Reviewerly/exHarmony huggingface dataset:
Advanced Face Mask Detection System

COVID-19 pandemic caused a lot of loss and it is really important to give attention to technologies and ways to slow its spread and eventually stop it. Now in past few months, the government tried to open various sectors of society such as school gyms, etc. but failed and had to shut them down again due to increase in cases; we have seen a gradual increase in cases when the government tries to open these sectors because many persons were not so much attentive and were not following protocols properly. One of them is not wearing proper masks. Mask is a non-pharmaceutical measure that is used against primary spread of COVID by droplets. For people to follow protocols and wear masks, we are proposing a face mask detection device which will be effective to make people more aware to wear face masks by warning them who are not putting masks on their faces and restricting them to enter public places such as school, colleges, gym, etc.
Enhanced cipher text-policy attribute-based encryption and serialization on media cloud data

Purpose There are various system techniques or models which are used for access control by performing cryptographic operations and characterizing to provide an efficient cloud and in Internet of Things (IoT) access control. Particularly in cloud computing environment, there is a large-scale distribution of these traditional symmetric cryptographic techniques. These symmetric cryptographic techniques use the same key for encryption and decryption processes. However, during the execution of these phases, they are under the problems of key distribution and management. The purpose of this study is to provide efficient key management and key distribution in cloud computing environment. Design/methodology/approach This paper uses the Cipher text-Policy Attribute-Based Encryption (CP-ABE) technique with proper access control policy which is used to provide the data owner’s control and share the data through encryption process in Cloud and IoT environment. The data are shared with the the help of cloud storage, even in presence of authorized users. The main method used in this research is Enhanced CP-ABE Serialization (E-CP-ABES) approach. Findings The results are measured by means of encryption, completion and decryption time that showed better results when compared with the existing CP-ABE technique. The comparative analysis has showed that the proposed E-CP-ABES has obtained better results of 2373 ms for completion time for 256 key lengths, whereas the existing CP-ABE has obtained 3129 ms of completion time. In addition to this, the existing Advanced Encryption Standard (AES) scheme showed 3449 ms of completion time. Originality/value The proposed research work uses an E-CP-ABES access control technique that verifies the hidden attributes having a very sensitive dataset constraint and provides solution to the key management problem and access control mechanism existing in IOT and cloud computing environment. The novelty of the research is that the proposed E-CP-ABES incorporates extensible, partially hidden constraint policy by using a process known as serialization procedure and it serializes to a byte stream. Redundant residue number system is considered to remove errors that occur during the processing of bits or data obtained from the serialization. The data stream is recovered using the Deserialization process.
Dynamic Authentication Using Visual Cryptography

Nowadays with the increase in cybersecurity threats, it has become very important to upgrade the cybersecurity standards for all web applications. Especially in the area of Internet Banking, user authentication is a major issue. An extra layer of security is necessary for user authentication. Two-factor authentication is providing extra layer security in banking services. Biometric-based authentication, password-based authentication, smart card-based authentication are some of the authentication techniques used in banking systems. But all these techniques rely on the security of the database. Another issue faced by the users is regarding the latency in receiving the SMS OTP during online banking. If the network connection is poor, then the user may face latency in receiving OTP. To overcome these issues, we can use the concept of visual cryptography. This paper aims to use the “visual cryptography technique” for the authentication of a user in Internet Banking systems. Visual cryptography was proposed by Naor and Shamir (Naor M, Shamir A (1995) Visual cryptography. In: Proceedings of the advances in cryptology—Eurocrypt) in 1994. VC is one of the most secure techniques which can be used for secret sharing, in which the secret information will be in the form of an image. During encryption, we can divide the image into different shares or transparencies and the decryption process can be done by superimposing the shares. There is no need for complex computations in decryption; even by using a normal human visual system, we can decrypt the image; this is one of the advantages of visual cryptography. This paper presents a technique that uses the concept of (2,2) VC scheme to create shares of a unique key generated during the time of customer account creation. One of these shares is given to the customer, and another share will be kept within the server. During the time of two-factor authentication instead of OTP, OTP positions will be generated, and according to that, the server-side share will undergo changes dynamically. And the server-side share will be displayed to the user. The user can superimpose his/her share with the server share to generate the OTP.
Gameplay Cognitive Decision Support Using Statistical and Non-statistical Parametric Fusion

