Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis Optimization Using Vader Lexicon on Machine Learning Approach

The current increase in global internet users, equivalent to 59.5 percent of the world’s population, affects active internet users globally. Almost some users have expressed their opinions on the internet, including in text, in the form of positive or negative views. Sentiment analysis aims to polarize information in the form of text in the state of positive, negative, and even neutral opinions. Vader Lexicon can extract sentiment features properly on datasets without labels so that datasets can be directly classified using machine learning methods, including naïve Bayes, support vector machine (SVM), random forest, neural network, decision trees, k-nearest neighbor (k-NN). The selection of extraction features among TF-IDF, word2vec, and word embedding (one hot coding) also increases the accuracy of machine learning methods. The experimental results show that the feature extraction of TF-IDF with the SVM method is the highest. The evaluation of model performance using k-fold cross validation obtained 92.6% accuracy for unlabeled and 80.3% for labeled datasets.
A Novel SVM-based Automatic Modulation Classifier

Automatic modulation classification (AMC) recreates an essential part in both cooperative and noncooperative communication applications. AMC is employed to automatically determine the modulation types of the transmitted signals. This is done by noting the recived information instances, which would be manipulated by the noise as well as fading channels. It is a middle process between the signal detection and the data retrieving. In this paper, a novel feature-based ACM is proposed and its performance is investigated. The ACM is based on the support vector machine (SVM) method. Different analog and digital modulation types are examined versus the ACM. The classifier accuracy is more than 98% for high SNR. Even more, the accuracy is around 90% for low SNR. The confusion matrix show no chaos between the different modulation types discussed.
Combining Named Entity Recognition and Emotion Analysis of Tweets for Early Warning of Violent Actions

Natural Language Processing techniques have gained popularity for the analysis of social media content. A number of techniques have been proposed to analyze various aspects such as public opinion, sentiments, and emotions expressed, opinion leaders, and extreme views. However, existing approaches take a retrospective approach that studies opinions after the occurrence of events. With the buildup of negative sentiments and extreme public opinion potentially leading to violent actions and civil disobedience, there is a need for a proactive and predictive approach that can offer early warning signs to government officials to intervene. In this work, we propose such an approach by combining two natural language processing techniques: Named entity recognition (NER) and emotions analysis. By tagging important entities within posts, such as prominent figures and important locations, and analyzing whether the tweets mentioning important entities carry negative emotions such as anger or violence, we are able to give insights about potential violent actions. Our framework was built and tested using 1290 tweets related to the 2020 US presidential election and the related US Capitol attack. The results obtained are promising and open the door for early intervention and appropriate preparedness for violent actions that may ensue from the buildup of negative sentiments and public views.
Textual Entailment Technique for the Bahasa Using BiLSTM

Recognizing Textual Entailment (RTE) is an important task in Natural Language Understanding (NLU). RTE aims to identify the entailment relationship between two text fragments (premise and hypothesis). Researchers on textual entailment have converged various languages. Research indicates that the use of deep learning approaches improved the accuracy performance of RTE in English. This study proposes an RTE model in Bahasa using a Bidirectional Long Short-Term Memory (BiLSTM). The method was trained on a large corpus in Bahasa. The corpus was generated by translating the SNLI corpus into Bahasa (SNLI Indo). The experimentation shows that the best training accuracy that the model produced, i.e., 79.37%, is when BiLSTM was combined with pre-trained IndoBERT as the word embedding method.
Application of Extreme Learning Machine-Autoencoder to Medium Term Electricity Price Forecasting

This paper proposes a new forecasting approach for medium-term electricity market prices based on an extreme learning machine-autoencoder (ELM-AE). The main idea behind is to use trained weights for hidden layer instead of randomly generated weights. The input hidden layer weights are obtained by solving a network with the same input outputs by the autoencoder method. To do so, a data-set is created using input data, where the ahead 24 hours are forecasted based on previous 168 data. The simulations have been performed on New York Independent System Operator prices and compared with the classic ELM demonstrating the high accuracy of the proposed method in both training and testing.
Generation of realistic synthetic data using Multimodal Neural Ordinary Differential Equations

