Reviewerly/exHarmony huggingface dataset:
Inequalities in connectivity and academic performance during non-face-to-face teaching due to the Covid-19 Pandemic

The outbreak of the COVID-19 virus has turned our world upside down. Universities were not the exception. Most administrators struggle to redesign higher education for a postCOVID world university. During the pandemic, however, many of the face-face courses turned to virtual, giving rise to or incrementing inequalities among students due to lack of equipment and network connectivity could affect their academic performance. This paper brings together evidence from various data sources and the most recent studies to describe what we know so far about the impacts of the COVID-19 crisis on inequalities across several key domain factors, including the availability of equipment and connectivity could affect their academic performance. The paper explores the effect of connectivity on the academic performance of our chemical and engineering students based on (i) the location of the home, (ii) the equipment, (iii) the type of connectivity, (iv) the number of people with those in which the equipment is shared and (v) online attendance at classes, finding that the ones with the greatest impact are the lack of synchronous attendance at class, the equipment, and internet connectivity.
Comparing K-Nearest Neighbors, Random Forest and Naïve Bayes Models to Classify Fetal Health Using Resampling Methods

To preserve the lives of pregnant women and their fetuses, Cardiotocography (CTG) data is used to check the fetus’s condition and for early intervention if necessary. As a result of the existence of imbalanced classes of fetus’s condition, the performance of some models is affected when applied to real data. Accordingly, this research aims to observe the effect of data resampling methods on models’ performance by adopting the CRISP methodology. The performance of k-Nearest Neighbors (KNN), Random Forest (RF), and Naive Bayes (NB) models are compared using resampled data with several methods with the help of the CorrelationAttributeEval feature selection method for high dimension reduction. The results stated that NB and RF models’ performance has not improved with the resampling methods, while KNN has significantly improved across all metrics. RF is considered a good classifier as it was the best, with an accuracy of 93.8%. NB was the only improved model using feature selection with imbalanced data. Also, NB has the worst performance in terms of accuracy compared to KNN and RF.
Voice Assistant System

Computers are getting better, quicker, and smarter in the current period. Their use is expanding in industries including medicine, business administration, and education, among others. Therefore, it is necessary to make computers easier to use and operate. The interface with computers is known to be made easier by digital virtual assistants. Most digital virtual assistants rely on voice to communicate with their users, making it difficult for the deaf and dumb to utilise such gadgets. Research in this area is motivated by a desire to make common computer tasks accessible to individuals of all abilities by way of a speech and gesture-based virtual assistant. Developing intuitive methods for human connection with machines is the focus of this study. The user can choose what to input into the virtual assistant. Users have the option to interact with a mouse pointer or communicate using voice and gestures.
Hierarchical Method in Cloud Security for Maintaining Data Integrity After Revocation

CloudComputing(CC) is part of the vast majority of promising ways to deal with the speedy rise in facts distribution. To avoid data leaks in the cloud, users must cipher their facts before it is allocated. Consumers with sparse computing assets may utilize the cloud tooutsource their huge compute tasks and benefit from their vast computing capacity, bandwidths, warehouse and also some relevant application which can be merged on a single paid manner. Permission rule is essential since it is the foremost protection against illegal access to the shared information. However, outsourced compute workloads typically include sensitized details, example firm monetary logs, registered enquiries of data or directly recognizable health datum, etc. Facts should be encrypted before being outsourced aiming to ensure complete info secrecy in cloud and beyond, preventing the information leaking. Clients run the danger of important exposure that goes unnoticed but was there in prior study. ABE's practical use is further hampered by the massive client decryption overhead it incurs. An effective solution to both the key escrow and key exposure issues is provided by the collaborative mechanism. It is our goal to provide and demonstrate, using the described security model, an actual HAS-BE with RABE-DI technique for data confidentiality and data integrity together. A major drawback of using standard data encryption is that it prevents the cloud from doing any useful operations on the text policy that underlies the encrypted data.
Privacy and Security of Big Data-A High Perspective Investigation

The technological development in various fields brings about the persistent development of the enormous information market. Huge information is drawing the attention of researchers in all the domains because of its greater utility. But, prior to the usage of numerous information innovation for huge scope, the security risks whereas preserving privacy should be taken care of. This paper surveys on the security issues and challenges faced and elaborates on the recent research carried out in maintaining highest protection by improvising confidentiality on numerous data. The effect on the attributes of the enormous information subsequent to authorizing the safety efforts is also focused.
An Intelligent Food Serving Robot Prototype with Android Application for Canteens

