Reviewerly/exHarmony huggingface dataset:
Arvs: A Video Display Application Based on Augmented Reality

ARVS is an application for displaying publicity video with augmented reality effect. We defined two-dimensional markers in advance. The system captures the frame image data of video sensor in real time, uses OpenCV library to identify the predefined markers, and calculates the pose of markers relative to video sensor. OpenGL library is also used to create a virtual cube model at the marker positions, and the video or image is displayed on the faces of the cube. The virtual cube is displayed in the interactive window together with the original frame image data collected by the video sensor to achieve the augmented reality effect. When the angle of the marker collected by the video sensor changes, the angle of the display video relative to the display plane also changes in real time; when multiple video sensors collect markers from different directions at the same time, the observed display videos will also be different, which has strong interactivity and interest. We tested the system and analyzed the efficiency and accuracy of the system. The result shows that the system has high reliability and good interaction.
OXGBoost: An Optimized eXtreme Gradient Boosting Algorithm for Classification of Breast Cancer

One of the most common diseases among the middle-aged women is breast cancer. The proposed system designed an efficient breast cancer detection and classification approach using an ensemble booster algorithm “XGBOOST” classifier. All the traditional approaches in machine learning have developed models with low variance or with high bias. So, the proposed system has evaluated the model with different evaluation metrics. In the world of machine learning, optimization has a great impact. To address this issue, the proposed system performed whirling of hyper-parameters during the classification process. Finally, the designed system is compared with conventional models. The major goal of the proposed system is to identify the breast cancer and classify the stage of cancer. So, the automated system can help the doctors to recommend the treatment or medicines and in turn the morality rate due to the breast cancer can be reduced.
History of Fiction as the History of Human Consciousness (Based on D. Lodge’s Work “Consciousness and the Novel”)

Fiction and the relevant conceptualizing academic discipline have been developing in parallel. Human consciousness is simultaneously a tool for understanding the world and its artistic representation and the object of human comprehension of our inner nature. Consciousness has been represented in fiction beginning from its perception as a material substance in the Middle Ages, through naming the emotions, expansion of the range of feelings depicted in the Renaissance literature, through the first-person narration and identification of the thought and the word, towards the discovery of the unconscious and the ways of its representation in the epoch of Modernism, and equation of the processes of consciousness and the principles of functioning of the artificial intelligence during the Postmodern period. Consciousness as a tool became an object of artistic discovery in the 20th century inspiring the emergence of the “novel of consciousness” genre. The English writer and critic D. Lodge in his work “Consciousness and the Novel” expounded the basic caveats about human consciousness, drawing attention to the contingence of fiction and science, as well as fiction and the history of its studies.
Recommendation System Using MixPMF

The objective of this paper is to the use of the Probabilistic Matrix Factorization (PMF) model which scales linearly with the number of observations and performs well on the large, sparse, and imbalanced music/movie dataset. In this project, we compare various PMF-based models and apply them to the recommendation system. We design and develop a Mix Probabilistic Matrix Factorization (MixPMF) model for music recommendation. This new model will take advantage of user network mapping and artist tag information and forms the effective rating matrix and thus will be efficient in recommending music/movies to new users. Simulation results show the advantage of our model.
An Improved Ants Colony Optimization for Mobile Robot Path Planning

One of the most popular swarm algorithms is the ants colony algorithm (ACO) due to its concept can be understood and implemented efficiently. Still, ACO has drawbacks, e.g., inability to find the shortest path, slow convergence speed, great blindness of path search, and many inflection points in dealing with path planning under a realistic environment. This study suggests an improved ACO addresses its drawbacks for mobile path planning in a running background. The initial pheromones are distributed unevenly in the grid map. The line between the starting point and the target point is considered a model for the path search tended robot be nearby. It added information of the current node, the next node, and the target points to the heuristic function by robot’s sensors that are considered the dynamic adjustment factor to achieve strong robot path guidance. The improved algorithm feasibility, effectiveness, and superiority are verified by comparing to the other optimal solutions. The compared results show that the proposed method produces a shorter and smoother path than the other methods.
Considerations and Strategies for Operationalizing Heuristic Evaluation Work

Heuristic evaluation (HE) is a popular usability inspection method that allows expert evaluators to document usability problems with the design of interfaces. The relatively low resource requirements and high utility of identifying usability issues and suggesting dimensions along which to correct them have made HE highly scalable. However, there are several limitations and concerns when operationalizing HE for a large enterprise, and education and communication along with combining HE with other usability techniques may greatly increase the utility of the work.
On the Additional Properties of Fuzzy Polynomial Implications of Degree 4