Individual organizations, such as hospitals, pharmaceutical companies, and health insurance providers, are currently limited in their ability to collect data that are fully representative of a disease population. This can, in turn, negatively impact the generalization ability of statistical models and scientific insights. However, sharing data across different organizations is highly restricted by legal regulations. While federated data access concepts exist, they are technically and organizationally difficult to realize. An alternative approach would be to exchange synthetic patient data instead. In this work, we introduce the Multimodal Neural Ordinary Differential Equations (MultiNODEs), a hybrid, multimodal AI approach, which allows for generating highly realistic synthetic patient trajectories on a continuous time scale, hence enabling smooth interpolation and extrapolation of clinical studies. Our proposed method can integrate both static and longitudinal data, and implicitly handles missing values. We demonstrate the capabilities of MultiNODEs by applying them to real patient-level data from two independent clinical studies and simulated epidemiological data of an infectious disease.
Realization of a sign-distinguishable higher-order optical differentiation

A differential operation, which can highlight features of images, is of significant importance in fields such as machine vision. As a front-end action, optical differentiation can overcome the shortcomings of traditional computer differentiation and runs at the speed of light. Compared with the conventional first-order optical differentiation technique, a higher-order one with the correct sign of a differential field obtained can facilitate the recognition of finer features, although relevant studies are relatively scarce. Here, we propose a method to realize sign-distinguishable arbitrary-order optical differentiation by coupling the momentum and polarization of a light field, the feasibility of which is further confirmed by a proof-of-principle experiment. We hope our method will promote the development of optical analog computing, image recognition, and other relevant applications.
Unified energy-efficient reconfigurable MAC for dynamic Convolutional Neural Network based on Winograd algorithm

There has been a dramatic proliferation of research concerned with Convolutional Neural Networks (CNNs) over the past decade. In the field of smart surveillance, multi-channel frames need to be processed simultaneously as real-time operations, which leads to intensive computation. Deep CNNs have tens of layers which lead to intensive computation. In order to solve tremendous computation pressure, lots of CNN accelerators have been proposed. Moreover, many works focus on the design of a multiply-and-accumulate (MAC) unit since ten billions of MAC computations induce enormous energy consumption for logical operations in CNNs. Although DynCNN (Tsai et al., 2020) improves the structure of conventional CNNs by using the high similarity property of consecutive frames, it generates redundant cells and leads to redundant operations. Therefore, this paper proposed an energy-efficient reconfigurable MAC and a high-performance CNN accelerator based on the Winograd Minimum Filtering Algorithm (WMFA). The proposed design not only dramatically enhances throughput but also acquires better energy efficiency in limited resources. Experimental results showed that the proposed Winograd processing element (PE) gives 8% to 12% power improvements. The DSP efficiency was improved in the range of from 1.48 × to 6.82 × as well compared with the previous works.
Reconfigurable electro-optical logic gates using a 2-layer multilayer perceptron

In this work, we aim to use the optical amplifiers, directional couplers and phase modulators to build the electro-optical gates. Thanks to the 2-layer-multilayer-perceptron structure, the inversion of matrix is performed to obtain the coupling ratio of the directional couplers and the phase delay of the phase modulators. The electro-optical OR, AND, XOR, NAND, NOR and XNOR gates are demonstrated. Moreover, we not only study the results under the ideal condition of device, but also discuss the imperfect situation with 1% error of fabrication or operation to study the tolerance of this system. Through our simulation results, the visibility of the gate output can be higher than 0.83. The gates can be fabricated in a silicon-based chip to develop the integrated optics computing system.
Load-Aware VM Migration Using Hypergraph Based CDB-LSTM

