Reviewerly/exHarmony huggingface dataset:
Analysis of phase errors introduced by single-shot color phase-shifting profilometry on colored objects

We aim to report an error analysis on single-shot profilometry when projecting a composite colored fringe pattern onto a surface containing different colors. Several studies have analyzed white surfaces using profilometry; however, when color is incorporated, new considerations must be taken into account. In this work, it is found theoretically and corroborated experimentally that each color introduces local phase variations that render in phase errors with a severity that is related to the crosstalk matrix of the projector–camera system. It is also shown that the phase errors are phase-shifting dependent and could be reduced with appropriate phase recovery algorithms. Experimental results are presented and compared with those obtained using a standard temporal four-step phase-shifting method.
Cryptocurrencies

A cryptocurrency is a digital or virtual currency that relies on cryptography in order to maintain security and perform versatile financial operations. The adoption of cryptocurrencies for various functions around the world has been increasing. This has resulted not only in the growth of the use of virtual currencies, but also in their volatility. Lucrative cryptocurrency investments have yielded impressive profits on the market recently. At the same time, there have always been questions concerning various aspects of their security and long-term credibility. The success of Bitcoin, its growing popularity, and increasing energy consumption have motivated a considerable number of financial companies to design and deploy numerous alternative cryptocurrencies. This chapter delves into three particularly interesting examples of the most successful and innovative crypto currencies different from Bitcoin and its close ally Ethereum. Namely, we examine the Litecoin, Cardano, and Dogecoin cryptocurrencies and their functionalities, dwelling on similarities and differences among them.
Blockchain Applications

Blockchain technologies have become one of the mainstream directions of the modern research since they bring significant benefits to many industrial applications. The success of blockchain techniques is explained by their remarkable capabilities to solve many practical issues currently hindering further advances in various industrial domains. Securely recording and sharing transactional data, establishing automated and efficient supply chain processes, and enhancing transparency across the whole value chain are some examples of the improvements that blockchain can bring. Blockchain offers an effective way to tackle these issues using distributed, shared and secure permissionless or permissioned ledgers of transactions. The employment of blockchain technologies and the possibility of applying them in different situations enable many industrial applications through increased efficiency and security, enhanced traceability and transparency, and reduced costs. In this chapter, we give a detailed description of a blockchain-based e-voting system and survey blockchain applications in finance, health, manufacturing, energy, logistics and other areas. The chapter explores the advantages of the blockchain technology, the benefits it can bring, and the challenges of incorporating blockchain in different applications.
Cryptographic Primitives

Blockchain and cryptocurrencies including Bitcoin rely on cryptographic methods to maintain security and privacy. Cryptography is a mathematical and computational practice of encoding and decoding data to ensure privacy. Bitcoin and blockchain use two cryptographic methods: digital signatures for the purpose of transaction authentication, and hash functions for the purpose of “mining”. This chapter introduces secret key cryptosystems, public key cryptosystems, hash functions and elliptic curve digital signature algorithm. It provides a cryptographic background for blockchain and cryptocurrencies including Bitcoin.
Bitcoin, Ethereum, Smart Contracts and Blockchain Types

Bitcoin and Ethereum are the two largest cryptocurrencies. They employ the blockchain technology, which makes it difficult to steal coins and tamper with data since all machines on the blockchain network have to achieve consensus to approve every transaction. Bitcoin is the first cryptocurrency introduced in 2008. Ethereum was introduced shortly after Bitcoin. It extended the way the blockchain technology was used beyond the scope of the first crypto currency. This chapter describes Bitcoin, Ethereum, blockchain, smart contract, and blockchain types.
Importance of clustering Improve of Modified Bee Colony Optimization (MBCO) algorithm by optimizing the clusters initial values

The modified bee colony algorithm is one of the excellent methods that has been proposed in recent years for data clustering. This MBCO algorithm randomly values the primary centers of the cluster by selecting a number of data from the data set, which makes the algorithm sensitive to the presence of noise and outgoing data in the data set and reduces its performance. Therefore, to solve this problem, the proposed method used three approaches to quantify the initial centers of the clusters. In the proposed method, first the initial centers of the clusters are generated by chaos methods, KMeans++algorithm and KHM algorithm to determine the optimal position for the centers. Then the MBCO algorithm starts working with these centers. The performance of the proposed method compared to a number of other clustering methods was evaluated on 7 UCI datasets based on 6 clustering evaluation criteria. For example, in the iris data set, the proposed method with chaos approaches, KHM and KMeans++with accuracy of 0.8725, 0.8737 and 0.8725, respectively, and the MBCO method with accuracy of 0.8678, and in terms of CH criteria, the proposed method with chaotic approaches, KHM and KMeans++reached values of 0.3901, 0.54848, 0.5147 and MBCO method of 0.3620, respectively. Better achieved. In general, the results of the experiments according to the 6 evaluation criteria showed better performance of the proposed method compared to other methods in most data sets according to the 6 evaluation criteria.
Time-Constrained Adversarial Defense in IoT Edge Devices through Kernel Tensor Decomposition and Multi-DNN Scheduling

