Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis System using Hybrid Word Embeddings with Convolutional Recurrent Neural Network

There have been wide ranges of innovations in sentiment analysis in recent past, with most effective ones involving use of various word embeddings methods for analysis of sentiments. GloVe and Word2Vec are acclaimed to be two most frequently used. A common problem with simple pre-trained embedding methods is that these ignore information related to sentiments of input texts and further depend on large text corpus for training purpose and generation of relevant vectors which is hindrance to researches involving smaller sized corpuses. The aim of proposed study is to propose a novel methodology for sentiment analysis that uses hybrid embeddings with a target to enhance features of available pre-trained embedding. Proposed hybrid embeddings use Part of Speech (POS) tagging and word2position vector over fastText with varied assortments of attached vectors to the pre-trained embedding vectors. The resultant form of hybrid embeddings is fed to our ensemble network-Convolutional Recurrent Neural Network (CRNN). The methodology has been tested for accuracy via different Ensemble models of deep learning and standard sentiment dataset with accuracy value of 90.21 using Movie Review (MVR) Dataset V2. Results show that proposed methodology is effective for sentiment analysis and is capable of incorporating even more linguistic knowledge-based techniques to further improve results of sentiment analysis.
Quantum boomerang effect in systems without time-reversal symmetry

In an Anderson localized system, a quantum particle with a nonzero initial velocity returns, on average, to its origin. This recently discovered behavior is known as the quantum boomerang effect. Time-reversal invariance was initially thought to be a necessary condition for the existence of this phenomenon. We theoretically analyze the impact of the symmetry breaking on the phenomenon using a one-dimensional system with a spin-orbit coupling and show that the time-reversal invariance is not necessary for the boomerang effect to occur. We explain this behavior giving sufficient symmetry conditions for the boomerang effect to occur when time-reversal symmetry is broken.
Local Explanations of Global Rankings: Insights for Competitive Rankings

Explaining complex algorithms and models has recently received growing attention in various domains to support informed decisions.Ranking functions are widely used for almost every form of human activity to enable effective decision-making processes.Hence, explaining ranking indicators and their importance are essential properties to enhance performance.Local explanation techniques have recently become a prominent way to interpret individual predictions of machine learning models.However, there has been limited investigation into explaining competitive rankings.This work proposes a hierarchical ranking explanation framework to capture local explanations for competitive rankings by defining a proper neighborhood construction approach.We explore various explanation techniques to identify the local contribution of ranking indicators based on the position of an instance in the ranking as well as the size of the neighborhood around the instance of interest.We evaluate the generated explanations for the Times Higher Education university ranking dataset as a benchmark of competitive ranking.The results reveal insights for a wide range of instances in the ranking list and indicate the importance of local explanations for competitive rankings.
Spatial distribution analysis of seismic activity based on GMI, LMI, and LISA in China

Abstract Recently, all kinds of geological disasters happen frequently on the earth. In China, there are countless earthquakes every year, which greatly affect the country’s economic level and development as well as the people’s life and health. The analysis of seismic activity is becoming more and more significant. In this article, the spatial distribution of China’s seismic activities was analyzed by using the provincial seismic data from 1970 to 2013. On the basis of spatial autocorrelation analysis theory, Global Moran’s I , Local Moran’s I , and the Local Indicators of Spatial Association are used to measure the geospatial distribution characteristics of China’s seismic activities. The research results show that earthquakes in mainland China have significant global autocorrelation characteristics as a whole, and the global autocorrelation coefficients are all positive. And the Z -value test ( P &lt; 0.05) shows that earthquakes in mainland China present a spatial agglomeration pattern. Furthermore, we observed a reduction trend in disparities of seismic activity among regions in China.
Transfer Learning for Operator Selection: A Reinforcement Learning Approach

In the past two decades, metaheuristic optimisation algorithms (MOAs) have been increasingly popular, particularly in logistic, science, and engineering problems. The fundamental characteristics of such algorithms are that they are dependent on a parameter or a strategy. Some online and offline strategies are employed in order to obtain optimal configurations of the algorithms. Adaptive operator selection is one of them, and it determines whether or not to update a strategy from the strategy pool during the search process. In the field of machine learning, Reinforcement Learning (RL) refers to goal-oriented algorithms, which learn from the environment how to achieve a goal. On MOAs, reinforcement learning has been utilised to control the operator selection process. However, existing research fails to show that learned information may be transferred from one problem-solving procedure to another. The primary goal of the proposed research is to determine the impact of transfer learning on RL and MOAs. As a test problem, a set union knapsack problem with 30 separate benchmark problem instances is used. The results are statistically compared in depth. The learning process, according to the findings, improved the convergence speed while significantly reducing the CPU time.
Hand Gesture Detection for Sign Language using Neural Network with Mediapipe

