Reviewerly/exHarmony huggingface dataset:
REVIEW SPAM DETECTION USING MACHINE LEARNING

Reviews, ratings, and experience stories left by customers on e-commerce websites and other online services are helpful to both buyers and sellers. The reviewer can foster more brand loyalty and aid in the understanding of other consumers' product experiences. Similar to how reviews help customers earn more profiles, reviews help businesses sell more things by enhancing customer satisfaction. However, suppliers may, unfortunately, abuse these review processes. For instance, one might fabricate positive evaluations to boost the reputation of a brand or attempt to denigrate rival brands' goods by posting fictitious reviews about them. Utilizing various machine learning techniques and tools are examples of existing solutions with supervision. Unlike previous work, I decided to use a wide range of vocabulary to work with, such as many datasets integrated into one enormous data set, rather than a confined dataset. Based on the reviews' text and emoji usage, sentiment analysis has been implemented. Review fraud is recognised and classed. The use of the Linear SVC, Support Vector Machine, and Random Forest algorithms yields the test results.
Digital Pedagogy for Mathematics and Technology Education: Exploring the Initiatives at One South African Teacher Education Institution

In the Fourth Industrial Revolution era, globally, the use of digital pedagogy is progressing swiftly. This chapter discusses the findings of a qualitative study that explored mathematics and technology education students’ perceptions and experiences of digital pedagogy in a developing country. The chapter focuses on one teacher education institution’s initiatives to empower mathematics and technology education students to prepare them for teaching and learning in the digital age. The study was framed by connectivism, a network learning theory guided by the view that learning is a lifelong process that continuously acquires new information. For this study, postgraduate mathematics and technology education students who are also practising school teachers were invited to interactive online workshops and interviews focusing on teaching and learning using digital tools. Apart from showcasing the effects of the digital age on the teaching and learning process, this study’s findings indicated the challenges of using digital pedagogy in a developing country. The advantages of virtual collaboration inspired by digital pedagogy are also discussed in this chapter. These findings have relevance globally and nationally when considering the perceptions, experiences, and implications of digital pedagogy in a developing country.
Local Dependence Latent Rank Analysis

This chapter describes a local dependence latent rank analysis (LD-LRA; Shojima, 2011), which combines a latent rank analysis (LRA; Chap. 6, p. 191) and the Bayesian network model (BNM; Chap. 8, p. 349). The LRA is a method used to classify students into latent ranks (ordinal latent classes), whereas BNM is a model used to find a good structure among the test items. An LD-LRA is thus a model that can classify students into ranks and find a good structure at each rank. This chapter also refers to a local dependence latent class analysis (LD-LCA) because the ranks reduce to classes if not ordered based on their ability level.
Human Body-Aware Feature Extractor Using Attachable Feature Corrector for Human Pose Estimation

Top-down pose estimation generally employs a person detector and estimates the keypoints of the detected person. This method assumes that only a single person exists within the bounding box cropped by detection. However, this assumption leads to some challenges in practice. First, a loose-fitted bounding box may include certain body parts of a non-target person. Second, spatial interference between several people exists owing to occlusion, so more than a single person can exist in the cropped image. In such scenarios, the pose estimation may falsely predict the keypoints of two or more persons as those of a single person. To tackle these issues, this paper proposes the human body-aware feature extractor based on the global- and local-reasoning features. The global-reasoning feature considers the entire body using transformer's non-local computation property and the local-reasoning feature concentrates on the individual body parts using convolutional neural networks. With those two features, we extract corrected features by filtering unnecessary features and supplementing necessary features using our proposed novel architecture. Hence, the proposed method can focus on the target person's keypoints, thereby mitigating the aforementioned concerns. Our method achieves noticeable improvement when applied to state-of-the-art top-down pose estimation networks.
Knowledge Distillation Algorithm of Feature Reconstruction Based on Feature Maps of the Middle Layer

Knowledge distillation is an effective model compression technology. As the main research branch, the knowledge distillation based on the middle layer focuses on extracting the network representation knowledge in the hidden layer to improve student learning performance. To fully utilize the process knowledge of the teacher model, an algorithm of the feature reconstruction based on middle-layer feature maps is proposed, which embeds middle-layer feature maps of the student network into the teacher network group by group. We introduce the intermediate soft-hard label contained process knowledge of the network with the help of pre-trained module group of the teacher network in loss function. This can increase the coupling of the teacher-student network in the process of knowledge transfer and improve the intrinsic knowledge inheritance of the compressed student model to the teacher network. The experimental results on the CIFAR-10 and CIFAR-100 verify the effectiveness and scalability of this method.
Composite Reuse Method Based On Graph Database

