Reviewerly/exHarmony huggingface dataset:
Research on robot path planning based on A*-weighted JPS Algorithm

Mobile robots are entering daily life quietly. How mobile robots can walk more efficiently in environments with complex obstacles has become a hot topic. For robot path planning, there are currently a variety of research algorithms, which can be divided into global path planning based on prior information and local path planning based on multi-sensor information. Commonly used robot path planning algorithms include artificial potential field method, ant colony algorithm, Genetic algorithm, annealing algorithm, etc., because these algorithms have too much calculation and are difficult to apply to environments with complex obstacles, this paper proposes an improved algorithm based on A* algorithm—weighted JPS algorithm for the above problems. This algorithm takes the expansion of sub-nodes as the optimization focus, reduces the number of random expansions of the current node, reduces the time for the robot to reach the target point, and uses the five-term interpolation method to smooth the planned path to reduce the robot’s travel process the probability of collision due to bypassing obstacles and cornering corners. Experimental results show that compared with the A* algorithm, the weighted JPS algorithm reduces the search time by 20% to 27%, and the length of the path to be explored is also reduced by 2% to 4%.
One-Way Cryptographic Hash Function Securing Networks

Due to the rapid growth of Internet technologies, voluminous data is generated every minute from multifarious sources. It is impossible for conventional tools to analyse and process such an immense volume of data. Cryptographic hashing is one of the best approaches to efficiently deal with accuracy and consistency of the data on the cloud. It also plays a crucial role in protecting message creditability during transmission. A new one-way cryptographic hashing algorithm has been suggested in this paper, which produces a digest of 128 bits for any message length (short, medium and long). With the basic properties of the hashing algorithm, the suggested algorithm is implemented and compared with existing ones. Outcomes shows that proposed algorithm is stronger than the current ones in terms of execution and protection.
An Efficient BoF Representation for Object Classification

The Bag-of-features (BoF) approach has proved to yield better performance in a patch-based object classification system owing to its simplicity. However, often the very large number of patch-based descriptors (such as scale-invariant feature transform and speeded up robust features, extracted from images to create a BoF vector) leads to huge computational cost and an increased storage requirement. This paper demonstrates a two-staged approach to creating a discriminative and compact BoF representation for object classification. As a preprocessing stage to the codebook construction, ambiguous patch-based descriptors are eliminated using an entropy-based and one-pass feature selection approach, to retain high-quality descriptors. As a post-processing stage to the codebook construction, a subset of codewords which is not activated enough in images are eliminated from the initially constructed codebook based on statistical measures. Finally, each patch-based descriptor of an image is assigned to the closest codeword to create a histogram representation. One-versus-all support vector machine is applied to classify the histogram representation. The proposed methods are evaluated on benchmark image datasets. Testing results show that the proposed methods enables the codebook to be more discriminative and compact in moderate sized visual object classification tasks.
A Green Pipeline for Out-of-Domain Public Sentiment Analysis

In the changing social and economic environment, organisations are keen to act promptly and appropriately to changes. Sentiment analysis can be applied to social media data to capture timely information of new events and the corresponding public opinions. However, currently both the social topics and trending words are changing just as rapidly as the target topics and domains that organisations are interested in investigating. Therefore, there is a need for a well-trained sentiment analysis model able to handle out-of-domain input. Current solutions mainly focus on using domain adaptation techniques, but these solutions require domain-specific data and inevitably introduce extra overheads. To tackle this challenge, we propose a green Artificial Intelligence (AI) solution for a sentiment analysis pipeline (GreenSAP) to gain a better understanding of the changing public opinions on social media. Specifically, we propose to leverage the expressively powerful capability of the pre-trained Transformer encoder, and make use of several publicly-available sentiment analysis datasets from various domains and scenarios to develop a pipeline model. A sarcasm detection model is also included to eliminate false positive predictions. In experiments, this model significantly outperforms its competitors on three public benchmark datasets and on two of our labelled out-of-domain datasets for real-world applications.
Averaging Dimensionality Reduction and Feature Level Fusion for Post-Processed Morphed Face Image Attack Detection

