Reviewerly/exHarmony huggingface dataset:
Face mask detection system using deep learning

In the present scenario due to Covid-19, there are no efficient face mask detection applications which are now in high demand for transportation means, densely populated areas, residential districts, large-scale manufacturers and other enterprises to ensure safety. This system can therefore be used in real-time applications which require face-mask detection for safety purposes due to the outbreak of Covid-19. This project can be integrated with embedded systems for application in airports, railway stations, offices, schools, and public places to ensure that public safety guidelines are followed. © 2021 Karadeniz Technical University. All rights reserved.
Development of the Oral Examination Assessment System (SIPENILAI) in Japanese Using Latent Semantic Analysis (LSA) Algorithm

SIPENILAI is an automatic oral exam and grading system that has been developed by the Electrical Engineering Department Universitas Indonesia. The system developed in this thesis uses the google speech recognition API and LSA methods for assessment. The Google speech recognition API processes voice input which is then converted as text. LSA method looks for similarities between two documents and the Frobenius norm for assessment scores. Based on testing that has been done the average value of SIPENILAI accuracy is 83.64% for fluent users and 76.89% for non-fluent users.
Economic authentic and anonymous data sharing with forward security

With the advancements of cloud services, sharing of information has never ever been easier, and intelligent prediction of shared information provides a slew of advantages also to individuals and society. When sharing data with a huge group of people, several factors must be considered, which include effectiveness, authentication, and the confidentiality of the cloud provider. A viable target for constructing an unidentified and genuine interoperability system is ring signature. It enables an owner to privately configure the data that can then be stored or analyzed in the cloud. However, in a conventional Public-Key-Infrastructure (PKI) setting, the expensive credential authentication becomes a limiting factor for this solution's scalability. Instead, an Identity based Ring Signature can be used, which eliminates the need for document verification. We enhance the safety of identity based ring signatures in this paper by adding forward protection: If a user's secret key is corrupted, all previously stored confirmations containing this user are still valid. The whole product is extremely crucial in any big information sharing system, because it is pointless to force all digital assets to conceptual metaphor their data if a single user's secret key is compromised. We demonstrate the practicality of our scheme by providing a tangible as well as efficient implementation and proving its security in good manner.
Design and Implementation of Chao-Cryptic Architecture on FPGA for Secure Audio Communication

The emerging growth in communication arena firmly demands a secure channel and needless to mention that security and privacy of the user are the top priorities in audio communication. To transmit audio signals safely, cryptography is being adopted widely. Chaos-based cryptography is one of the effective strategies that attract researchers to develop more techniques. Characteristics of chaotic systems such as ergodicity and stochasticity are the main motivators for using them in encryption schemes. This work presents an field programmable gate array (FPGA) governed chaotic audio encryption scheme based on Lorenz attractor. This scheme of encryption undergoes two consecutive steps of operations, namely confusion by linear feedback shift register (LFSR) and diffusion through the random numbers generated in X, Y, and Z planes of the chaotic attractor. A study has also been carried out to analyze the best diffusion from the obtained results. Evaluation of the proposed scheme includes entropy, correlation, histogram, PSNR, and MSE. Further, NIST SP 800-22 batteries of test have been performed to ensure that the encrypted audio data possesses more randomness. Moreover, this work consumed 15,422 logic elements and 14.22 mW of dynamic power to accomplish the design. Noticeably, the proposed work has achieved 10.346 Mbps as a throughput to encrypt 216 × 16 bit samples of audio data.
Improving Relation Extraction Using Semantic Role and Multi-task Learning

Relation extraction (RE) aims at identifying the relationship between two given entities and plays an essential role in natural language processing (NLP). Most of existing relation extraction models use convolutional or recurrent neural network and fail to capture the in-depth semantic features from the entities. These models also only focus on the training data and ignore external knowledge. In this paper, we propose a relation extraction model that makes use of external knowledge and the semantic roles of entities. In our model, we first adopt RoBERTa to make use of the knowledge learned from the unsupervised pretraining corpus. Then we obtain the semantic role embeddings and propose an entity attention network to select important words for relation extraction. We also offer the multi-task learning module and further improve our model by learning from auxiliary tasks. Our model obtains a Macro-F1 score of 89.96% on the benchmark dataset, outperforming most of the existing methods. More ablation experiments on two different datasets show that semantic role information and multi-task learning can help improve the relation extraction.
Article Review: Survey Fuzzy Logic and Aprior Algorithms Employed for E-learning Environment

