Reviewerly/exHarmony huggingface dataset:
Application of Blockchain Technology in Healthcare: An Analysis

Blockchain technology was first used in Bitcoin, as the underlying concept It has also been widely applied in many other sectors, such as healthcare, fmance, etc. In the healthcare domain, this technology keeps patients&#x2019; data secure, verifying healthcare products for their authenticity, maintaining the drugs supply chain, etc. For diagnosing disease and its treatment at remote places, the concept of blockchain has been added with telehealth and telemedicine. This paper discusses the application of blockchain technology in healthcare, especially Electronic Health Records (EHRs), Supply Chain Management, Telehealth, and Mobile-based Healthcare (mHealth). This work also discusses the comparative analysis of different published papers on healthcare and its limitations.
Finite Impulse Response (FIR) Filters and Kalman Filter for Object Tracking Process

Object tracking is a subject of great interest in different fields of research. In the process of estimating the trajectory of a target in a displacement sequence, the target is not always located exactly, the process tracking can be accompanied by variations in the position and size of the image. The differences observed can be considered as colored measurement noise (CMN). We treat the.se variations as Gauss-Markov color measurement noise. The standard Kalman, Optimal FIR, Optimal Unbiased FIR, and Unbiased FIR filters are tested in simulated displacement sequences to demonstrate the best performance. The object trajectory estimation process was carried out in two stages: “predict” and “update”. The results showed good performance of the FIR and Kalman filters in the object tracking process under conditions of the process and data noise. While at higher data and process noise values, the FIR filters showed better performance.
Cryptanalysis of Candidate Obfuscators for Affine Determinant Programs

At ITCS 2020, Bartusek et al. proposed a candidate indistinguishability obfuscator ( $$i\mathcal {O}$$ ) for affine determinant programs (ADPs). The candidate is special since it directly applies specific randomization techniques to the underlying ADP, without relying on the hardness of traditional cryptographic assumptions like discrete-log or learning with errors. It is relatively efficient compared to the rest of the $$i\mathcal {O}$$ candidates. However, the obfuscation scheme requires further cryptanalysis since it was not known to be based on any well-formed mathematical assumptions. In this paper, we show cryptanalytic attacks on the $$i\mathcal {O}$$ candidate provided by Bartusek et al. Our attack exploits the weakness of one of the randomization steps in the candidate. The attack applies to a fairly general class of programs. At the end of the paper we discuss plausible countermeasures to defend against our attacks.
Towards a Taxonomy of Software Maintainability Predictors: A Detailed View

To help practitioners and researchers choose the most suitable predictors when selecting from existing Software Product Maintainability Prediction (SPMP) models or designing new ones, a literature review of empirical studies on SPMP identified a large number of metrics or factors used as predictors of maintainability. However, there is a redundancy and ambiguity in both the naming and meaning of these predictors. To address this terminology issue, a one-level taxonomy of the SPMP predictors identified in the literature review have been proposed. This paper now proposes a more detailed two-level taxonomy where the first level refers to four categories, namely, software design, software size, quality attributes (or factors), and software process, the second to sub-categories, and predictors inventoried from empirical studies on SPMP.
A hybrid feature selection method using multi-objective Jaya algorithm

Feature selection (FS) is a preprocessing procedure in machine learning that seeks to extract model predictors from a large geographic domain dataset to enhance prediction accuracy. They extract the most important features from a dataset and exclude those that could hinder estimation methods&#x2019; accuracy. However, as the number of observations grows, the dimensionality of the feature space grows, posing a considerable computational and prediction accuracy problem for numerous traditional feature selection approaches. In the context of software engineering, several feature selection strategies are studied. Normalized mutual information (NMI) and Jaya Algorithm are combined in this paper to create a hybrid feature selection algorithm. The proposed Normalized Mutual Information Jaya Algorithm (NMIJA) selection approach significantly decreases data dimension and minimizes classification redundancies. The reduced dataset achieves the highest classification accuracy when compared to typical feature selection algorithms. China, Desharnais, Kemer, and Maxwell are some of the datasets that were used. The numerical findings support the suggested model&#x2019;s validity.
A Novel Biologically Inspired Structural Model for Feature Correspondence