As the world faces a COVID epidemic, one of the most critical rules to observe is social separation. There are some situations where social separation is difficult to maintain, such as canteens. The proposed technology equips a college canteen with an autonomous food serving robot, allowing us to preserve social distance. People in canteens confront challenges such as long lines and food service delays. When it comes to college canteens, students only have a limited amount of time for refreshment, resulting in a rush at the canteen. Our self-serving food robot will serve the food to the clients without fail; all they have to do is order meals using the mobile app. The system relies on a mobile application to place orders and a robot to deliver the food. Users will be able to summon the robot using the help button in the mobile app, which will result in canteen trash management. For routing and finding the best way to the table, we employ a combination of sensors and Radio Frequency Identifier (RFID) technology. Our solution will benefit the admin in addition to keeping the customers happy. Making a robot will be less expensive than hiring a human waiter. The system not only has a rechargeable wallet payment interface, but also net banking, card payment, and UPI payment possibilities.
Dissected Urdu Dots Recognition Using Image Compression and KNN Classifier

Character recognition for the Urdu language is always a tedious task because of its complexity as Urdu words are composed of ligatures that further consist of primary and secondary components. The Secondary components further consist of Urdu diacritical marks which play an important role in recognition of the Urdu language. This paper presents the technique for recognizing these secondary components to improve the recognition accuracy of the Urdu language. For this, three types of features of secondary components are extracted i.e. invariant moments, DCT, and DFT. The trained data set consists of a total of 228 features out of which 28 are invariant moment features, 100 are DCT features and 100 are DFT features. These extracted features are classified using the KNN classifier for recognition. After extraction of variant and invariant features of characters, the KNN is evaluated with different threshold values. When the threshold value of KNN is considered as 1, then the KNN achieves 89.6% recognition rate for variants and invariants features. But, if the value of k is set to be 2 the recognition rate of KNN (97.57%) increases. Thus, the value of k improves the character recognition rate.
Performance assessment of solar radiation based on satellite data in a Moroccan city

Solar radiation data in a relevant location on earth is a key component for all applications of solar energy. In the current study, four models are assessed for forecasting solar irradiation at a particularized location in southern Morocco. The satellite-derived radiation data for the years of2005 to 2020 are analyzed. The comparisons of predicted solar irradiation from studied models and satellite values have been performed using many statistical indicators, including the relative mean bias error (rMBE), the relative root mean square error (NRMSE), t-statistic (TS) and the determination coefficient (R2). Based on these indicators, the predicted solar irradiation values were in good correlation to satellite values. The obtained results in this study can be employed as basic information to estimate the accuracy of future solar energy systems appropriate in the studied location and in other sites in Morocco with identical climatic conditions where there are no solar radiation measurements.
LSTM Based Sentiment Analysis on Conversations in Health

Sentiment analysis from health data is to classify texts in the field of health with sentiment analysis. Research analysis on emotion covers as many fields as politics, economics and health. Sentiment analysis from health data will contribute to human-computer interaction, since they perceive emotions in their machines and make them look like human beings. In this article, the existing data in the field of health has been classified by sentiment analysis. For the realization of this sentiment analysis, the deep learning model lstm deep learning model was used. LSTM has been very successful in processing long texts. In this method used, first of all, the data for model training was made ready with text processing methods. Model training was carried out with this data set, which was prepared with the LSTM deep learning model for model training. As a result of this model training, our success score was 94%, and when we compared it with the results of the articles and studies I examined in the literature, we see that our model gave a very good result. When this result is compared with other well-known classifiers, the efficiency of the proposed algorithm is convincingly proven.
Towards Secured Big Data Analytics in the Cloud: Two Case Studies

Securing and dealing with big data in the cloud has become a challenging issue. Best techniques, methods, and practices should be applied to ensure privacy and smooth cloud computations. Meanwhile, data analytics techniques play an important role in utilizing big data. This paper presents two different case studies that use the same datasets and perform the same processes, but they are implemented in different environments. Each case study has different scopes and aims to provide security and privacy for the data owner. Furthermore, a hybrid encryption algorithm is used to secure data using Elliptic Curve Cryptography (ECC) and Fully Homomorphic Encryption (FHE). Besides, clustering has been chosen as an instance of data analytics. Moreover, a distributed computing environment is defined to decrease the clustering time. Experiments show that the suggested distributed computing method can decrease the clustering time by up to 48% in the first case study and by up to 8.3% in the second case study.
Proposing Algorithm to Localize and Extract Facial Information Using FaceNet and MTCNN

