Reviewerly/exHarmony huggingface dataset:
K-Means Algorithm: An Unsupervised Clustering Approach Using Various Similarity/Dissimilarity Measures

Clustering is an unsupervised method of classifying data objects into similar groups based on some features or properties usually known as similarity or dissimilarity measures. K-Means is one of the most popular clustering methods that come under the hard clustering group. In this clustering method, any data object can belong to a single cluster. On the other hand, in soft clustering methods (e.g., fuzzy c-means clustering), the data object can be clustered in more than one cluster with some degree which is specified by the membership value with the limitation imposed as the summation of these membership values should be equal to individual. While the clustering method of K-Means is a comparatively old technique, it still has tremendous popularity in terms of being used in applications for data grouping and machine learning. In this article, K-Means approach with five different distance measures such as Euclidean, Squared Euclidean, Half Squared Euclidean, Cosine, and City Block distance has been explored. A comparative study is made based on the performance of these similarity criteria on real-time Edible oil dataset acquired using MIR spectroscopy. In addition, it attempts to investigate the measure of similarity for a specific collection of unique patterns carrying data. In the MATLAB R2015b environment given by Mathworks, the K-Means algorithm with different similarity and dissimilarity measures were formulated and implemented.
The influence of CLAHE on the accuracy stability of the automatic classification of Mars surface lineament structure based on DEM image

Abstract This article introduces training ResNet to automatically classify the lineament structure of Mars surface (DEM image), and then use CLAHE to pre-process the samples to improve the stability of accuracy. The linear structure of Mars surface is mainly divided into two types: dorsum and vallis, and crater is added as a representative of non-lineament structure. We have prepared a sample set of 300 samples for each class, divided into training set, test set, and validation set at 6:2:2. Without pre-processing, the highest accuracy rate reached 98.33% (for crater), 100.00% (for dorsum), 98.33% (for vallis), 89.44% (for total). However, the accuracy of Dorsum fluctuates greatly and frequently. After CLAHE pre-processing, the fluctuation of the accuracy of dorsum is significantly reduced.
Pallet Detection Based on Halcon and AlexNet Network for Autonomous Forklifts

Pallet detection is the key step of cargo handling for autonomous forklifts. Due to the influence of complex background, the recognition rate of pallet detection is low. In order to improve the recognition rate, a pallet detection method based on point cloud is proposed. In this method, time-of-flight (ToF) camera is used to collect the point cloud. AlexNet network which is provided by Halcon software is used for deep learning, and deep learning is used to extract the region of interest of pallet contour. The extracted region of interest is processed to obtain the regions of the pallet contour, and the pallet center is solved. The experimental results show that the precision of pallet detection can reach 92.0%. This method has high recognition rate in complex background, and has reference value for the design of pallet detection system of autonomous forklifts.
Aspect Based Sentiment Analysis With Combination Feature Extraction LDA and Word2vec

A product review is needed by a customer before he buys a product. Currently, several platforms can be used to provide product reviews, one of which is the beauty product. Every customer can read beauty product reviews, not only from one aspect of the review but it can be from several aspects of the review. it is difficult for consumers to find all the reviews from various aspects quickly. Therefore, in this study, a combination of LDA modeling methods and Word Embedding Word2vec were used, to obtain sentiments from each of the predetermined aspects of the review. In this study, the accuracy of the combination of LDA will be compared with the Word2vec Skip-gram and Continuous-bag-of-word (CBOW) models. From the two combinations, it is found that the combination accuracy of LDA and Word2vec Skip gram is 80.36%, and for CBOW is only 74.37%. Meanwhile, the SVM and K-Fold Cross-Validation algorithms are used to find the accuracy of sentiment predictions on the aspects of price, packaging, and fragrances. Compared to the other two aspects, the packaging aspect has the highest accuracy at 89.71%.
Comparison of Machine Learning Methods for Tamil Morphological Analyzer