The adaptive education frameworks within e-learning platforms are designed to ensure that each learner's learning experience differs. To provide adaptive e-learning programs and research materials adapted to adaptive learning, this type of educational approach attempts to integrate the ability to understand and identify the unique needs of an individual in the context of learning with the skills needed to use suitable learning pedagogy and to improve the learning process. Thus, designing realistic student profiles and templates based on an overview of their affective states, level of experience, and individual personality traits and abilities is essential. The data collected can then be used and used effectively for the creation of an adaptive learning system. These learner models can be used in two ways once learned. The first is to educate the pedagogy of the integrated educational method suggested by the experts and designers. The second objective is to provide the framework dynamic self-learning ability based on teachers and students' behaviours to build effective pedagogy and adapt e-learning environments automatically according to pedagogies. Artificial intelligence algorithms can, for various reasons, be useful, including their ability to improve and mimic human reasoning and decision-making (learning-teaching model) and to reduce the source of uncertainty in order to achieve an effective background of learning. These leadership skills ensure progress for both the learner and the system over the lifelong learning process. In the following document, we present a survey on growing and relevant issues in the field of artificial intelligence algorithms, their advantages and drawbacks (fuzzy logic and A prior algorithm), as well as the importance of using these strategies to make e-learning system smarter and more efficient.
Outlier Detection for Sensor Data Streams Based on Maximum Frequent and Minimum Rare Patterns

Identifying outliers in data is an essential assignment in data mining. It aims to find the data that is significantly different from other data in the data streams, and scholars have proposed many outlier detection methods in recent years. Because pattern-based outlier recognition technology clearly illustrates the causes of anomalies, they have received extensive attention. However, the existing pattern-based outlier identification techniques main use frequent patterns or rare patterns with perform the outlier identification operations, which will affect the precision from outlier identification. To improve the exactness of outlier detection on data streams, in this paper, the maximum frequent patterns and minimum rare patterns are combined together and regarded as a whole as the pattern used in the process of outlier detection, and then it redefines the deviation index, so as to perfectly measure the abnormal degrees of outlier in the data streams. Dependent upon those search patterns and characterized deviation index, Novel maximum frequent and minimum rare pattern-based outlier detection method are put forward, that is, specific MFaMRP-OD, which can faultlessly recognize those possibility outliers.
Cross-modal learning for saliency prediction in mobile environment

The existing researches reveal that a significant impact is introduced by viewing conditions for visual perception when viewing media on mobile screens. This brings two issues in the area of visual saliency that we need to address: how the saliency models perform in mobile conditions, and how to consider the mobile conditions when designing a saliency model. To investigate the performance of saliency models in mobile environment, eye fixations in four typical mobile conditions are collected as the mobile ground truth in this work. To consider the mobile conditions when designing a saliency model, we combine viewing factors and visual stimuli as two modalities, and a cross-modal based deep learning architecture is proposed for visual attention prediction. Experimental results demonstrate the model with the consideration of mobile viewing factors often outperforms the models without such consideration.
Development of the method of visualization for production facilities with application of augmented reality technologies

This article reviews the experience of application of VR-AR technologies (virtual and augmented reality technologies) for visual demonstration of production facilities in the process of engineering and operation. The three-dimensional modeling in CAD systems is commonly used as a means for development of construction documentation, visualization of work at different stages of engineering, and preparation of objects for engineering analysis. The authors believe that the use of AR-solutions not in parallel, but along with modern CAD systems for solution of engineering and design problems would allow avoiding design flaws and simplify the process of demonstration of projects. Based on the blueprints and three-dimensional models of the parts and assembly of two-stage reduction gear used &amp;nbsp;for lifting a thermal furnace in metallurgical production, within the framework of development of the Unity multimedia application is developed the AR application for visual demonstration of the device. As the guide mark for visualization is selected the assembly drawing of the reduction gear in digital graphical format edited in the Vuforia portal. The guide mark database is imported into the Unity environment, the logic of implementation of the technique for demonstrating the assembly of the device and separate parts is written in script program. The application is installed on a smartphone and tested; pointing the camera at the printed assembly drawing allows seeing the 3D model of the reduction gear from different angles, hide any details and scrutinize the device. The AR-applications developed according to the proposed method can be used in the process of training operational personnel, which would improve the performance and quality of work during installation and maintenance of industrial facilities, as well as for displaying the projects during exhibitions and presentations of new products.
Anov: A Framework for Rapid Construction and Development of Web-Based Large Screen Visual Dashboard