Facial recognition systems from photos or videos are widely applied in daily life such as surveillance systems, access management, and security investigations. It can be compared with other forms of biometrics such as fingerprint or iris recognition systems. Among the facial recognition systems developed so far, Facenet introduced by Google brings a high accuracy rate. Therefore, in this paper, we will build a face recognition system using MTCNN| to detect faces and extract features using Facenet and SVM to classify and recognize faces. The proposed algorithm consists of 5 steps, namely, preparing data for training; face detection from data with MTCNN; extracting the features of each face with the FaceNet Keras model; classifying feature vectors by SVM, and performing face recognition. The algorithm results in an accuracy of up to 99.63% with data from only 5 to 15 images for a person. This proves that the algorithm is feasible when applied in practice.
An overview of Secure Communication in Smart Cities: Issues and Cryptographic Solution

Smart cities are one of the core infrastructures of the Modern World. The primary goal of smart cities is to improve the quality of life of people and services. Internet of Things (IoT) devices and information and communication technology are the primary building blocks of smart city infrastructure (ICT). ICTs and IoT devices have prompted many privacy concerns about communication in smart cities. The current study attempts to illustrate the fundamental ideas of security issues that address smart cities and reveal previously unknown cyber-attacks based on existing literature and recent targeted smart cities. Further, this study has uncovered and elaborated on several security issues. Cryptography is one of the most prominent methods for securing all types of communication. We have focused on all possible cryptographic concepts used in IoT security and smart cities scenario. It will also assist incoming researchers in formulating their proposals for developing robust security protocols to serve end users better.
Developing A Parallel Program For Image Similarity Search Using Hashing Methods

Nowadays, images play an essential role in people’s lives as they document their memories and affairs. Therefore, a large number of images have been generated in different storage. As a result, there was a need to search for similar images in this colossal quantity. While enormous data processing requires speed in execution, it needs the parallel processing method. This study aims to develop a program to search for similar images using the image hashing methods, the Average Hashing, and the Difference Hashing, then to compare their performance. In addition, it aims to explore the effect of using parallelization on the speedup and efficiency of the program. The first part’s results stated that the performance of the Average Hashing and the Difference Hashing methods was affected by the type of data used, despite their effectiveness in detecting identical images. As for the second part, the parallel program reduced the execution time by almost half using two workers, while the best speedup value was when using eight workers. In terms of efficiency, it achieved the highest results with a value greater than 0.9 when using two workers.
Issues, Challenges, and Opportunities in Food Recommender Systems

Recommendation systems are commonly used to provide consumers with customized services. They are primarily designed to provide recommendations or ideas that address the problems of consumers and can be applied to a variety of industries. Food recommendation systems are used to propose meals to users based on their purpose, assisting users in acquiring the most suitable meals. We explain four main types of meal recommendation system and algorithms and techniques utilized to implement them for the first time in this work. This paper also provides a full overview of several FRS studies, breaking them down as simply as possible. Finally, we explore the current issues in FRS as well as the field’s future potential. This paper will pose a reference to those seeking a detailed breakdown of FRS in the most coherent way possible.
Fine-Tuned Faster R-CNN for Universal Lesion Detection

Universal Lesion Detection in computed tomography scan images is an important task for clinical diagnosis. Manuel and conventional medical methods are time-consuming and error-prone. Automated detection of lesions at an early phase is important to increment the chances of treatment. In this paper, we proposes a fine-tuned deep learning-based method using Faster R-CNN and 1cycle training for universal lesion detection more accurately. The learning rate is one of the most significant hyperparameter to improve deep neural networks performance. Training with the well learning rate strategy can make a significant contribution to improve the model. The experiments showed that the proposed approach improves significantly Faster R-CNN performance for universal lesion detection.
Software Complexity Analysis of Integrated Accounting Information Systems for Cooperatives using the Use Case Point (UCP) Method

In recent years, many cooperatives in Indonesia are not able to present financial reports properly, impeding the Office of Cooperatives and Small and Medium Enterprises (SMEs) to supervise these cooperatives. To address this problem, it is necessary to develop an integrated Cooperative Accounting Information System. The system will integrate data at the Office of Cooperatives and SMEs with data in cooperatives in their working areas so as to produce a consolidated performance report. Considering the wide scope of the integrated accounting information system, it is necessary to analyze the complexity of the software to further discover the resources needed for the development of the system in the future. Software complexity was measured using Use Case Point (UCP), which can produce an estimation of resource needs such time and costs consumed in developing the system. The results revealed that the Integrated Accounting Information System has a level of complexity with UCP of 149.2, project work effort of 1,223 hours, project duration of 29 weeks, with an estimated cost of IDR 86,099,200. Based on this estimation, it is expected that the planning of the resources needed can be conducted more accurately and system development can run well as planned.
Comparison of SQL and NoSQL databases with different workloads: MongoDB vs MySQL evaluation