Surprises are an inevitable part of any gaming application. These surprises eventually make decision-making a tough task. Cognitive learning always works in actions and associative responses. Gaming and cognition have a close association while decision-making in gaming or economics applications. These applications always respond to opponents’ moves using statistical variants. These responses are always affected due to surprises. These surprises contribute to increasing uncertainty in decision-making. These surprises are not quantified and considered before action or response selection. This research is focusing on hypotheses as using non-statistical information such surprises can be optimized. There is also a need to associate quantified surprises and associated statistical variants. Here the model is proposed using data fusion techniques to use statistical and non-statistical parameters. This intern improves cognitive decision-making. The system can be adapted to other applications like gamification, economics applications, etc.
A Queue Management System for Cloud Data Processing

With cloud computing, businesses have to manage their systems effectively due to the amount of data they store every day. The Hadoop Distributed File System with MapReduce is an efficient and widely used cloud data processing platform that parallelizes data processing operations. However, Hadoop’s efficiency is heavily reliant on job scheduling. Centralized schedulers offer predictable execution at the cost of resource utilization; distributed schedulers, on the other hand, maximize cluster utilization but have lengthy job completion times when assignments are heterogeneous. The introduction of queues at operational nodes may help to resolve this issue. This paper introduces a queue management system to reduce job execution time while utilizing the system resources as effectively as possible. To achieve rapid job completion times, we can define policies that can be used to manage active queues, in which we specify which task will be executed next when a running task terminates. The proposed system’s performance is evaluated using three parameters: total time, average time, and capacity utilized.
Technologies for designing and implementing user self-service workstations in libraries

Using Radio Frequency Identification (RFID) technology in libraries has become the reality. Their design and implementation methods are well defined. However, lacking financial and technical resources in Russian regions prevent them from using commercial solutions in regional libraries. Based on the experience of implementing RFID-technologies in Russian libraries, experts of the Kuzbass State Library for Children and Youth (Kemerovo) accomplished preproject research and commissioned self-service workstation (with designed hardware, developed database, established interaction with OPAC-Global ALIS). The stages of designing self-service station for Kuzbass State Library for Children and Youth are characterized. The requirement to the hardware comprising www-server, sensor display, receipt printer, and 3D-scannerfor barcode, are specified. MySQL environment was chosen for designing and implementing of self-service workstation configuration. The authors present its advantages and disadvantages in library services. The corresponding use statistics for Kuzbass State Library for Children and Youth is provided. These workstations save user time, enable to computerize many processes and to improve the technology of library services on the whole.
Training generalizable quantized deep neural nets

While a number of practical methods for training quantized DL models have been presented in the literature, there exists a critical gap in the theoretical generalizability results for such approaches. Although empirical evidence often suggests a high tolerance of DL architectures to variations of training procedures, existing theoretical generalization analyses are often contingent on the specific designs of training algorithms, e.g., in stochastic gradient descent (SGD). This specialization makes such generalizability results inapplicable to the case of quantized DL models. In view of this critical vacuum, this paper provides several almost-algorithm-independent results to ensure the generalizability of a quantized neural network at different levels of optimality. These results include the characterizations of a computable, quantized local solution that ensures the generalization performance and an algorithm that is provably convergent to such a local solution.
The promise of beginnings: Unpacking ‘diversity’ at Oculus VR

This paper presents a case study of the inaugural year of Launch Pad, a diversity initiative by Meta’s virtual reality (VR) subsidiary, originally known as Oculus. As industry-led discourse presented VR as a vision of opportunity and change in the tech industry, Launch Pad presented a vision of social progress through improved diversity. However, a variety of contextualizing factors within that first year complicate these visions of progress, including the gendered and racialized norms of the tech industry, the politics of Oculus’s co-founder and the mixed feelings of the first beneficiaries of the program. I argue that even if Launch Pad is a good faith effort to address historical and current forms of marginalization and underrepresentation in the tech industry, such efforts must go much further than mentorship and tokenized inclusion, requiring a genuine recognition of the need for systemic change.
Open-Source Software Projects Curating Model for Empirical Software Engineering Studies