Feature correspondence is an essential issue in computer science, which could be well formulated by graph matching (GM). However, traditional GM is susceptible to outliers, thus limiting the applications. To address the issue, we present a biologically inspired feature descriptor (BIFD) corresponding to the simple and complex cell layers in primary visual cortex, which shows robust performance against deformations. Furthermore, we propose a novel biologically inspired structural model (BISM) for feature correspondence by fusing the graph structural information and appearance information described by BIFD in the images. The proposed BIFD imitates the cortical pooling operation across multiscale and multiangle cell layers, which makes BISM robust to outliers and distortions. To demonstrate the validity of the proposed method, we evaluate it in feature correspondence tasks on the public databases. The experimental results on synthetic data prove the validity of the proposed method.
Situational Awareness for Law Enforcement and Public Safety Agencies Operating in Smart Cities – Part 2: Platforms

This chapter continues the discussion from the previous chapter on the use of Internet of Things (IoT) concepts, technologies, and processes, in support of situational awareness applications for law enforcement and public safety agencies operating in smart city environments. This chapter focuses on the elements that comprise a situational awareness platform (SAP) and the types of SAPs that are in current use or are rapidly evolving. Then, some practical challenges affecting the actual rollout of these platforms in urban and municipal police departments are discussed. Some ethics issues are also highlighted, along with a brief assessment of ongoing research in this fast-evolving field.
Is Technology Affecting the Way Our Minds Operate? Digital Psychology of Users in the Era of Digitalization

In recent years, digital media has become pervasive in modern cultures. The number of internet users is at an all-time high, not just because of laptops and PCs, but also because of smartphone penetration, which makes it simpler and more accessible to people of all ages. Digital psychology has recently become an essential tool for organizations and scholars to better understand client psychological behaviour. The current study focuses on the literature on digital psychology since the advent of online goods buying and selling. It's tough to accept abrupt changes in one’s manner of doing things. For this purpose, the Scopus database is selected to extract data on digital psychology. The keywords for this study used are digital psychology, and after a detailed screening process, the final of sixty articles is included in the review. In order to gather data on digital psychology, the Scopus database was used. The study keywords included “digital psychology”, and the final sixty papers are included in the review following a thorough screening procedure. For data selection and rejection, the methodological element adheres to the PRISMA declaration from 2015. Year basis, journal base, and most referenced part are all discussed in the description section. The writers classify the data based on their results, settings, and processes. Digital environment, psychology, and behaviour are three key areas in which the literature is classified.
Ripple Effects: New Frameworks for Learning in Postcommodity’s Sound Art

This essay focuses on the art collective Postcommodity and examines several of the group’s site-specific public art projects involving sound. These works demonstrate key aspects of postdigital aesthetics through their investigation of networked infrastructures, algorithms, and big data in contemporary culture, and are created through collaborative, dialogical processes that propose ‘new frameworks for learning’. Many of Postcommodity’s projects can be considered in relation to Paulo Freire’s theories on popular education and ecopedagogy, through their critique of settler colonialism and their interest in experimenting with prefigurative action. Like other forms of ecopedagogy, these projects confront the extractive nature of capitalism and ask questions about the impact of climate change and the creation of sustainable communities. Postcommodity has described their approach to making these projects as emerging through a ‘shared Indigenous lens and voice’—a description that conveys the dual importance of the visual and the sonic in their work. Analyzing their sound art highlights productive alliances between postdigital practices, ecopedagogy, and decolonial aesthetics, and invites questions about how small-scale actions ripple outwards and produce larger transformations.
Concept Testing

In this chapter, we focus on concept testing. It is important to invest adequate time in concept testing to discover the most important attributes/benefits to craft into the “minimum viable product” (MVP) for later development and testing with your first niche market of focus. As such, our learning objectives in this chapter are to:
Intelligent Document Classification using Machine Learning

In this era of digitalization, everything is smart and digitalized. All the documents are presented, prepared and shared as soft copies. Classifying those soft copy documents is gaining an important insight in recent times. It is attracting digital word with its impact in different fields like spam filtering, email routing, language identification, genre classification, sentimental analysis, readability assessment. Document grouping plays an important role in different applications that help with sorting out, ordering, looking and briefly speaking to a lot of data. In this report, the uses of document classification and important steps are used for classifying the document or text by considering a small use case to know how document classification is done, basic steps of document classification, processing and analyzing the documents that are collected.
Autonomous Wireless Technology Detection in Seamless IoT Applications