Abstract With the advent of the era of big data, extract and display effective information from massive amount of data has become a hot research field. Visualization is the most direct way for users to perceive and understand data. That’s why data dashboard with its cool dynamic visual effect has become so popular. In this paper, we propose a framework for rapid construction and development of web-based large screen visual dashboard—Anov. Anov dedicates to three goals: high efficiency, innovative AI user interaction and 3D scene. First, this framework provides a “30% configuration, 70% customization” development scheme which shortens the development cycle by at least one third. Second, Anov adopts a hierarchical architecture model to achieve high reusability, easy extensibility, high reliability, and easy maintenance. Finally, this paper introduces Anov’s wide range of application field and rich user experience through multiple cases to demonstrate the practicality of the framework.
A Novel Image Caption Model Based on Transformer Structure

The Image Caption algorithm based on the transformer structure is different from the traditional codec model based on convolutional neural networks and recurrent neural networks. Our proposed algorithm uses a convolutional neural network to extract image features and completely relies on the self-attention mechanism to process image features and generate sentences describing image content. The experimental results verified the validation of our method using BLEU, CIDEr and other indicators on the MSCOCO data set.
An Intelligent Photographing Guidance System Based on Compositional Deep Features and Intepretable Machine Learning Model

Photography is the activity of recording precious moments which are often difficult to make up afterwards. Therefore, taking the correct picture under proper guidance assistance is important. Although there are many factors that can determine a good photo, in general, photos that do not follow the composition rules usually look bad that make the viewer feel uncomfortable. Acting as a solution, in this paper, we propose an intelligent photographing guidance system using machine learning. The guidance is based on a tree-based interpretable machine learning model that can give reasons for decisions. There are two categories of features for guidance, which are traditional image features and deep features. Traditional features include prominent lines and image maps, such as saliency map and sharpness map, each of which exists in a multi-scale Gaussian pyramid. Deep features are extracted during the establishment of a CNN-based image composition classifier. We use these two categories of features as inputs for the interpretable machine learning model to establish a feasible photographing guidance system. The guidance system references our composition classifier with precision rate of 94.8%, and recall rate of 95.0% where the comprising tree-based interpretable model is capable of guiding camera users to alter image contents for obtaining better aesthetical compositions to take photos of good quality.
Government Expenditure Data Exploration &amp; Analysis Using Python

The goal of improving cost efficiencies is a constant endeavor of all organizations. This is especially true for governments, where public perception often has the ability to affect budget allocations. The data used in this analysis consisted of publically available state expenditures from 2018 and 2019 for the state of Idaho. The dataset contains the record of over 2 million state expenditures across all state agencies. The data analysis was performed using Python and the Pandas library. Visualizations were created using the Matplotlib package. The data exploration showed that Idaho’s Departments of Health and Welfare, Education and Transportation spent the most in this time period. The analysis also determined which Summary Objects, Sub-Object and Vendors experienced the greatest changes between the two years. Comparisons were also done using publicly available data on reported budget allocations by the states of Arkansas, California, Texas and Montana to see how spending differs between Idaho and these states based on percentage and per capita. Finally, suggestions for improvement in the areas of health care and employee transportation were given. These include methods of improving competition in health care, reducing travel through expanded teleconferencing and providing incentives to employees for reduced travel cost. Keywords: data science, budget analysis, python, pandas, government spending
Enhancing Learning Performance of Students in Higher Education with Adaptive Navigation Support

Abstract The study work on the development of a system prototype to aid the students in managing their assignments. The prototype employs Adaptive Navigation Support as an approach in providing personalized recommendation for the quiz that students must work on. With this prototype, the students can manage their quizzes better and complete more quizzes. In addition, the students are able to monitor their progress as well as their performance in particular course. Prototyping methodology was used to develop the prototype in mobile environment.
Adaptive path planning control of snake-like robot based on reinforcement tracking learning

