Reviewerly/exHarmony huggingface dataset:
The Scope and Applications of Nature-Inspired Computing in Bioinformatics

Charles Darwin postulated the concept of “survival-of-the-fittest” and evolution in general. He discussed how nature selects the best candidate under different situations who are fit enough to survive and reproduce. This analogy has inspired many computational scientists, bioinformaticians, and computational biologists to develop techniques that can learn, adapt, and evolve to find optimal solutions for complex problems. Biologists are heavily dependent on computational methods and strategies to analyze humongous biological and medical data. Nature-inspired computing (NIC) encapsulates an ensemble of myriad studies of computer science, statistics, mathematics, and biological sciences where the essence is to adapt and develop robust competing techniques just like nature. It is a novel approach to optimization algorithms that are motivated by the dynamics of the biological evolution of our natural milieu. Over the past decade, various nature-inspired optimization algorithms have been deployed to solve complex problems in bioinformatics, engineering, and other sciences. With the glorious artificial intelligence (AI) revolution in biological sciences, there have been times when some problems are nonlinear in nature with multiple constraints and some techniques are hard to deploy. To solve high dimensionality issues and time complexity in such cases, NIC algorithms are the best choice to be used to solve complex optimization problems. This chapter highlights the commonly used NIC algorithms and their applications in biological sciences and bioinformatics.
LifeGuard: An Improvement of Actor-Critic Model with Collision Predictor in Autonomous UAV Navigation

The needs for autonomous unmanned aerial vehicle navigation (AUN) have been emerging for recent years due to the growth of the logistic industry and the need for social distancing during the pandemic. There have been different methods trying to overcome the AUN task, and most of them have focused on deep reinforcement learning (DRL). But the results were still far from satisfactory, and even if the result was good, the environment was usually too trivial and simple. We report in this paper one of the causes of low success rate for AUN in our previous work, which is the apprehensive behavior of agents. After numerous episodes of training, when the agent faces risky scenes, it often moves back and forth repeatedly until running out of the limited steps. Hence, in this paper, we propose a new role, LifeGuard, into the popular DRL model, Actor-Critic, to tackle the apprehensive behavior and expect a better success rate. In addition, we developed a pilot method of unsupervised classification for sequential data to further enhance our reward function from previous work, augmentative backward reward function. The experimental results demonstrated that the proposed method can eliminate the apprehensive behavior and gain higher success rates than the state-of-the-art method, FORK, with lesser effort.
Between Immersion and Deimmersion. Adaptation to 360° Technology in <i>The New York Times</i> Daily 360

In recent years, many journalistic organisations have experimented with 360° video technology. One of the reasons for the interest is its potential ability to facilitate the public’s emotional engagement. However, this seems to come into conflict with the prevailing idea that news should be presented in an emotionally detached manner. The New York Times is a media outlet that, more than most, conjures up notions of professional and high-quality journalism. The present article chooses the NYT Daily 360 project as a case study to examine the tension between traditional journalist ethics and the emotional bias that seems to characterise immersive technology. We conducted a content analysis to identify the elements that, by emphasizing the mediation between the user and the narrated facts, generate a “deimmersion” effect. As a result, we have observed that, in general, the immersive properties of the videos tend to be mitigated by resources with an opposite effect. Reasoning from this compromise solution between immediacy and hypermediacy, the discussion is based on the problem of the distance between the user and the narrator. To this end, reference is made to theoretical tools from journalism and media studies as well as from art history, literary studies, and semiotics.
A latent slice sampling algorithm

Motivated by a sampling algorithm for discrete spaces, a variation of the slice sampler for continuous spaces is introduced. It utilizes latent variables and is related to Neal's slice sampler. The key difference is that the additional latent variables allow the sequential stepping out or doubling procedures, which makes the basic slice sampler difficult to use in high dimensional problems, to be avoided. On the other hand, the latent slice sampling algorithm is applicable on high dimensional problems where the variables can all be treated in a single block.
The quantitative research on length and area perception: A guidance on shape encoding in visual interface

Shape encoding is one of the most important ways to convey information in a visual interface. Length and area are two crucial visual design elements for shape encoding. By exploring the quantitative research of length and area perception, we hope to provide a basis for shape encoding in a visual interface. Three groups of visual estimation experiments were conducted to investigate the perceived quantitative difference in length and area. A total of 32 observers (12 men and 20 women, ages ranging from 20 to 26 years, Mean = 21.7, SD = 1.9) were asked to perform three visual estimation tasks. Observers' subjective estimation variation was measured as a function of the following independent variables: length estimation, absolute area, and relative area estimation. The results showed that there was no computational noise in length perception. The span of shape is the primary estimation element for area estimation; In addition, length estimations of solid and dashed lines obey Weber's law (R2 = 0.997), while for the relative area ratio, R2 = 0.636, indicating somewhat less linearity, and the perceptual sensitivity is square compared to a circle, which is more sensitive than triangle; Finally, the separation effect appeared in the estimation of relative area, and there is a specific logarithmic relationship between the length and the area perception bias. The findings of this study provide valuable guidelines for designing more efficient and user-friendly shape encoding and visual interfaces in the future.
Latent energy preserving embedding for unsupervised feature selection