Facial morphing detection is critical when applying for a new passport and using the passport for identity verification due to the limited ability of face recognition algorithms and people to detect morphed photographs. As a result of face recognition systems' vulnerability to morphing attacks, the value of detecting fake passports at the ABC gate is undeniable. Nonetheless, identifying morphed images after they have been altered using image operations like sharpening, compression, blurring, print-scan and resizing is a significant concern in Morphing Attack Detection (MAD). These image operations can be used to conceal the morphing artefacts, which makes MAD difficult. Several researchers have carried out MAD for print-scan images; few researchers have done MAD for compressed images; however, just one paper has considered image sharpening operation. Hence, this paper proposes a MAD technique to perform MAD even after image sharpening operation using averaging dimensionality reduction and feature level fusion of Histogram of Oriented Gradient (HOG) 8 × 8 and 16 × 16 cell size. The 8 × 8 pixels cell size was used to capture small-scale spatial information from the images, while 16 × 16 pixels cell size was used to capture large-scale spatial details from the pictures. The proposed technique achieved a better accuracy of 95.71% compared with the previous work, which reached an accuracy of 85% when used for MAD on sharpened image sources. This result showed that the proposed technique is effective for MAD on sharpened post-processed images.
Quantum Computing and Machine Learning: In Future to Dominate Classical Machine Learning Methods with Enhanced Feature Space for Better Accuracy on Results

Quantum Computing is new standard which will contribute computational efficiency on to the many operational methods of classical computing. Quantum computing motivates to use of quantum mechanics such as superposition and entanglement for making new standard of computation which will be far different than classical computer. The quantum computing concept need to understand Qubit which is nothing but Quantum Bit that differs quantum computing from classical computing. Classical bit, which can be either Zero 0 or One 1 in single state at a time moment, a Qubit or Quantum Bit can be Zero 0 and One 1 at same time called as in superposition state. Quantum Computers will use quantum superposition and quantum entanglement are the two basic laws of quantum physics principles. Computational tasks which are non-computable by classical machine can be solved by quantum computer and these computational tasks defines heavy computations those expects large size data processing. Machine learning on classical space is very well set but it has more computational requirements based on complex and high-volume data processing. This paper surveys and propose model with integration of quantum computation and machine learning which will make sense on quantum machine learning concept. Quantum machine learning helps to enhance the various classical binary machine learning methods for better analysis and prediction of big data and information processing.
Collision Avoidance of Unmanned Aerial Vehicles In an Urban Environment

This research proposes a collision-avoidance approach for unmanned aerial vehicles (UAVs) without local minima and inaccessible goal issues in an urban environment considering the enhanced potential field (EPF). The EPF formulated in a two-dimensional environment is extended into a three-dimensional environment by combining the vertical and horizontal maneuvers. The proposed approach is validated by simulation studies with various scenarios in an artificially generated urban environment considering UAV dynamics and sensor limitations.
Proposed Hybrid model for Sentiment Classification using CovNet-DualLSTM Techniques

The fast growth of Internet and social media has resulted in a significant quantity of texts based review that is posted on the platforms like social media. In the age of social media, analyzing the emotional context of comments using machine learning technology helps in understanding of QoS for any product or service. Analysis and classification of user’s review helps in improving the QoS (Quality of Services). Machine Learning techniques have evolved as a great tool for performing sentiment analysis of user’s. In contrast to traditional classification models. Bidirectional Long Short-Term Memory (BiLSTM) has obtained substantial outcomes and Convolution Neural Network (CNN) has shown promising outcomes in sentiment classification. CNN can successfully retrieve local information by utilizing convolutions and pooling layers. BiLSTM employs dual LSTM orientations for increasing the background knowledge accessible to deep learning based models. The hybrid model proposed here is to utilize the advantages of these two deep learning based models. Tweets of users for reviews of Indian Railway Services have been used as data source for analysis and classification. Keras Embedding technique is used as input source to the proposed hybrid model. The proposed model receives inputs and generates features with lower dimensions which generate a classification result. The performance of proposed hybrid model was compared using Keras and Word2Vec and observed effective improvement in the response of the proposed model with an accuracy of 95.19%.
Contourlet and Gould Transforms for Hybrid Image Watermarking in RGB Color Images

The major intention of this work is to introduce a novel hybrid image watermarking technique for RGB color images. This hybrid watermarking algorithm uses two transforms such as Contourlet and Gould transform. The Contourlet transform is used as first stage while the Gould transform is used as second stage. In the watermark embedding phase, the R, G and B channels are transformed using Contourlet transform. The bandpass directional sub band coefficients of Contourlet transformed image are then divided into sub-blocks. The sub-blocks are then transformed using Gould transform and the watermark information is embedded on the initial coefficients of each sub-blocks. After the embedding of watermark, inverse of Gould and Contourlet are taken to obtain the watermarked image. The reverse operation is performed to extract the watermark. Results obtained from attack-based experimentation for checking robustness implicate the superiority of this method in comparison with conventional methods. Better values are obtained for Embedding Rate, Structural Similarity Index Metric (SSIM) and Peak Signal to Noise Ratio (PSNR).
Distributional Measures of Semantic Abstraction