It is well known that fuzzy implication functions have a very flexible definition that encompasses lots of different potential families. Therefore, the additional properties that the family members fulfill define the behavior these operators will have when modeling fuzzy conditionals or applied to any other application. In this way, in this paper, we study the additional properties of fuzzy polynomial implications of degree 4, a recent family of fuzzy implications which has been recently introduced as a family whose members have a simple expression, appealing for applied researchers. It is proved that members of this family fulfill some of the additional essential properties of fuzzy implication functions.
Rewriting Algorithms

Data restore is an important phase in the storage management stage of data deduplication systems. During a restore, a recipe (i.e., the fingerprint sequence of a backup) is read, and the containers serve as the prefetching unit. After deduplication, the chunks of each backup are often physically scattered, also known as fragmentation problem, which severely decreases restore performance. In this chapter, we discuss the problem and present the solution to alleviate the problem. The rest of this chapter is organized as follows: Section 5.1 presents the state-of-the-art algorithms for addressing the fragmentation problem. Section 5.2 describes the design and implementations of our proposed History-Aware Rewriting algorithm for data center. Section 5.3 describes our another causality-based deduplication performance booster for both cloud backup and restore operations.
Multi-agent Cooperative Framework for Autonomous Wall Construction

Ankit, Kumar Tony, Lima Agnel Jana, Shuvrangshu Ghose, DebasishUnmanned aerial vehicle (UAV) applications with pick-and-place operation are plenty, and the same prevails in unmanned ground vehicle (UGV) domain. But low payload capacity for a UAV and the limited sensing capability of a UGV limit them to automate heavy-duty and large-scale construction. This complementary nature of these agents can be utilized together to cater to the needs of long-term autonomous construction. Thereby, we propose a software framework with its algorithmic details for multi-vehicle collaboration for autonomous pick-and-place operation. Three UAVs and a UGV coordinate among themselves to pick bricks of different sizes and place them at a specific location in a predetermined orientation. At the core of the decision-making process, distance-based optimization is done to generate the route plan for the agents. Generated route plan is then sent to agents via a scheduler which keeps their operations in check and, in case of failures, helps them recover autonomously. The framework provides end-to-end details on multi-vehicle pick-and-place operation, keeping collisions and failures in check. The software is developed in ROS and Gazebo environment and ready to implement on hardware. The modeling approach makes it easy to be modified and deployed to cater to any application such as warehouse stock management and package delivery, besides several other applications.
Proposed Architecture Diagrams for developing an electronic system based on block chain technology to secure confidential data in university

The current system inside Egyptian universities s relies on the minimum and traditional methods of securing data that do not keep pace with the current digital development. Therefore, the education sector needs to take advantage of the advantages of block chain technology in securing and encrypting data from the risks of hacking and leakage of records. Through the above, an electronic system for generating a data encryption key based on block chain technology and the AES algorithm has been proposed.
Construction of a Personalized English Learning System Based on Machine Learning

In recent years, the development of countries around the world has gradually globalized, and the communication between countries has increased. English has gradually become important. English learning is a comprehensive learning process, including listening, speaking, reading and writing, but each part is independent and interrelated. Due to the characteristics of learning English, it also puts forward requirements for the English learning system: how to build a multi-functional and integrated English learning environment to make users learn English easily. At the same time, the Internet has been developing continuously, and the number of English learning materials has become very large and rich. How users find suitable resources for learning English from huge materials has also become a problem that has to be considered, which directly affects users’ learning time, learning cost and learning interest. In view of the above problems in English learning, this paper studies a personalized English learning system based on machine learning by using the theoretical knowledge of natural language processing, information retrieval and machine learning, so as to meet the needs of personalized organization and search of resources in the construction of English learning system. Combined with the above algorithms, a dynamic English resource retrieval system is constructed in the local retrieval framework, and a system solution of English resource retrieval evaluation, indexing and fast retrieval is proposed. At the same time, in order to meet the needs of word pronunciation and listening in English learning, the functions of reading and listening words are introduced into the system, which is improved on the basis of single model single feature (SMSF) algorithm, and a SMSF combination method is developed. The experimental results show that the accuracy rate can reach 89%. Finally, create a multi-functional and integrated personalized English learning environment for users.
New Metallogenic Belts at the Central Part of México: Time-Space Distribution of Hydrothermal Au-Ag, Fluorite, Hg-Sb and Skarn Ag-Cu-Zn-Pb Mineral Deposits