The ever-increasing use of Internet of Things (IoT) devices results in the implementation of multiple wireless technologies that would not only cater their data rate requirements but also support various applications. To optimize the energy efficiency and security of the wireless transmission, it is imperative to identify the wireless technologies in various IoT implementations. Many of the existing approaches are based on measuring only the receiving signal strength indicator (RSSI). However, such approaches may not work well because of transmit power control and complex channel variations among different wireless technologies. In this article, we propose an autonomous wireless detection scheme that considers multiple distinguishable physical (PHY)-layer settings for real-time identification of wireless technologies for real-time applications. Specifically, the proposed scheme relies on the PHY-layer measurements of the targeted spectrum. Transmission settings, such as bandwidth, carrier frequency, and RSSI are estimated from the raw in-phase and quadrature-phase (I/Q) measurements. In addition, a symbol-level extraction scheme is implemented to extract unique features of modulation settings. These aforementioned features are applied to a machine learning process to identify the received wireless technologies. Compared with raw I/Q measurements, the extracted features are much simplified and, thus, the machine learning classifier can be designed with a simple structure for fast processing on IoT nodes. The proposed schemes are primarily evaluated theoretically, followed by implementing them on a USRP software-defined radio (SDR)-based hardware testbed. The evaluation results demonstrate high accuracy in the real-time detection of different wireless technologies for seamless IoT applications.
Design of IoT-enabled, scalable mobile application for ASHA workers in COVID-19 data management

In COVID-19 pandemic, ASHAs are frontline workforces in Rural and Urban India. During the outbreak of this national emergency, they were trained to check the public health parameters and conduct surveys to collect medical information from public, hospitals and quarantine camps. The collected data was submitted to Zonal Admin for further curative action. This entire process was time consuming and ineffective. For fast data gathering by ASHA workers and its management we have developed an IoT enabled mobile application ASHA Bot. This integrated solution consists of a portable handheld device to capture body temperature with no direct contact and SpO2 during the mass screening. Further, this device was interfaced with “mobile App Asha Bot”. We have exemplified the utility of ASHA Bot for ASHA workers, in collecting on filed data to gather day-to-day statistics and its management by zonal officers during the pandemic. The proposed app is further scalable for other statistical needs and could be used by the stakeholders for COVID-19 data management.
Post-COVID-19 Learning Context: The Impact of Remote Leadership on Blended Learning in Higher Education

The Covid-19 pandemic has impacted the society, and this pandemic has prompted higher education institutions to shift to remote work at the administrative and educational level, which prompted the leaders of educational institutions to practice remote leadership as one of the appropriate leadership styles in post-COVID 19 era. Blended learning is one of the methods offered by different academics and scientific researchers as a sustainable and best-fit alternative for educational establishments for at least a few quarters post-pandemic. Therefore, the present study aims to examine the impact of remote leadership on blended learning effectiveness in higher education institutions. Survey data were collected from 385 instructors in UAE universities via an online survey distributed (google forms). Path coefficients analysis was used to test the instructor’s perspective by scales and the measurement model fit, using SEM. The findings of this study Remote Leadership (RL), leadership traits, technology adoption, technology skills, trust in leadership and learning flexibility are more likely to adapt to Blended Learning (BL) with (58.9%). The more positive the perceptions, the more adaptable the instructors will be, and the more prepared they will be to use a BL approach in the post-pandemic era for their professional growth, did only when necessary by (41.1%) in order to maintain remote leadership measures.
A Block-Based Data Hiding Technique Using Convolutional Neural Network

In this paper, we propose a block-based data hiding method using convolutional neural network. The capability of a convolutional neural network to automatically learn the feature is exceptional. Hence, these recent times have seen a predominant usage of this kind of deep learning network in numerous research areas. Initially, the cover image is divided into a non-overlapping block. Convolutional neural network classifies these blocks into either smooth block or texture block. Normally, the changes made to the texture areas are imperceptible to human eye when compared to the smooth areas. So, our data embedding algorithm will select only the texture block for data embedding process. Hence, visual distortion created by the embedding process is less, and a high-quality stego image is obtained. Experimental results proved that our proposed method produced high-quality stego images, and an average of 2 dB increase in the peak signal-to-noise ratio was observed in comparison to the recent works in this domain.
The Integration of Network Application Platform to Promote the Process of Regional Education Informatization in the Era of Big Data

