Reviewerly/exHarmony huggingface dataset:
Sentiment/Opinion Review Analysis: Detecting Spams from the good ones!

In this era of Whatsapp, Facebook, Twitter, Instagram, and various other social media platforms, we all are connected to each other's thought in one way or another. The Internet has brought us closer to everybody's work, place, plans, ethics, feelings, and emotions. We are much more interested in showing off our day, commenting and reviewing each and everything we came across throughout the day, knowing others opinion on the same and identifying how and why are they different from ours. Reviews also help in identifying the market conditions and strategies, and it could be done via Sentimental analysis as it helps us in identifying the things that are in trend and helps the organizations, businesses to utilize and expand accordingly. It can also be used in general by people themselves to look for which movie to watch to which laptop to buy, but when we encounter spam reviews we sometimes do not know whether they are fake or not in reality, but they do change our point of view. In this article, we go through this in a step by step format of different papers and summarize for other readers how we can identify the correct emotions and differentiate between the real and fake reviews. Using some researches, we get to know in-depth about how to choose the correct dataset, and the challenges faced.
A Novel Approach of Text Summarization using Assamese WordNet

Automatic Text Summarization techniques in Assamese language is still in an immature stage compared to other Indian languages. In this study, we have reviewed some of the studies in other Indian languages for having a better understanding of the problems. Furthermore, we have also put in our efforts to understand keeping in view the opinion of Assamese linguists as how we could overcome the shortcomings of Assamese Text Summarization and provide an effective solution that could serve as fundamental concepts in this particular field. An extractive method for text summarization is proposed in this paper to evaluate the sentences of the input document based on the merging of semantic and statistical characteristics. It is applied considering the importance of sentence and diversity. Moreover, we have applied Synsets from Assamese WordNet to calculate the word frequency. Besides identifying and weighing the salience sentences, the concept of vector centrality and cosine similarity is used and the result is represented in graph representation. A threshold value to indicate the important sentences is applied. The effectiveness of our proposed method is demonstrated through a set of experiments conducted using ROUGE measure and the evaluation is showed in terms of Recall.
Mobile Robot Obstacle Avoidance in labyrinth Environment Using Fuzzy Logic Approach

The navigation of autonomous mobile robots has in recent times gained interest from many researchers in different areas such as in the industrial, agricultural, and military sectors. This paper presents the development of fuzzy logic controller (FLC) motion for guiding a nonholonomic mobile robot to navigate in an environment area cluttered with static obstacles. The controller was programmed with 3 inputs distance from a pioneer p3dx robot that is equipped with 16 ultrasonic sensors, 2 outputs and 27 fuzzy rules that allow robot to move and avoid static obstacles. To validate the feasibility and effectiveness of the proposed model we used V-REP and MATLAB softwares. The simulations results showed that the mobile robot could navigate successfully into labyrinth environment thanks to the performance of our intelligent controller.
Sequential Monte Carlo Filtering with Long Short-Term Memory Prediction

Recursive Bayesian estimation builds on the use of suitable mathematical models in order to accurately describe the evolution of an object based on imperfect sensor data. Sequential Monte Carlo (SMC) techniques have been developed to overcome the need of restrictive models, however more sophisticated motion models can require high-dimensional particle states, resulting in a need for many particles and hence greater computation. Additionally, most conventional prediction methods assume low-order Markovian transitions and therefore tend to ignore the majority of the target history that may be relevant to the prediction. In this article, a variation of the Particle Filter (PF) is introduced which uses a Long Short-Term Memory (LSTM) neural network to perform the prediction step. In contrast to existing techniques, the network is trained to predict the relative displacement between object states rather than absolute positions to gain rotation and translation invariance. Experiments on simulated trajectories show that the approach is more robust against low probability of detection and occlusion than the conventional PF.
Extraction of Noise-Robust Speaker Embedding Based on Generative Adversarial Networks

In the field of speaker verification, the speaker systems based on x-vector framework are widely used in many scenarios. However, it suffers from the performance degradation caused by noise disturbance. In this paper, we firstly analyzed the noisy robustness of x-vector by training the networks using a mixture dataset which includes clean data and corrupted data. Then, we proposed a novel adversarial strategy against noise interference and extracted the noise-robust speaker embedding with x-vector. The proposed adversarial method named as triple-net GAN employs three connected networks: a generator network (G), a discriminator network (D) and a classifier network (C). The spectral coefficients of clean and noisy speech utterances are fed to the G, of which the structure is nearly the same as x-vector. The outputs of G are transferred in a parallel way to the D and C. And the labels of D are set binary for clean data and corrupted data, while the labels of C are set corresponding to speaker identities, which aims to learn the speaker embedding features invariant to the noise. Finally, we executed the experiments with different variants of triple-net GAN to verify the denoising capability of the proposed adversarial method. Experimental results on Librispeech corpus demonstrate that our proposed method could achieve a better performance under the noisy environments.
Visual Cryptography for colour images using multilevel thresholding

