Reviewerly/exHarmony huggingface dataset:
Blockchain Technology

Blockchain technology has received much attention from academicians and industry practitioners because they perceive that it can contribute to the bottom line of corporations. Current literature shows a significant need for adopting blockchain technology in the finance and banking sector to bring digital disruption. The main objective of this chapter will be to identify the principles of blockchain technology and how it impacts the performance of the financial and banking sector. The secondary aims of this chapter will be to examine the challenges faced by the banking and insurance sector and analyse the characteristics of blockchain technology to suggest a model that collaborates with the other ecosystems to overcome those challenges. The chapter identifies models that have been suggested by other researchers and finally proposes a framework for the adoption of blockchain technology in the finance and banking sector.
A Study of Variance and its Utility in Machine Learning

Abstract: With the availability of inexpensive devices like storage and data sensors, collecting and storing data is now simpler than ever. Biotechnology, pharmacy, business, online marketing websites, Twitter, Facebook, and blogs are some of the sources of the data. Understanding the data is crucial today as every business activity from private to public, from hospitals to mega mart benefits from this. However, due to the explosive volume of data, it is becoming almost impossible to decipher the data manually. We are creating 2.5 quintillion bytes per day in 2022. One quintillion byte is one billion Gigabytes. Approximately, 90% of the total data is created in the last two years. Naturally, an automatic technique to analyze the data is a necessity today. Therefore, data mining is performed with the help of machine learning tools to analyze and understand the data. Data Mining and Machine Learning are heavily dependent on statistical tools and techniques. Therefore, we sometimes use the term – “Statistical Learning” for Machine Learning. Many machine learning techniques exist in the literature and improvement is a continuous process as no model is perfect. This paper examines the influence of variance, a statistical concept, on various machine learning approaches and tries to understand how this concept can be used to improve performance.
Dimensionality reduction of independent influence factors in the objective evaluation of quality of experience

Big Data analytics and Artificial Intelligence (AI) technologies have become the focus of recent research due to the large amount of data. Dimensionality reduction techniques are recognized as an important step in these analyses. The multidimensional nature of Quality of Experience (QoE) is based on a set of Influence Factors (IFs) whose dimensionality is preferable to be higher due to better QoE prediction. As a consequence, dimensionality issues occur in QoE prediction models. This paper gives an overview of the used dimensionality reduction technique in QoE modeling and proposes modification and use of Active Subspaces Method (ASM) for dimensionality reduction. Proposed modified ASM (mASM) uses variance/standard deviation as a measure of function variability. A straightforward benefit of proposed modification is the possibility of its application in cases when discrete or categorical IFs are included. Application of modified ASM is not restricted to QoE modeling only. Obtained results show that QoE function is mostly flat for small variations of input IFs which is an additional motive to propose a modification of the standard version of ASM. This study proposes several metrics that can be used to compare different dimensionality reduction approaches. We prove that the percentage of function variability described by an appropriate linear combination(s) of input IFs is always greater or equal to the percentage that corresponds to the selection of input IF(s) when the reduction degree is the same. Thus, the proposed method and metrics are useful when optimizing the number of IFs for QoE prediction and a better understanding of IFs space in terms of QoE.
Diver tracking in unknown structured clutter background using a force-based GM-PHD filter

This paper considers the problem of tracking multiple human divers using a high-resolution 2D active sonar. The proposed solution can handle the real-world challenges of a time-varying number of targets, complex correlated diver dynamics affected by external factors (e.g., water current, activities of neighbouring divers and the intent of the divers themselves), and unknown spatial non-homogeneous clutter intensity due to the complex non-stationary underwater environment with structured clutter. The proposed algorithm uses the probability hypothesis density (PHD) filter with a novel force-based diver state evolution model, along with a log-Gaussian Cox process (LGCP) model for a global clutter spatial intensity estimation. In addition, the posterior Cramér-Rao lower bound (PCRLB), which quantifies the best possible accuracy in the presence of kinematic interactions among targets in an uncertain underwater structured environment, is derived as the benchmark for performance evaluation. Simulation results demonstrate the improved performance of the proposed method over the standard PHD filter.
Extralinguistic Consultation in English–Chinese Translation: A Study Drawing on Eye-Tracking and Screen-Recording Data

