Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Human Motion Recognition Information Processing System Based on LSTM Recurrent Neural Network Algorithm

With the comprehensive development of national fitness, men, women, young, and old in China have joined the ranks of fitness. In order to increase the understanding of human movement, many researches have designed a lot of software or hardware to realize the analysis of human movement state. However, the recognition efficiency of various systems or platforms is not high, and the reduction ability is poor, so the recognition information processing system based on LSTM recurrent neural network under deep learning is proposed to collect and recognize human motion data. The system realizes the collection, processing, recognition, storage, and display of human motion data by constructing a three-layer human motion recognition information processing system and introduces LSTM recurrent neural network to optimize the recognition efficiency of the system, simplify the recognition process, and reduce the data missing rate caused by dimension reduction. Finally, we use the known dataset to train the model and analyze the performance and application effect of the system through the actual motion state. The final results show that the performance of LSTM recurrent neural network is better than the traditional algorithm, the accuracy can reach 0.980, and the confusion matrix results show that the recognition of human motion by the system can reach 85 points to the greatest extent. The test shows that the system can recognize and process the human movement data well, which has great application significance for future physical education and daily physical exercise.
A Large-Scale English Multi-Label Twitter Dataset for Cyberbullying and Online Abuse Detection

In this paper, we introduce a new English Twitter-based dataset for online abuse and cyberbullying detection.Comprising 62,587 tweets, this dataset was sourced from Twitter using specific query terms designed to retrieve tweets with high probabilities of various forms of bullying and offensive content, including insult, profanity, sarcasm, threat, porn and exclusion.Analysis performed on the dataset confirmed common cyberbullying themes reported by other studies and revealed interesting relationships between the classes.The dataset was used to train a number of transformer-based deep learning models returning impressive results.
Finding BERT’s Idiomatic Key

Sentence embeddings encode information relating to the usage of idioms in a sentence.This paper reports a set of experiments that combine a probing methodology with input masking to analyse where in a sentence this idiomatic information is taken from, and what form it takes.Our results indicate that BERT's idiomatic key is primarily found within an idiomatic expression, but also draws on information from the surrounding context.Also, BERT can distinguish between the disruption in a sentence caused by words missing and the incongruity caused by idiomatic usage.
BERT Goes Shopping: Comparing Distributional Models for Product Representations

Word embeddings (e.g., word2vec) have been applied successfully to eCommerce products through prod2vec.Inspired by the recent performance improvements on several NLP tasks brought by contextualized embeddings, we propose to transfer BERT-like architectures to eCommerce: our model -Prod2BERT -is trained to generate representations of products through masked session modeling.Through extensive experiments over multiple shops, different tasks, and a range of design choices, we systematically compare the accuracy of Prod2BERT and prod2vec embeddings: while Prod2BERT is found to be superior in several scenarios, we highlight the importance of resources and hyperparameters in the best performing models.Finally, we provide guidelines to practitioners for training embeddings under a variety of computational and data constraints.
Impact of ict on writing and reading skills

Due to the potential of ICT to enhance the development of reading and writing skills, it has become an emerging field of scientific research. The purpose of this work has been to examine scientific literature that investigate the use of ICT in the development of writing and reading skills for the years 2010 and 2020 in five databases. A descriptive and quantitative methodology has been followed to present the most important characteristics and results of the 16 selected scientific articles. In analysis of the research findings we explored the most significant bibliometric data and the effectiveness of ICTs in enhancing the writing and reading skill of students. The results confirmed that, despite the fact that scientific production in this field is still underdeveloped, digital technologies can help in the development of reading and writing skills. There is a need to improve teacher training for the effective integration of technologies in both teaching and learning of reading and writing.
Development of English Writing Skills through Blended Learning among ESL Learners in Malaysia

Developing writing proficiency is complex as students need to master specific rules and acquire certain skills. This complexity often obstructs students’ interest and eventually affects their comprehension and performance in language learning. Research has highlighted the significance of English as a Second Language (ESL), particularly in the Malaysian context. This study, therefore, aims to evaluate the effectiveness of blended learning in developing English writing skills, particularly among ESL learners at a Polytechnic in Malaysia. The study adopted quasi-experimental analysis in which pre-test and post-test were used as data-gathering instruments. The data were gathered from a purposive sample of 60 ESL students separated into an experimental group and a control group. The data were analyzed using the Mann-Whitney U test via the Statistical Package for Social Sciences (SPSS). The findings showed that blended learning is significantly effective in developing writing performance among ESL students in the selected institution. Further analysis revealed a slight improvement among students who learned through the traditional approach. This study highlights useful findings that could be useful in designing English curriculums, particularly in higher institutions. However, this experiment focuses on students’ performance, further research may explore instructors’ practices in Malaysian higher intuitions.
Blockchain-based Cultivating Ideas for Growth: A New Agronomics Perspective