Visual Cryptography Scheme, is even known as VCS. It is actually a cryptography method to encrypt an image by dividing it in parts called shares, where decryption is performed using Human Visual System (HVS), only by overlapping all these shares. The image to be encrypted is called a secret image. Visual Cryptography can be used for binary image, grayscale image as well as colour image. This paper implements visual cryptography technique for colour images, by using multilevel thresholding so as to enhance the obtained quality contrast of reconstructed image.
Recent Advances of Nature-Inspired Metaheuristic Optimization

Metaheuristic approaches receive a great interest in the area of optimization, especially when exact methods are missing, or the cost is extremely high. Besides the possibility to report good solutions in reasonable time, metaheuristic techniques are widely applicable. There are diverse categories of techniques that differ in number of search agents (or solutions), solution representation, and movement mechanism in search space. Just mentioned ingredients are determined according to the motivation or inspiration philosophy behind the technique. Nature-inspired optimization category is very popular and has proven high efficiency in many problems. It contains famous subclasses like evolutionary algorithms, swarm intelligence, and single-based techniques. Famous and classical examples of each subclass are genetic algorithm, particle swarm, and ant colony optimization, and simulated annealing, respectively. Nature-inspired optimization family grows so fast, and many members have joined it recently, for example, emperor penguin colony (2019), seagull optimization algorithm (2019), sailfish optimizer (2019), pity beetle algorithm (2018), emperor penguin optimizer (2018), multi-objective artificial sheep algorithm (2018), salp swarm algorithm (2017), electromagnetic field optimization (2016), sine cosine algorithm (2016), moth-flame optimization (2015), grey wolf optimizer (2014), flower pollination algorithm (2012), bat algorithm (2010), cuckoo search algorithm (2009), firefly algorithm (2008), and many others. There are many proposed hybridization and cooperation methods between techniques to produce improved versions of original ones. Nature-inspired techniques have been used in many application areas like theoretical computer science, engineering and control, forecasting, medical field, finance, management, operation research, and others. Also, new scientific disciplines like renewable energy, robotics, and navigation are feasible areas to make use of nature-inspired techniques. This chapter sheds light on six so recently new techniques that belong to nature-inspired optimization class.
Design a Prototype of The Application System of Classification and Calculating Motor Vehicles on Highway

The growth rate of the number of motor vehicles has a tendency that is always increasing. The increase in the number of motorised vehicles which is not followed by the addition of roads is a cause of traffic jams on the highway. This study aims to design an application system that is used to determine the level of traffic density on certain road sections by using the background subtraction method to classify and count the number of vehicles passing on that road section. From the results of software testing, three types of vehicles can be classified, namely motorcycles, cars, and trucks/buses with an accuracy of 96%, 83%, and 87.15%, respectively.
Personalized Learning Resource Recommendation Algorithm of Mobile Learning Terminal

With the popularization of mobile devices such as the Internet and smart phones, people's learning styles are undergoing profound changes and mobile learning terminals are becoming more and more popular. In this paper, the model of personalized learning resource recommendation algorithm based on mobile learning terminal was studied, and a personalized learning model based on recommendation algorithm was designed. The algorithm can analyze the user's learning history data in the mobile learning terminal and make accurate learning resource recommendation according to the user's learning level and personal preference. Finally, the experimental results show that personalized learning resource recommendation model based on recommendation algorithm can effectively predict the direction of users' learning needs, and expand the new research directions in personalized learning resources recommendation.
An Appearance-based Approach to Detect the Wrong-way Movement of Vehicles Using Deep Convolutional Neural Network

To guarantee the enforcement of traffic rules, the identification of traffic rule violators is an exceptionally alluring yet difficult assignment to implement and the detection of the wrong-way movement of vehicles is one of them. In this paper, an appearance-based approach is proposed which detects the front and back side of the vehicles on a highway with the help of a deep convolutional neural network and decides whether a vehicle is moving along the wrong-way or not based on the user expectation to see the side of a vehicle on each side of the highway using a handcrafted region divider algorithm. The effectiveness of this strategy has been assessed on a primary data-set built on real-time traffic videos captured from several significantly busy highways of Dhaka Metropolitan City and proven quite productive with an accuracy of 96% on successful detection of wrong-way movement of vehicles.
Multi-lingual Transformer Training for Khmer Automatic Speech Recognition