Both linguistic and extralinguistic consultations are essential in translation practice and have been commonly investigated as an integral topic in previous studies. However, since extralinguistic information is usually longer in extent and not specifically designed for a linguistic purpose, extralinguistic consultations involve different search strategies compared with linguistic consultations. Drawing on eye-tracking and screen-recording data, this study compares linguistic and extralinguistic consultations in terms of cognitive resources allocation and information processing patterns in English-Chinese translation. It also explores the differences among 17 language learners, 20 student translators, and 21 professional translators, and the effect of extralinguistic consultation on their translation quality. The findings are as follows: (1) all participants allocate more attention and lower cognitive load to extralinguistic consultations than to linguistic consultations; (2) participants' translation experience levels and their attention allocated to extralinguistic consultation show an inverted U-shaped relationship; and (3) participants who consult extralinguistic information before drafting or devote more attention to extralinguistic consultation produce target texts with significantly higher scores.
Development of a Personalized Recommendation System for E-Commerce Products for Distributed Storage Systems

Because the distributed storage system is based on network technology, it can store data in multiple independent low-cost physical storage devices, and it is also suitable for large-capacity storage, so it has become more and more popular. Today, common applications of distributed storage systems include cloud storage services, data center storage services, and P2P storage services. Typical ones are GFS, HDFS, OceanStore, and Dynamo. Due to regional and economic differences, the development level of global e-commerce (b2c) is very inconsistent. b2c contains the following key tags: buying and selling, which is the core of the website platform. E-commerce provides business users with transparent information and high-quality cheap products. Logistics is the basic guarantee for customers to execute transactions, and it is also a strict indicator of the website platform. There will be many visits during the operation of the e-commerce system, and the number of users in the early stage will increase exponentially. A safe and efficient e-commerce system can provide users with one-stop transaction support and convenient transaction processes. The personalized recommendation system has formulated some rules for certain fields, based on these rules, and defined certain types of knowledge for certain items to meet the needs of certain users and use the defined reasoning rules to generate recommendation results.
Automated People Counting in Public Transport

This paper is elaborating on problems in public transport in the context of Smart Cities and Internet of Things (IoT). The means of public transport are commonly overcrowded and on the other hand some lines could be designed inefficiently. This affects passengers’ comfort and also the financial site of public transport companies. The field of counting people in public transport is specific with its variety and limitations regarding setup in vehicles, which we took into account while designing the embedded system. We propose a solution—an embedded system with an array of infrared sensors. Approach uses image processing means (gauss filter, sliding average, and thresholding) for object detection, followed by the detection of direction by correspondence of objects positions across images. We performed controlled experiments with one and four participants in different scenarios which were compared to other similar solutions. We have achieved satisfying results up to 95%.
Improve User Experience by Adjusting Draft Design through Retouching System for Paper-cutting Production

This study proposed a system for quantifying artistic production difficulty and adjusting difficulty via retouching, with an aim to improve production. A paper-cutting artwork was created controlling a knife to cut the designed paper. Paper-cutting draft designs have two parts: a white and black areas for cutting-out and painting, respectively. The difficulty level of the picture was measured based on the thickness of the cutting borderlines. Thus, in this study, a retouching system that reduces the difficulty of draft design by adjusting the line thickness was developed. Further, the effects of this system on a novice production was experimentally investigated. Consequently, the system was confirmed to improve accuracy and psychological state (flow state) via retouching the draft design.
Adoption and Maintenance of the Next Generation Integrated Library Systems (ILS) in Academic Libraries

The next-generation library integrated systems (ILS) are becoming increasingly popular throughout the world. However, determining appropriate decision-making aids for the implementation and maintenance of the next-generation ILS is complicated and difficult to manage. This chapter is based on a systematic literature review related to the adoption and maintenance of next-generation ILS in academic libraries published between 2016-2022. Roger's diffusion of innovation (DoI) was used as a framework to examine ways of understanding and accepting new technologies. Existing research indicates that academic libraries in South Africa are using next-generation ILS to ensure interoperability between the various systems, platforms, and devices that are part of modern library systems. Despite its potential benefits, there are significant barriers to the adoption of ILS such as lack of technical knowledge and skills in using emerging technologies and the perception among LIS professionals worldwide that they will negatively impact their jobs and lead to unemployment.
The Impact of Non-Verbalization in Think-Aloud: Understanding Knowledge Gain Indicators Considering Think-Aloud Web Searches