Software projects are common inputs in Empirical Software Engineering (ESE), and they are often selected without following a specific strategy, leading to biased samples. To avoid this problem, researchers choose to use publicly available datasets instead of picking the projects themselves. However, some datasets are not maintained, containing old versions of projects, or even deprecated ones. This may raise some representativeness issues due to major changes in development practices and technologies over time. The main goal of this research is to develop a procedures model to construct and maintain a software project dataset with their product quality metrics, to support the development of ESE studies.
A Novel Hysynset-Based Topic Modeling Approach for Marathi Language

Topic modeling is an unsupervised technique which allows representing the corpus using various topics which are inherent but hidden. These topics in turn contain different relevant words, and the relevance is calculated using latent features. The topic model is built using latent dirichlet allocation (LDA) which is constructed on the corpus and is based on latent features. The vector space model for Marathi is VSMM used by the existing system which does not consider the different relationships between the words based on the meaning of the words. Hysynset vector space model for Marathi (HSVSMM) is proposed which uses Hyponyms-hypernyms relationship along with synonyms and formulates group known as Hysysnset. It reduces dimensions along with betterment in the quality of topics. The term ‘Hysynset’ is used first time in the research world. The comparative analysis of topic models using VSMM and HSVSMM is carried out using several parameters like entropy, purity, coherence measure, etc. The corpus contains more than 1200 text documents in the form of different Marathi stories.
A Survey on Detecting and Preventing Hateful Comments on Social Media Using Deep Learning

Freedom is that the right that is expressed by everybody. However, under the guise of free speech, this privilege is being abused to discriminate against and harm others, either physically or verbally. Hate speech is the regarding for this class of religious bigotry. Hate speech is depicted as language used to show scorn toward an individual or a gathering of people dependent on characteristics like ritual, ethnicity, gender specific, ethnic group, handicap, and heterosexuality. It can take the form of speech, writing, gestures, or displays that target someone due of their affiliation with a particular group. Hate speech has been more predominant in recent years, both in individual and Internet. Hateful content is reared and shared on social media and other online sites, which in the end leads to hate crime. The developing utilization of online media stages and data trade has brought about critical advantages for mankind. However, this has resulted in a number of issues, including the spread and dissemination of hate speech messages. Late investigations utilized a scope of AI and profound learning strategies with text mining techniques to automatically detect hate speech messages on real-time datasets to handle this developing issue on social network forum. Hence, the aim of this paper is to survey the various algorithms to detect the hateful comments and predict the best algorithms in social media datasets.
Survey on Convolutional Neural Networks-Based Object Detection Methods

The world is undergoing a deep and vast digital transformation and every day millions of images are produced and shared. There is an urgent need to extract valuable information from these images and use it for various applications. Object detection is at the forefront of it and there are many methods/algorithms which can be used for it. As a subfield of computer vision, object detection has been going through fast-paced changes. It is regarded as an exceptionally complex topic in the field of computer vision as it is the amalgamation of object classification as well as object localization. In this paper, we have taken an in-depth look at some of the widely varied state-of-the-art methods of object detection such as RetinaNet, ResNet, and ConvNet. These networks can be differentiated based on a variety of different features such as loss functions, data augmentation and feature extraction. We have compared them with their baseline models and analysed them to identify the most accurate methods. In our literature survey, we have studied the best in class techniques and have presented a brief overview of the current situation of object detection.
Investigating i-Vector Framework for Speaker Verification in Wild Conditions

Nowadays, most of the mobile and handheld devices use speech and speaker verification systems (SVS). Even though these systems give satisfactory performance in constrained conditions, there are a number of real-life unconstrained conditions where their performance is not satisfactory. Such SVS cannot accurately authenticate a person when deployed in applications where varying environmental or channel conditions. The actual conditions may be very much different than those used during system training. This creates a large uncertainty in verification scores obtained during evaluation phase of the system. In this regard, we have implemented a verification system using state of the art i-vector-based approach. It is based on total variability subspace (TVS) that benefits in modeling both session and channel variabilities using a single low-dimensional space instead of two different subspaces. Our experiments are conducted using the data taken from speakers in the wild (SITW) database, and the equal error rate (EER) value we have obtained is 23.16%.
Decentralization of Traditional Systems Using Blockchain