The development of deep learning technology has resulted in great contributions in many artificial intelligence services, but adversarial attack techniques on deep learning models are also becoming more diverse and sophisticated. IoT edge devices take cloud-independent on-device DNN (deep neural network) processing technology to exhibit a fast response time. However, if the computational complexity of the denoizer for adversarial noises is high, or if a single embedded GPU is shared by multiple DNN models, adversarial defense at the on-device level is bound to represent a long latency. To solve this problem, eDenoizer is proposed in this paper. First, it applies Tucker decomposition to reduce the computational amount required for convolutional kernel tensors in the denoizer. Second, eDenoizer effectively orchestrates both the denoizer and the model defended by the denoizer simultaneously. In addition, the priority of the CPU side can be projected onto the GPU which is completely priority-agnostic, so that the delay can be minimized when the denoizer and the defense target model are assigned a high priority. As a result of confirming through extensive experiments, the reduction of classification accuracy was very marginal, up to 1.78%, and the inference speed accompanied by adversarial defense was improved up to 51.72%.
Consistency properties for the nearest neighbour estimator of the density function and applications based on <i>α</i>-mixing samples

In this paper, some asymptotic results such as weak consistency, strong consistency, uniformly strong consistency and the corresponding rates for the nearest neighbour estimator of the density function are established under α-mixing samples. The results improve and extend some previous ones in the literature. As applications, the corresponding results on the consistencies for the estimator of the failure rate function in reliability theory are presented.
Image processing techniques for the segmentation of cervical cancer

The most prevalent type of cancer in women worldwide is uterine cervical cancer. The majority of cervical cancer (CC) cases can be avoided by participating in screening programmes that look for precancerous lesions. Colposcopic cervigrams or images from digital colposcopy have been acquired in raw form. This study presents a novel framework that combines image enhancement, pre-processing, and image segmentation to identify cervical cancer. Three phases make up this framework: the Dual Tree Discrete Wavelet Transform (DTDWT) for pre-processing, the Curvelet transform and Contour Transform (CC) for image improvement, and the K-means clustering for segmentation.
‘‘The future is now’’ — a European perspective on the future of science communication

The Future of SciComm 2.0 conference was a one-day event in Brussels on April 26th 2022. Focusing on the future of European science communication, sixty participants from twelve countries with different expertise discussed the current challenges and possible solutions for the field. Key themes centred around disinformation, communicating global challenges, evidence-based practices and institutional structures woven through the plenary opening, afternoon workshops and the closing public panel discussion. The conclusion is a need for an European science communication ecosystem that is transdisciplinary, connected and cooperative in practice, weaving between policy, research and industry. Finally, citizen science and open science could be included as scholarly praxes to facilitate societal interconnectivity.
Comparison of Syllabification

On the topic of syllabification, the types of writing and the changes that writing goes through are compared based on the stories told by Tolkappiyam Nannool. In the Tamil grammar which was followed in the Initial method, the grammar was heavily concerned with punarchi. Just as syllabify is related to verbal grammar, it can be seen in Tolkappiyam and Nannool grammar books that devote a large part of syllabify in written grammar. In this way, it is necessary to find out about the stories that are included in the two books, Tolkappiyam and Nannool. The syllabify is one of the characteristics of Tamil language. As words continue, they join each other naturally without change and subject to deformation. This pattern is one of the main reasons why words need to be added and separated. The combination of the final letter of the adverb and the initial letter of the adverb is called Eluttu punarchi. The stories about Punarchi are told in six iyaals such as Tolkappiyam Punarial and in three iyaals such as Nannool Uyirirru Punarial. The aim of this study is to compare the rules for writing in the works mentioned in these two grammar books.
A Historical Overview of Conceptual Approaches in Mineral Exploration with Special Reference to Uranium Exploration in India