The most popular way of interfacing with most computer systems is a mouse and keyboard. Hand gestures are an intuitive and effective touchless way to interact with computer systems. However, hand gesture-based systems have seen low adoption among end-users primarily due to numerous technical hurdles in detecting in-air gestures accurately. This paper presents Hand Gesture Detection for American Sign Language using K-Nearest Neighbor with Mediapipe, a framework developed to bridge this gap. The framework learns to detect gestures from demonstrations, it is customizable by end-users, and enables users to interact in real-time with computers having only RGB cameras, using gestures.
Emoji Prediction Using Emerging Machine Learning Classifiers for Text-based Communication

We aim to extract emotional components within statements to identify the emotional state of the writer and assigning emoji related to the emotion.Emojis have become a staple part of everyday text-based communication.It is normal and common to construct an entire response with the sole use of emoji.It comes as no surprise, therefore, that effort is being put into the automatic prediction and selection of emoji appropriate for a text message.Major companies like Apple and Google have made immense strides in this, and have already deployed such systems into production (for example, the Google Gboard).The proposed work is focused on the problem of automatic emoji selection for a given text message using machine learning classification algorithms to categorize the tone of a message which is further segregated through n-gram into one of seven distinct categories.Based on the output of the classifier, select one of the more appropriate emoji from a predefined list using natural language processing (NLP) and sentimental analysis techniques.The corpus is extracted from Twitter.The result is a boring text message made lively after being annotated with appropriate text messages
Dependency multi-weight-view graphs for event detection with label co-occurrence

• A multi-weight-view graph is proposed to capture enriched semantic and structural information automatically. • A label co-occurrence graph is proposed to enhance dependency between different event labels. • DMWVG is an end-to-end model without external tools, knowledge, or feature engineering. • DMWVG model detects events without identifying trigger words. • DMWVG model obtains remarkable performance on MLEE, BioNLP 09, BioNLP 11, and ACE 2005 datasets. Event detection is an extremely important step for extracting events, and its goal is to detect the occurrences of events and categorize them. Prior works use either a fine-grained approach or a coarse-grained approach to detect events. However, both approaches encounter two problems: (1) Enriched feature information obtained from external tools probably introduces unnecessary errors. (2) Ignoring the phenomenon of event co-occurrence in a sentence degrades the performance of event detection. To address these issues, a novel framework called D ependency M ulti- W eight- V iew G raph (DMWVG) is proposed to detect events without trigger tagging and external feature engineering. Specifically, DMWVG leverages the tree transformer to automatically generate syntactic relations with different weights to mitigate the errors generated by external syntactic parsing tools. In addition, an event correlation matrix is constructed with the help of the event label co-occurrence to deliver essential information on triggers and to promote the performance of event detection. Finally, the experimental results indicate that our approach is competitive with existing SOTA methods on four datasets (75.77% F1-score on ACE 2005, 82.11% F1-score on MLEE, 75.61% F1-score on BioNLP 09, and 72.20% F1-score on BioNLP 11).
Federated Multi-Task Attention for Cross-Individual Human Activity Recognition

Federated Learning (FL) is an emerging privacy-aware machine learning technique that applies successfully to the collaborative learning of global models for Human Activity Recognition (HAR). As of now, the applications of FL for HAR assume that the data associated with diverse individuals follow the same distribution. However, this assumption is impractical in real-world scenarios where the same activity is frequently performed differently by different individuals. To tackle this issue, we propose FedMAT, a Federated Multi-task ATtention framework for HAR, which extracts and fuses shared as well as individual-specific multi-modal sensor data features. Specifically, we treat the HAR problem associated with each individual as a different task and train a federated multi-task model, composed of a shared feature representation network in a central server plus multiple individual-specific networks with attention modules stored in decentralized nodes. In this architecture, the attention module operates as a mask that allows to learn individual-specific features from the global model, whilst simultaneously allowing for features to be shared among different individuals. We conduct extensive experiments based on publicly available HAR datasets, which are collected in both controlled environments and real-world scenarios. Numeric results verify that our proposed FedMAT significantly outperforms baselines not only in generalizing to existing individuals but also in adapting to new individuals.
Analysis of Experimental Research Results Focused on Improving Student Psychological Health

