Reviewerly/exHarmony huggingface dataset:
Recent trends in histopathological image analysis

Histopathological images provide a plethora of phenotypic information that forms the basis for proven to be the gold standard for cancer diagnosis and monitoring the progression of the disease in cancer patients. However, such images are challenging to analyse, even for experienced pathologists. Moreover, manual analysis is a tedious and costly task in terms of labour, time, etc. The manual analysis is also affected by intra- and inter-observer disagreement, as reported in several studies. Therefore, computer-aided diagnosis (CAD) systems are being explored to speed up the analysis process. Nowadays, artificial intelligence (AI)-based solutions are quite popular in the medical domain, and deep learning (DL) is becoming the most popular methodological choice for researchers to analyse histopathological images. Usually, feature extraction, image segmentation and histopathological image classification are the popular tasks for which several machine learning (ML) approaches and deep models have been developed. There are also few works that are designed for Internet-of-Things-based applications while also addressing security concerns. Therefore, this chapter briefly presents the recent developments in the automated histopathological analysis of cancer. We further summarise different publicly available datasets and also emphasise the key challenges along with limitations of emerging DL techniques for CAD of cancer. We also provide an insight into possible avenues for future research in this area. It helps the researchers working in this area to leverage the opportunities and challenges that direct towards innovative developments in the field.
Comparison of the consumers' need for touch in terms of high-touch and low-touch products in online and offline retail stores

In the current marketing literature, sensory marketing as an innovative marketing strategy has a growing interest. In order to manage sensory marketing effectively, it is crucial to handle each sensory stimuli in detail. Among the five senses, touch and its' effects on consumer behaviour have long been ignored in marketing literature. Based on the limited theoretical and empirical grounds of haptic (touch) issues, this study aims to empirically investigate the differences of need for touch between online and offline retail stores for high-touch and low-touch products. Analysis of covariance (ANCOVA) was used to test for differences among treatment groups. According to the results of the study, there was only a statistically significant difference in the context of the need for autotelic touch among the groups examined in the study. In the conclusion, some suggestions for future research and for the retailer and marketing managers are put forward.
Three level weight for latent semantic analysis: an efficient approach to find enhanced semantic themes

Latent semantic analysis is a prominent semantic themes detection and topic modelling technique. In this paper, we have designed a three-level weight for latent semantic analysis for creating an optimised semantic space for large collection of documents. Using this novel approach, an efficient latent semantic space is created, in which terms in documents comes closer to each other, which appear far away in actual document collection. In this approach, authors used two dataset: first is a synthetic dataset consists of small stories collected by the authors; second is benchmark BBC-news dataset used in text mining applications. These proposed three level weight models assign weight at term level, document level, and at a corpus level. These weight models are known as: 1) NPC; 2) NTC; 3) APC; 4) ATC. These weight models are tested on both the dataset, compared with state of the art term frequency and it has shown significant improved performances in term set correlation, document set correlation and has also shown highest correlation in semantic similarity of terms in semantic space generated through these three level weights. Our approach also shows automatic context clustering generated in dataset through three level weights.
Real-time Virtual Machine Energy-Efficient Allocation in Cloud Data Centers Using Interval-packing Methods

The reduction of power consumption, which can lower the operation costs of Cloud providers, lengthen the useful life of a machine, as well as lessen the environmental effect caused by power consumption, is one of the critical concerns for large-scale Cloud applications. To satisfy the needs of various clients, Virtual Machines (VMs) as resources (Infrastructure as a Service (IaaS)) can be dynamically allocated in cloud data centers. In this research, we study the energy-efficient scheduling of real-time VMs by taking set processing intervals into account, with the providers' goal of lowering power consumption. Finding the best solutions is an NP-complete problem when virtual machines (VMs) share arbitrary amounts of a physical machine's (PM) total capacity, as demonstrated in numerous open-source resources. Our strategy treats the issue as a modified interval partitioning problem and takes into account configurations with dividable capacities to make the problem formulation easier and assist save energy. There are presented both exact and approximate solutions. The proposed systems consume 8–30% less power than the existing algorithms, according to simulation data.
Blockchain’s Impact On Telecom in India