With the development of science and technology service industry, make more and more the number of service resources, service resources will exist between a relationship, you can make multiple services together to complete the same thing, but in the service users demand for combination, due to the needs of users the same or similar, makes combination will be the same or similar, for the same combination of similar cases, After comparing MySQL and Neo4j databases, a combinatorial multiplexing method combining graph database and improved VF2 algorithm was proposed. First, the service combination was stored in Neo4j database, and then the improved VF2 algorithm was used for subgraph matching. Experimental results show that compared with MySQL database, this method can reduce the number of service combination.The improved VF2 algorithm can reduce the time of matching combination compared with VF2 algorithm.
Expert System for Breast Cancer Prediction using Ensemble Learning

Breast cancer (BC) is the most frequent life-threatening illness in women, as well as the main cause of cancer mortality. Over the last two decades, BC research has made significant advances in our understanding of the disease, resulting in more effective and less toxic medicines. However, precise BC prediction is always crucial for effective therapy. Ensemble learning techniques have been used for accurate prediction of BC since the last few years. This paper also proposes an Expert System for Breast Cancer Detection (ESBCD) for better prediction of BC with machine learning analytics. The ESBCD preprocesses the BC data to impute the missing values and to remove the outliers, and then uses ensemble learning technique for classification.
Prologue to Quantum Computing and Blockchain Technology

Quantum computers utilize the features of quantum physical science to save information and perform calculations. This can be amazingly beneficial for a specific task where they could limitlessly outperform even our best supercomputers. Until now, we have depended on supercomputers to take care of the most critical issues. Supercomputers are extremely huge traditional computers, frequently with a large number of traditional Central processing unit and Graphical processing unit centres; this is the reason we need quantum computers. Blockchain (BC) is a process of recording data that makes it complicated to change, mutilate/cheat the framework. The most obvious and notable advantage of BC is that it eliminates the need for a centralized trusted third party in distributed applications. The fundamental thought of BC is straightforward where the focal authority cannot adjust the information. This chapter focuses on the overview, challenges, importance of quantum computing, and BC technology in various applications.
Adaptive Label-Constrained Correlation Filter for UAV Tracking

Traditional discriminative correlation filter (DCF) tracking algorithms always use ideal Gaussian functions as labels to train filters, which has reached promising performance in usual scenarios. However, due to challenges such as camera motion, occlusion and similar targets appearing frequently in unmanned aerial vehicle (UAV) tracking scenarios, these trackers using the identical and fixed labels often lead to over-fitting and model degradation and therefore perform poorly in UAV tracking. Accordingly, we present a new framework named Adaptive Label-Constrained Correlation Filter (LCCF) to adaptively construct a more realistic label function for each frame. Specifically, we propose adaptive label constrain regularization terms to assist in the construction of the desired realistic label function. In addition, we introduce an additional temporal regularization term to ensure the temporal consistency, thus avoiding using an additional fixed learning rate. Broad tests on multiple challenging UAV datasets have strongly established the comparative advantage of LCCF over deep and DCF methods. Moreover, LCCF fulfills the real-time tracking requirements with a tracking speed of 43 FPS. Remarkably, our approach delivers new best performance on VisDrone.
E-Government: Hybrid Cloud Deployment Architecture for the Nigerian Diplomatic Service

Organizations are increasingly embracing the offerings of cloud computing technologies and its attendant services. This is predominantly due to its underlying benefits of cost efficiency, ease of scalability, availability, etc. Recently, governments around the globe are also embracing the offerings of cloud technologies to offer their varied but much needed services to their citizens. The global penetrability of the diplomatic service makes an ideal case of an organization that can utilize the benefits and offerings of cloud computing services. With increasing demand for consistent uptime and timely responses, diplomatic services are progressively being offered online and real time. This paper provided a hybrid cloud computing architecture for both deployment and services offering for the diplomatic service using Nigerian diplomatic service as reference point. TOGAF ADM was utilized to identify and align organizational, transformational, and technological aspirations to cloud computing technologies’ offerings. The hybrid cloud deployment architecture comprises of a private cloud with an on-premise hosting, a diplomatic regional cloud (community cloud) deployed in each continent in an AZ, and a public cloud referred to as diplomatic service wide cloud connecting all the diplomatic service regional cloud hubs. AWS was used for the technological outlay of the hybrid cloud computing service architecture.
IoE-Based Genetic Algorithms and Their Requisition