Mineralization associated with magmatic rocks occurs at the central part of México. The most characteristic types of mineralization are 1) Hydrothermal Au-Ag deposits, 2) The world class Fluorite hydrothermal deposits, 3) Hg-Sb hydrothermal low temperature deposits and 4) Ag-Cu-Zn-Pb skarn deposits. The age of mineralization extends from 43 to 15 Ma and is related to small stocks and dikes of quartz monzonites and porphyritic diorites to subvolcanic-volcanic felsic-mafic rocks. The four belts are oriented NW-SE and are the inheritance of the opening of the Gulf of Mexico.
An Incremental Extreme Learning Machine Prediction Method Based on Attenuated Regularization Term

As a powerful tool for regression prediction, Incremental Extreme Learning Machine (I-ELM) has good nonlinear approximation ability, but the original model has the problem that the uneven output weights distribution affects the generalization ability of the model. This paper proposes an Incremental Extreme Learning Machine method based on Attenuated Regularization Term (ARI-ELM). The proposed ARI-ELM adds attenuation regularization term in the iterative process of output weights, reduces the output weights of the hidden node in the early stage of the iteration and ensuring that the new nodes after multiple iterations are not affected by the large regularization coefficient. Therefore, the overall output weights of the network reach a relatively small and evenly distributed state, which would reduce the complexity of the model. This paper also proves that the model still has convergence performance after adding the attenuated regularization term. Simulation results on the benchmark data set demonstrate that our proposed approach has better generalization performance than other incremental extreme learning machine variants. In addition, this paper applies the algorithm to specific weight prediction scene of intelligent manufacturing dynamic scheduling, and also gets good results.
Sentimental Analysis to Predict Women Violence in India Using Machine Learning

In our living society, women are suffering so many significant problems everywhere; in this way, we analyzed based on various factors to identify the majority of women’s who reside in countless cities including metropolitan cities of India feel unsafe while going out to work or while traveling in public transports. We observed users posts or comments, encounter negative comments, sexual harassment, and abused languages on various platforms like Twitter, Facebook, and similar social networks and collect the necessary information to give the best way to resolve the violence solutions utilizing government and NGO services. In this research paper, we proposed a new system that uses an SVM algorithm to analyze the proper guidelines for unsafe methods to reach health environments based on analyzing the tweets by checking the intelligent way.
Extreme Learning Machine Based on Adaptive Matrix Iteration

Under the continuous optimization and development of various algorithms in machine learning, the performance of the algorithm model on classification and regression prediction problems has become an important evaluation metric for the quality of algorithms. In order to solve the problems of low testing accuracy and unsatisfactory generalization performance of the models trained by the traditional extreme learning machine, this paper proposes an extreme learning machine algorithm based on adaptive convergence factor matrix iteration. This algorithm optimizes the calculation method of solving the hidden layer output weight matrix, while retaining the network structure model of the traditional extreme learning machine. This algorithm is implemented with a matrix iterative method that includes an adaptive convergence factor to compute the output weight matrix. As a result, it can adaptively select the optimal convergence factor according to the structure of the iterative equations, and thus use iterative method to solve linear equations efficiently and accurately upon ensuring the convergence of the equations. The experiment results show that the proposed algorithm has better performance in model training efficiency and testing accuracy, compared with the traditional extreme learning machine, the support vector machine, and other algorithms for data classification and regression prediction.
Securing Unauthorized Access to Cloud Data Storage

In today’s era, cloud data storage is a popular choice for data storage. Cloud technology gives computing and information storage services through the internet. Expansion in the measures of cloud users has raised protection and security concerns. Security of users’ information was a major concern in cloud storage. This paper tried to simulate an experimental setup that provides a better security alternative for cloud architecture. We have applied the feed-forward backpropagation technique of artificial neural networks. The benefits of storing encrypted passwords are stored in the form of a weight matrix and which is almost impossible to crack.
Proposing a Performance Measurement Framework for Higher Education Institutions by Using Fuzzy Delphi and Best Worst Method (Bwm)

This paper proposes a novel framework to measure the performance of higher education institutions (HEIs). To our knowledge, there is little research in this area, in particular, with the use of multiple criteria decision making (MCDM) methods. Data gathering tools in this research were semi-structured interview, experts panel and questionnaire. This study employed the fuzzy Delphi method (FDM) to filtering out the criteria extracted from the literature, and Best-Worst Method (BWM) to find the relative weight of the criteria and indicators. A case from a public university in Iran was employed to illustrate the proposed framework. A seven-category framework of performance measurement of HEIs is developed. Findings show that “education” and “human capital” are the most important criteria. Further, “number of patents and inventions”, “ratio of faculty/students”, and “students’ satisfaction with teaching quality” are the most important performance indicators, respectively. This research proposes a new performance measurement framework for HEIs based on the transformational approach. More importantly, a novel integration of FDM with BWM is employed in performance measurement for HEIs which, to our knowledge, the first time in the literature.
Compact-VG: A Small-scale Dataset for Scene Graph Generation

