Reviewerly/exHarmony huggingface dataset:
A Sentiment Analysis Framework on COVID-19 in Major Cities of Malaysia based on Tweets using Machine Learning Classification Model

Twitter is one of the famous social media platforms for people to share their stories and opinions on any situations, such as the COVID-19 pandemic. With the indirect influence of tweets on users and the rise in cases of COVID-19 in Malaysia, it is important to monitor information related to the pandemic in order to avoid misinformation, panic, or confusion among public. As the data from tweets are also one of the useful raw data sources that can be used for data visualization, this project aims to design and develop a web-based system for visualizing the status of pandemic in Malaysia based on the data collected from Twitter. There are four phases in the methodology of this project: (i) Planning, (ii) Analysis, (iii) Design and Development, and (iv) Testing and Documentation. In the planning and analysis phases, the data will be collected from March 2020 to March 2021 and will be filtered by using keywords and hashtags, such as #COVID19 and #Coronavirus, as well as the location tagged on the tweets. The collected data then will be pre-processed to remove any unwanted texts. The classification of the data is based on sentiment analysis using one of machine learning models that is Support Vector Machine (SVM). The performance of the classification model will be evaluated using the evaluation model: (i) accuracy, (ii) recall, (iii) precision, and (iv) F1-measure. The final output of this project is the data visualization of the sentiment analysis on COVID-19 in Malaysia based on two of its major cities: Kuala Lumpur and Klang.
Novel Autonomous Algorithms of Path Planning for Mobile Robots: A Survey

As the demand for autonomous mobile robots has risen sharply, the collision-free path planning/navigation problem has become and still is a focus of attention for many researchers. This paper covers a rangeof path planning approaches for various types of mobile robots, such as ground mobile robots, unmanned aerial vehicles, and autonomous vehicles. The literature is classified into two categories: global path planning and reactive path planning. To help readers comprehend the flow within each category, we analyze and compare each category from the perspectives of environmental modeling, optimization criteria, and different methods for path planning. In comparison to earlier survey articles, we place a greater emphasis on Artificial Intelligence (AI) and self-learning navigation methods. In particular, different robot kinematic models are also discussed in thisarticle. Finally, we indicated some future research directions.
Designing Compact Convolutional Filters for Lightweight Human Pose Estimation

Existing methods for human pose estimation usually use a large intermediate tensor, leading to a high computational load, which is detrimental to resource-limited devices. To solve this problem, we propose a low computational cost pose estimation network, MobilePoseNet, which includes encoder, decoder, and parallel nonmaximum suppression operation. Specifically, we design a lightweight upsampling block instead of transposing the convolution as the decoder and use the lightweight network as our downsampling part. Then, we choose the high-resolution features as the input for upsampling to reduce the number of model parameters. Finally, we propose a parallel OKS-NMS, which significantly outperforms the conventional NMS in terms of accuracy and speed. Experimental results on the benchmark datasets show that MobilePoseNet obtains almost comparable results to state-of-the-art methods with a low compilation load. Compared to SimpleBaseline, the parameter of MobilePoseNet is only 4%, while the estimation accuracy reaches 98%.
Project Activities of Shevchenko National Preserve in Informational Society

To realization of the project digitalisation and study the possibilities of consolidating museum, library and archival documentary and information resources as a means of preserving, popularizing, putting into scientific circulation the cultural heritage of the Shevchenko National Reserve and integrating the local database into the general information space within the implementation of social project. Methodology of investigation is based on the general scientific methods: analysis, synthesis, formal logic, federal consolidation of information resources and visualization of the results of investigation. Preconditions of ormation of consolidated datebase of documentary and informational resources of Shevchenko National Preserve are highlighted, its entry to the general informational space and steps of realization of the project are suggested. It is defined that project format of the activities of Shevchenko National Preserve which accumulates museum, library and archival communicative systems broadens the range of its social and cultural functions and turns it into active participant of cultural, political and socioeconomic life of the society. It is found out that consolidation of documentary and informational resources of Shevchenko National Preserve in general informational space is need of time and is dictated by growing request. If modern visitor who needs fast and qualitative access to informational resources at a convenient time and in a convenient way for him. Despite high demand of the users, implementation of consolidated documentary and informational resources is a long - term business and requires immediate step-by-step solution.
A Novel SPA Countermeasure for SM2 Hardware Implementation with FPGA