One of the most important considerations when selecting a database is how relational (SQL) and non-relational (NoSQL) data structures will interact. While all options are viable, consumers should take certain distinctions into account before choosing. Since SQL databases are vertically scalable, you can typically scale server components like CPU, RAM, or SSD. NoSQL databases, on the other hand, support horizontal scaling. As a result, you can increase the capacity of your NoSQL database by fragmenting (data partitioning), or by adding extra servers. Why, then, is it still challenging to choose the instance that is most appropriate for a given application and requires the least amount of runtime? Because data that will be conveyed via the internet uses a cloud in computer networks as a metaphor. To determine which model to utilize, it is required to conduct a comparison study of SQL-oriented database engines. SQL has a form created for another side of non-productive data and is offered in the form of ordered data, but NoSQL databases are horizontally expandable. Workload management solutions are therefore also in charge of automating organizational procedures, i.e., they carry out activities without requiring manual employee attendance. For businesses trying to implement continuous delivery methods and enhance the effectiveness of customer service delivery, they are unavoidable.
Comparative Study between Quantum and Classical Methods: Few Observations from Portfolio Optimization Problem

Classical computers have a limitation in solving NP-hard (nondeterministic polynomial) problems, which cannot be solved in polynomial time. However, the Quantum Computing paradigm is inherently capable of solving such types of problems. One of the major milestones inclined to a quantum -ready era is a relative comparison of execution speed and overall efficiency of current classical versus quantum models. In our approach, we are initiating the foremost step towards addressing the above milestone by performing a comparative study between quantum optimization techniques (available on both gate-model as well as quantum annealers) versus classical by solving a finance domain problem, that is, portfolio optimization using Markowitz mean-variance optimization. Historical market data from Jan’2011 till Dec’2016 of 48 NSE stocks were considered to build the portfolio by optimizing the mean-variance based on Harry Markowitz’s Modern Portfolio Theory. We approach this first classically and then on gate-model based quantum computer using Qiskit SDK, followed by D-wave solvers to build efficient portfolio and compare which among these frameworks are mature enough in this NISQ era to solve portfolio optimization problem related to the finance domain. Our results are comparable and from our experimentation performed on D-wave annealers and gate-model simulators, we observed that implementations using quantum methods were faster than the corresponding implementation of classical methods.
Self-Autonomous Car Simulation Using Deep Q-Learning Algorithm

This paper has proposed a car game using the algorithm Deep Q-Learning for simulation of self-driving autonomous car. It creates an environment where the self-driving car moves in left right up down direction starting from source to destination. Q-Learning is a RL algorithm which is an of f-policy algorithm that is completely different from a Deep Q-Learning as it replaces a normal Q table with neural network. It maps neural network with input states as their actions and Q values. This Game is reward based where agent gets the reward when it reaches the destination and does not hit any obstacles else it gets penalty which is then kept as an experience for future plays.
Performance analysis of quantum algorithms on Param Siddhi-AI system

The realization of certain computational problems using quantum mechanics opened up interesting new possibilities which may be better and beyond the scope of classical supercomputers. Simulation of quantum circuits on classical computers is necessary to analyze the properties of superposition and entanglement. Here, we used the SDKs of optimized libraries and tools of cuQuantum for accelerating quantum computing workflows on FP64 and TF32 based accelerated system for performance analysis. In this paper, the performance of quantum algorithms (Shor’s algorithm, Quantum Fourier Transformation, and Sycamore circuit), implemented by the CIRQ simulator, is analyzed on the PARAM HPC-AI system. Execution time and relative speed up for CPU only and accelerators having multiple precision capability enabled run is discussed in detail on PARAM Siddhi-AI system. The graphs are plotted to give a real-time analysis of the quantum circuit algorithms with the increase in the number of qubits. For Shor’s algorithm, Quantum Fourier Transformation, and Sycamore circuit, the maximum speed up, between CPU only and GPU enabled run, is observed as ~143x, ~115x, and 104x for 30, 32, and 32 qubits respectively.
Performance Analysis: Vehicle Routing using Quantum Annealing

Vehicle routing refers to the generic problem of finding the optimal routes for a given fleet of vehicles with respect to a given measure of optimality and subject to a given set of constraints. Finding the exact solution to such a problem is NPhard. Hence, we often use heuristics to find feasible solutions. We use the DWave Hybrid solver to find a solution to the vehicle routing problem, and compare the time complexity of finding the solution as well as the quality of the solution found against a classical alternative, namely Gurobi v9.5. We observe that the quantum annealer shows better scaling with respect to size when compared to Gurobi.