Reviewerly/exHarmony huggingface dataset:
An interactive wandering Wolf Pack algorithm for solving High-dimensional complex functions

High-dimensional complex function optimization is a significant problem in engineering applications. Wolf pack algorithm (WPA) has a good performance in the optimization of high-dimensional complex functions, however in solving high-dimensional, multi-peak complex optimization problems, there are still some disadvantages, such as low precision and ease to fall into local optimum. Thus, this paper proposes an interactive wandering wolf pack algorithm (IWWPA). IWWPA uses an interactive wandering strategy based on differential evolution algorithm to enhance the global exploration ability of scout wolf; adopts adaptive striding step length, centripetal siege strategy and optimizes the termination condition of calling behavior, which improves the efficiency of the algorithm; in the late stage of the iteration, the Gaussian-Cauchy combined mutation operator is introduced to avoid the algorithm from falling into the local optimum and "premature". In the paper, the convergence of the algorithm is analyzed by using Markov process, and then IWWPA and 6-population intelligent algorithm are used to test 14 benchmark functions and 4 variable dimension test functions in 500 and 1000 dimensions. The simulation results show that the improved algorithm has better accuracy and speed performance in solving high-dimensional complex functions.
Digital Web Archive Collection Framework Based on Intelligent Management Information System and Optimization

Digital web archive collection framework based on intelligent management information system and optimization is designed in this paper. Digital file management can not only strengthen resource sharing and enhance the actual effect of the management, but also help avoid accidental damage to files and increase the retention rate of files. The proposed system is designed from the perspective of intelligent system and data analysis. The intelligent information system has a high knowledge innovation function, which can solve unstructured affairs and is in the dominant position in decision-making, and is a human guide. The data analysis model can help to obtain the efficient analytic results. The simulation compared with the state-of-the-art models have provided the evidence of the efficiency.
Infinite classes of generalised complete permutations

In Pasalic et al. (2016) a construction allowing for high levels of modification was presented. It can be used to construct several important combinatorial structures, among them are examples of complete permutations. Here the method is used to construct infinite classes of generalised complete permutations F(x), where both F(x) and F(x)+hD(x) are permutations, not just F(x)+x. In the article three families of the function hD(x) are considered: hD(x) being a function multiplying the vector x with a vector D, a permutation matrix D, or a linear mapping matrix D. Most existing results related to complete permutations use the finite field notation, while in this article we are developing permutations based on the vector space structure. The case where D is a binary vector needs to be emphasised. In this case the permutation F(x) is defined in such a way that F(x)+DTx remains a permutation for any of the 2n−3 vectors D as defined in Eq. (5). Let S be the set of all such vectors. We present for arbitrary n>4 construction of an S-complete permutation F(x), where |S|=2n−3. Additionally, we prove that each of these permutations F has such a corresponding linear subspace L that the pair (F,L) satisfies the (C)-property and can be used to construct a huge infinite class of bent functions in Carlet’s C class. In Mandal et al. (2016) it was proven that finding such pairs (F,L) is a difficult problem.
Robust Class Parallelism - Error Resilient Parallel Inference with Low Communication Cost

Model parallelism is a standard paradigm to decouple a deep neural network (DNN) into sub-nets when the model is large. Recent advances in class parallelism significantly reduce the communication overhead of model parallelism to a single floating-point number per machine per iteration. However, traditional fault-tolerance schemes, when applied to class parallelism, require storing the entire model on the hard disk. Thus, these schemes are not suitable for soft and frequent system noise such as stragglers (temporarily slow worker machines). In this paper, we propose an erasure-coding based redundant computing technique called robust class parallelism to improve the error resilience of model parallelism. We show that by introducing slight overhead in the computation at each machine, we can obtain robustness to soft system noise while maintaining the low communication overhead in class parallelism. More importantly, we show that on standard classification tasks, robust class parallelism maintains the state-of-the-art performance.
Context Based Searching in Cloud Data for Improved Precision of Search Results

The World Wide Web has been spread its branches in all the areas of the day to day life of humans. It is considered as the largest data repository in the world that works as a key driving force for many novel architectures in the area of information technology. With the expansion of the volume of content, it has gotten hard to construct an intuitive web search using conventional keyword search. Probably the worst issue of traditional search engines is that typically they depend on mere keyword processing. An idea is proposed here to ameliorate the process of searching with data separated from the semantic model of the domain. With the improvement of network storage services, the cloud architecture has proved that it is inexpensive, easy to manage, highly scalable and has a wide access limit. These features attract an ever-increasing number of enterprises and make them decide to redistribute enormous amounts of information to a third party. This method will help heaps of medium or small enterprises dispose of expenses of development and maintenance, thus it has wide market possibilities. But the companies must pay for the bandwidth they use to access the data they store in the cloud which may end up as an economic burden on mediocre enterprises when a large number of their employees fetch data from the cloud through networks. Such huge expense may keep users stay away from the conveniences of cloud usage. In this situation, returning large volumes of data as a search result is not a good solution. The returned search results must be small in volume and highly precise. This brings forth the requirement of an optimized approach for searching in clouds and fetching results. The use of context-based searching is very beneficial in this regard. Ontology is the backbone of semantic web technologies.
Tweets Reporting Abuse Classification Task: TRACT

In recent decades, we have noticed a considerable increase in reports or confession posts of abuse victims on Twitter. Most of the time victims do not report it to their guardians or the concerned authorities. Teenagers and minorities are the most affected group of abuse. Part of these victims tweets about their incident to let go of pain and suffering or as a cry for help. Identifying such reports is challenging, to address such an important task. In this study, we define a new task, tweets reporting abuse classification task (TRACT), and construct a new dataset related to the online abuse reporting. A detailed comparison with existing supervised models and detailed error analysis explores the merit of our proposed model.
Cycles, Randomness, Discrete Logarithms, and Key Exchange