SM2 algorithm has been widely used in the field of financial IC cards. However, it is easy to be attacked by the side channel, and Simple Power Analysis (SPA) is the most common attack method. An atomic point addition and point doubling algorithms is proposed to be used in SM2 algorithm against SPA. Based on the software and hardware co-design with SAKURA-G FPGA board, the correctness of the algorithm is verified in the 256-bit SM2 digital signature algorithm, and the power consumption curves are collected. Experiments show that the atomic algorithm improves the ability to resist SPA in SM2.
Radar-Thermal Sensor Fusion Methods for Deep Learning Hand Gesture Recognition

Miniature radar sensors typically excel in capturing radial movements, while the thermal sensor can easily capture lateral movements; this complementary nature motivates the fusion of measurements from both sensors for enhancing the accuracy of hand-gesture recognition. This paper presents fusion techniques for combining the signal from commercially available miniature radar with infrared thermal sensor to improve the performance of recognizing hand-gestures. To achieve this, two parallel deep-learning networks are used to establish the detection probabilities of each gesture class separately using the radar and thermal sensors. The detection probabilities are then fused using several methods, including (i) weighted average score with optimized weights, (ii) logistic regression, (iii) multilayer perceptron, and (iv) random forest algorithm. The performance of these different methods is analyzed and compared where the best fusion technique shows very high classification accuracy above 99 % in successfully recognizing 14 different hand-gesture types. This result is significantly higher compared to the performance of individual sensors.
Efficient Batch Scheduling of Large Numbers of Cloud Benchmarks

This paper proposes a framework for efficiently running benchmarks on one or multiple cloud environments. It is essential that users and businesses utilize benchmarks to understand the performance of their network or the cloud environment where they host their virtual network and machines. When performing a large number of benchmarks, we have found it necessary to construct a tool that allows us to automatically and efficiently schedule the benchmarks we need to run without exceeding our resource limits or interfering with other benchmarks. We have developed a benchmarking tool called PKB_scheduler that accepts benchmark configuration files, and will construct an optimal graph of virtual machine usage. PKB_scheduler will use this graph to dynamically schedule batches of benchmarks to run, seeking to reduce the total number of batches and thus the overall time of benchmark execution.
Exploratory data analysis on Reddit data: An efficient pipeline for classification of flairs

Internet has now become a platform for people to learn new things, share their opinions, and communicate with each other from anywhere. As technology is growing, the number of internet users are growing as well. With the increase in number of users, the amount of data is also enormously increasing. Social networking sites like Reddit, Facebook, Twitter have gained global popularity as a platform through which people can create individual public profiles, interact with real friends, share their interests and opinions, and post messages on any topics. Each post is tagged for filtering purposes. These tags are called flairs in the Reddit world. In this paper, a comparative data analysis using existing Machine Learning and Natural language processing techniques is provided to detect the flair of each Reddit post. Proper data analysis was done on the data using different features and a pipeline of various natural language processing techniques like Count Vectorization and Tfldf Transformation, and various machine learning techniques like K-Nearest Neighbor (KNN), Decision Tree, Support Vector Machines (SVM), and Logistic Regression was used to research on the data, and classify the flairs
Modern Hash Collision CyberAttacks and Methods of Their Detection and Neutralization

Abstract This article discusses the issues related to the possibility of realization of collision cyberattacks (based on hash collisions). Since post–quantum cryptography has become relevant, classical cryptosystems do not provide the sufficient resistance to the modern quantum cyberattacks. Systems based on outdated hashing algorithms become vulnerable to cyberattacks with hash collision. As replacement for unreliable algorithms, such as various modifications of MD5 and SHA–1, new algorithms have been created, for example, SHA–3 standard based on the Keccak function and AES–based hashing. This article discusses modern collision cyberattacks and possible methods of their detection. Because of this study, theoretical description of cyberattacks with hash collision was considered; modern cyberattacks on hash collisions and possible ways of detecting and countering them (weak hash detection) are described; software tool that detects vulnerable and unreliable hash is implemented; software testing is carried out. Based on the conducted research, it can be concluded that the main advantages of implementing software tool are effective detection of vulnerable hash, the ability to generate new hash protected from collisions, convenient and user– friendly interface, small memory requirements and small size of the program code.
Transformation of pedagogical consciousness in the context of digitalization of society