Morphological Analysis is the study of word formation which explains how a word is evolved from smaller pieces called root word. Morphological analysis is an important task in natural language processing applications, namely, POS Tagging, Named Entity Recognition, Sentiment Analysis, and Information Extraction. The heart of the morphological analysis process is to find out the root words from the given documents that is exactly matched with the corpus list. There are many research works that have been done in this area of research however not much contribution has been made in domain specific in the area of domain specific analysis regional languages. Morphological analysis for regional languages is complex and demands extensive analysis of natural language rules and syntax pertaining to specific regional language of focus. In order to improve the quality of natural language processing, generally research works are restricted to domain specific analysis. Morphological analysis in Tamil language documents is quite complex and valuable for Tamil NLP process. Our work focuses on a comparative study of three different approaches in performing morphological analysis on the regional language called Tamil. The scope our work is restricted to Gynecology domain text in represented in Tamil language. The analysis of morphological process is done in three different machine learning methods for the Gynecological documents. The performance analysis is carried out on the three morphological analysis models, namely, Rules-based lemmatizer (IndicNLP), Paradigm-based Tamil Morphological Analyzer (Tacola), and N-gram-based lemmatizer (UDPipe), and our experimental results proved that paradigm-based finite state model gives optimal results (0.96).
Learning a Similarity Metric Discriminatively with Application to Ancient Character Recognition

The process of learning good representation in deep learning may prove difficult when the data is insufficient. In this paper, we propose a Siamese similarity network for one-shot ancient character recognition based on a similarity learning method to directly learn input similarity, and then use the trained model to establish one shot classification task for recognition. Multi-scale fusion backbone structure and embedded structure are proposed in the network to improve the model's ability to extract features. we also propose the soft similarity contrast loss function for the first time. It ensures the optimization of similar images with higher similarity and different classes of images with greater differences while reducing the over-optimization of back-propagation leading to model overfitting. A large number of experiments show that our proposed method has achieved high-efficiency discriminative performance, and obtained the best performance over the methods of traditional deep learning and other classic one-shot learning.
Framework Towards Higher Data Privacy by Novel Data Integrity Scheme

Data integrity is one essential concern when it comes to using the store sensitive and valuable data over distributed storage units of cloud computing. This paper review existing approaches towards data integrity in cloud computing to find that the existing public verification scheme is mainly carried out assuming the pre-defined legitimate data auditor. However, if the data auditor goes rogue, it is quite a complex scenario process to ensure data integrity. Therefore, the proposed system offers a novel scheme that assesses the legitimacy of all the actors involved in the data integrity process that was not considered in existing approaches and was found to offers resistive against the majority of threats connected with data integrity. The simulated outcome of the study shows that the proposed system offers better response time, invalidating the test data with highly controlled memory consumption.
HCapsNet: A Text Classification Model Based on Hierarchical Capsule Network

In text classification tasks, RNNs are usually used to establish global relationships. However, RNNs have the problems that the semantic information coding of key words is not prominent and cannot be calculated in parallel. In addition, hierarchical information of text is usually ignored during feature extraction. Aiming at the above problems, a text classification model based on hierarchical capsule network (HCapsNet) is proposed. In order to capture the hierarchical features, text is divided into granularities and constantly aggregate according to the characteristics of the data. A parallel LSTM network fused with self-attention is utilized to complete the encoding of multiple natural sentences. Then, we construct sentence features into sentence capsules to extract richer semantic information. The spatial relationship between sentence capsule as part and chapter capsule as whole is established by dynamic routing algorithm. Our experiments show that HCapsNet gives better results compared with the state-of-the-art methods on six public data sets.
Exploiting Insertion Symbols for Marginal Additions in the Recognition Process to Establish Reading Order

In modern and medieval manuscripts, a frequent phenomenon is additions or corrections that are marginal or interlinear with regard to the main text-blocks. With the recent success of Long-Short-Term-Memory Neural Networks (LSTM) in Handwritten-Text-Recognition (HTR) systems, many have chosen lines as the primary structural unit. Due to this approach, establishing the reading order for such additions becomes a non-trivial problem, because they must be inserted between words inside line-units at undefined locations. Even a perfect reading order detection system ordering all lines of a text in the correct order would not be able to deal with inline insertions. The present paper proposes to include markers for the insertion points in the recognition training process, those indicators can then teach the recognition models themselves to detect scribal insertion markers for marginal or interlinear additions.
Automated Segmentation of Fetal Ultrasound Images Using Feature Attention Supervised Network