Blockchain is considered to be one of the paradigms shifting technology in the 21st century. Before the advent of Blockchain, people could not think of a Peer-to-Peer computing system to be existing because of the key issues in having such technology such as issues with security and privacy, issues with scaling such a technology. Blockchain has garnered considerable attention in recent years, which has generated a wide range of applications. However, with new inventions in these fields such as smart contracts, new and improved secured protocols this dream does not seem that farfetched after all. Blockchain is the front runner for changing the Peer-to-Peer network system and having a safe and secure form for having transactions. Proof of work (PoW) is a decentralized consensus technique that requires network participants to spend time solving a complex mathematical puzzle to prevent anyone from manipulating the system. The primary problem with this technology is the lack of awareness among people; the general public still gets confused with Blockchain and Bitcoin. Even with these existing conditions, several companies have demonstrated use cases of this technology in various fields like finance, supply chain, smart contracts, and many other territories.
E-Commerce Web Portal Using Full-Stack Open-Source Technologies

E-commerce applications are tools for accessing the Internet as well as purchasing products and services. People nowadays do not prefer to go out to buy; instead, they prefer the goods and services to be delivered at their place. Such technologies are evolving day by day as a huge advancement of technology is observed with a rapid pace. This paper offers a fresh perspective on how open-source technologies can be used in building e-commerce platforms, thereby reducing various costs such as licensing fees of enterprise software and tools. This full-stack web application enables the user to buy products from an online platform. Apart from buying, the user is provided with a wide variety of features such as selecting products of a particular category, filtering the products on the basis of color, size, and many other features that make this application more user friendly. Also, the data which is received from the front end or posted on the front end is in an unstructured json format. It is tough to store such data in a tabular format. So as to store such kind of unstructured data, we need a NoSQL database, i.e., MongoDB.
Face Recognition-Based Automatic Attendance System

In today's digital world, in almost every field, the face recognition technology plays a vital role. The attendance marking system has become difficult and interesting. This system of automation is used for surveillance, authentication, recognition of the face of a specific person, and has many more benefits. Everyone is adopting the conventional method of taking attendance these days, this consumes more time, and there could be possibilities for proxy participation. We used several libraries in this automation framework, such as OpenCV, face recognition, and Harr-cascade classifier. Using a Haar-cascade classifier, face detection and recognition are executed. And, in the Excel sheet, the attendance is revised.
Addressing Item Cold Start Problem in Collaborative Filtering-Based Recommender Systems Using Auxiliary Information

Recommender systems (RS) are becoming increasingly popular and are emerging as a viable answer to the problem of information overload. The collaborative filtering-based (CF) recommender system has trouble recommending new items or to new users because there are no ratings for new items or by new users. These issues are known as new item cold start (ICS) and new user cold start (UCS) problems. In many domains, such as ecommerce, we have auxiliary information such as an image and a textual description for each item. These auxiliary data can be used to produce feature vectors for new items, which can subsequently be used to find similarities between new items and existing items in the system, allowing for their recommendation, and alleviating item cold start problem. In this paper, we propose to extract feature vectors from images and textual descriptions of items using deep neural networks such as pretrained VGGNet and BERT, and then use these feature vectors for similarity calculation, allowing CF-based RS to predict ratings of new items accurately or with low error. We present 3 models that use only image, only text, and both images and text as auxiliary information, and tests on a large dataset from Amazon show that the suggested strategy is effective, with an MAE ranging from 0.77 to 0.86 on a 1 to 5 rating scale.
Text-to-Speech Synthesis of Indian Languages with Prosody Generation for Blind Persons

The major form of communication for humans is speech in order to exchange their thoughts and feelings in a better manner. There are a number of research works that focused on automatic text-to-speech conversion in various fields, and its applications are helpful for visually impaired and other physically challenged persons also. This paper mainly focused on implementing text-to-speech synthesis using Python for various Indian languages such as Bangla, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Punjabi, Tamil, and Telugu. The main parameters considered for analysis are execution time and audio file size generated for the given text. The main phases involved in the proposed system are text-to-speech conversion for various Indian languages and text-to-speech conversion with prosody generation for various Indian languages, and compare which method gives better results. Finally, it is analyzed which Indian language gives better performance with prosody generation based on the analyzing parameters.