The transition from the informatization stage to the stage of digitalization in the world, including in our country, occurred very quickly. This gives society advantages, but also makes you think about the problems that have appeared in connection with this. Digitalization in education opens up wide opportunities for learning people of different ages throughout their lives, but at the same time requires cardinal changes in the entire education system. All this leads to the transformation of pedagogical consciousness. Since colossal changes in digital technologies, including in the education system, are proceeding rapidly, the consciousness of many teachers does not have time to rebuild. As a result of the study, it was found that this leads to stress, dissatisfaction, fears and irritation. Only a few are interested and excited. There is a process of awareness of one's Self in a changing pedagogical space, one's place in it. The results of the study confirm that the speed of introduction of new digital technologies in the education system depends on the pain of the transformation of pedagogical consciousness. The course Active Methods of Socio-Psychological Learning on the basis of the Master's program Psychological and Pedagogical Education of Samara University allows you to prepare teachers to solve problems related to the transformation of pedagogical consciousness in the context of digitalization of education.
Market-Based Robots Cooperative Exploration In Unknown Indoor Environment

Multi-robot coordination is a useful and widely studied issue, particularly in the CSLAM application. For the exploration task in an unknown environment, it can reduce exploration time and enhance system efficiency. This paper mainly discusses the allocation algorithm for multiple robots. A good allocation algorithm can optimize the behavior of the robot team. This paper improves a market-based cooperative method, a new logic is added to prevent any robot from falling into idleness, and the assignments of every robot will be sorted by their new auction price each iteration. Two typical indoor environments are discussed over the algorithm efficiency, simulation of five kinds of contrastive algorithms to exploring the environment. Results show that the method proposed in this paper does the best for multi-robot exploration in the structural unknown environment.
Parallel Quantum Computation Approach for Quantum Deep Learning and Classical-Quantum Models

Abstract The paradigm of Quantum computing and artificial intelligence has been growing steadily in recent years and given the potential of this technology by recognizing the computer as a physical system that can take advantage of quantum mechanics for solving problems faster, more efficiently, and accurately. We suggest experimentation of this potential through an architecture of different quantum models computed in parallel. In this work, we present encouraging results of how it is possible to use Quantum Processing Units analogically to Graphics Processing Units to accelerate algorithms and improve the performance of machine learning models through three experiments. The first experiment was a reproduction of a parity function, allowing us to see how the convergence of a given Quantum model is influenced significantly by computing it in parallel. For the second and third experiments, we implemented an image classification problem by training quantum neural networks and using pre-trained models to compare their performances with the same experiments carried out with parallel quantum computations. We obtained very similar results in the accuracies, which were close to 100% and significantly improved the execution time, approximately 15 times faster in the best-case scenario. We also propose an alternative as a proof of concept to address emotion recognition problems using optimization algorithms and how execution times can be positively affected by parallel quantum computation. To do this, we use tools such as the cross-platform software library PennyLane and Amazon Web Services to access high-end simulators with Amazon Braket and IBM quantum experience.
Learning spatial filters from EEG signals with Graph Signal Processing methods

In this paper, we propose to learn a spatial filter directly from Electroencephalography (EEG) signals using graph signal processing tools. We combine a graph learning algorithm with a high-pass graph filter to remove spatially large signals from the raw data. This approach increases topographical localization, and attenuates volume-conducted features. We empirically show that our method gives similar results that the surface Laplacian in the noiseless case while being more robust to noise or defective electrodes.Clinical relevance- The proposed method is an alternative to the surface Laplacian filter that is commonly used for processing EEG signals. It could be used in cases where this standard approach does not provide satisfying results (low signal-to-noise ratios due to a low number of epochs, defective electrodes). This could be particularly interesting in case of an electrode defect, as it can happen in clinical practice.
Weather Forecasting Uses Backpropagation Algorithm Artificial Neural Network Model for Agricultural Planning in Three Villages at Three Sub-Districts of Gowa Regency

Abstract Weather is a condition of air in a narrow place, which is a condition based on symptoms of temperature, air pressure, humidity, wind, rainfall, sun exposure, cloud conditions, wind speed and others. This study aims to develop weather prediction models that are used for planning agricultural cultivation activities. The method used in predicting climate is Backpropagation Artificial Neural Network technique based on rainfall data in 1996-2019 in Samata Village, 1975-2019 in Julu Bori Village and 2007-2019 in Tete Batu Village. The results showed that the climate classification according to Oldeman in Samata Village dan Julu Bori Village were in the C1 climate type suitable for planting one-time rice crops and crops twice in one year while Tete Batu Village was in the B3 climate type suitable for planting rice crops twice and crops once a year.
Research on Geological Disaster Forecast Method Based on PCA Structure BP Model