Segmentation of anatomical structures from ultrasound images requires the expertise of an experienced clinician, but developing a machine automated segmentation process is complicated because of the existence of characteristic artifacts. In this article, we present a novel end-to-end network that enables automated measurements of the fetal head circumference (HC) and fetal abdomen circumference (AC) to be made from 2-dimensional (2D) ultrasound images during each pregnancy trimester. These measurements are necessary, because the HC and AC are used to predict gestational age and to monitor fetal growth. Automated HC and AC assessments are valuable for providing independent and objective results and are particularly useful for application in developing countries where trained sonographers are in short supply. We propose a scale attention expanding network that builds a feature pyramid inside the network, and the intermediate result of each scale is then concatenated to the feature with a fusion scheme for the next layer. Furthermore, a scale attention module is proposed for selecting the most useful scale and for reducing scale noise. To optimize the network, a deep supervision method based on boundary attention is employed. Results of experiments show that the scale attention expanding network obtained an absolute difference, Hausdorff distance, and dice similarity coefficient of 1.81 ± 1.69%, 1.22 ± 0.77%, and 97.94%, respectively, which were top results in the HC18 data set, and respective results on the abdomen set were 2.23 ± 2.38%, 0.42 ± 0.56%, and 98.04%. The experiments conducted demonstrate that our method provides a superior performance to existing fetal ultrasound segmentation methods.
Research on VR Tourism Application Based on 5G Technology — Taking Guilin Lijiang as an Example

The combination of VR technology and tourism is an inevitable trend in the development of the digital age, which can create a brand-new travel experience model and change the way people travel. It plays an important role in expanding tourism promotion to enhance the interactive, entertaining and immersive experience of tourism, and increase tourism attractiveness. Guilin introduces the concept of 5G+VR tourism to better show Guilin’s tourism resources and promote the development of tourism. The paper puts forward a set of VR travel application software based on 5G technology through the SWOT analysis of Lijiang River in Guilin.
Service Selection using Ethereum

The paper presents an Ethereum based service selection framework that is decentralized in nature and aids people in finding the best services using a smart contract. We have used Solidity, a language to develop the smart contract for Ethereum, to tackle the problem that occurs in broker-based systems or centralized systems. As for the result, we show that the functionalities performed by a broker can be implemented on Ethereum smart contract. We present a methodology that one can refer to develop Ethereum smart contract for service selection.
Wide-range routing method for lunar exploration rovers using multi-objective optimization

This paper presents a wide-range routing method for multiple rovers deployed for a lunar polar exploration mission. In the mission scenario considered in this paper, multiple exploration points are divided into several sets of points to be assigned to multiple rovers. Subsequently, routes between the exploration points are generated for each rover. This assignment and routing problems are well known as the multiple Traveling Salesman Problem (mTSP). The scenario aims to minimize two objective functions: the sum and standard deviation of operation time among the multiple rovers. This problem is then subject to the multi-objective mTSP (MOmTSP). Therefore, the proposed method is composed of a three-step procedure for solving the MOmTSP. First, the k-means algorithm divides an exploration area into several regions for the number of rovers. Subsequently, a routing algorithm with the space-filling curve and 2-opt algorithm determines the order of exploration points in each region. A local path planner then generates a feasible path between two exploration points. The simulation results using the proposed method with 80 cases are statistically analyzed. We confirm that the proposed method solves the multiple optimization problems by finding the routes that can equalize the rover's operation time and minimize their deviations.
On the Innovation of Foreign Language Teaching in the Era of Artificial Intelligence

As human society steps into the era of artificial intelligence, artificial intelligence has a profound impact on education, which provides necessary conditions and have new requirements for the innovation of foreign language teaching. The innovation of foreign language teaching in the era of artificial intelligence is mainly reflected in four aspects: the innovation of educational organization form, the innovation of education and teaching mode, the innovation of teachers’ skills and concepts, and the innovation of teaching resources selection and application. To improve the level of campus intelligence, to carry out highly customized learning, to create a smart classroom, and to establish a collaborative relationship between teachers and artificial intelligence are the ways and methods of foreign language teaching innovation. In this process, society, schools, teachers and students should cooperate well and work closely to create a better and new situation of foreign language teaching in the era of artificial intelligence.
A Feature Fusion Model For Person Identification Using Top-view Image

In this paper, Top-view images taken by the bird'seye view digicam can reduce the limitation of the camera installation position and solve the problem of the face in image being blocked. we count on people's clothes, hair color, and physique do now not exchange in a quick length of time. We propose a person identification method from a bird's-eye view image by fusing features. To obtain a high discrimination rate even with a small number of learning images, we extract effective features (1) HOG (2) gray level co-occurrence matrix (3) color histogram in the traditional computer vision field. And (4) VGG16 in deep learning field. And then we fused these features and applied them to train the SVM classifier. The effectiveness of the proposed approach used to be tested from many comparative experiments.
COVID-19 Early Symptom Prediction Using Blockchain and Machine Learning

