to be transformed into an online format. The research was conducted at the University of Dhaka and the University of Rajshahi in Bangladesh, as well as at the Peter the Great St. Petersburg Polytechnic University in Russia. The analysis of the participants’ answers helped to identify the following psychological issues on learning continuity by the university students, such as: attention, motivation, emotion and anxiety of students. According to the research, students from Bangladesh got tired faster, felt loneliness more acutely, and experienced health problems than their Russian colleagues during online learning. Russian students took notes of their lectures more often, claimed to use various memorization techniques, spent more time online while studying, and were more demanding of handout materials and presentations. Bangladeshi students faced more difficulties with concentration and focusing their attention during online training. The motivation and interest of Bangladeshi students in online learning were significantly lower than Russian students’ learning motivation. Also, the level of stress and anxiety among Bangladeshi students was higher. Thus, psychological issues on learning continuity based on learner satisfaction are vital to minimize the negative impact of rapid changes in the educational process and ensure effective online education during digital transformation.
Multi-scale Motion-Aware Module for Video Action Recognition

Due to the lengthy computing time for optical flow, recent works have proposed to use the correlation operation as an alternative approach to extracting motion features. Although using correlation operations shows significant improvement with negligible FLOPs, it introduces much more latency per FLOP than convolution operations and increases noticeable latency as a larger searching patch is applied. Nonetheless, shrinking the searching patch in correlation operation is doomed to degrade its performance owing to the inability to capture larger displacements. In this paper, we propose an effective and low-latency Multi-Scale Motion-Aware (MSMA) module. It uses smaller searching patches at different scales for efficiently extracting motion features from large displacements. It can be installed into and generalizes well on different CNN backbones. When installed into TSM ResNet-50, the MSMA module introduces $$\approx $$ 17.6% more latency on NVIDIA Tesla V100 GPU, yet, it achieves state-of-the-art performance on Something-Something V1 & V2 and Diving-48.
Research on modulation recognition of underwater acoustic communication signal based on deep learning

Abstract Marine information technology plays a important role in the development of marine resources development, marine climate early warning, and other industries. Underwater acoustic communication technology can help us better access marine information. The performance of the underwater acoustic signal modulation recognition algorithm depends on the accuracy of feature extraction. however, due to excessive underwater noise, many traditional algorithms can not recognize features well. For this reason, this paper proposes a modulation recognition network for aquatic communication signals based on deep learning. ResNet can capture the characteristics of deep features and combines ResNet with a modulation recognition network. Finally, the experiment proves the effectiveness of this method.
Optimization of Flood Prediction using SVM Algorithm to determine Flood Prone Areas

Flooding is one thing that can slow down the economic pace in the affected area. Bandung is called the city of flowers and the city of fashion because the nickname makes Bandung a city with a variety of fashions growing in multiple places as a starting point for the buying and selling process. Not only did Bandung spawn fashions that became hits every year, but it also had many Meccas of traditional food preparation that were extraordinarily unique and interesting. Creating a flood-prone area model can make it easier to provide information for communities in Bandung Prefecture that belong to flood-prone and non-flood-prone areas. The SVM algorithm is a technique that can be used in the case of classification and regression, which is very popular lately. SVM is in a class with Artificial Neural Networks (ANN) in terms of features and conditions of problems that can be solved, and to be able to increase its accuracy it uses what can be optimized with PSO (Particle Swarm Optimization), where the test data is used BNPB official website data, BPS Bandung District and BMKG processed. The accuracy rate generated by using the SVM algorithm is 85.71% and the generated AUC is 0.841, while the accuracy rate generated by using the PSM-based SVM algorithm is 97.62%. and AUC produced at 1,000.
REALISASI KRIPTOGRAFI PADA FITUR ENKRIPSI END-TO-END PESAN WHATSAPP

This journal explains the process that occurs in data wrapping and the algorithm used by the security features in WhatsApp end-to-end encryption. This system guarantees that messages sent between two parties, cannot be intercepted by hackers who intercept the communication lines. In WhatsApp communication, sending data is confidential because it has gone through an encryption process. Encryption is a process carried out to secure a data (which is called plaintext) into hidden data (called ciphertext). Ciphertext is data that cannot be read easily.
Digital Rupee and its impact on the Indian Economy

With the launch of Digital Currency by RBI, India is expected to witness a substantial growth in the digitization to witness a substantial growth in the digitization of money and take a step further towards facilitating a cashless economy, digital rupee will add resilience associated with innovation in the way payments are made it will also boost innovation in the cross-border payment space, the markets will develop more huge cases in time to come as per their specific requirement. This paper gives a guided four of the growing literature on CBDC on the microeconomic considerations related to operational architectures, technologies and privy.
Revamped Dual-key Stealth Address Protocol for IoT Using Encryption and Decentralized Storage