This article describes the current telecom ecosystems workflow for the commercial distribution of advertising messages. Here, we contrast the current telecom ecosystem workflow with a blockchain-based workflow to solve the unsolicited commercial communication (UCC) problem. Blockchain technology and its effects on telecom are explained. We are talking about the technological and legislative approaches to deal with the spam problem. The discussion is limited to the Indian context.
Performance evaluation of oversampling algorithm: MAHAKIL using ensemble classifiers

Class imbalance is a known problem that exists in real-world applications, which consists of disparity in the existence of sample counts of different classes that results in biased performance. The class imbalance issue has been catered by many sampling techniques which may either fall into an oversampling approach that solves issues to a greater extent or under sampling. MAHAKIL is a diversity-based oversampling approach influenced by the theory of inheritance, in which minority samples are synthesised in view of balancing the class using Mahalanobis distance measure. In this study the performance of MAHAKIL algorithm has been tested using various ensemble classifiers which are proved to be effective due to its multi hypothesis learning approach and better performance. The results of the experiment conducted on 20 imbalanced software defect prediction datasets using six different ensemble approaches showcase XGBoost provides better performance and reduced false alarm rate compared to other models.
Interoperable blockchain systems

This chapter covers the concept of blockchain interoperability, which is considered as one of the crucial features of blockchain technology. Interoperability techniques enable people to see and access information across various blockchain networks. Interoperability implementation will increase the communication rate of blockchains. Blockchain technology works on sharing and integration of data, which is more strengthened by making use of interoperability. It aids to increase the advent and success rate of existing and emerging projects. Interoperability between blockchains can be achieved through cross-chain technology, which allows the exchange of value as well as information across networks and blockchain-agnostic protocols that provide an abstraction layer. Some of the approaches that are addressed in this chapter are public connectors, hybrid connectors, and blockchain of blockchains. The benefits of blockchain interoperability include ease in information transfer, providing diverse functionalities such as cross-chain transactions, smooth execution of smart contracts, enhancing multi-token transactions with the creation of multi-token wallet systems, and collaboration and partnerships between the blockchain ecosystems. Once interoperability among the major public blockchains is achieved, it will unlock diverse functionality, including payments, smart contracts, and data storage.
The Ultimate Medium for People with Disabilities?

Techno-utopian visions of virtual reality as the “ultimate” medium do not always live up to the hype for people with disabilities. VR game developers promise to expand realms of play, but their products are often plagued by accessibility issues. VR clinical applications envision more engaging and effective therapies but often rely on medicalized models of care. Meanwhile, VR disability simulations designers make the controversial claim that they encourage empathy by immersing users in experiences of impairment. Centring disability experiences and drawing on disability media studies and advocacy reveals ways VR designers could better support goals of expanding diverse human experience.
Searching the Deep Web

Learning to search the “deep web” includes understanding of online data sources like government databases, court records, legislation, patents and political campaign contributions. We also cover how to search key social media platforms (including Facebook, Twitter and LinkedIn as well as Reddit, Telegram and smaller social network sites) to find information and sources for stories.
Scraping Social Media

We explain how to download data from social media, such as all tweets from a specific user, all comments on a Facebook post and ads running on social networking platforms. Scraping techniques, like Python libraries and simpler methods like Google Sheet formulas, will also provide an introduction to the power of programming languages in data acquisition as well as forming readers’ “data frame of mind.” We will incorporate Application Programming Interfaces (APIs) as a source of data in storytelling as well.
Advanced Spreadsheets and R

This chapter focuses on such higher-level analysis skills. It covers common functions beyond SUM (average, median, mode, count), creating “IF” statements (e.g., to avoid getting error messages when dividing by zero) and an introduction to correlation and other statistical concepts. The chapter also explains how to create formulas with text – to combine or split the content of cells. We also cover how to use “paste special” to turn formulas into values and to transpose data (making rows columns and vice versa). The chapter then teaches how to make pivot tables to group (summarize) data. At the end of the chapter are exercises to reinforce the above skills. We’ll also explore the fundamental differences between working with Google Sheets and working with Excel and how to use R and R Studio to analyze large datasets.
Blockchain for securing the IoT-based smart wireless surveillance cameras