With the continuous promotion, development, popularization and improvement of mobile Internet technology, people’s daily life, work and study will be gradually free from the constraints of time, space and region. The network has developed into an indispensable part of people’s daily life and study in modern society. In the field of education, with the development of educational informatization, higher requirements are put forward for the construction of educational service platform and educational database. The purpose of this paper is to study how to use the big data era to integrate the campus network application platform to promote the process of regional education information construction. This paper mainly uses the interview method, content analysis method and practice summary method and other exploratory research methods to study the key influencing factors of informatization promoting the development of regional education modernization, and the specific practices and typical experience of informatization promoting the construction of education modernization in the city, and summarizes the practical experience of informatization promoting education modernization in the city. The research results show that the development process of education informatization in our city has been developing year by year since 1978, and the relevant data of regional informatization is typical. In addition, the four generations project can be carried out on a pilot basis to continuously explore and optimize the development of urban education informatization.
Intertwining History and Places: The Design of TongSEE Location-Based Augmented Reality Application for History Learning

Visualizing the past and engaging learners in real-world learning contexts is important for history learning. Location-based Augmented reality (AR) technologies offer new possibilities for supporting place-based learning, by tracking users’ position and superimposing layers of visual information on the real world. This paper presents a location-based AR application TongSEE that enables users to learn history in authentic context. We describe the design approaches of the interface, physical interaction, and contextual guidance of the application. Twenty participants were evaluated using the application. The results show that, in general, TongSEE is a promising educational tool that helps learners better understand the history and increase their interest in history learning. Our study contributes insights into how location-based AR technologies could be designed to support place-based learning and users’ perspective on this learning method.
Real-Time Solution of Unsteady Inverse Heat Conduction Problem Based on Improved Whale Optimization Algorithm and Parameter-Adaptive Pid

To the common unsteady inverse heat conduction problem in the industrial field, a real-time solution strategy of improving whale optimization algorithm (IWOA) and parameter-adaptive Proportional-integral-differential (PID) is proposed in the paper. This strategy improves the initial solution distribution, global search capability and population diversity generalization of traditional WOA, which effectively improves the parameters-adaptive capability of PID. In solving the unsteady inverse heat conduction problem, the IWOA-PID feedback control model is first established to adaptively obtain the optimal PID parameters at each time. Then, the deviation between the calculated temperature and the measured temperature of the measured point obtained by solving the direct heat conduction problem (DHCP) is used as the system input. Finally, the real-time heat flux at the boundary of the heat conduction system is solved iteratively. The experimental results shown that the inverse algorithm proposed in the paper can accurately retrieve the variation of the boundary heat flux in real-time, and has good resistance and self-adaptability.
Reidentification of Objects From Aerial Photos With Hybrid Siamese Neural Networks

In this article, we consider the task of reidentifying the same object in different photos taken from separate positions and angles during aerial reconnaissance, which is a crucial task for the maintenance and surveillance of critical large-scale infrastructure. To effectively hybridize deep neural networks with available domain expertise for a given scenario, we propose a customized pipeline, wherein a domain-dependent object detector is trained to extract the assets (i.e., subcomponents) present on the objects, and a siamese neural network learns to reidentify the objects, exploiting both visual features (i.e., the image crops corresponding to the assets) and the graphs describing the relations among their constituting assets. We describe a real-world application concerning the reidentification of electric poles in the Italian energy grid, showing our pipeline to significantly outperform siamese networks trained from visual information alone. We also provide a series of ablation studies of our framework to underline the effect of including topological asset information in the pipeline, learnable positional embeddings in the graphs, and the effect of different types of graph neural networks on the final accuracy.
A Novel Cipher Technique Using Substitution and Transposition Methods

The most fitting issue in the technological era is to provide security as most of the communications are done through the internet. Various classical encryption techniques such as Caesar cipher, Hill cipher, One-time pad, Rail- Fence techniques, and Columnar transpositions ciphers were designed to secure the data used in communication. These techniques are vulnerable to various active and passive attacks. To enhance security, we propose a new algorithm based on substitution and transposition techniques in which some bits are deleted and permuted in another place. The structure of ciphertext obtained through this algorithm is complex and thus, provides a high level of security. It can be used for the text of any length.
Using Ant Colony Optimisation to Select Features Having Associated Costs

Software Product Lines (SPLs) strongly facilitate the automation of software development processes. They combine features to create programs (called products) that fulfil certain properties. Testing SPLs is an intensive process where choosing the proper products to include in the testing process can be a critical task. In fact, selecting the best combination of features from an SPL is a complex problem that is frequently addressed in the literature. In this paper we use evolutionary algorithms to find a combination of features with low testing cost that include a target feature, to facilitate the integration testing of such feature. Specifically, we use an Ant Colony Optimisation algorithm to find one of the cheapest (in terms of testing) combination of features that contains a specific feature. Our results show that our framework overcomes the limitations of both brute force and random search algorithms.