Blockchain technology unarguably has over a decade gained widespread attention owing to its often-tagged disruptive nature and remarkable features of decentralization, immutability and transparency among others. However, the technology comes bundled with challenges. At center-stage of these challenges is privacy-preservation which has massively been researched with diverse solutions proposed geared towards privacy protection for transaction initiators, recipients and transaction data. Dual-key stealth address protocol for IoT (DkSAP-IoT) is one of such solutions aimed at privacy protection for transaction recipients. Induced by the need to reuse locally stored data, the current implementation of DkSAP-IoT is deficient in the realms of data confidentiality, integrity and availability consequently defeating the core essence of the protocol in the event of unauthorized access, disclosure or data tampering emanating from a hack and theft or loss of the device. Data unavailability and other security-related data breaches in effect render the existing protocol inoperable. In this paper, we propose and implement solutions to augment data confidentiality, integrity and availability in DkSAP-IoT in accordance with the tenets of information security using symmetric encryption and data storage leveraging decentralized storage architecture consequently providing data integrity. Experimental results show that our solution provides content confidentiality consequently strengthening privacy owing to the encryption utilized. We make the full code of our solution publicly available on GitHub.
A New Query Expansion Approach for Improving Web Search Ranking

Information systems have come a long way in the 21st century, with search engines emerging as the most popular and well-known retrieval systems. Several techniques have been used by researchers to improve the retrieval of relevant results from search engines. One of the approaches employed for improving relevant feedback of a retrieval system is Query Expansion (QE). The challenge associated with this technique is how to select the most relevant terms for the expansion. In this research work, we propose a query expansion technique based on Azak &amp; Deepak's WWQE model. Our extended WWQE technique adopts Candidate Expansion Terms selection with the use of in-links and out-links. The top two relevant Wikipedia articles from the user's initial search were found using a custom search engine over Wikipedia. Following that, we ranked further Wikipedia articles that are semantically connected to the top two Wikipedia articles based on cosine similarity using TF-IDF Vectorizer. The expansion terms were then taken from the top 5 document titles. The results of the evaluation of our methodology utilizing TREC query topics (126-175) revealed that the system with extended features gave ranked results that were 11% better than those from the system with unexpanded queries.
Currency or commodity competition? Bitcoin price trends in the post-pandemic era

In the post-pandemic era, two issues including the currency competition between BTC and the US dollar and the competition between the commodity and monetary medium functions of BTC are critical. By applying the Markov switching model, the cyclical nature of the numbers of additional confirmed COVID-19 cases and deaths are verified on daily basis. So, these two factors are assumed to follow the Ornstein–Uhlenbeck process. Then, we estimate parameters to establish the structural characteristics of post-pandemic era and the start of post-pandemic era. In order to clarify these two issues, we use vector autoregression for testing the related macrocosmic and financial variables and BTC. Systematic evidences are provided regarding the relationships among BTC, related macrocosmic, related financial variables, related COVID-19 variables. Our findings provide a useful insight into currency competition and commodity competition on the basis of the impulse response of BTC to US dollar fluctuation and the impulse response of BTC to expected inflation and volatility in the post-pandemic era. These findings indicate increased currency competition between Bitcoin and the US dollar in the post-pandemic era. Therefore, currency competition should be more valued than Commodity Competition in the post-pandemic era. This provides a useful guideline for Bitcoin’s management.
Round-Efficient Secure Inference Based on Masked Secret Sharing for Quantized Neural Network

Existing secure multiparty computation protocol from secret sharing is usually under this assumption of the fast network, which limits the practicality of the scheme on the low bandwidth and high latency network. A proven method is to reduce the communication rounds of the protocol as much as possible or construct a constant-round protocol. In this work, we provide a series of constant-round secure protocols for quantized neural network (QNN) inference. This is given by masked secret sharing (MSS) in the three-party honest-majority setting. Our experiment shows that our protocol is practical and suitable for low-bandwidth and high-latency networks. To the best of our knowledge, this work is the first one where the QNN inference based on masked secret sharing is implemented.
Roman Urdu Sentiment Analysis of Reviews on PSL Anthems

Due to the easy access of internet and smart devices, people are becoming habitual to give their feedback on what they hear or watch, online. These reviews are very valuable for all sorts of users. Due to the widespread online activities, the count of these reviews has raised tremendously. This fact makes it humanly impossible to analyse them manually. So it needs time that reviews to be analysed and use patterns to be found and explored through the automated channel. This led to a new field of research known as Sentiment Analysis. This paper is targeting to design a model to perform sentiment analysis of Roman Urdu text using the reviews of Pakistan Super League’s official song. To perform this analysis five different techniques-- Naïve Bayes Kernal, Random Forest, Logistic Regression, K-Nearest Neighbour and Artificial Neural Network, are applied. Naïve Bayes Kernal and Logistic Regression correctly predicted 97.00% reviews.
A Divide and Conquer Sequential Monte Carlo Approach to High Dimensional Filtering