Wireless smart surveillance cameras are the need of the smart city, an urgent requirement to minimize crime or any disaster, to improve the discipline in the society in many contexts. Smart cities have been growing with the evolution of the Internet of Things (IoT), as it has the power to bind the Operational Technology (OT) with Information Technology (IT), resulting in automation of the smart city machinery like smart traffic systems, smart streetlights etc. Security of IoT devices is one of the concerns in the rising cyber-attacks. Blockchain technology can be used to register all the cameras with their respective edge computing device as secured blocks in the blockchain and will allow only the authorities to access the contents on demand from the distributed network of cameras. Cameras generate a huge volume of data, storing and retrieving all these contents to/from the cloud directly may be a slow process as well as a waste of storage. This problem may be overcome through edge computing technology by adding cameras under the blockchain. Edge computing devices will filter the contents and will send the labeled pictures and videos to cloud storage thus simplifying the search and saving a lot of time for the authorities. Edge computing is also useful for taking quick action or alerting about the situation automatically based on the input from any of the cameras in a smart system. In this chapter, a model for a secured wireless IoT based network of smart surveillance cameras for a smart city with blockchain is proposed. This model describes the architecture of smart surveillance cameras with edge computing devices along with blockchain for unbreakable security to empower the authorities with the original contents in real-time as well as automation of the smart city machinery.
Blockchain for industry 4.0

Blockchain is a promising technology and is highly appreciated in today’s industry. It has recently attracted the attention of governments, the scientific community, and organizations, with applications in key areas including insurance, energy, finance, transportation, logistics, and many more. This technology can help with a range of industry concerns, including transparency, trust, reliability of data processing, and security. A blockchain is simply a distributed database or public ledger that records all transactions or digital events that have been conducted and divided between the members of the parties. The consent of a majority of the system’s members validates each transaction in the public ledger. This chapter begins with an introduction to blockchain technology that covers the overview of blockchain technology and the use of this technology in Industry 4.0. The second part covers the structure of blockchain, how it works, its tiers, and why is it important. After that, the third part covers the theoretical foundation of this technology. The fourth part covers the characteristics, then types and advantages and disadvantages, and the last SWOT analysis of this technology. The goal is to provide readers an understanding of what blockchain is and how blockchain technology works.
Safeguarding blockchains from adversarial tactics

Blockchain-enabled apps, as a game-changing technology, has the potential to transform the IT solution landscape not only for enterprises but also for governments and academic institutions. With the increasing number of organizations adopting or incorporating blockchain-based solutions into their existing product architecture in domains such as finance, supply chain, health care, etc., it is evident that blockchain technology is along the path of mass adoption. However, blockchain applications and platforms are generally perceived to be secure as the underlying technology is based on proven cryptographic primitives. In this chapter, we review both conventional and blockchain-specific attack vectors that can pose a serious financial, operational, and reputational risk to businesses and their end consumers. This chapter breaks the blockchain attack surface into four layers: network, smart contract, access layer, and application layer, to establish various attack vectors that could be leveraged by a malicious adversary to compromise the security posture of the blockchain network or application. The difference between tactics and techniques adopted by adversaries while targeting public and private blockchain solutions is highlighted. We are using Ethereum & Hyperledger Fabric blockchain as reference platforms to establish the attacks through case studies. Concepts presented in this chapter are meant to help blockchain developers and architects to avoid security issues and design flaws and to develop blockchain solutions that are inherently resilient to cyber-attacks.
Using Marchenko–Pastur SVD and Linear MMSE Estimation for Reducing Image Noise

The degradation in visual quality of images is often seen due to a variety of noise added inevitably at the time of image acquisition. Its restoration has thus become a fundamental and significant problem in image processing. Many attempts are made in recent past to efficiently denoise images. But, the best possible solution to this problem is still an open research problem. This paper validates the effectiveness of one such popular image denoising approach, where an adaptive image patch clustering is followed by the two step denoising algorithm in Principal Component Analysis (PCA) domain. First step uses Marchenko–Pastur law based hard thresholding of singular values in the singular value decomposition (SVD) domain and the second step removes remaining noise in PCA domain using Linear Minimum Mean-Squared-Error (LMMSE), a soft thresholding. The experimentation is conducted on gray-scale images corrupted by four different noise types namely speckle, salt &amp; pepper, Gaussian, and Poisson. The efficiency of image denoising is quantified in terms of popular image quality metrics peak signal-to-noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM), and the denoising time. The comprehensive performance analysis of the denoising approach against the four noise models underlies its suitability to various applications. This certainly gives the new researchers a direction for selection of image denoising method.
Resume Review