The COVID-19 outbreak has resulted in unprecedented and difficult times for world's population. Social distancing and self-isolation have become very important to reduce the spread. This called upon the creation of numerous applications that have used proprietary models for symptoms-tracking and contact-tracing around the world to mitigate the spread. In most of the applications data collected is stored in a centralized database without verification and hence, the data is not reliable. In this study, a decentralized application for COVID-19 symptoms tracking using Blockchain is proposed in order to enhance reliable data collection for training Machine Learning (ML) models. The Blockchain integration in this application will help in collecting COVID-19 symptoms data from the patients with trust. In addition to this, the data would be first verified by an entity of the decentralized network (e.g. a COVID-19 testing lab). Then, with the consent of the patient, this data is provided to the centralized system for retraining the ML. In short, the main advantage of this architecture is that the data from the users is collected and checked by a laboratory first and then provided to the ML model. The process helps in identifying the incorrect ML prediction and further train the ML model with reliable data for accurate prediction. Moreover, the trust of the users is earned as the data transfer happens with their consent and, besides, all transactions are recorded on the Blockchain, which is possible with the help of the Distributed Ledger Technology (DLT).
Examining the Effect of Self-explanations in Distributed Self-assessment

Self-assessment is a twofold activity consisting of self-evaluation and self-explanation, which are considered imperative metacognitive strategies in learning science. Although the self-explanation effect has been scaffolded in numerous learning systems, it remains unclear whether the effect can still occur to students in a voluntary setting of learning such as remote learning that requires self-regulation to persist in making progress. Furthermore, it is inconclusive what students' behavioral patterns can be when they exercise self-evaluation and self-explanation overtime. In this study, we investigate the effectiveness of self-assessment and the embedded self-explanation by dissecting semantic elements in the explanations in a multilevel analysis. The result showed that the low-performing students were challenged by the complexity of topics, which resulted in an increased error rate when they ventured into more learning opportunities. However, the self-explanation effect might occur to them and improved their performances, especially when they reflected on the content of questions that were relevant to the concepts. In summary, this study provides an insight into effective self-assessment. Specifically, it shows that students can potentially improve performances by writing compact and relevant explanations over time.
Cyber Security Certification Programmes

Although a large and fast-growing workforce for qualified cybersecurity professionals exists, developing a cybersecurity certification framework has to overcome many challenges. Towards this end, an extended review of the cybersecurity certifications offered currently on the market from 9 major issuing companies is conducted. Moreover, the guidelines for the definition of a cybersecurity certification framework as they are provided from the recent Cyber Security Act and framework of ENISA, NIST and ISO/IEC 17024 are covered. A vast comparison among the presented cybersecurity certifications is given, based not only on the cybersecurity domain covered but also the required level of candidate’s experience. A proposed certification program has been also analyzed based on the learning pathways and the knowledge areas described in FORESIGHT.
QoE-assured Live Video Streaming Based on Coalition Game in 5G eMBMS Networks

The scenario that quantities of users subscribing to a same live video content cluster together in a spatially local area poses challenges to cellular operators even in 5G unicast networks. In this regard, we propose a network paradigm exploiting eMBMS and edge computing, which relieves resource starvation in both backbone and wired access networks by grouping users and distributing the desired content to each multicast group only once. However, problems are raised by operators to perform an optimal server-side decision: how to partition users with the heterogeneity and dynamic of channel conditions, how to fairly and optimally allot resources considering both unicast and multicast users, and how to maximize the overall QoE of this live video service. To cope with these coupled problems, we formulate an optimization model based on coalition game with QoE assured, which defines a fair allocation strategy according to respective contributions and a dynamic grouping method. Subsequently, we propose a heuristic algorithm with a low-time complexity that guarantees QoE for most users and shows conspicuous reduction of annoying stalling events. Noticeably, numerical simulations reveal the fairness and near-optimality of our algorithm compared with state-of-the-art approaches in multiple scenarios.
Formal Analysis of Symbolic Authenticity

Authenticated encryption schemes are ways of encrypting messages which simultaneously assure the secrecy and authenticity of data. Designing authenticated encryption schemes can be error-prone. In this paper, we consider the authenticity of authenticated encryption schemes . We introduce the notion of symbolic authenticity, and present two inference systems for verifying symbolic authenticity. The first inference system works for authenticated encryption schemes for messages of fixed length. It is sound, complete and terminating. The second one works for authenticated encryption schemes for messages of arbitrary length. It is sound, terminating, and complete under some condition. These inference systems can be used to automatically synthesize authenticated encryption schemes.