Live Virtual Machine (VM) migration is one of the foremost techniques for progressing Cloud Data Centers' (CDC) proficiency as it leads to better resource usage.The workload of CDC is often dynamic in nature, it is better to envisage the upcoming workload for early detection of overload status, underload status and to trigger the migration at an appropriate point wherein enough number of resources are available.Though various statistical and machine learning approaches are widely applied for resource usage prediction, they often failed to handle the increase of non-linear CDC data.To overcome this issue, a novel Hypergrah based Convolutional Deep Bi-Directional-Long Short Term Memory (CDB-LSTM) model is proposed.The CDB-LSTM adopts Helly property of Hypergraph and Savitzky-Golay (SG) filter to select informative samples and exclude noisy inference & outliers.The proposed approach optimizes resource usage prediction and reduces the number of migrations with minimal computational complexity during live VM migration.Further, the proposed prediction approach implements the correlation co-efficient measure to select the appropriate destination server for VM migration.A Hypergraph based CDB-LSTM was validated using Google cluster dataset and compared with state-of-the-art approaches in terms of various evaluation metrics.
Automatic Clustering of User Behaviour Profiles for Web Recommendation System

Web usage mining, content mining, and structure mining comprise the web mining process.Web-Page Recommendation (WPR) development by incorporating Data Mining Techniques (DMT) did not include end-users with improved performance in the obtained filtering results.The cluster user profilebased clustering process is delayed when it has a low precision rate.Markov Chain Monte Carlo-Dynamic Clustering (MC 2 -DC) is based on the User Behavior Profile (UBP) model group's similar user behavior on a dynamic update of UBP.The Reversible-Jump Concept (RJC) reviews the history with updated UBP and moves to appropriate clusters.Hamilton's Filtering Framework (HFF) is designed to filter user data based on personalised information on automatically updated UBP through the Search Engine (SE).The Hamilton Filtered Regime Switching User Query Probability (HFRSUQP) works forward the updated UBP for easy and accurate filtering of users' interests and improves WPR.A Probabilistic User Result Feature Ranking based on Gaussian Distribution (PURFR-GD) has been developed to user rank results in a web mining process.PURFR-GD decreases the delay time in the end-to-end workflow for SE personalization in various methods by using the Gaussian Distribution Function (GDF).The theoretical analysis and experiment results of the proposed MC 2 -DC method automatically increase the updated UBP accuracy by 18.78%.HFRSUQP enabled extensive Maximize Log-Likelihood (ML-L) increases to 15.28% of User Personalized Information Search Retrieval Rate (UPISRT).For feature ranking, the PURFR-GD model defines higher Classification Accuracy (CA) and Precision Ratio (PR) while utilising minimum Execution Time (ET).Furthermore, UPISRT's ranking performance has improved by 20%.
Tasks Scheduling in Cloud Environment Using PSO-BATS with MLRHE

Cloud computing plays a significant role in Information Technology (IT) industry to deliver scalable resources as a service.One of the most important factor to increase the performance of the cloud server is maximizing the resource utilization in task scheduling.The main advantage of this scheduling is to maximize the performance and minimize the time loss.Various researchers examined numerous scheduling methods to achieve Quality of Service (QoS) and to reduce execution time.However, it had disadvantages in terms of low throughput and high response time.Hence, this study aimed to schedule the task efficiently and to eliminate the faults in scheduling the tasks to the Virtual Machines (VMs).For this purpose, the research proposed novel Particle Swarm Optimization-Bandwidth Aware divisible Task (PSO-BATS) scheduling with Multi-Layered Regression Host Employment (MLRHE) to sort out the issues of task scheduling and ease the scheduling operation by load balancing.The proposed efficient scheduling provides benefits to both cloud users and servers.The performance evaluation is undertaken with respect to cost, Performance Improvement Rate (PIR) and makespan which revealed the efficiency of the proposed method.Additionally, comparative analysis is undertaken which confirmed the performance of the introduced system than conventional system for scheduling tasks with high flexibility.
Topics and changing characteristics of knowledge organization research in the 21st century: a content analysis