A properly formatted and detailed resume is the fastest way for you to skip the first barrier toward your senior software engineering job. During the time that I was focusing on consulting full-time, I would edit, and rewrite resumes for people that were having issues just to get to the interview process. The advice that I am giving in this chapter is what has worked for me, clients, and friends' resumes that I have edited with a 100% interview rate.
Best Code Practices: How to Organize Code

I saved the best for last. You didn't think I would go this entire chapter without mentioning my coding and processes best practices, did you? Do you even know me?!
Salary Information: Getting Your Biggest Bag

People have jobs because they need to make money to support themselves by paying bills for housing, food, utilities, medical needs, and to have some money to buy some fun things when everything is paid for. I went to the coding boot camp to become a software engineer because I knew that I would no longer be living in poverty and would immediately be elevated to middle class upon completing the course and passing the interview. Money is why people work.
Using personal data and data-driven technologies for research and public health in the context of the COVID-19 pandemic

This chapter presents legal, ethical, and other aspects regarding the use of personal data for scientific research and public health research. The potential benefits and challenges presented by data-driven research are guided by and weighed against ethical and legal standards and the threats of abuse that are prominent in the use of sensitive data. The aspects of data-driven technology, or Big Data, must be considered when assessing the impact of analyzing personal data in the modern age. Such digital technologies have the potential to solve problems, but are also the root of potential issues. Contact tracing during the COVID-19 pandemic presents a particularly salient example of why data-driven technologies must be carefully regulated and must protect the privacy interests of those studied.
Pandemic surveillance and US foreign surveillance

The Foreign Intelligence Surveillance Act of 1978 (FISA) authorized surveillance based on showing a court probable cause to believe that the target of the surveillance was an "agent of a foreign power." From that point, the United States' surveillance capabilities were greatly expanded in the interests of national security. Section 702 of the FISA Amendments Act of 2008 (FAA) introduced a way to seek authorization for systemic surveillance of non-US persons outside the US through the Foreign Intelligence Surveillance Court. Some frame the pandemic as a national security matter and that pandemic surveillance should fall within the surveillance authority of FISA and FAA. Post-9/11, the government greatly expanded its surveillance capacities, much as the surveillance capacities of the government are being expanded in light of the COVID-19 crisis. After the Snowden disclosures of 2013, the media revealed how this expansion of programmatic surveillance could be far-reaching and include US citizens as well as noncitizens. These revelations appear to have motivated the 2020 Schrems II decision by the Court of Justice of the European Union, finding that the EU-US Privacy Shield on transatlantic data transfers did not adequately protect EU persons' data as required under the European Union's General Data Protection Regulation (GDPR). The United States has a lot of work to do if it wants to meet the requirements of the GDPR and the CJEU's expectations of privacy. It is important to keep in mind the legal limitations that might exist under US surveillance law, what interests must be balanced, and what potential abuses might occur.
Regulating privacy and data ethics in the context of the UK's contact tracing apps

This chapter addresses the privacy and security elements of the United Kingdom's COVID-19 contact tracing app and strategy. The UK National Health Service (NHS) outsourced for a centralized COVID-19 contact tracing app with limited transparency and feedback, leading to significant consequences. Additionally, the COVID-19 app faced difficulties interfacing with the Apple and Google API, and interoperability was challenged by the European Union's stricter data privacy regulation in a post-Brexit United Kingdom. An automated contact tracing system has many potential advantages over a manual contact tracing program, but can be greatly hampered by inattention to the broader sociotechnical environment and interventions essential to its success.
Privacy and pandemic surveillance apps in Latin America

Fourteen Latin American governments deployed digital technology to control the COVID-19 pandemic. COVID-19 apps can be differentiated by their features, privacy vulnerabilities, and adoption levels. Most importantly, they provide different levels of data protection and privacy, with some apps not offering any privacy policy in countries like Guatemala and Paraguay that do not have any personal data protection law. This chapter addresses different problems that hinder widespread use of these technologies in Latin America, and how this has unintentionally and unexpectedly protected personal data.