Abstract The BP neural network prediction method constructed by PCA and the geological hazard prediction method based on the MM5 numerical model were used to establish geological hazard classification short-term objective forecast models. The calculation results show that these two objective forecast methods have a good fitting effect on historical samples. The independent sample’s trial report effect is also good; based on the above two objective forecasting methods, through correction, the comprehensive forecast product is finally obtained.
Clustering as feature selection method in spam classification: uncovering sick-leave sellers

Purpose This paper aims to propose a novel way of using textual clustering as a feature selection method. It is applied to identify the most important keywords in the profile classification. The method is demonstrated through the problem of sick-leave promoters on Twitter. Design/methodology/approach Four machine learning classifiers were used on a total of 35,578 tweets posted on Twitter. The data were manually labeled into two categories: promoter and nonpromoter. Classification performance was compared when the proposed clustering feature selection approach and the standard feature selection were applied. Findings Radom forest achieved the highest accuracy of 95.91% higher than similar work compared. Furthermore, using clustering as a feature selection method improved the Sensitivity of the model from 73.83% to 98.79%. Sensitivity (recall) is the most important measure of classifier performance when detecting promoters’ accounts that have spam-like behavior. Research limitations/implications The method applied is novel, more testing is needed in other datasets before generalizing its results. Practical implications The model applied can be used by Saudi authorities to report on the accounts that sell sick-leaves online. Originality/value The research is proposing a new way textual clustering can be used in feature selection.
Formal Verification of a Genetic Fuzzy System for Unmanned Aerial Vehicle Navigation and Target Capture in a Safety Corridor

As complex control for unmanned aerial vehicles (UAV) is implemented for more safety-critical applications, there is a need to be able to verify that their behavior is correct. Most current methods rely on numerical techniques that can miss critical evaluation points. In this work a Genetic Fuzzy System is developed for navigation of a UAV to waypoints while inside a safety corridor. The developed Fuzzy System is then evaluated using Formal Methods in order to show adherence to behavioral safety specifications. The results confirm that the specifications are either valid, or return counterexamples showing where they do not hold.
Improved Spatio-temporal Action Localization for Surveillance Videos

We present an improved spatiotemporal action localization framework that operates in an online manner. Current state of the art approaches have achieved remarkable results mainly due to the advancements in action recognition models. These approaches have commonly followed a two-stage pipeline consisting of a region proposal stage and an action classification stage. Recently, the improvement in spatiotemporal action localization models have focused on improving the action classification stage. As a result, the outputs generated in the region proposal stage are suboptimal. We believe that the proposal stage remains a crucial component in determining the overall model performance. As a result, we adopt a tracking model in place of the existing proposal models to generate more accurate and robust regions of interest (RoI). We evaluate our approach on a private CCTV surveillance dataset and on the challenging JHMDB-21 benchmark. We are able to achieve promising results on our private dataset and achieve good results for the JHMDB-21 benchmark.
Improving thai-lao neural machine translation with similarity lexicon

It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions. Thai-Lao is a typical low-resource language pair of tiny parallel corpus, leading to suboptimal NMT performance on it. However, Thai and Lao have considerable similarities in linguistic morphology and have bilingual lexicon which is relatively easy to obtain. To use this feature, we first build a bilingual similarity lexicon composed of pairs of similar words. Then we propose a novel NMT architecture to leverage the similarity between Thai and Lao. Specifically, besides the prevailing sentence encoder, we introduce an extra similarity lexicon encoder into the conventional encoder-decoder architecture, by which the semantic information carried by the similarity lexicon can be represented. We further provide a simple mechanism in the decoder to balance the information representations delivered from the input sentence and the similarity lexicon. Our approach can fully exploit linguistic similarity carried by the similarity lexicon to improve translation quality. Experimental results demonstrate that our approach achieves significant improvements over the state-of-the-art Transformer baseline system and previous similar works.
Introducing Subjective Knowledge Graphs

Knowledge-based applications that deal with uncertainty usually represent it by means of a confidence score that expresses the probability that a given fact is true. However, different users may have distinct opinions about the same fact, something that is not considered in existing proposals. This is critical in a number of areas where individual opinions need to be taken into account when making informed decisions, particularly when these are to be made by consensus. This paper introduces Subjective Knowledge Graphs (SKG), an extension to Probabilistic Knowledge Graphs that considers the individual opinions of separate users about the same facts, and allows reasoning about them. We show how SKGs can be implemented using standard graph databases and how the results of the queries can be enriched with the associated degrees of uncertainty.