Minerals and metals find mentioned in India's 'Shruthies' (Vedic) and 'Smruthies' (Ithihasas and Puranas) but the exploration concepts had remained latent. After a huge hiatus, the Industrial Revolution of the mid-18th century can be said to have heralded the need to seek mineral resources world-wide. Ancient mining activities were the obvious precursors used by the prospectors to seek new mineral resources. The 'Spatial and Temporal' association of the 'metallogenic provinces' thus also became clearer. The 'mineral system concept' came up since 1990s and are being used for seeking new Mississipi Valley Type, Pb−Zn−Cu and SEDAX type deposits along passive, continental margins. The 'Common Earth Model' came up in the new millenia for petroleum exploration with inferences on the depth aspects petroleum basins. Such a fusion of concepts and advanced exploration techniques have helped in the preparation of 'prospectivity maps' for seeking 'green field terrains' to discover new mineral resource bases. The second world-war which demonstrated the fission-power of uranium ushered in the race for atomic minerals, especially uranium, throughout the world including India. Crustal evolution and the time-bound character of specific uranium deposit-types marked a major shift in uranium exploration concepts world-wide including India. Metamorphic, vein type deposits of Singhbhum, Jharkhand (Jaduguda, Narwapaharh, Turamdih and others explored since the early 1950s), the sandstone types at Domiasiat and Wakhyn in Meghalaya, the albitite-hosted uranium at Rohil, Rajasthan and the unique, low-grade, large tonnage, carbonate-hosted Tummallapalle group of deposits in Andhra Pradesh discovered and proved since the late 1970s, form the main resource base of uranium in India.
Calibration of transverse ray and pupil aberrations for light field cameras

The accuracy of reconstructing depth maps or performing digital refocusing in light field cameras depends largely on how well the spatial and angular samples of light rays can be obtained. Ray sample errors induced by optical aberrations in a light field camera may be digitally corrected using the ray tracing data when its nominal lens design is available. However, the most commonly nominal lens prescription is not accessible to end users. Additionally, even if available, due to tolerances in optomechanical design, the ray tracing data can be inaccurate. We propose a calibration method based on measurements of fiducial markers on a checkerboard for modeling the imaging properties of light field cameras. The calibration accounts for vignetting, transverse ray errors, as well as pupil aberration, and can be applied to light field camera modeling of arbitrary pupil sampling systems. We further demonstrate the utility of the method for calibrating a tri-aperture camera that captures simultaneous stereo views via artificially induced transverse ray errors.
Personalized Recommendation Algorithm for Movie Data Combining Rating Matrix and User Subjective Preference

The film industry has also caught the fast train of Internet development. Various movie resources have come into view. Users need to spend a lot of time searching for movies they are interested in. This method wastes time and is very bad. The article proposes an NMF personalized movie recommendation algorithm, which can recommend movies to users based on their historical behavior and preference. The research results of the article show the following: (1) the experiment counts movie reviews of different users in the same time span. The results show that 48.42% of users have only commented on a movie once, 79.76% of users have posted less than or equal to 5 comments, and 89.92% of user reviews have posted less than or equal to 10 times. (2) In the comparative experiments of the NMF algorithm in different dimensions, the effect of the NMF-E algorithm is much better than that of the NMF-A algorithm. The accuracy, recall, and F1 value of the NME-E algorithm are all 3 types. The experimental results show that the NME-E algorithm is the best among all algorithms. (3) In the experiment to test the effectiveness of the NMF personalized recommendation algorithm, comparing the experimental results, the MAE value of the improved NMF personalized recommendation algorithm is lower than that of the unimproved algorithm. When the number of neighbors is 10, the highest value of the improved MAE of the previous algorithm is 0.837. After the improved algorithm, the MAE value is the highest (0.83), and the MAE value has dropped by 0.007, indicating that the error is smaller after the improved algorithm, and the result of recommending movies is more accurate. The recall value of the four algorithms will increase as the number of neighbors increases. Among them, the recall value of the NMF algorithm proposed in the article is the highest among several algorithms. The highest value can reach 0.200, which is higher than the highest value of other algorithms. It shows that the recommendation effect of NMF algorithm is the best. (4) According to the results of the questionnaire, after using the NMF personalized recommendation algorithm, users’ satisfaction increased from 20% to 50%, an increase of 30%, and their dissatisfaction decreased from 15% to 8%, a decrease of 7%. Relative satisfaction increased from 52% to 55%, an increase of 3%, satisfaction increased from 35% to 60%, an increase of 25%, and dissatisfaction decreased from 40% to 20%, a decrease of 20%, indicating that the algorithm can meet the requirements of most people.
A Value Factorization Method for MARL Based on Correlation between Individuals

Value factorization is a popular method for cooperative multi-agent deep reinforcement learning, which effectively solves explosion of state-action spatial dimension and partial observability problems. However, most existing algorithms only consider the impact of individuals rather than correlation between individuals, which leads to poor coordination between agents in complex environments. In order to resolve this problem, this paper proposes a multi-agent deep reinforcement learning value factorization method based on correlation between individuals, CI-VF, which promotes coordination between agents effectively. Firstly, the individual value function vectors are obtained according to the output of individual networks in each round. Secondly, a Spearman correlation coefficient matrix can be calculated by the vectors to measure the correlation degree of agents, and the joint correlation coefficient can be obtained to optimize joint value function. Next, we use optimized joint value function to train individual networks. Experimental results show that our method outperforms QMIX and other baselines in various scenarios under the StarCraft Multi-Agent Challenge environment.
Visual Attention and Motion Estimation-Based Video Retargeting for Medical Data Security