This article provides an in-depth study of distributional measures for distinguishing between degrees of semantic abstraction . Abstraction is considered a “central construct in cognitive science” (Barsalou, 2003) and a “process of information reduction that allows for efficient storage and retrieval of central knowledge” (Burgoon et al., 2013). Relying on the distributional hypothesis, computational studies have successfully exploited measures of contextual co-occurrence and neighbourhood density to distinguish between conceptual semantic categorisations. So far, these studies have modeled semantic abstraction across lexical-semantic tasks such as ambiguity; diachronic meaning changes; abstractness vs. concreteness; and hypernymy. Yet, the distributional approaches target different conceptual types of semantic relatedness, and as to our knowledge not much attention has been paid to apply, compare or analyse the computational abstraction measures across conceptual tasks. The current article suggests a novel perspective that exploits variants of distributional measures to investigate semantic abstraction in English in terms of the abstract–concrete dichotomy (e.g., glory–banana ) and in terms of the generality–specificity distinction (e.g., animal–fish ), in order to compare the strengths and weaknesses of the measures regarding categorisations of abstraction, and to determine and investigate conceptual differences. In a series of experiments we identify reliable distributional measures for both instantiations of lexical-semantic abstraction and reach a precision higher than 0.7, but the measures clearly differ for the abstract–concrete vs. abstract–specific distinctions and for nouns vs. verbs. Overall, we identify two groups of measures, (i) frequency and word entropy when distinguishing between more and less abstract words in terms of the generality–specificity distinction, and (ii) neighbourhood density variants (especially target–context diversity) when distinguishing between more and less abstract words in terms of the abstract–concrete dichotomy. We conclude that more general words are used more often and are less surprising than more specific words, and that abstract words establish themselves empirically in semantically more diverse contexts than concrete words. Finally, our experiments once more point out that distributional models of conceptual categorisations need to take word classes and ambiguity into account: results for nouns vs. verbs differ in many respects, and ambiguity hinders fine-tuning empirical observations.
Machine Learning-Based Indoor Scenes Identification Using Thepade’s SBTC and Local Binary Patterns

Now day’s images are an essential thing in human life. The images are get captured everywhere in the world. Scene classification is one of the important problems. The images are taken now and then at many different places. These different places have many different scenes like airport, bakery, kitchen, mountain, etc. Scene classification is mostly in two forms, which are indoor scene classification and outdoor scene classification. A difficulty in scene classification causes problems in the research and image-processing field. The image classification gets difficult because of the many distinct objects where present in the different sceneries, this makes a machine get confused about the categories of the scene. The present problem for the scene classification gives poor accuracy for classification. The object varies in different shapes, sizes in numerous locations, which is also one of the reasons behind the poor accuracy of the indoor scene classification. Here, we are going to perform feature extraction for pattern recognition and different machine learning models for image classification using the MIT Indoor Scene dataset. Here, paper gives Thepade's SBTC n-ary feature extraction method on RGB and LUV color plane models alongside LBP. The comparison of the given three feature extraction methods Thepade's SBTC with RGB color plane has given better accuracy.
Support Technologies

AbstractAt the conclusion of this chapter, you will be better prepared to administer the following:–Select relevant technologies to design, organize, and deliver an engaging distance learning experience depending upon the affordances and constraints at your institution–Design and assess learning in a distance learning context using the appropriate traditional or authentic tools and strategies–Curate and produce media for digital engagement and content presentation following ethical and legal guidelines–Conceptualize ways to personalize learners’ distance learning experiences using established practices and emerging trends in technology.
Integrated Learning Factory Design on Future Learning

1st WoW-SAIPIS 2021 is an article writing workshop centered on social sciences and education, open and welcoming contributions from various disciplines and approaches that meet at intersections. We are interested in scholarship that crosses disciplinary lines and speaks to readers from a variety of theoretical and methodological perspectives. In other words, 1st WoW-SAIPIS 2021 will be a forum for scholars as they speak to a wider audience of our many sub-fields and specialties, rather than a location for narrower conversations that are more appropriate in more specialized conferences. We suggest the following broad areas of research: Social Sciences and Laws History and Cultural Studies Interdisciplinary Studies Moral and Humanities Policy, Politics, and Communication Education The proceedings represent the latest in research, development, deployment and projects on a range of topics presented at the International Conference of Social Science and Education. The papers published under each conference are sorted according to the themes and topics defined under each event. Papers are published according to the events at which they were presented by the authors. They can be searched by theme,
Campus Sentiment Analysis with GAN-based Data Augmentation

