Reviewerly/exHarmony huggingface dataset:
Exploring Blockchain-Based Innovations for Economic and Sustainable Development in the Global South: A Mixed-Method Approach Based on Web Mining and Topic Modeling

Information and Communication Technologies such as blockchain can significantly contribute to achieving the Sustainable Development Goals (SDGs) in the Global South (GS). Grounded in the innovation forecasting literature, this paper explores blockchain-based innovations and research in the context of economic and sustainable development to answer the following research questions: what is the status quo of blockchain innovations and research in the Global South?; and further, how can blockchain innovations contribute to economic and sustainable development? We follow a mixed-method approach based on web mining and topic modeling with unstructured empirical data on blockchain innovators from Crunchbase, their websites, and academic publications to address these research questions. Our results indicate that blockchain innovators already engage in various application areas beyond the financial sector, addressing critical development issues. However, the empirical data on blockchain research and innovations suggests a largely untapped potential in various promising application areas relevant for the SDGs, such as sustainable energy or integrity verification. The major contributions of this paper are an overview and status quo of entrepreneurial innovation activities and research on blockchain technology in the GS, an analysis of these areas in the context of the SDGs, and a lean methodological approach for innovation forecasting.
Robot Motion Path Planning Using Artificial Bee Colony Algorithm

The very purpose of this paper is to introduce a new idea for mobile robot motion path planning, i.e., also known as navigation problem in robotics. Specific objective is to use the swarm intelligence of honey bees in the path planning process. The problem was located at the time of forecasting the path for an autonomous robot in an unknown environment. The nature of the problem is NP-complete and to overcome that many heuristic methods had been introduced. The paper recommends a new approach, i.e., artificial bee colony or ABC algorithm to overcome the same problem. This algorithm has discretized further in simpler algorithms. The algorithm avoids the incomplete paths and finds out the goal seeking path. To demonstrate the algorithm, MATLAB software has been used.
Concept Drift Aware Analysis of Learning Engagement During COVID-19 Pandemic Using Adaptive Windowing

Change is the only thing in the real world which has been known to last forever. It takes various forms and progressions, ranging from gradual in some cases and abrupt in the others to even constantly incremental in yet other cases like ageing. Machine learning (ML) algorithms, in its simplest definitions, use the statistical analysis of static past data records to make predictions about the future and have reached a fair amount of accuracy on diverse data sets across different application domains. There exists an inherent contradictory friction between real life analysis and machine learning models based on above definitions, and it gets compounded while capturing the ever-changing data from streaming sources. Concept drift is a principle used for description of unpredictable variations in streaming data sourced from the real world through a given time period. The drift phenomenon occurring even in a single feature, if left unaddressed leads to silent decay and can play havoc with the accuracy of a previously accurate ML model. With increasing prevalence and scale of real-world deployments of ML analytics, models cannot remain invariant to instability of data distributions and must adapt to concept drift. We analyse the occurrence and effect of concept drift in the COVID-19 online education data sourced from LearnPlatform edtech Company in this paper. The data set has almost 20 million entries related to engagement index and can be fairly assumed to be big data for processing purposes. A comparative case analysis for the accuracy of concept drift aware modelling using adaptive windowing (ADWIN) vis-a-vis the basic ML counterpart to predict the student engagement based on digital connectivity and education technology has been carried out for the study.
Constructing CM Fields for NFS to Accelerate DL Computation in Non-Prime Finite Fields

The hardness of discrete logarithm problem (DLP) over finite fields is the security foundation of many cryptographic protocols. When the characteristic is not small, the state-of-the-art algorithms for solving DLP are the number field sieve (NFS) and its variants. In the relation collection step, to translate the relations between prime ideals to those of elements one needs to use the Schirokauer map. Besides, if the number field has non-trivial automorphisms, one can use them to accelerate the factor-base logarithms computation. However, the Schirokauer map is not compatible with automorphisms. To exploit automorphism efficiently, we focus on the method to construct fields in NFS such that the fields on both sides have non-trivial automorphisms with the logarithms of units being zero: 1) we construct two families of CM polynomials of arbitrary even degree with small coefficients, corresponding to the automorphisms being <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x\mapsto -x$ </tex-math></inline-formula> or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x\mapsto 1/x$ </tex-math></inline-formula> ; 2) we show how to combine these polynomials with the JLSV1 and Conjugation polynomial selection methods on both sides; and 3) we also generalize our method to the multiple number field sieve and the extended tower number field sieve.
Deep Hashing With Multi-Central Ranking Loss for Multi-Label Image Retrieval