Currently, there are three challenges for constructing reliable ASR systems for the Khmer language: (1) the lack of language resources (text and speech corpora) in digital form, (2) the writing system without explicit word boundary, and (3) the pronunciation model is not well studied. In this paper, to avoid the extensive work on selecting proper acoustic units (e.g., phones, syllables) and preparing the frame-level labels on the traditional DNN-HMM framework, we directly use words or characters as the label using state-of-the-art transformer-based end-to-end model. Moreover, we use the multi-lingual training framework to tackle the low-resource data problem. All experiments are performed on the Basic Expressions Travel Corpus (BTEC) datasets. The experiments show that the proposed multi-lingual transformer-based end-to-end model can achieve significant improvement compared to the DNN-HMM baseline model <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> The work was performed during Mr. Kak Soky was in NIPTICT. He is currently with Ministry of Education, Youth, and Sports (MoEYS), Cambodia.
Multiway k-Cut in Static and Dynamic Graphs: A Maximum Entropy Principle Approach

This work presents a maximum entropy principle based algorithm for solving minimum multiway k-cut problem defined over static and dynamic digraphs. A multiway k-cut problem requires partitioning the set of nodes in a graph into k subsets, such that each subset contains one prespecified node, and the corresponding total cut weight is minimized. These problems arise in many applications and are computationally complex (NP-hard). In the static setting this article presents an approach that uses a relaxed multiway k-cut cost function; we show that the resulting algorithm converges to a local minimum. This iterative algorithm is designed to avoid poor local minima with its run-time complexity as ~ O(kIN <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ), where N is the number of vertices and I is the number of iterations. In the dynamic setting, the edge-weight matrix has an associated dynamics with some of the edges in the graph capable of being influenced by an external input. The objective is to design the dynamics of the controllable edges so that multiway k-cut value remains small (or decreases) as the graph evolves under the dynamics. Also it is required to determine the time-varying partition that defines the minimum multiway k-cut value. Our approach is to choose a relaxation of multiway k-cut value, derived using maximum entropy principle, and treat it as a control Lyapunov function to design control laws that affect the weight dynamics. Simulations on practical examples of interactive foreground-background segmentation, minimum multiway k-cut optimization for non-planar graphs and dynamically evolving graphs that demonstrate the efficacy of the algorithm, are presented.
Aggregating Filter Feature Selection Methods to Enhance Multiclass Text Classification

Text data usage has increased rapidly and simultaneously resulted in setbacks, such as high dimensionality of text data becoming a prominent problem. Hence, this study aimed to assess the application of filter feature selection techniques on text data. In this study, features were ranked from highest to lowest by the selected filter feature selection methods in each generated feature subset. Thereafter, a new feature subset was obtained using the proposed method. This study yielded that the accuracy of Information Gain is 1.12 percentage points higher in comparison to the accuracy of Chi-square. Moreover, classification accuracy obtained from aggregation exhibits a rise of 0.93 percentage points compared to the accuracy of Information Gain and 2.05 percentage points against Chi-square. Classification accuracy improved when the features are aggregated. On Precision, in comparison to that of the aggregation, results show the differences in percentage points of 1.41 and significant 11.64 for Information Gain and Chi-square respectively. About Recall, there is a 5.54 percentage points improvement on Information Gain and 3.03 percentage points improvement on Chi-square. Then, in F1, the score for aggregation is quite low. It may mean that the classifier has problems with false positives or false negatives. Thus, the classifier needs to be checked using a confusion matrix or check on the dataset, which was not done in the experiment. Dataset imbalance was also not addressed in this study. For future work, the imbalanced class-dataset issue should be addressed. Also, the performance of other filter methods could be compared as well as utilize other classifiers that support multiclass tasks to determine which is suitable for multiclass text classification.
High Impulse Noise Intensity Removal in Natural Images Using Convolutional Neural Network

This paper introduces a new image smoothing filter based on a feed-forward convolutional neural network (CNN) in presence of impulse noise. This smoothing filter integrates a very deep architecture, a regularization method, and a batch normalization process. This fully integrated approach yields an effectively denoised and smoothed image yielding a high similarity measure with the original noise free image. Specific structural metrics are used to assess the denoising process and how effective was the removal of the impulse noise. This CNN model can also deal with other noise levels not seen during the training phase. The proposed CNN model is constructed through a 20-layer network using 400 images from the Berkeley Segmentation Dataset (BSD) in the training phase. Results are obtained using the standard testing set of 8 natural images not seen in the training phase. The merits of this proposed method are weighed in terms of high similarity measure and structural metrics that conform to the original image and compare favorably to the different results obtained using state-of-art denoising filters.
Dual process theory 2.0