Web searching and knowledge gain are intertwined processes that share mental and physical activities at the core of both human cognition and hypertext theory, such as identifying, comparing, linking, and combining different subsets of existing or new information. As a consequence of the improvement of our ability to retrieve information across multiple sources provided by Web search engines, the necessity to understand how a user’s knowledge evolves through a Web search session increased. Previous works focused on understanding the knowledge gained in Web searches by using think-aloud protocols. From the user’s verbalization of her searching procedures, it is possible to identify her cognitive processing. Notwithstanding, we argue that user’s searching and browsing behaviors should be analyzed not only through the verbalization periods, as usually accepted by think-aloud studies, since not all cognitive decisions are made consciously, some are unconscious or subconscious. Hence, it is possible to identify more knowledge gained than it would be attainable focusing solely on what was verbalized. In this sense, we evaluated the statistical significance level derived from the relationship between verbal and non-verbal search periods mapped from online information searching strategy indicators. Then, we identified a positive association regarding non-verbalization and some indicators related to knowledge gain concepts and discovered that the values of non-verbal periods tend to increase as the values of particular indicators related to knowledge gain also increase. The knowledge gain concepts were identified using constructs representing cognitive absorption, comprehension, elaboration, and memory. Concerning the impact of Think-Aloud on knowledge gain processes, we found out that verbalization does affect how participants handle their search tasks. However, our result also showed a predominance of non-verbal periods during metacognitive-based searching activities, which may indicate that Think-Aloud protocols should not only rely on verbalization for indication of knowledge gain. Although verbalization may not disrupt the thought process, it might cut in on the cognitive process as the participant tries to explain her action while performing it. A search engine could use the identified indicators to account for the knowledge gained during search sessions, which would make it more adapted to identify user information needs and promote personalized information-adding.
Super Resolution Image Visual Quality Assessment Based on Feature Optimization

Most existing no-referenced image quality assessment (NR-IQA) algorithms need to extract features first and then predict image quality. However, only a small number of features work in the model, and the rest will degrade the model performance. Consequently, an NR-IQA framework based on feature optimization is proposed to solve this problem and apply to the SR-IQA field. In this study, we designed a feature engineering method to solve this problem. Specifically, the features associate with the SR images were first collected and aggregated. Furthermore, several advanced feature selection algorithms were used to sort the feature sets according to their importance, and the importance matrix of features is obtained. Then, we examined the linear relationship between the number of features and Pearson linear correlation coefficient (PLCC) to determine the optimal number of features and the optimal feature selection algorithm, so as to obtain the optimal model. The results showed that the image quality scores predicted by the optimal model are in good agreement with the human subjective scores. Adopting the proposed feature optimization framework, we can effectively reduce the number of features in the model and obtain better performance. The experimental results indicated that SR image quality can be accurately predicted using only a small part of image features. In summary, we proposed a feature optimization framework to solve the current problem of irrelevant features in SR-IQA, and an SR image quality assessment model was proposed consequently.
Color image restoration based on saturation-value total variation plus L1 fidelity*

Abstract Color image restoration is one of the most important tasks in image processing. In this paper, we propose and develop a novel saturation-value total variation (SVTV) plus L1 fidelity (SVTV-L1) model for color image restoration. SVTV is incorporated in the proposed model for regularizing the objective color image, meanwhile, L1 norm is considered for data fitting term in the proposed energy functional. Theoretically, we investigate the properties of the objective functional and the objective model. Note that the proposed energy functional including L1 norm is nonsmooth. We develop a smooth approximation approach to study the existence and properties of the solution of the proposed optimization problem. Numerically, Poisson noise and mixed noise have been tested and studied in the experimental results. We formulate an effective and efficient algorithm to solve the proposed minimization problem based on the framework of alternating direction method of multipliers. Numerical examples are presented to demonstrate that the performance of the proposed SVTV-L1 model is better than that of other testing methods in terms of visual quality, peak signal-to-noise ratio, and structure similarity.
An Improved Canny-Zernike Subpixel Detection Algorithm

This paper proposed a new subpixel detection method that detects subpixel edges directly, as opposed to the previous method, which requires crossing the entire image. It shows superior subpixel detection on the edge directly, which enhances subpixel edge detection speed significantly. In order to overcome the problem of noise reduction, this paper employs bilateral filtering. The method first performed coarse localization with the improved operator to determine the coordinates and gradient direction of the edge points. Then, the Zernike moment algorithm was used for subpixel repositioning of edge points. Finally, subpixel level edge positioning of the image is obtained. The detection algorithm is used to identify the edges of large-size workpieces, and the results reveal that the approach has superior positioning accuracy, noise immunity, and fast detection speed.
New Strategies To Improve Differential-Linear Attacks With Applications To Chaskey