The path adaptive planning control of snake-like robot is influenced by uncertain disturbance of environment variables and joint mechanism components, which leads to poor stability of path planning parameter output. In order to improve the stability of path adaptive planning control of snake-like robot, a path adaptive planning control method of snake-like robot based on reinforcement tracking learning is proposed. The kinematics model of snake-like robot path adaptive planning is constructed, constraint parameter modeling of snake-like robot path adaptive planning control is carried out, path distribution parameters of snake-like robot are collected by using sensitive components such as attitude sensor information output instrument and accelerometer, and path spatial distribution parameters of snake-like robot are fused by using enhanced tracking learning method and input into time control actuator. Aiming at the error of unknown disturbance in path planning parameter control of snake-like robot, attitude parameter fusion and inertia error correction are carried out by using enhanced parameter fusion learning method, so as to realize stable control of snake-like robot path adaptive planning. Simulation results show that this method has good stability in path adaptive planning control of snake-like robot, strong positioning and tracking ability of attitude parameters, and improves the robustness of snake-like robot moving forward.
Continuous Ant-Based Neural Topology Search

This work introduces a novel, nature-inspired neural architecture search (NAS) algorithm based on ant colony optimization, Continuous Ant-based Neural Topology Search (CANTS), which utilizes synthetic ants that move over a continuous search space based on the density and distribution of pheromones, strongly inspired by how ants move in the real world. The paths taken by the ant agents through the search space are utilized to construct artificial neural networks (ANNs). This continuous search space allows CANTS to automate the design of ANNs of any size, removing a key limitation inherent to many current NAS algorithms that must operate within structures of a size predetermined by the user. CANTS employs a distributed asynchronous strategy which allows it to scale to large-scale high performance computing resources, works with a variety of recurrent memory cell structures, and uses of a communal weight sharing strategy to reduce training time. The proposed procedure is evaluated on three real-world, time series prediction problems in the field of power systems and compared to two state-of-the-art algorithms. Results show that CANTS is able to provide improved or competitive results on all of these problems while also being easier to use, requiring half the number of user-specified hyper-parameters.
Building a Scheme for Assuring Certainty over the Internet based Trade for Cloud Applications

It is evident that the cloud computing platform offers plentiful chances for scattered and shared resources, services along with the information of the entire globe. The private cloud the information of the globe is prevailing within the cloud environment. Currently various types of internet-based procedures are implemented over the cloud environment. The present frameworks are not secure for executing internet-based firms seamlessly over the cloud platform. Moreover, the users lack effort in distinguishing the larger online business where the setbacks are depressed user and trade to perform in cloud environment. The motivation is to model a dynamic internet dependent business model using encryption focused on safety and swayed framework aiding in resolving the existing issues over the internet-based trade. Finally, the assessments are conducted in terms of the results and the modelled framework helping to disclose the correctness of the proposed model.
Analysis of different machine learning classifiers on MP election commission and breast cancer big dataset

This paper is a unique effort to resolve the scaling issues of machine learning using the multi-node environment of Big Data. For this purpose, it incorporates the concept of machine learning with big data. Machine learning is a branch of Artificial Intelligence which trains the computers to learn without being explicitly programmed. Machine learning works on the development of computer programs and software that work according to the input dataset. In machine learning, classification is used to identify the class of instances. In this the category or class or a group provides the new observation to which it belongs on the basis of the training dataset. Training datasets contain those instances whose class is already known. An algorithm that implements classification is known as a classifier. The main aim of this paper is to evaluate how different classifiers work on two different datasets, one is a real time dataset of MP Election nomination (2018), while the other is a standard Breast Cancer dataset, released by University of Wisconsin (1995). There are various classifiers available like Decision Trees, K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Naïve Bayes etc. Different classifiers work differently on different datasets, giving different accuracy according to the kind and the size of the dataset. So we have used different classifiers for our work. When we were applying machine learning classifier on a real-time dataset, i.e. MPSEC dataset, Accuracy goes down. Similarly, these classifiers apply to Standard Breast Cancer dataset then results show outstanding accuracy. This research work shows the exciting patterns to study the case of a real-time and standard dataset using different machine learning classifiers with a combination of big data. This research work also incorporates the concept of parallelization using a multi-nodes environment which is able to deal with scalability issues.