Dual process theory (DPT) states that cognitive performance is the result of two types of processing, usually termed Type 1 and Type 2. Thompson and Newman (this volume, p. 121) clearly and succinc...
Random Forest Based Searching Approach for RDF

The blend of digital and physical worlds changed the Internet significantly. Accordingly, trends to collect, access, and deliver information have changed over the Web. Such changes raised the problems of information retrieval. Search engines retrieve requested information based on the provided keywords which is not an efficient way for rich information retrieval. Consequently, the fetching of the required information is difficult without understanding the syntax and semantics of the content. The multiple existing approaches to resolve this problem by exploiting linked data and semantic Web techniques. Such approaches serialize the content leveraging the Resource Description Framework (RDF) and process the queries using SPARQL to resolve the problem. However, an exact match between RDF content and query structure is required. Although it improves the keyword-based search, it does not provide probabilistic reasoning to find the relationship accuracy between the query and results. In this perspective, this paper proposes a machine learning (random forest) based approach to predict the fetching status of RDF by treating RDFs’ requests as a classification problem. First, we preprocess the RDF to convert them into N-Triples format. Then, a feature vector is constructed for each RDF using the preprocessed RDF. After that, a random forest classifier is trained for the prediction of the fetching status of RDFs. The proposed approach is evaluated on an open-source <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DBpedia</i> dataset. The 10-fold cross-validation results indicate that the performance of the proposed approach is accurate and surpasses the state-of-the-art.
SoCo-ITS

Intelligent Transport System (ITS) is a culmination of technological and application systems that are contrived to improve the performance of road transportation and upgrade the commuter's experience. The integration of Internet of Things (IoT) with the transport system has contributed to the development of ITS. In this paper, we concentrate on the commercial servitization standpoint of the application. We structure and formulate an ontology called Service-Oriented Context Ontology for Intelligent Transport System: SoCo-ITS. This ontological framework abets in identifying appropriate services required by the commuters in transit based on their situation, predilection and ITS environmental information. We discuss the detailed implementation description and also accentuate its role in ITS through a use case scenario and an exemplar application portraying the importance of the proposed ontological model.
Text Classification Based on Topic Modeling and Chi-square

This paper compares two topic modeling algorithms - Latent Dirichlet Allocation (LDA), Latent Semantic Index (LSI), and a feature selection algorithm chi-square to extract news feature words. After feature extraction, the three classifiers (Logistics Regression, Naive Bayes and SVM) are compared in news classification. Based on the test results, combined LSI and Logistics Regression gives the highest result compared to the other algorithms, with precision of 96% and recall of 95%.
Research study on applying SLAM-Based Augmented Reality technology for gamification history guided tour

This study employed simultaneous-localization-and-mapping-based augmented reality technology in a guided tour for a historic site. Virtual objects integrated real environment to promote users' senses of immersion, and a gamification mechanism was applied to enhance their experience and learning outcomes. The historic site of this study was Nishi Honganji Temple in Taiwan. The literature and historical materials related to the temple were analyzed, and its historical appearance was reconstructed using a novel technology. The effectiveness of simultaneous-localization-and-mapping-based augmented reality in historical and cultural representation and learning enhancement was explored. The results indicated that use of the technology was effective for enhancing users' learning, and most of the participants reported having a positive experience.
An Analysis of the Method for Determining the Lowest Sensitivity of Solarblind Ultraviolet Corona Cameras

The current techniques to determine the lowest detectable energy associated with solar-blind ultraviolet corona inspection cameras are scrutinized. The current methodology is analysed, using statistical techniques on a number of different types of camera brands. Several recommendations and adaptions based on various investigations are presented.
Integrated Collision Avoidance for Unmanned Aircraft Systems with Harmonic Potential Field and Haptic Input

The growing interest and trend for deploying unmanned aircraft systems (UAS) in civil applications requires robust traffic management approaches that can safely integrate the unmanned platforms into the airspace. Although there have been significant advances in autonomous navigation, especially in the ground vehicles domain, there are still challenges to address for navigation in the dynamic 3D environment that airspace presents. An integrated approach that facilitates semi-autonomous operations in dynamic environments and also allows for operators to stay in the loop for intervention may provide a workable and practical solution for UAS integration in the airspace. In this paper, a new integrated approach is presented for the problem of collision avoidance that applies a variant of potential field method along with haptic-based tele-operation for added safety in high-risk situations.