Purpose Knowledge organization (KO) has been advancing at a progressively rapid pace under the influence of information technology. This study aims to explore the topics, characteristics, and trends of KO research in the 21st century. Design/methodology/approach The full text of 4,360 KO-related articles published from 2000 to 2021 is collected. Through content analysis, this study identifies the topics, research methods, and application areas of each article, and the statistics are presented through a series of visualizations. Findings In total, 13 main topics, 105 sub-topics, 16 research methods, and 57 application areas are identified. Notably, classification has always been an important topic, while linked data, automated techniques, and ontology have become popular topics recently. Significant changing features have also occurred. The versatile use of research methods has increased, with empirical research becoming the mainstream. Application areas show a trend of refinement from subject areas to specific scenarios. Construction techniques present a combination of automated techniques, crowdsourcing, and experts. Originality/value KO has evolved and diversified due to technological developments. This study is the first to focus on the continuous changing features over an extended, 21-year period, as opposed to sampling a few years. It also provides clues and insights for researchers and practitioners interested in KO to understand how it has changed in the Semantic Web and big data context.
Spatial and temporal characteristics of surface solar radiation in China and its influencing factors

The total-sky direct solar radiation at Earth’s surface (SRS) not only has an important impact on the earth’s climate and ecology, but also is a crucial parameter for solar photovoltaic power. SRS determines whether photovoltaic power plants can be built in the region and directly affects the efficiency of photovoltaic power generation. Therefore, the spatial and temporal distribution characteristics of SRS have a very important guiding significance for the construction of photovoltaic power stations. This study discusses the temporal and spatial characteristics of SRS and its influencing factors in China during 1961–2020 using ERA5 data and the empirical orthogonal function (EOF), rotated empirical orthogonal function (REOF), and ensemble empirical model decomposition (EEMD) methods. Our investigation reveals that the high-value SRS center is located on the southwestern Tibetan Plateau, while the low-value center occurs on the northeastern Yunnan–Guizhou Plateau and in the Sichuan Basin. Seasonal variability in SRS means that maximum values occur uniformly in summer, followed sequentially by spring, autumn, and winter. The spatial distribution of the leading SRS EOF mode exhibits a dipole pattern between the southern Tibetan Plateau and other regions. Combined with the time series, SRS in China underwent an interdecadal transition around the year 2000. The regression analysis shows that this pattern is mainly affected by surface air temperature, total precipitation, relative humidity and cloud cover. The time series evolution of SRS primarily reflects the interannual variability in annual-mean and four seasons; the variance contributions of decadal variability and secular trend are minor through the EEMD. The REOF separates Chinese SRS into 11 central regions, the top 5 being the western Tibetan Plateau, western Northwest China, the eastern Tibetan Plateau, northern Xinjiang, and North China. The relationship between SRS and meteorological parameters shows that SRS is positively correlated with surface air temperature and wind speed but negatively correlated with total precipitation, relative humidity, low and total cloud cover, and aerosol concentrations.
Secure approximate pattern matching protocol via Boolean threshold private set intersection

Approximate pattern matching (APM) measures whether the Hamming distance between two strings is less than a threshold value. APM has been widely utilized, such as gene matching and facial recognition. Yet, the genetic data are privacy-sensitive, resulting that the owners are unwilling to share the raw data. This inspires us to explore how to securely perform APM. After revisiting threshold private set intersection (TPSI), we first propose and formalize a functionality named Boolean threshold private set intersection (BTPSI). The new proposed BTPSI primitive returns a Boolean value (0 or 1) to the user, rather than the actual elements in TPSI. We then construct a secure protocol for the BTPSI functionality with semihonest security. Besides, we first combine oblivious transfer and BTPSI to achieve the efficient construction of secure approximate pattern matching (SAPM) protocol in a semihonest model. Furthermore, we implement our SAPM protocol to demonstrate its real practicality. The performance result shows that when the text length is 2 20 ${2}^{20}$ and the pattern length is 2 10 ${2}^{10}$ , the total runtime is less than 3 s.
Script Event Prediction via Multilingual Event Graph Networks