The development of nourishment and food is an essential requirement of nations for their affluence and welfare. Agriculture is the science and specialty of developing plants and livestock. In sedentary human civilization, agriculture plays a key role. The agribusiness needs to do a ton of work to keep up and fabricate the purchaser’s trust with regards to the food quality check. A blockchain-based agricultural arrangement holds a guarantee for the agro-business with its capacity to acquire clarity and transparency in the framework. Minimal effort agrarian protection plans are progressively seen as instruments for giving social assurance to the individuals influenced by floods or dry seasons. In assisting with the diminishing effects, they endure because of such occasions. Therefore, this chapter introduces the idea of blockchain technology, extensive literature survey, including a review of blockchain development in the nourishment sector followed by the cases in the food supply chain. This chapter presented various opportunities and an ideal architecture of how blockchain will function in the farming sector. Finally, the chapter concludes by featuring open research difficulties in this field.
FisheyeDistanceNet++: Self-Supervised Fisheye Distance Estimation with Self-Attention, Robust Loss Function and Camera View Generalization

FisheyeDistanceNet [1] proposed a self-supervised monocular depth estimation method for fisheye cameras with a large field of view (&gt; 180°). To achieve scale-invariant depth estimation, FisheyeDistanceNet supervises depth map predictions over multiple scales during training. To overcome this bottleneck, we incorporate self-attention layers and robust loss function [2] to FisheyeDistanceNet. A general adaptive robust loss function helps obtain sharp depth maps without a need to train over multiple scales and allows us to learn hyperparameters in loss function to aid in better optimization in terms of convergence speed and accuracy. We also ablate the importance of Instance Normalization over Batch Normalization in the network architecture. Finally, we generalize the network to be invariant to camera views by training multiple perspectives using front, rear, and side cameras. Proposed algorithm improvements, FisheyeDistanceNet++, result in 30% relative improvement in RMSE while reducing the training time by 25% on the WoodScape dataset. We also obtain state-of-the-art results on the KITTI dataset, in comparison to other self-supervised monocular methods.
Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solution

In this paper, we investigate the driving factors behind concatenation, a simple but effective data augmentation method for low-resource neural machine translation.Our experiments suggest that discourse context is unlikely the cause for concatenation improving BLEU by about +1 across four language pairs.Instead, we demonstrate that the improvement comes from three other factors unrelated to discourse: context diversity, length diversity, and (to a lesser extent) position shifting.
X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic Parsing

Task-oriented compositional semantic parsing (TCSP) handles complex nested user queries and serves as an essential component of virtual assistants. Current TCSP models rely on numerous training data to achieve decent performance but fail to generalize to low-resource target languages or domains. In this paper, we present X2Parser, a transferable Cross-lingual and Cross-domain Parser for TCSP. Unlike previous models that learn to generate the hierarchical representations for nested intents and slots, we propose to predict intents and slots separately and cast both prediction tasks into sequence labeling problems. After that, we further propose a fertility-based slot predictor that first learns to detect the number of labels for each token, and then predicts the slot types. Experimental results illustrate that our model can significantly outperform existing strong baselines in cross-lingual and cross-domain settings, and our model can also achieve a good generalization ability on target languages of target domains. Furthermore, we show that our model can reduce the latency by up to 66% compared to the generation-based model.
CLUSTERING AND INDEXING OF MULTIPLE DOCUMENTS USING FEATURE EXTRACTION THROUGH APACHE HADOOP ON BIG DATA

Bigdata is a challenging field in data processing since the information is retrieved from various search engines through internet. A number of large organizations, that use document clustering,fails in arranging the documents sequentially in their machines. Across the globe, advanced technologyhas contributed to the high speed internet access. But the consequences of useful yet unorganized information in machine files seemto be confused in the retrieval process. Manual ordering of files has its own complications. In this paper, application software like Apache Lucene and Hadoop have taken a lead towards text mining for indexing and parallel implementation of document clustering. In organizations, it identifies the structure of the text data in computer files and its arrangement from files to folders, folders to subfolders, and to higher folders. A deeper analysis of document clustering was performed by considering various efficient algorithms like LSI, SVD and was compared with the newly proposed updated model of Non-Negative Matrix Factorization. The parallel implementation of hadoopdevelopedautomatic clusters for similar documents. MapReduce framework enforced its approach using K-means algorithm for all the incoming documents. The final clusters were automatically organized in folders using Apache Lucene in machines. This model was tested by considering the dataset of Newsgroup20 text documents. Thus this paper determines the implementation of large scale documents using parallel performance of MapReduce and Lucenethat generate automatic arrangement of documents, which reduces the computational time and improves the quick retrieval of documents in any scenario.
The Challenges of Drone Application in the Construction Industry