With the explosive growth of various images, large-scale image retrieval has attracted ever-growing attention. Deep hashing methods have achieved great success on single-label retrieval. However, the multi-level similarities between samples in multi-label scenarios have not been fully explored. In this letter, based on the in-depth analysis of the complex semantic similarities of multi-label images, the Multi-Central Ranking Loss (MCR Loss) for deep hashing is proposed to construct a powerful metric space that not only preserves the fine-grained similarities of multi-label images but also has low quantization error. The proposed MCR Loss utilizes learnable hash centers and similarities of data-to-data pairs to optimize the metric space, which greatly alleviates the embedding conflict caused by proxy-based supervision, and reduces the quantization error. The proposed method is compared with several existing state-of-the-art hashing methods on two public multi-label benchmarks. Experimental results show that the proposed method achieves state-of-the-art performance on several ranking evaluation metrics.
Neglect-Zero Effects in Dynamic Semantics

The article presents a bilateral update semantics for epistemic modals which captures their discourse dynamics [54] as well as their potential to give rise to fc inferences [58]. The latter are derived as neglect-zero effects as in [3]. Neglect-zero is a tendency in human cognition to disregard structures that verify sentences by virtue of an empty witness set. The upshot of modelling the neglect-zero tendency in a dynamic setting is a notion of dynamic logical consequence which makes interesting predictions concerning possible divergences between everyday and logico-mathematical reasoning.
An RFS Time-Frequency Model for Decomposing Chirp Modes With Dynamic Cross, Appearance, and Disappearance

In this paper, the decomposition and retrieval of the chirp modes with dynamic cross, appearance, and/or disappearance in a signal are formulated as a process of tracking time-frequency (TF) trajectories. Analyses show that the amplitude and frequency evolutions of each chirp mode in the signal can be described by polynomial prediction models. When all these evolutions are viewed as state equations, it is illustrated that the random appearance and/or disappearance of these modes will make the state equations into a state random finite set (RFS). If the amplitudes and frequencies obtained by the short-time spectrum of the signal are taken as the measurements of the state equations, a measurement RFS model in the presence of data association and detection uncertainties is coined when the statistical properties of the existing and newborn chirp modes as well as the noise/clutter are taken into consideration. So, a RFS TF model for tracking the TF trajectories in a signal is established. It is then demonstrated that a modified probability hypothesis density filter can be used to infer the amplitude and frequency posterior distributions of the chirp modes in the signal when the intensity of the RFS states is modelled as a Gaussian mixture. Numerical simulation experiments verify the effectiveness of our model and analytical results. Meanwhile, a superior performance is achieved especially for the high readability, extractability, and decomposability of instantaneous frequency trajectories with dynamic cross, appearance, and disappearance in a signal.
Machine Learning Approach to Analyze Breast Cancer

Machine learning has opened a new era of effective and easy ways of data analysis in biological research. The use of high throughput data analysis tools and libraries facilitated cancer research among many other benefits. Breast cancer is one of the main reasons for mortality among women. Apart from traditional molecular detection, it is also necessary to find an alternate approach. With the advent of modern technology, clinicians and healthcare providers can improve diagnosis accuracy and detection. In this study, we use a simple layer-based deep learning approach to investigate the malignancy of breast cancer. Our finding includes shows over 97% accuracy with the ANN-based machine learning approach.
Optimized Throttling for OAuth-Based Authorization Servers

Responsiveness is a key requirement for web-based enterprise software systems. To this end, throttling (or rate limiting) is often applied to block illegitimate traffic from outside-facing components and protect their computational resources. OAuth-based authorization servers are among the most popular components facing the web. Alas, the OAuth protocol introduces severe challenges to throttling. The OAuth protocol flow introduces indirections to client requests that make it hard to determine their source to apply rate limits. Moreover, fixed limits perform poorly in cases varying between high and low request loads. In this paper we propose solutions for both issues and provide an efficient solution for throttling in the context of OAuth. This includes integrated methods for a) cooperative throttling of authorization and resource servers as well as b) dynamic rate limiting as part of the throttling algorithm. We evaluate our approach based on a real-world use-case of enterprise CRM.
Comparative Modeling of CPU Execution Times of AES and ECC-AES Hybrid Algorithms Over Mobile Cloud Environment