Predicting what happens next in text plays a critical role in building NLP applications. Many methods including count-based and neural-network-based have been proposed to tackle the task called script event prediction: predicting the most suitable subsequent event from a candidate list given a chain of narrative events (context). However, two problems including event ambiguity and evidence bias hinder the performance of these monolingual approaches. The former means that some events in the event chain are ambiguous. The latter means that both the wrong and correct candidate events can obtain sufficient support from the event context. In this article, we propose a novel multilingual approach to address two issues simultaneously. Specifically, to alleviate the event ambiguity problem, we project the monolingual event chains to parallel cross-lingual event chains, which can provide complementary information for monolingual event disambiguation. To deal with the evidence bias problem, we construct two monolingual event graphs and a cross-lingual event aligned graph to fully explore connections between events. What’s more, we design a graph attention mechanism to model the confidence of the complement clues, which controls the information integration from various languages. By modeling the events with graphs instead of pairs or chains, the model can compare the candidate subsequent events simultaneously and choose the more suitable subsequent event as the final answer. Extensive experiments were conducted on the widely used New York Times corpus for script event prediction task and experimental results show that our approach outperforms previous models.
Facial Emotion Recognition Analysis Based on Age-Biased Data

This paper aims to analyze the importance of age-biased data in recognizing six emotions using facial expressions. For this purpose, a custom dataset (adults, kids, mixed) was constructed using images that separated the existing datasets (FER2013 and MMA FACILE EXPRESSION) into adults (≥14) and kids (≤13). The convolutional Neural Networks (CNN) algorithm was used to calculate emotion recognition accuracy. Additionally, this study investigated the effect of the characteristics of CNN architecture on emotion recognition accuracy. Based on the variables of Accuracy and FLOP, three types of CNN architectures (MobileNet-V2, SE-ResNeXt50 (32 × 4 d), and ResNeXt-101 (64 × 4 d)) were adopted. As for the experimental result, SE-ResNeXt50 (32 × 4 d) showed the highest accuracy at 79.42%, and the model that learned by age obtained 22.24% higher accuracy than the model that did not learn by age. In the results, the difference in expression between adults and kids was greatest for fear and neutral emotions. This study presented valuable results on age-biased learning data and algorithm type effect on emotion recognition accuracy.
Assessment of photovoltaic power output using the estimated global solar radiation at Vuwani Science Resources Centre

The focus of this study was to assess the solar radiation in locations with limited meteorological data. In 2019, monthly average temperature values that were collected from the South African University Radiometric Network (SAURAN), USAid Venda station in Vuwani, Limpopo, were inputted in the temperature-based empirical model, Hargreaves–Samani (H−S) model to calculate global solar radiation. The modelled monthly average solar radiation values were compared to the ground-based and two satellites data sources. The performance of the model showed strong correlation between all the measurements (observed) solar radiation with R2 of 0.83, 0.94, and 0.92 for ground-based, NASA, and CAMS satellites derived data, respectively. It can be inferred that the modelled global radiation has a better error range as compared to both ground-based and satellite-derived observations. The strong correlation validated that the estimated radiation through the H−S model as a reliable input for photovoltaic(PV) power output models for a 255 W panel installed at the study site. Therefore, careful site assessment of weather conditions is necessary for a better assessment of potential P V power output and recommendation of the suitable solar panel. The study has proven that the PV power output can be estimated in areas with limited weather data with known solar panel. The outcomes of this study can be useful for proper design of power generation system of any size in any location.