Drone technology is currently being used in many areas, and the increasing prevalence of drones is evident in all areas. Drones were first used for military purposes and now they have become an important part of military actions. In recent years, drone technology has become widespread in various industries including journalism, filmmaking, aerial photography, shipping, delivery. However, drone technology is still in its infancy and has not had a significant impact on the construction industry in Malaysia. Therefore, the study's objective was to assess the challenges preventing the application of drone technology in the Malaysian construction industry. The methodology used for this research was qualitative, which is data collection through interviews with three drone experts. The respondents in this research were directors of construction companies that use unmanned aerial vehicles for their operations. The findings showed that drones faced the challenges of unfamiliarity, lack of knowledge, reticence to adopt new technologies, privacy issues, and security issues. These challenges faced by drone technology willnot prevent its application in the Malaysian construction industry. However, government support is important in promoting the use of drone technology and raising people's awareness of drone technology in the construction industry.
A Privacy Protection Method of Lightweight Nodes in Blockchain

Aiming at the privacy protection of lightweight nodes based on Bloom filters in blockchain, this paper proposes a new privacy protection method. Considering the superimposition effect of query information, node and Bloom filter are regarded as the two parties of the game. A privacy protection mechanism based on the mixed strategy Nash equilibrium is proposed to judge the information query. On this basis, a Bloom filter privacy protection algorithm is proposed when the probability of information query and privacy, not being leaked, is less than the node privacy protection. It is based on variable factor disturbance, adjusting the number of bits’ set to 1 in the Bloom filter to improve the privacy protection performance in different scenarios. The experiment uses Bitcoin transaction data from 2009 to 2019 as the test data to verify the effectiveness, reliability, and superiority of the method.
Dynamic Volume-Hiding Encrypted Multi-Maps with Applications to Searchable Encryption

We study encrypted storage schemes where a client outsources data to an untrusted third-party server (such as a cloud storage provider) while maintaining the ability to privately query and dynamically update the data. We focus on encrypted multi-maps (EMMs), a structured encryption (STE) scheme that stores pairs of label and value tuples. EMMs allow queries on labels and return the associated value tuple. As responses are variable-length, EMMs are subject to volume leakage attacks introduced by Kellaris et al. [CCS'16]. To prevent these attacks, volume-hiding EMMs were introduced by Kamara and Moataz [Eurocrypt'19] that hide the label volumes (i.e., the value tuple lengths). As our main contribution, we present the first fully dynamic volume-hiding EMMs that are both asymptotically and concretely efficient. Furthermore, they are simultaneously forward and backward private which are the de-facto standard security notions for dynamic STE schemes. Additionally, we implement our schemes to showcase their concrete efficiency. Our experimental evaluations show that our constructions are able to add dynamicity with minimal to no additional cost compared to the prior best static volume-hiding schemes of Patel et al. [CCS'19].
The bassoon tonehole lattice: Links between the open and closed holes and the radiated sound spectrum

The acoustics of the bassoon has been the subject of relatively few studies compared with other woodwind instruments. One reason for this may lie in its complicated resonator geometry, which includes irregularly spaced toneholes with chimney heights ranging from 3 to 31 mm. The current article evaluates the effect of the open and closed tonehole lattice (THL) on the acoustic response of the bassoon resonator. It is shown that this response can be divided into three distinct frequency bands that are determined by the open and closed THL: below 500 Hz, 500–2200 Hz, and above 2200 Hz. The first is caused by the stopband of the open THL, where the low frequency effective length of the instrument is determined by the location of the first open tonehole. The second is due to the passband of the open THL, such that the modes are proportional to the total length of the resonator. The third is due to the closed THL, where part of the acoustical power is trapped within the resonator. It is proposed that these three frequency bands impact the radiated spectrum by introducing a formant in the vicinity of 500 Hz and suppressing radiation above 2200 Hz for most first register fingerings.
THE ONLINE PBL (PROJECT-BASED LEARNING) EDUCATION SYSTEM USING AI (ARTIFICIAL INTELLIGENCE)