This paper presents an extended comparative study on the modeling of CPU execution times for AES and a hybrid encryption algorithm AES-ECC. The encryption key is generated using the ECC algorithm, and then, this key is passed to the AES algorithm to encrypt the content of a data file. The ECC key integration with the AES encryption algorithm results in substantial increase in the security of the encrypted data. However, this study shows that the hybrid encryption algorithm utilizes more CPU execution time to encrypt the data file, when compared to the AES algorithm. Applications which demand high security are suggested to use the hybrid algorithm, whereas applications which demand faster execution are suggested to use the AES algorithm. The performance metric used for this study is the CPU execution time and is calculated using the C#.net framework and the assembly code.
An Improved Query Similarity Model for Online Health Community Forum Using Cross-Attention Mechanism on Siamese Network

Nowadays, many users join online health community forums to seek information, acquire knowledge, and hear how patients handle similar illnesses. However, finding similar patients is time consuming and laborious. In this circumstance, this study provides an automated query similarity method to find similar queries already discussed. Siamese neural network is a proven technique to assess the semantic similarity between sentences. However, Siamese neural network cannot alone provide satisfactory results due to long-term dependencies among words between sentences. Hence, this paper is an improved system of a previous study that proposed a Siamese neural network with a transfer learning technique for medical query similarity tasks. To examine the semantic similarity of the queries, a cross-attention method is constructed using a Siamese neural network (CASNN). The cross-attention mechanism manages the interactive semantic relationship between words. To handle the presence of numerous medical terms in the query, the model is also fine-tuned on a large medical question–answer dataset using a transfer learning approach. The best model (CASNN), when compared to other variations of the model, could obtain an F1-score of 89%. The experimental results show that the proposed method effectively obtains a better generalization ability among medical queries than the previous method.
A State of the Art Approaches to Question Generation Techniques

Automatic question creation for educational purposes has recently piqued the interest of scholars from several fields. In this study, we look at a few different techniques to create instructional question generating software. Although there are several approaches for automatic question generating, only a few educational systems that use question generation have been created and used in real classrooms. We also provide research recommendations for using question technology in computer-assisted educational systems.
Development of Contextual Crop Ontology for Effective Information Retrieval

Agriculture in India has a diversity of natural elements such as wider cultivable land, different agro-climatic zones, different soil types, and suitable seasons for cultivating a variety of crops. Indian agriculture is largely reliant on natural resources and favorable weather conditions. Therefore, under a vast agricultural diversity, strong information support and knowledge system are required for the farmer community to achieve a high yield. Accurate and timely information and instruction are vital to the agricultural processes that should be delivered to farmers properly. The available agricultural websites, mobile applications, and software provide only general practices to the farmers and do not address the solution for specific problems. So there is a need for a system that should give contextual information based on geographical area, climatic condition, soil nature, previous experience, and current state of the crop. The information provided by the system can be applied for their cultivation and marketing. The system is designed by using ontology for the agriculture domain and thereby providing information retrieval for this RDF repository. In this paper, farmer-based crop ontology is developed that will be useful for guiding the farmers by providing instructions and information related to crop cultivation and management of fertilizers based on their situation of the crop developing stage.
Automation in Project Management 4.0 with Artificial Intelligence

In today’s world, digital transformation is common and innovative in each factor of life. Now, most of the human works are being done by robots and machines in project management. Artificial intelligence has the potential to change the way of project management (PM) tasks which are currently performed and can be changed and controlled in the recent future. Companies are searching out diverse approaches to beautify the performance with taking care of several options like reliability, accountability, etc., all simultaneously. Industries have initialized construction of AI embedded tools to assist the team to control their challenge(s). This study explores how our existing PM profession can lead to change the strategy by using AI inputs and how AI helps in enhancing project management and transforming it as Project Management 4.0. It enables to automate various time-taking venture of control that is crucial for success. AI also helps in evolving task automation to efficient analytics of the project, searching for resources to help, and building efficient strategies.
To Model or Not to Model? Assessing the Value of Ontology-Driven Conceptual Modeling

Modeling plays an important role in representing and supporting complex human design activities. For example, Ontology-Driven Conceptual Modeling (ODCM) creates concrete artifacts representing conceptualizations of particular domains. However, the development, management, and usage of these artifacts require investments of resources that should be worth it. Often, stakeholders neglected the trade-off analysis of the benefits and investments in ODCM experiences because of the lack of tools to assist them with this task. In this context, the aim of this research is to develop a method to identify when worth investing in ODCM experiences based on an analysis of value. To propose the method, we will develop and correlate knowledge and artifacts regarding the quality and value of the modeling process and product, the return on modeling effort, and domain debt.
A Concept and a Multitenant Web Application for Interactive Software Architecture Analysis