As is a fundamental yet challenging topic in machine learning and pattern recognition, feature selection has received much attention. Since data are often unlabeled in real-world applications, unsupervised feature selection (UFS) methods have aroused extensive attention. In this article, we present a novel latent energy preserving embedding (LEPE) method for UFS. First, we rewrite subspace learning into the form of energy preserving. Then, a novel self-representation learning strategy is utilized in the feature selection framework, in which the low-rank and sparse constraints are imposed on the representation matrix. In addition, we utilize a graph Laplacian to mine the manifold information of data. Meanwhile, we use an ℓ2,1-norm for feature selection. To validate the effectiveness of the proposed LEPE method, we conduct extensive experiments on six real-world datasets. Experimental results illustrate the effectiveness of the proposed method.
Two-directional two-dimensional fractional-order embedding canonical correlation analysis for multi-view dimensionality reduction and set-based video recognition

Set-based video recognition is an important application in practice, and many specialized approaches have been proposed. However, most of these methods either only use one kind of visual features for classification, or are sensitive to the noises and the number of training images when using several kinds of features, resulting in limited discrimination. To explore a possible solution to these issues, in this paper, a novel efficiently and effectively dimensionality reduction method, named two-directional two-dimensional fractional-order embedding canonical correlation analysis ((2D)2FECCA), is proposed. (2D)2FECCA borrows the idea of fractional-order embedding to correct the estimation of sample covariance matrices, which can significantly reduce the influence of noise disturbance and effectively utilizing the discriminative information from different view features. In addition, several set-based video recognition schemes are introduced to determine the labels of the test videos. Extensive experimental results on four familiar single image based databases and two video based benchmark databases demonstrate the effectiveness of the proposed method. These quantitative assessments reinforce the significance as well as the importance of embedding the proposed method in other intelligent systems application areas.
Heterogeneous sets in dimensionality reduction and ensemble learning

Abstract We present a general framework for dealing with set heterogeneity in data and learning problems, which is able to exploit low complexity components. The main ingredients are (i) A definition of complexity for elements of a convex union that takes into account the complexities of their individual composition – this is used to cover the heterogeneous convex union; and (ii) Upper bounds on the complexities of restricted subsets. We demonstrate this approach in two different application areas, highlighting their conceptual connection. (1) In random projection based dimensionality reduction, we obtain improved bounds on the uniform preservation of Euclidean norms and distances when low complexity components are present in the union. (2) In statistical learning, our generalisation bounds justify heterogeneous ensemble learning methods that were incompletely understood before. We exemplify empirical results with boosting type random subspace and random projection ensembles that implement our bounds.
Preservation of Data Integrity in Public Cloud Using Enhanced Vigenere Cipher Based Obfuscation

Today's internet world is moving to cloud computing to maintain their public data privately in a secure way. In cloud scenarios, many security principles are implemented to maintain the secure transmission of data over the internet. And still, the main concern is about maintaining the integrity of our own data in the public cloud. Mostly, research works concentrate on cryptographic techniques for secure sharing of data but there is no such mentioned works available for data integrity. In this paper, a data masking technique called obfuscation is implemented which is used to protect the data from unwanted modification by data breaching attacks. In this work, enhanced Vigenere encryption is used to perform obfuscation that maintains the privacy of the user's data. Enhanced Vigenere encryption algorithm combined with intelligent rules to maintain the dissimilarity between the data masking to perform encryption with different sets of rules. This work mainly concentrates on data privacy with reduced time complexity for encryption and decryption.
A case for DOT: theoretical foundations for objects with pattern matching and GADT-style reasoning