We propose a divide-and-conquer approach to filtering which decomposes the state variable into low-dimensional components to which standard particle filtering tools can be successfully applied and recursively merges them to recover the full filtering distribution.It is less dependent upon factorization of transition densities and observation likelihoods than competing approaches and can be applied to a broader class of models.Performance is compared with state-of-the-art methods on a benchmark problem and it is demonstrated that the proposed method is broadly comparable in settings in which those methods are applicable, and that it can be applied in settings in which they cannot.
From Trace Elements to Petrogenesis: A Machine Learning Approach to Determine Ore Deposit Type from Trace Elements Analysis of Apatite

The diverse suite of trace elements incorporated into apatite in ore-forming systems has important applications in petrogenesis studies of mineral deposits. Trace element variations in apatite can be used to distinguish between different types of rocks as well as discriminating between deposit types, and thus have potential as mineral exploration tools. Such classification approaches commonly employ two-variable scatterplots of apatite trace element compositional data. While such diagrams offer easy and convenient visualization of compositional trends, they often struggle to effectively distinguish deposit types because they do not employ all the high-dimensional (i.e. multi-element) information accessible from high-quality apatite trace element analysis. To address this issue, we employ, for the first time, a supervised machine learning-based approach (eXtreme Gradient Boosting, XGBoost) to correlate apatite compositions with ore deposit type, utilizing high-dimensional information. We evaluated 8629 apatite trace element data from five deposit types (porphyry, skarn, orogenic Au, iron oxide copper gold, and iron oxide-apatite) along with unmineralized apatite to discriminate between apatite in mineralized vs unmineralized systems. We could show that the XGBoost classifier efficiently and accurately classifies high-dimensional apatite trace element data according to the ore deposit type (overall accuracy: 94% and F1 score: 89%). Interpretation of the model using the SHAPley Additive exPlanations tool (SHAP) shows that Th, U, Eu and Nd are the most indicative elements for classifying deposit types using apatite trace element chemistry. Our approach has broad implications for the understanding of the sources, chemistry and evolution of melts and hydrothermal fluids resulting in ore deposit formation.Keywords: Machine learning; apatite; Trace elements; Ore deposit type; XGBoost
Temporal Pattern Attention for Multivariate Time Series of Tennis Strokes Classification

Human Action Recognition is a challenging task used in many applications. It interacts with many aspects of Computer Vision, Machine Learning, Deep Learning and Image Processing in order to understand human behaviours as well as identify them. It makes a significant contribution to sport analysis, by indicating players’ performance level and training evaluation. The main purpose of this study is to investigate how the content of three-dimensional data influences on classification accuracy of four basic tennis strokes: forehand, backhand, volley forehand, and volley backhand. An entire player’s silhouette and its combination with a tennis racket were taken into consideration as input to the classifier. Three-dimensional data were recorded using the motion capture system (Vicon Oxford, UK). The Plug-in Gait model consisting of 39 retro-reflective markers was used for the player’s body acquisition. A seven-marker model was created for tennis racket capturing. The racket is represented in the form of a rigid body; therefore, all points associated with it changed their coordinates simultaneously. The Attention Temporal Graph Convolutional Network was applied for these sophisticated data. The highest accuracy, up to 93%, was achieved for the data of the whole player’s silhouette together with a tennis racket. The obtained results indicated that for dynamic movements, such as tennis strokes, it is necessary to analyze the position of the whole body of the player as well as the racket position.
A Multi-Granularity Word Fusion Method for Chinese NER

Named entity recognition (NER) plays a crucial role in many downstream natural language processing (NLP) tasks. It is challenging for Chinese NER because of certain features of Chinese. Recently, large-scaled pre-training language models have been used in Chinese NER. However, since some of the pre-training language models do not use word information or just employ word information of single granularity, the semantic information in sentences could not be fully captured, which affects these models’ performance. To fully take advantage of word information and obtain richer semantic information, we propose a multi-granularity word fusion method for Chinese NER. We introduce multi-granularity word information into our model. To make full use of the information, we classify the information into three kinds: strong information, moderate information, and weak information. These kinds of information are encoded by encoders and then integrated with each other through the strong-weak feedback attention mechanism. Specifically, we apply two separate attention networks to word embeddings and N-grams embeddings. Then, the outputs are fused into another attention. In these three attentions, character embeddings are used to be the query of attentions. We call the results the multi-granularity word information. To combine character information and multi-granularity word information, we introduce two fusion strategies for better performance. The process makes our model obtain rich semantic information and reduces word segmentation errors and noise in an explicit way. We design experiments to get our model’s best performance by comparing some components. Ablation study is used to verify the effectiveness of each module. The final experiments are conducted on four Chinese NER benchmark datasets and the F1 scores are 81.51% for Ontonotes4.0, 95.47% for MSRA, 95.87% for Resume, and 69.41% for Weibo. The best improvement achieved by the proposed method is 1.37%. Experimental results show that our method outperforms most baselines and achieves the state-of-the-art method in performance.