The students' psychological health in high education is still being a problematic issue.This article describes the importance of ensuring students' psychological health in higher education today and related empirical research results.The aim of this paper is exploring and analysis research results focused on students' psychological health.To determine the students' psychological health there were used special psychodiagnostic methods.The components of the psychological health were divided as 1) satisfaction level; 2) perceptions of a healthy lifestyle; 3) emotional stability; 4) psychoemotional state; and 5) attitude towards themselves.Obtained results on students' psychological health indicate specific conclusions about various psychological health indicators and the relationship between behaviour and internal health, including in emotional, cognitive and behavioural areas.The results of this research work showed that one of the important indicators of students' psychological health is a decrease in the level of emotional distress and emotional instability (neuroticism, nervousness), a positive change in students' internal state, and an increase in students' satisfaction with their educational environment.The results could be used in the high education system, especially measuring and monitoring students' psychological health.
PESA: Prioritized experience replay for parallel hybrid evolutionary and swarm algorithms - Application to nuclear fuel

We propose a new approach called PESA (Prioritized replay Evolutionary and Swarm Algorithms) combining prioritized replay of reinforcement learning with hybrid evolutionary algorithms. PESA hybridizes different evolutionary and swarm algorithms such as particle swarm optimization, evolution strategies, simulated annealing, and differential evolution, with a modular approach to account for other algorithms. PESA hybridizes three algorithms by storing their solutions in a shared replay memory, then applying prioritized replay to redistribute data between the integral algorithms in frequent form based on their fitness and priority values, which significantly enhances sample diversity and algorithm exploration. Additionally, greedy replay is used implicitly to improve PESA exploitation close to the end of evolution. PESA features in balancing exploration and exploitation during search and the parallel computing result in an agnostic excellent performance over a wide range of experiments and problems presented in this work. PESA also shows very good scalability with number of processors in solving an expensive problem of optimizing nuclear fuel in nuclear power plants. PESA's competitive performance and modularity over all experiments allow it to join the family of evolutionary algorithms as a new hybrid algorithm; unleashing the power of parallel computing for expensive optimization.
Application of Improved Satin Bowerbird Optimizer in Image Segmentation

Aiming at the problems of low optimization accuracy and slow convergence speed of Satin Bowerbird Optimizer (SBO), an improved Satin Bowerbird Optimizer (ISBO) based on chaotic initialization and Cauchy mutation strategy is proposed. In order to improve the value of the proposed algorithm in engineering and practical applications, we apply it to the segmentation of medical and plant images. To improve the optimization accuracy, convergence speed and pertinence of the initial population, the population is initialized by introducing the Logistic chaotic map. To avoid the algorithm falling into local optimum (prematurity), the search performance of the algorithm is improved through Cauchy mutation strategy. Based on extensive visual and quantitative data analysis, this paper conducts a comparative analysis of the ISBO with the SBO, the fuzzy Gray Wolf Optimizer (FGWO), and the Fuzzy Coyote Optimization Algorithm (FCOA). The results show that the ISBO achieves better segmentation effects in both medical and plant disease images.
Automated Detection of Cervical Cancer

Cervical Cancer is an abnormal growth in the cervix - the lower part of the uterus which joins to the vagina. Human Papilloma Virus (HPV) is identified as the main cause for cervical cancer. Cervical cancer is the second most deadly disease among women next to breast cancer in developing countries. However, it is considered to be the most preventable female cancer if identified at an early stage. The cancerous cells may spread to other parts of the body if not identified at an early stage. Pap Smear test and acetic acid test are usually done for cancer screening. In pap test cells are taken from the vagina and cervix and are examined under a microscope for the presence of an abnormal cell. In acetic acid test, the change in features after and before the application of acetic acid is analyzed to find the existence of abnormal cell. Automated screening is becoming most common than manual screening because the latter is erroneous. This paper surveys the different automated methods available for screening the abnormal cells in pap images.
An Effective Randomization Framework to POW Consensus Algorithm of Blockchain (RPoW)