Medical data security is an important guarantee for intelligent medical system. Medical video data can help doctors understand the patients’ condition. Medical video retargeting can greatly reduce the storage capacity of data on the premise of preserving the original content information as much as possible. The smaller volume of medical data can reduce the execution time of data encryption and threat detection algorithm and improve the performance of medical data security methods. The existing methods mainly focus on the temporal pixel relationship and foreground motion between adjacent frames, but these methods ignore the user’s attention to the video content and the impact of background movement on retargeting, resulting in serious deformation of important content and area. To solve the above problems, this paper proposes an innovative video retargeting method, which is based on visual attention and motion estimation. Firstly, the visual attention map is obtained from eye tracking data, by K-means clustering method and Euclidean distance factor equation. Secondly, the motion estimation map is generated from both the foreground and background displacements, which are calculated based on the feature points and salient object positions between adjacent frames. Then, the visual attention map, the motion estimation map, and gradient map are fused to the importance map. Finally, video retargeting is performed by mesh deformation based on the importance map. Experiment on open datasets shows that the proposed method can protect important area and has a better effect on salient object flutter suppression.
Analytic Methods

The design process involves different activities: analytical/research activities, design activities and validation activities. In this chapter, we go into methods to support the analytical activities, and summarise methods for doing user research and for analysing qualitative data. The outcomes of the user research may be captured by means of user profiles/personas, requirements and use cases and scenarios. Finally, we go briefly into methods for conducting task analysis.
State-of-the-art Automatic Evaluation Methods

During the last two decades, automatic evaluation has been significant for NLP tasks because it is necessary to measure the scope of NLP systems in terms of robustness and efficacy. In the context of Automatic Text Summarization (ATS), proposed methods are evaluated depending on how well they can produce informative, coherent, readable, responsive, and non-redundant summaries. Therefore, such methods are focused on linguistic and content analysis of summaries. This chapter explores the different state-of-the-art evaluation methods that evaluate summaries with or without human references. Moreover, it describes two novel alternatives related to the optimization of content metrics to improve automatic evaluation.
Conclusions and Future Considerations for the ETS

Nowadays, the Evaluation of Text Summaries (ETS) plays an essential role in automatically summarizing documents because it allows the researcher to quantify how good/bad a summary is. In this chapter, we present the conclusions about the challenges of the ETS task developed in the book. Our principal hypothesis was that linear optimization con metrics might improve the correlation between automatic and manual evaluation. To demonstrate this, we have described and provided experimental evidence that linear optimization improves the ETS. In addition, we present a description of new considerations and perspectives that will address the ETS in the future.
A Novel Methodology Based on Linear Optimization of Metrics for the ETS

In previous chapters, we have described the background, fundamentals, and state-of-the-art methods for the ETS, including the experimental research of how linear optimization has improved the performance of NLP tasks. Although we have covered several aspects of the ETS, it is necessary to describe how linear optimization is applied to enhance automatic evaluation. Therefore, this chapter presents a general description of the proposed methodology to perform linear optimization of metrics.
Experimenting with Linear Optimization of Metrics for Single-Document Summarization Evaluation

In Chap. 6 , we have described the stages of the proposed methodology to perform linear optimization. However, experimenting with such stages is necessary to demonstrate its improvements over automatic evaluation. This chapter outlines the experiments of linear optimization of content metrics in Single-Document Summarization Evaluation.
Background of the ETS

The management of textual information on the Internet is an issue that the industry and research have faced through the Automatic Text Summarization (ATS). Over the last six decades, several studies have been carried out, proposing computerized methods that extract the most important information from text documents in the form of summaries. However, the ATS goes in hand with the Evaluation of Text Summaries (ETS) because we can measure the effectiveness of proposed methods, systems, and approaches. This chapter describes the background behind the ETS in-depth. The main intention of this chapter is to introduce the reader to the basic ideas and historical context of the ETS across time.
Linear Optimization for Solving Other NLP Tasks

Identifying confusable drug names and detecting source code re-use are two NLP tasks typically addressed by similarity measures. However, although their use has achieved promising results, each measure is focused on capturing different aspects of each drug name/source code pair. In view of this situation, previous works have proposed linear optimization of similarity measures through Genetic Algorithms (GA). This chapter explores how linear optimization has improved the performance of both tasks from a methodological point of view.