The idea of the Internet of Everything (IoE) originated at Cisco and it has four pillars people (associate people in more pertinent and relevant ways), process (conveying the right knowledge to the right person at the right time), data (for making a better conclusion, transforming data into intelligence), and things (for making intelligent decision-making, objects and physical devices are connected through the internet, which is often called “Internet of things” (IoT)), and it embedded these four pillars with the intelligent connection. IoE is the superset of IoT. The IoE-based genetic algorithm serves as a vigorous adaptive approach to remedy exploration and optimization issues. For optimization problems, it employs the principle of natural evolution. The principle of natural evolution is sometimes referred to as the “principle of natural selection” or “survival of the fittest,” which was given by Charles Robert Darwin.
Towards measuring the aggregated debt of trustworthiness level

The management of technical debt related to non-functional properties such as security, reliability or other trustworthiness dimensions is of paramount importance for critical systems (e.g., safety-critical, systems with strong privacy constraints etc.). Unfortunately, diverse factors such as time pressure, resource limitations, organizational aspects, lack of skills, or the fast pace at which new risks appears, can result in an inferior level of trustworthiness than the desired or required one. In addition, there is increased interest in considering trustworthiness characteristics, not in isolation, but in an aggregated fashion, as well as using this knowledge for risk quantification. In this work, we propose a trustworthiness debt measurement approach using 1) established categories and subcategories of trustworthiness characteristics from SQuaRE, 2) a weighting approach for the characteristics based on an AHP method, 3) a composed indicator based on a Fuzzy method, and 4) a risk management and analysis support based on Monte Carlo simulations. Given the preliminary nature of this work, while we propose the general approach for all trustworthiness dimensions, we elaborate more on security and reliability. This initial proposal aims providing a practical approach to manage trustworthiness debt suitable for any life cycle phase and bringing the attention to aggregation methods.
Design of Mathematical Online Training Interactive Framework based on AI-assisted Data Modeling Algorithm Visualization Process

Based on the artificial intelligence algorithm, the data model of the optimal time series is researched and designed, and the data is classified by collecting and processing the time series data. A dynamic visual demonstration process is formed and applied to the design of the interactive framework for mathematical online training. The test results show that the 16 classic experimental algorithms of artificial intelligence courses included in the platform can be clearly and dynamically demonstrated, and the detailed description of the principles and steps of algorithm execution can help students deeply understand the execution process of the algorithm. The analysis of interactive behavior is intelligent, and can carry out targeted training according to the problems that arise in the learning process of students, thereby improving the learning efficiency of students.
An Effective Traffic Sign Image Classification and Detection Using CNN

Traffic signs are important parts of modern transportation systems because they keep roads safe and help drivers. There are two types of traffic signs: symbol-based and text-based. It is very important for real-world applications like autonomous driving, traffic monitoring, and driver safety to be able to read traffic signs. Therefore, traffic sign identification is a difficult problem since various sizes, illuminations, and sounds impact sign detection and recognition. Traffic sign detection systems based on convolutional neural networks face difficulties in adverse weather conditions, as well as a shortage of training data and difficulty in detecting objects. The training is done using the German Traffic Sign Dataset and resulted in a high rate of traffic sign recognition.
A Survey on E-Commerce Sentiment Analysis

Sentimental Analysis for products and services available on various e-commerce website and applications has been an important and crucial research task in current era. With the ease of e-commerce and m-commerce every seller tries to show their service and products by promoting on various platforms. Opinion mining, as well referred as sentiment analysis, is a significant element in natural language processing. It is used to evaluate what individuals or audiences consider about the products and services currently offered on collective media channels or social media platforms or e-commerce sites. To detect sentimental polarity a better method should be chosen. We have reviewed some of the research work, those have been tested and proven as good research work for sentimental analysis, as reviews of any products are available on website or applications related to products or services. But it is difficult to find out sentiments when large number of reviews from various sources are collected. You can find different reviews from different sources. There are lakhs of products reviews and services available on various e-commerce portal. To check manually review is difficult task. An automated review system without bios is need of the time.
Hyperparameter Optimization for Object Detection Network

Object detection is a growing research area in the field of neural networks and deep learning with many different applications. One of the most popular applications is in the development of self-driving cars. Hyperparameter optimization is a common mechanism to improve the performance of a deep learning network. However, a lot of the research today tends to use heuristics to set the hyperparameter values. In this study, several methods are discussed and implemented to provide specific techniques that researchers can use to find optimal settings for their network. The experiments are conducted on a camera radar fusion network for detecting and classifying objects in the Nuscenes dataset scene collection. Training the model with the optimization techniques led to a 56.2% improvement in accuracy from the baseline model.
TG-SMR: A Text Summarization Algorithm Based on Topic and Graph Models