Blockchain technology has become a buzzword due to its intuitive applications and its decentralized network architecture. Blockchain is a decentralized system that uses peer -to - peer networking and appropriate consensus algorithm for secure and reliable transactions and records them all in an immutable ledger as chain of blocks. The world got introduced to the blockchain technology, when Satoshi Nakamoto released Bitcoin in 2009[2]. Though this technology is famous for being the backbone of cryptocurrencies, it has got into various industry domains and many applications have been developed using blockchain [11]. The consensus algorithm used in Blockchain architecture influences how agreement is made to add a new block among all the nodes in the network. PoW (Proof of work) is the consensus algorithm applied in the Bitcoin network architecture and many other blockchain applications. PoW requires each node to solve a cryptographic puzzle with adjusted difficulty, to get the privilege to add a new block to the current chain. The first node that solves the puzzle will have this privilege and some reward. Proof-of-Work (PoW) uses extensive amounts of electric power and steep computing hardware as an effect of their consensus procedure [1]. This proposed work presents an effective randomization framework that reduces the execution time required to calculate the hash value. The number of instruction-set required to compute the PoW consensus is also reduced. This approach helps in maintaining a fair decentralized network to construct blockchain applications.
Forecasting New Student Candidates Using the Random Forest Method

College education institutions regularly hold new student admissions activities, and the number of new students can increase and can also decrease. University of PGRI Semarang (UPGRIS) on the development of new student admissions for the 2014/2015 academic year up to 2018/2019 with so many admissions selection stages. To meet the minimum comparison requirements between the number of students with the development of human resources, facilities, and infrastructure, it is necessary to predict how much the number of students increases each year. To make a prediction system or forecasting, the number of prospective new students required a good forecasting method and sufficiently precise calculations to predict the number of prospective students who register. In this study, the method to be taken is the Random Forest method. For the evaluation of forecasting models used Random Sampling and Cross-validation. The parameter used is Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2). The results of this study obtained the five highest and lowest study programs in the admission of new students. Therefore, UPGRIS will make a new strategy for the five lowest study programs so that the desired number of new students is achieved
AudioKey: a usable device pairing system using audio signals on smartwatches

Smartwatches are expected to replace smartphones in some applications with better user experience because of a greater range of features and new innovations such as audio recording, activity recognition, and data transmission. In this paper, we develop a system called AudioKey, aiming to pair two smartwatches by generating a unique secret key between them. Compared with existing works, our system does not need extra infrastructure to synchronise devices and trigger the key generation process, and only uses the existing sensors (gyroscope and microphone) that are deployed on most smartwatches. AudioKey triggers the key generation process on two devices at the same time by detecting the handshake between two normal users. A secret key is extracted from both the frequency domain and the time domain of audio signals and used to authenticate each other or encrypt the sensitive data. Evaluation results collected on nine volunteers in three different scenarios show that our system can achieve a bit generation rate of 13.4 bits/s with the mean key agreement rate of 96.7% for a 128-bit secret key, while a strong attacker can only achieve a mean key agreement of 10.8%.
Employer Attractiveness and Employee Performance: An Exploratory Study

Improving employee performance always receives researchers and organisational managers' attention as this factor contributes directly to the organisational competitive advantage.Therefore, academic researches, as well as practices, focus on the factors affecting employee performance.However, employer attractiveness, primarily defined as the benefits of an organisation perceived by potential candidates, has not received sufficient attention.Studies regarding employer attractiveness have emphasised on the potential candidates, rather than current employees.Therefore, this study focuses on discovering a new direction -the impact of employer attractiveness on current employees, namely, employee performance.The research method used in this study is the mixed method, especially PLS-SEM analysis tool.Research data is collected from 937 employees from 37 organisations and enterprises in Vietnam.Research results discover the statistically significant relationship between employer attractiveness and employee performance.The results have helped confirm and develop the employer attractiveness factor; especially the new safety value factor.
Benefit and Challenge of Blockchain Technology in Pharmaceutical Supply Chain Management

Pharmaceutical Industry Supply Chain Management (SCM) has been an invigorating research topic because the industry strives to explore innovative ways to secure the medicine distribution and to ensure the authenticity of medicine consumed by the patients. A surge in the counterfeit drug has called an urgent effort for the industry to eradicate. In this scope, the fake medicine harm serious health problem for patients. To cope with this urgency, Blockchain offers the new innovative technology that captures the security and accountability in the financial industry for decades. In this spectrum, the new generation of blockchain with smart contract feature enables the utilization of blockchain in an enterprise setting such as Supply Chain Management Systems. In this point, this study explored contemporary for benefit and challenge blockchain adoption based on Systematic Literature Review methodology. At a closer look, the research indicates the impact of the Blockchain Adoption Technology in Pharmaceutical Industry SCM in for four aspects, i.e. supplier, producer, logistic/distributor, and customer. In a specific range, this research explores significant factors from ten leading journals (one thousand two hundred and fourteen papers) and selects solely eighteen relevant documents. The examination reveals the critical research development of blockchain technology adoption in Pharmaceutical SCM and twenty-three essential factors.