Kumar, Aiswarya S. Nair, Jyothisha J.High-level image understanding includes phases like object detection, predicate classification, and attribute classification. The outputs from each phase are merged to build a scene graph, which arranges the elements in a structured manner. Scene graphs have shown their proficiency in various tasks like image retrieval, visual question answering, and image generation. However, data is an essential aspect for such tasks, especially when the models are too complex. We introduce Compact-VG, a refined subset of the popular dataset visual genome. This subset contains 200 object categories, 10 predicates, and 16 attributes. Studies show that, even when we consider only the most common categories of objects, predicates, and attributes, the extracted dataset is still very rich, with a mean of 14.1 objects, 18.5 attributes, and 19.7 relationships per image. Dataset is available at https://github.com/Aiswarya2021/Scene2Graph .
A Novel Automated Human Face Recognition and Temperature Detection System Using Deep Neural Networks—FRTDS

This paper proposes a novel FRTDS (Face Recognition and Temperature Detection System) that is contactless and performs real-time face recognition. The system had proved to be fast, built at low-cost, and efficient in user authentication. FRTDS consists of numerous algorithms and techniques that have been implemented to improve the performance of the entire system with the help of Deep Neural Networks. FRTDS can capture images from a video stream and can detect faces from 70–90 cm away from the camera. An interactive front-end recognizes and displays the identity of the person. FRTDS also includes a temperature sensor to monitor the health of the person, before they enter any premises. The recognized face along with temperature data is stored at the back-end with the current time and date. This paper also presents a novel customized tool that eases the process of dataset creation and augmentation, and a novel prediction discrepancy elimination algorithm.
Feddcs: A Distributed Client Selection Framework for Cross Device Federated Learning

The robustness of a trained machine learning model depends upon the variety and veracity of the training data. Federated learning (FL) offers the platform for a collaborative eco-system that involves multiple parties (clients) and a global server orchestrating a global model training without clients exposing their raw data to each other. One of the key aspects of FL is appropriate client selection with relevant training data and resources to last throughout the FL process. Existing client selection approaches are centralised, making the assumption that the participatory clients are resource-full and restrict their type as mobile phones. Moreover, centralised client selection makes difficult for the global server to monitor the client(s) resources to last the training process. A distributed client selection framework (FedDCS) for cross-device FL is proposed in this paper. FedDCS employs a novel distributed, data-type, resource, and time aware protocol for FL client selection to minimize client(s) drop-outs, model convergence time, number of message exchanges, and time taken to achieve a certain level of accuracy. Extensive experiments are performed to evaluate the performance of FedDCS and the results show 100 %Â client retention during FL model training, 82.64 % and 36.53 % improvement in the time taken to reach the desired testing accuracy, and 17.85 % and 9.67 % improvement in model convergence time, for IID and Non-IID setting respectively as compared to the state-of-the-art. In addition, FedDCS also results in a significant reduction in the number of message exchanges during FL.
Object Tracking for Autonomous Vehicle Using YOLO V3

Accuracy and performance of an object detection model have always been the main requirements for an object tracking system. In this project, the performance of machine learning based object detection using YOLO v3 technique will be investigated. Two models were provided where one model is trained using online Common Objects in Contact (COCO) dataset only, and the other model is trained with additional images from Universiti Malaysia Pahang (UMP) with several different locations dataset. The performance of the trained models were evaluated using mean Average Precision (mAP), and precision techniques. The model with highest precision was selected to be implemented on actual road test. The results show that the model 2 has the highest precision and was able to detect every class of objects. Each output box had displayed the class and the distance to the objects from the RGBD camera of the vehicle. It is observed that the first model that was trained to perform the mAP value of 90.2% and a performance of 0.484 precision. For the second model, it can be seen that the accuracy of the detections are higher than that of model 1. Therefore, model 2 has a better performance with a value of 0.596 precision.
Chemical Reaction Optimization (CRO) for Maximum Clique Problem

The maximum clique problem (MCP) finds the largest complete subgraph or clique of a given graph. The target here is to maximize the size of the clique. In this article, we have proposed a chemical reaction optimization (CRO)-based metaheuristic algorithm to solve the maximum clique problem. Two datasets have been used to measure the performance of the proposed method. The proposed method gives better results with less average errors in comparison to the state-of-the-art methods for the two datasets.