Recently, automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization.However, most of the computing methods that are used in real systems are based on graph models, which are characterized by their simplicity and stability.Thus, this paper proposes an improved extractive text summarization algorithm based on both topic and graph models.The methodology of this work consists of two stages.First, the well-known TextRank algorithm is analyzed and its shortcomings are investigated.Then, an improved method is proposed with a new computational model of sentence weights.The experimental results were carried out on standard DUC2004 and DUC2006 datasets and compared to four text summarization methods.Finally, through experiments on the DUC2004 and DUC2006 datasets, our proposed improved graph model algorithm TG-SMR (Topic Graph-Summarizer) is compared to other text summarization systems.The experimental results prove that the proposed TG-SMR algorithm achieves higher ROUGE scores.It is foreseen that the TG-SMR algorithm will open a new horizon that concerns the performance of ROUGE evaluation indicators.
Implementation of the Weighted Product Method to Specify scholarship’s Receiver

Scholarship assistance for children of educational age, especially during the COVID-19 pandemic, has provided some relief for some families affected by the economic turmoil during the COVID-19 pandemic, where some parents were laid off, or their parents were reduced in salary. This scholarship at least helps students to continue their dreams of obtaining and continuing the education they are pursuing so that in the end, they can continue their dreams for a brighter future for the welfare of themselves, their families, and the nation. However, on the other hand, new problems arise with the limited number of scholarships awarded and the manipulation of student data to obtain these scholarships. Therefore, this paper discusses the implementation of decision-making applications in scholarships using the weighted product method that underlies the awarding of scholarships with four criteria: parents of students participating in the family program of hope, orphan status, and several dependents of parents, and parents’ income.
Deteksi Gestur Tangan Berbasis Pengolahan Citra

Hand sign language is a medium of communication for people with disabilities (deaf and speech impaired). However, in social practice, persons with disabilities may have to communicate with non-disable persons who do not understand sign language. These problems can be overcome with the help of translators or normal people learning sign language through existing media such as videos. Unfortunately, this method will probably cost a lot of money and time. In respons to this issue, the present study designed a sistem to detect hand gestures based on image processing. The method used is the You Only Look Once (YOLO) algorithm. The YOLO algorithm can detect and classify objects at once without being influenced by the light intensity and background of the object. This algorithm is a deep learning method that is more accurate than other deep learning methods. From this research, the system can detect and classify hand gestures with different backgrounds, light intensity, and distances with an accuracy rate above 90%.
Analysis of the Lowest Memory Consumption (Memory Usage) Through Running Different Cryptography Techniques for Different Types of Images

Abstract Nowadays security of image communication systems is highly important. It is essential to provide protection for image data confidentiality from illegal users. The detection and identification of unauthorized users is still a difficult and complex task. Many researchers have proposed several works to provide security of the transmission of the image. The objective of this article is to provide a comparative study of the current techniques that have been widely used. This article concentrates on various types of cryptography algorithms that are existing, like AES, MAES, DES, 3DES, RSA, and Blowfish, and investigation the lowest energy consumption (memory usage) through running several cryptographic algorithms on different image formatting.
Atlas of a changing earth

Data visualization breakdown, showing the data processing, data fusion, and design that went into creating a cinematic scientific visualization of a collapsing ice cap.
Digital Forensics and Crime Scene Investigation

Abstract You’ve watched the TV shows, but what really happens – or should happen – when a digital forensics professional arrives on a crimescene? Andrew Moore, Lecturer Practitioner at Anglia Ruskin University, shares the facts.
Handwritten and Printed Text Identification in Historical Archival Documents

Historical archival records present many challenges for OCR systems to correctly encode their content, due to visual complexity, e.g. mixed printed text and handwritten annotations, paper degradation, and faded ink. This paper addresses the problem of automatic identification and separation of handwritten and printed text in historical archival documents, including the creation of an artificial pixel-level annotated dataset and the presentation of a new FCN-based model trained on historical data. Initial test results indicate 18% IoU performance improvement on recognition of printed pixels and 10% IoU performance improvement on recognition of handwritten pixels in synthesised data when compared to the state-of-the-art trained on modern documents. Furthermore, an extrinsic OCR-based evaluation on the printed layer extracted from real historical documents shows 26% performance increase.