Recent advances have seen the rapid development of online media, which offers various communication channels for people to express opinions. The fast-growing social media platforms have generated tremendous data that can be transformed into business and social value through modern machine learning algorithms. One of the crucial learning tasks is sentiment analysis, which refers to identifying the tendency of subjective information in an expression. Prior efforts in sentiment classification have explored a broad spectrum of methods that have achieved impressive performance gains from the predictive modeling perspective. However, the potential of data augmentation has not been sufficiently explored in this task, and we aim to fill this gap. This study proposes a novel sentiment analysis framework powered by two performance boosters, including a data augmentation method based on a transformer-based generative adversarial network (GAN) and RoBERTa, a robustly optimized Bidirectional Encoder Representations from Transformers (BERT) pretraining model, to improve the prediction accuracy from both the data and the model side. We conduct extensive experiments on a campus sentiment classification dataset. We show that the GAN-based data augmentation method can generate high-quality synthetic samples to increase the size and diversity of the training set. Compared with other model options and augmentation methods, the RoBERTa model enhanced by transformer-based GAN (TransGAN) presents a superior performance in prediction accuracy, validating the efficacy of the proposed framework.
Research and Implementation of Choreographer Technique Based on Music Feature Extraction

With the deepening of China’s reform and opening up, people’s living standards have been continuously improved, which has promoted the rapid development of our country’s culture and arts, such as music. As an important part of music, dance plays an important role in the culture and art of our country. People can use dance gestures to express moods or inner feelings that cannot be expressed in words. In order for the dance to be received and favored by people, it is necessary to have better ideas to express emotions, which is related to the choreographer of the dance. This article takes choreographer as the research object, uses the music feature extraction technology in machine learning to the choreographer, applies the extracted music feature information to the choreographer, and synchronizes the audio and video to achieve better dance effect, which has important research value.
A Survey on Deep Learning Aided Telepathology and its Methodologies

Dynamics in Telepathology has been an emerging field since 1986. Recent Researches on Telepathology have concentrated much on Digital Pathology and Computer-Aided Pathology with an emerging power of computations like Edge Computing Technologies and so on., which can even facilitate live microscopic sessions to a remote place. Although such platforms allows a pathological consultant or pathologists team to retrieve, manage and process a real or surpassed microscopic image simultaneously, deployed in a remotely located web servers or any handheld devices, these systems still lag in Image Processing with specific Applications and also Speeding up of image Transmission Time and less interest on Cyber Human Systems. Most importantly the system shows less focus on the security of patient&#x0027;s Data. In today&#x0027;s computer world, integrated systems with increasingly complex and functional software tools are being developed and are becoming co-partners in the pathology diagnostic toolbox on the road to personalized medicine. Recent studies have described various barriers that can spread worldwide by validating telemedicine technology for diagnostic purposes. Surgical remote pathology is of paramount importance in covering its entire range. These technical tools undoubtedly facilitate digital pathology and computational pathology, opening up new approaches to the study of the calculation of individual diseased tissues, and the practice of pathology in distant tissues. Transform rural areas without pathologists for free. Due to technical factors, impressions on workflow, and information technology infrastructure implementation of Digital Pathology is still challenging for pathologists which must have to be considered. Still, the implementation of Telepathology is inefficient in developing countries due to the lack of IT Infra Structure and accessibility.
Cybercrime regulations: need for a new international approach?

Cybercrimes, cyber terrorism and cyber war are the pandemic of the 21st century. This problem existed before, but lately they have spread with a rapid speed, and have modified from their basic appearance, which makes them difficult to follow. The purpose of this paper is to show the need for a cyber convention of a new kind, and to explain the need for better international legal framework. The paper is “dealing “first with the European Convention of Cybercrime and its use. Also having in mind that the cyber field is not only consisted from cybercrimes/ offences but also with cyber terrorism and cyber wars, the second part of the paper isdedicated to the need of a new Digital convention. The last part is giving a brief overlook of the Macedonian legislation considering the cybercrimes.