This paper proposes the project-based learning (PBL) education system which uses Artificial Intelligence (AI) instead of teachers' direct instruction.Kanazawa Institute of Technology (KIT) applies PBL to its design and engineering programme as Project Design (PD) Programme.Students form small groups to discuss real-life problems and sometimes collaborate with local communities.However, in order to prevent COVID-19 infection, students cannot go to school.Instead, e-Syllabus and the web meeting Zoom (videoconferencing software) are used to take lectures at home.Adding to the PBL class implementation experience under the impact of COVID-19 and to the review results of the progress of digital technology, the outline, and the feature of the on-line PBL education system using chatbot and AI is proposed.Although the system is still under development, some of the components are introduced.
COVID-19 PANDEMIC AND HERDING BEHAVIOUR IN CRYPTOCURRENCY MARKET

In this paper, we examine the presence of herding in cryptocurrency market for four distinct sub-periods (Pre and During COVID-19 period, bear and bull markets) using daily closing prices of 5 largest cryptocurrencies by market capitalization (Bitcoin, Ethereum, XRP, Stellar and Tether) from April 20, 2019 to January 31, 2021. The study employs cross-sectional absolute deviations (CSAD) model to test herd behavior and the results of the study provide evidence of herd behavior in the whole market for the selected period under study. The study also proofs the presence of herding during COVID-19 period and in positive market returns. These indicate that, investors in the cryptocurrency market, during COVID-19 periods, and in bullish market are inclined to the investment behavior of other peer investors in the market. The study is significant to investors, regulators and players in the cryptocurrency market so as to deepen their understanding of herding behavior since herding is thought to increase the volatility of the market. &#x0D; The study is significant to investors, regulators and players in the cryptocurrency market so as to deepen their understanding of herding behavior since herding is thought to increase the volatility of the market.
Credit Card Fraud Detection System

Credit card fraud is a serious criminal offense. It costs individuals and financial institutions billions of dollars annually. According to the reports of the Federal Trade Commission (FTC), a consumer protection agency, the number of theft reports doubled in the last two years. It makes the detection and prevention of fraudulent activities critically important to financial institutions. Machine learning algorithms provide a proactive mechanism to prevent credit card fraud with acceptable accuracy. In this paper Machine Learning algorithms such as Logistic Regression, Naïve Bayes, Random Forest, K- Nearest Neighbor, Gradient Boosting, Support Vector Machine, and Neural Network algorithms are implemented for detection of fraudulent transactions. A comparative analysis of these algorithms is performed to identify an optimal solution.
Optimization Landscape of Quantum Control Systems

Optimization is ubiquitous in the control of quantum dynamics in atomic, molecular, and optical systems. The ease or difficulty of finding control solutions, which is practically crucial for developing quantum technologies, is highly dependent on the geometry of the underlying optimization landscapes. In this review, we give an introduction to the basic concepts in the theory of quantum optimal control landscapes, and their trap-free critical topology under two fundamental assumptions. Furthermore, the effects of various factors on the search effort are discussed, including control constraints, singularities, saddles, noises, and non-topological features of the landscapes. Additionally, we review recent experimental advances in the control of molecular and spin systems. These results provide an overall understanding of the optimization complexity of quantum control dynamics, which may help to develop more efficient optimization algorithms for quantum control systems, and as a promising extension, the training processes in quantum machine learning.
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Solving equations over finite fields is an important problem from both theoretical and practice points of view. The problem of solving explicitly the equation Pa(X)=0 over the finite field FQ, where Pa(X):=Xq+1+X+a, Q=pn, q=pk, a∈FQ⁎ and p is a prime, arises in many different contexts including finite geometry, the inverse Galois problem [1], the construction of difference sets with Singer parameters [9], determining cross-correlation between m-sequences [10] and to construct error correcting codes [5], cryptographic APN functions [6], [7], designs [21], as well as to speed up the index calculus method for computing discrete logarithms on finite fields [11], [12] and on algebraic curves [18]. In fact, the research on this specific problem has a long history of more than a half-century from the year 1967 when Berlekamp, Rumsey and Solomon [2] firstly considered a very particular case with k=1 and p=2. In this article, we discuss the equation Pa(X)=0 without any restriction on p and gcd⁡(n,k). In a very recent paper [15], the authors have left open a problem that could definitely solve this equation. More specifically, for the cases of one or two FQ-zeros, explicit expressions for these rational zeros in terms of a were provided, but for the case of pgcd⁡(n,k)+1 FQ− zeros it was remained open to compute explicitly the zeros. This paper solves the remained problem, thus now the equation Xpk+1+X+a=0 over Fpn is completely solved for any prime p, any integers n and k.