Many programming languages in the OO tradition now support pattern matching in some form. Historical examples include Scala and Ceylon, with the more recent additions of Java, Kotlin, TypeScript, and Flow. But pattern matching on generic class hierarchies currently results in puzzling type errors in most of these languages. Yet this combination of features occurs naturally in many scenarios, such as when manipulating typed ASTs. To support it properly, compilers needs to implement a form of subtyping reconstruction: the ability to reconstruct subtyping information uncovered at runtime during pattern matching. We introduce cDOT, a new calculus in the family of Dependent Object Types (DOT) intended to serve as a formal foundation for subtyping reconstruction. Being descended from pDOT, itself a formal foundation for Scala, cDOT can be used to encode advanced object-oriented features such as generic inheritance, type constructor variance, F-bounded polymorphism, and first-class recursive modules. We demonstrate that subtyping reconstruction subsumes GADTs by encoding λ 2, G µ , a classical constraint-based GADT calculus, into cDOT.
MatrixSim: A new method for detecting the evolution paths of research topics

• A new method for detecting the evolution paths of research topics called MatrixSim is proposed. • The community structure of co-word networks is maximally preserved. • The similarity of topic communities in terms of both words and relations is integrated through MatrixSim. • Two sets of experiments are designed to verify the validity and feasibility in research topic evolution analysis. • Methodological support is provided for researchers to conduct prospective research activities. In this study, MatrixSim, a new method for detecting the evolution paths of research topics based on matrix similarity, was proposed. In the analysis of research topic evolution with the help of co-word networks, in contrast to traditional methods of topic evolution path detection, such as cosine similarity and edge similarity, MatrixSim is based on the local community structure of topic communities in co-word networks and considers the similarity of research topics in both nodes and edges, that is, words and inter-word relations. Using the library and information science field as an example, two sets of experiments were designed for topic similarity detection and subject-specific research topic evolution analysis to evaluate and verify the performance of MatrixSim in detecting the evolution paths of research topics and its validity and feasibility in research topic evolution analysis. The results confirm that MatrixSim performs well in detecting the evolution paths of research topics. It can correlate important research topics, help describe the research development process in scientific fields, reveal the internal evolutionary features of research topics, and thus discover and track the research frontiers in scientific fields. This study provides significant methodological support for researchers conducting prospective research activities.
Some Misconceptions about Software in the Copyright Literature

The technical complexity and functionality of computer programs have made it difficult for courts to apply conventional copyright concepts, such as the idea/expression distinction, in the software copyright case law. This has created fertile ground for significant misconceptions. In this paper, we identify fourteen such misconceptions that arose during the lengthy course of the Google v. Oracle litigation. Most of these misconceptions concern application programming interfaces (APIs). We explain why these misconceptions were strategically significant in Oracle's lawsuit, rebut them, and urge lawyers and computer scientists involved in software copyright litigation to adopt and insist on the use of terminology that is technically sound and unlikely to perpetuate these misconceptions.
Programming Languages and Law: A Research Agenda

If code is law, then the language of law is a programming language. Lawyers and legal scholars can learn about law by studying programming-language theory, and programming-language tools can be usefully applied to legal problems. This article surveys the history of research into programming languages and law and presents ten promising avenues for future efforts. Its goals are to explain how the combination of programming languages and law is distinctive within the broader field of computer science and law, and to demonstrate with concrete examples the remarkable power of programming-language concepts in this new domain.
Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks

The sharing of high-quality traffic information plays a crucial role in enhancing the driving experience and safety performance for vehicular networks, especially in the development of electric vehicles (EVs). The crowdsourcing-based real-time navigation of charging piles is characterized by low delay and high accuracy. However, due to the lack of an effective incentive mechanism and the resource-consuming bottleneck of sharing real-time road conditions, methods to recruit or motivate more EVs to provide high-quality information gathering has attracted considerable interest. In this paper, we first introduce a blockchain platform, where EVs act as the blockchain nodes, and a reputation-based incentive mechanism for vehicular networks. The reputations of blockchain nodes are calculated according to their historical behavior and interactions. Further, we design and implement algorithms for updating honest-behavior-based reputation as well as for screening low-reputation miners, to optimize the profits of miners and address spatial crowdsourcing tasks for sharing information on road conditions. The experimental results show that the proposed reputation-based incentive method can improve the reputation and profits of vehicle users and ensure data timeliness and reliability.
NeX360: Real-Time All-Around View Synthesis With Neural Basis Expansion

We present NeX, a new approach to novel view synthesis based on enhancements of multiplane images (MPI) that can reproduce view-dependent effects in real time. Unlike traditional MPI, our technique parameterizes each pixel as a linear combination of spherical basis functions learned from a neural network to model view-dependent effects and uses a hybrid implicit-explicit modeling strategy to improve fine detail. Moreover, we also present an extension to NeX, which leverages knowledge distillation to train multiple MPIs for unbounded 360 ° scenes. Our method is evaluated on several benchmark datasets: NeRF-Synthetic dataset, Light Field dataset, Real Forward-Facing dataset, Space dataset, as well as Shiny, our new dataset that contains significantly more challenging view-dependent effects, such as the rainbow reflections on the CD. Our method outperforms other real-time rendering approaches on PSNR, SSIM, and LPIPS and can render unbounded 360 ° scenes in real time.
Fuzzy systems research in the United States of America and Canada: A bibliometric overview