The Architecture Tradeoff Analysis Method (ATAM) is a well-known method for the early evaluation of software architecture decisions based on the formulation of quality scenarios. Although widely recognized as beneficial, several limitations of ATAM have become apparent over time that limit its applicability. In this paper, we propose the concept of an interactive web-based application that mitigates some of these limitations while opening the possibility for further analysis based on collected ATAM projects, e.g. based on artificial intelligence. Thereby, we address the following limitations of ATAM: a general need for tool support, the need for an overview of ATAM results, the need for a consistent documentation of an ATAM process, the need for interactive and collaborative process execution among stakeholders, and the benefits of supporting multitenancy for architecture analysis. This applies in particular to systems with microservice architecture in order to document a large number of individual components and to make the documentation accessible to everyone involved in the system. The proposed concept, Interactive Software Architecture Analysis (ISAA), consists of a requirements analysis, an analysis model, a concept for interactive visualization, and a set of use cases. We demonstrate the feasibility of our approach by a software prototype in the form of custom modules for the Drupal Content Management System.
Analysis and Rendering of Deauthentication Attack Using IoT Technology

The Internet has developed exponentially over the years and has become an identity. Along with development, different kinds of networks such as LAN, WLAN, and WAN came into picture. Wireless local area network (WLAN) is the most used communication network in relatively smaller areas such as houses, schools, and offices. WLAN follows security such as WEP and encryption, but there are still vulnerabilities in the lower layer of the protocol stack. The vulnerabilities in the IEEE 802.11 have proved to be a medium for denial-of-service attack. Due to susceptibility of management frames, WLAN is vulnerable to medium access control-based (MAC) denial-of-service attacks. The results of these DOS attacks can range from man-in-the-middle attack to network congestion. By spoofing the MAC address of any legitimate user, deauthentication can be rendered to break the connection between the user and the Internet access point. There have been various amendments in the protocols to avoid the attacks on the MAC layer. But, much success hasn’t been registered till date. Various algorithms such as medium access control spoof detection and prevention (MAC SDP DoS) perform spoof detection and prevention to render MAC layer attacks affectless. This paper talks about the technique for rendering the DoS deauthentication attack and also present the analysis of the packet that is sent to attack on MAC layer.
Detection of Age and Gender from Facial Images Using CNN

This work focuses on the existing application of gender and age classification from an image. In this paper, convolutional neural network is used to accurately classify a person’s gender and age through a single face image. Gender can be predicted to one of ‘Male’ or ‘Female’ and age predicted to be one of the following: (0–2), (4–6), (8–12), (15–20) (25–32), (38–43), (48–53), (60–100) from 8 nodes. Guessing the accurate age of an individual through image can be difficult due to variables such as make-up, lighting, and expressions. Therefore, rather than using regression, we consider it a classification model. Evaluations on the ‘Audience’ dataset, with a combination of simple preprocessing, obtained the performance in gender and age recognition. We experienced some patterns like face recognition, face detection, age recognition by implementing convolutional neural networks (CNNs) for age and gender predictions. The automatic age and gender detection has become increasingly important, particularly as social networks and social media which are rapidly grown. This demonstrates that the use of deep neural networks (CNNs) is increasing significantly to showcase the performance of observed accuracy for age and gender classification which are higher than another existing claim. We used the cloud services to compute and accessing the dataset from cloud storage.
Multifold Secured Bank Application Authentication Service Using Random Visual Cryptography and Multimodal Steganography with Blockchain Technology

DATA protection is becoming increasingly critical as the demand for online services grows, which biometrics authentication can help with. To communicate biometric data authenticating services over the Internet in a secure channel is essential. To ensure the security and the existence of the biometric information over the communication channel Random Visual Cryptography and Multimodal Mask Steganography is used. The biometric features captured are embedded using multimodal mask steganography, considering mask image and cover image. The resultant stego image shares are created using Random visual cryptography technique. The resultant shares are placed in blockchain using blockchain technology to improve security aspects of the cloud application and reliable transmission of data. Combining proposed Random Visual cryptography and multimodal mask steganography methods with blockchain technology ensures high reliable authentication system of server room access or banking application.