Abstract Differential-linear cryptanalysis, as the combination of differential and linear cryptanalysis, is an efficient way to attack many kinds of ciphers. Recently, various refinements to this cryptanalytic technique have been proposed, especially with good effects on ARX ciphers. In the current framework of a differential-linear attack, a cipher $E$ is often divided into three parts: a differential part $E_1$, a linear part $E_2$ and a connective part $E_m$. It is a challenging problem to deal with the connective part when building a differential-linear distinguisher, and for ARX ciphers, estimating the correlation of $ E_m $ experimentally under given input difference $\Delta _m$ and output linear mask $\Gamma _m$ is the main approach so far. In this paper, we discuss the effects of $ \Delta _{m} $ and $ \Gamma _{m} $ on the correlation of $ E_m $ for the first time. As a result, we propose a new strategy to find $\Delta _m$ and $\Gamma _m$ to build differential-linear distinguishers with high correlations for ARX ciphers based on algebraic equations derived from their round functions. For the key recovery parts of differential-linear attacks, we also find a new partitioning technique which will reduce the time complexity. Based on our new methods, we improve the differential-linear attack on 7-round Chaskey.
A quantum steganography scheme using quantum wavelet transforms

Quantum steganography is a technology which can hide information effectively for achieving copyright protection. In this paper, a quantum steganography scheme using quantum wavelet transform and the novel enhanced quantum representation for quantum image is proposed, and this scheme can embed the secret text of size [Formula: see text] into a [Formula: see text] gray-scale carrier image. In the scrambling operation, the text is scrambled by the scrambling circuit including bit-reverse, quantum equal and CNOT gate to improve its security. Before embedding, the carrier image is first transformed by 1-level quantum Haar wavelet transform, and then its low-frequency part is divided into blocks. The simulation results suggest that the proposed scheme has good performance in visual quality and robustness. Finally, through the analysis of circuit complexity, the scheme has low complexity O(n).
Blockchain Application in Retirement Planning Investment

Retirement planning is a crucial step in any individual's life, yet many complain of dissatisfaction with the plan they made before retirement. Many variables, the uncertainty of life, and socio-economic shocks make this planning difficult. At times, an action plan needs some adjustments, which can be made through financial institutions. Any loan or scheme offered to an individual involves risk calculation. Transparency in records and the complete history of an individual's financial transactions can help institutions access risk correctly. Linking transactions and other details with blockchain technology can improve transparency and can make retirement saving plans viable for individuals. This chapter explains the relevance of blockchain technology in context to retirement planning investment. This chapter also suggests a blockchain model that can be implemented by financial institutions to make their products favorable.
Analysis of Key Barriers in Blockchain in Banking

In this chapter, the authors holistically study different barriers in the path of implementation of blockchain technology in the growth of the banking sector. Over the last decade, blockchain technology has received a lot of attention. This disruptive force of digital technology is becoming an imperative factor around the world by changing the business models. This chapter includes three different research phases. In the first stage, barriers were identified from the literature followed by interviews with experts of the industry. Finally, the authors design an ISM impact matrix cross-reference multiplication model. For the mass application of blockchain technology, policymakers tend to remove the barriers that have been shown in this chapter. On the basis of their importance, the blockchain technology application tends to ease by eliminating the highlighted barriers. This study is one of the preliminary attempts at the implementation of blockchain by identifying barriers and ranking them according to their importance.
Google Home, Listen: Building Helper Intelligences for Non-Verbal Sound

Voice assistants have become a mainstay of our increasingly smart domestic spaces. Within this design-through-research inquiry, how new forms of intelligence might creatively respond to everyday interactions. In this, we are particularly interested in non-verbal communication and expression as a design material for interaction design. In this demonstration, we present a prototype illustrating this opportunity: a tinyML-enabled helper that augments the Google Home and enables it to recognize and respond to everyday phenomenon such as a sneeze, a door opening, or rapping on a table. The helper offers a creative toolkit for designers and end-users to quickly explore, understand, and imagine the affordances of everyday sounds.
Cryptocurrency

Cryptocurrencies started gaining popularity in 2017 because of the exponential returns despite their introduction in the year 2009. Cryptocurrency is now also seen as an investment opportunity by many especially after various cryptocurrencies have given unimaginable returns. Favourite investment avenues for investors were mutual funds, stock market, bonds, fixed deposits, etc., but cryptocurrencies have made their presence felt in recent times as an investment option and have seen a lot of inflow. Various start-ups are facilitating the investment in cryptocurrencies, making it feasible and reachable for every household to invest in in the hope of high returns. This chapter tries to analyse the returns that various cryptocurrencies have given since inception and compare the same with global benchmarks like NASDAQ, Nikkei, Sensex, etc. It is observed that despite very high volatility, cryptocurrencies were able to achieve very high returns as compared to stocks. The study also found a high positive correlation between the returns given by global indices and cryptocurrencies.