The objective of this paper is to apply bibliometric techniques to analyze the evolution of fuzzy systems research (FSR) in the United States of America and Canada. The aim is to visualize the progression of FSR since Zadeh's first publication of fuzzy sets and the regional impact that these ideas have in the scientific community. This work obtains data retrieved from the Web of Science (WoS) scientific database. Using a hybrid methodology designed to maximize the obtention of representative results, we analyze 20,317 documents published between 1965 and 2020. The main findings show that Zadeh is the most influential author and that Pedrycz is the most productive author at FSR. Journals such as Fuzzy Sets and Systems (FSS) and IEEE Transactions on Fuzzy Systems (TFS) publish most of the FSR articles. The results show a greater contribution by the United States of America compared to Canada in FSR. This analysis contributes to visualization and evolution in the field of study of FSR in diverse regions.
Experimenting System Dynamics Model to Assess the Impacts of Urban Regeneration Processes

Cities are complex and dynamic systems characterized by a high level of uncertainty and vulnerability that involve social, economic, and environmental components. In this context, both urban regeneration and urban transformation processes have to face complexity and uncertainty. This research belongs to the wide research field that aims at developing new evaluation approaches that are able to manage both the complexity and the impacts of the urban regeneration process. This paper proposes the System Dynamics Model (SDM) to analyze an urban regeneration process of the Aurora neighborhood, in the city of Turin. The aim is to identify the mutual relationships that occur among the social, economic, and urban components that are involved in this process. In detail, this research gives specific attention to the analysis of social components of the area with the aim of focusing on the multiculturality of the area under investigation. The result is a preliminary insight that identifies the probable multidimensional impacts generated by the regeneration project. This application underlines thus the suitability of the SDM to support the decision-makers in the development of urban strategies within the perspective of a sustainable, multi-cultural and inclusive city.
Customer Knowledge Management in Enterprise Software Development Companies: Organizational, Human and Technological Perspective

Abstract In this study, Knowledge-Based View (KBV) and Theory of Technology in a Generic Customer Knowledge Management (CKM) Framework were assimilated to demonstrate the Organizational, Human and Technological antecedent factors that enable CKM processes to improve software product quality. A Theoretical CKM Framework was developed by extracting Human, Organizational and Technological factors from the literature, then, the “Technique for Order of Preference by Similarity to Ideal Solution” (TOPSIS) Multi-Criteria Decision Making (MCDM) method was applied to find the importance level of factors to CKM development in software companies. The weight and priority of factors were determined by 31 experts in enterprise software development companies. The results show that, from an expert viewpoint, CKM antecedent factors are categorized into high priority and low priority groups. Organizational factors such as “Customer Involvement”, “Customer-Centric Culture” and “CKM Strategy Development” are high priority.
Exemplar-based large-scale low-rank matrix decomposition for collaborative prediction

Recommender system is one of the most effective tools to solve the problem of information overload. As a popular method for recommendation system, regularized singular value decomposition (RSVD) has the advantage of prediction accuracy. However, with the growing of the size of rating matrix A, RSVD suffers from both 'out of memory' and high computational cost. To alleviate these disadvantages, we utilize CUR decomposition approach to reduce the memory consumption before RSVD is applied. Additionally, the rating matrix A often is sparse, so we propose a novel column sampling algorithm and sparseness measure to solve the data sparse problem. The main computation cost in the original RSVD is to compute MTN and MNT respectively. While replacing N by CUR or C in RSVD, we can reduce the computational cost from O(m2n) to O(c2m+tcnkc) (where c is the size of data subspace, m and n are the size of the input matrix, t is the number of iterations, kc is the number of features). Because CUR is explicitly expressed in terms of a small number of actual columns and actual rows of the original data matrix, the result of matrix decomposition has better interpretability. The advantage of what we devised CUR+RSVD and C+RSVD collaborative prediction approaches is that, they not only can deal with the large scale matrix rapidly but also preserve the sparsity of the original matrix, more interestingly, our approaches have higher prediction accuracy. Experimental results on Movielens, Joke data set, etc. show that what we proposed methods can handle the sparsity issue on large-scale low-rank matrix effectively. Compared with RSVD, it can achieve far better prediction accuracy and recommendation result, meanwhile save about 70% training time at the same dataset.