With symmetric cryptography, it is necessary for the two parties who wish to communicate to have access to a common key so that one party can encrypt a message and the other party can decrypt the message. This would limit the ability of two parties who have not communicated in the past to engage in the kind of secure communication necessary for electronic commerce, for example. In this chapter we describe how number-theoretic constructs that create seemingly-random sequences of integers can be used to allow two parties to exchange information that would allow them to agree upon a common cryptographic key, even if no other communication has taken place between them. This exchange of key information can be done by exponentiation modulo a large prime number, in a manner similar to that of RSA encryption, or using elliptic curve groups in the same fashion. We will also cover the basics of the index calculus method that can be used, although with difficulty, to attack this kind of key exchange.
BEGSS! Pruning Candidate Selection for ConvNets

Deep learning has been used to get human-level performance on classification on some of the renowned problems like speech and object recognition. It is very difficult to port deep learning algorithms to resource-limited devices because of their computational cost. However, many ideas have been proposed by the researchers to reduce the computational cost and in this work, we addressed the same problem. We propose one of the promising techniques i.e., Pruning. Pruning starts with learning a large-sized network and then removing the least adversarial parameters. Since in feature map pruning, all the outgoing and incoming kernels are removed, which affects the parameters. Therefore, achieving a high pruning ratio is difficult. To achieve a high pruning ratio, we need to select the candidate for pruning intelligently. Therefore, we propose BEGSS! the pruning candidate selection based on Best of random, Entropy, Gray threshold, Sparsity, and Signal strength. We first computed the statistics of each feature map and select the least adversarial based on these statistics for pruning. The experimental results show that increasing pruning ratio results in degradation of network performance. On Cifar-10 dataset, introducing 70% pruning in the network resulted in 50% performance degradation but on the other hand, the inference time is reduced by 60% of actual inference time. To compensate for the performance degradation, we retrained the pruned network. After retraining the network, we found that the accuracy is improved even for a high pruning ratio. The proposed techniques performed better than existing absolute weight sum voting technique.
Shrinkage Priors for Nonparametric Bayesian Prediction of Nonhomogeneous Poisson Processes

We consider nonparametric Bayesian estimation and prediction for nonhomogeneous Poisson process models with unknown intensity functions. We propose a class of improper priors for intensity functions. Nonparametric Bayesian inference with kernel mixture based on the class improper priors is shown to be useful, although improper priors have not been widely used for nonparametric Bayes problems. Several theorems corresponding to those for finite-dimensional independent Poisson models hold for nonhomogeneous Poisson process models with infinite-dimensional parameter spaces. Bayesian estimation and prediction based on the improper priors are shown to be admissible under the Kullback--Leibler loss. Numerical methods for Bayesian inference based on the priors are investigated.
An Automatic Perception of Blood Sucker on Thin Blood Splotch Using Graphical Modeling Methods

Parasite is a host bacterium known as a plasmodium which lives on a different organism. This parasite is susceptible to malaria, dengue, typhoid, etc. The involvement of parasite in the blood cells will also lead to death of the humans. It is also very important to identify and diagnose the parasite in early blood film images in order to save human life. Therefore, the key slogan of this paper is to identify in less time the parasite in red blood cells using a new image processing technique by blood film images in early phases. Aim: In this article, the primary focus is on identifying the blood sucker which occur in red blood cells using thin blood film images in less time using a modern image processing system, in early stage. Method: In several steps, the procedure used detects the presence of blood sucker on photographs of blood films. The first step is to obtain the image from an optical microscope laboratory. Using the standard method, the image is then transferred into the grayscale image. The output image which is a grayscale image is transformed into the single-color image i.e., monochrome image which contains the pixel values using the “Binary Threshold method”. This monochrome image is then transformed into a matrix format and printed with binary values i.e., zero’s and one’s. Conclusion: The output matrix method will be displayed with the binary values by either one or zero which represents the presence or absence of blood sucker. If all zeroes are displayed in whole image, then no blood sucker presence can be reached in that case, and if any ones are displayed in the blood film images, it may be found that the blood film images contain a blood sucker.
Integrating Blockchain with Edge Computing for a Secure and Reliable Data Flow

The cryptocurrency technology “blockchain” has stood out and gathered a lot of attention. Blockchain has been utilizing in many applications, such as supply chain management and many other application areas of the Internet of Things. But there are some scalability barriers for blockchain, which results in degrading of its potential to brace those services that require multiple and different transactions. However, the edge computing maximizes the data computation speed, and it makes it possible to extend the benefits and services of cloud resources to the edge end of the devices but also faces some complexities and issues regarding its security feature. A single system that has the features of both blockchain and edge computing can handle the abovementioned issues to effectively maintain robust, reliable, and secure access in real time as the data computation and storage are distributed at the edges [1]. The integration provides multiple benefits like performing validation of operations securely at the edge end and storing data [1, 2]. The challenge to create intelligent application systems and architectures has increased by numerous types of devices, connected to the Internet. The challenge is to make these applications and architectures to process generated data continuously over a period of time. This chapter proposes the combination of blockchain and edge computing to gain an allotted, supple, upgradable, and secure streams of data.
Research and Exploration of Metadata in Artificial Intelligence Digital Library

Abstract With the surge of information, the intelligent management of library management research has been the general trend. With the gradually in-depth development of artificial intelligence, intelligent library management system to intelligent library sharing platform and intelligent Metadata platform as the foundation, the library can be based on artificial intelligence strong deep learning, calculation and responsiveness, according to library management process, collect related information, information analysis and decision-making of the library, to gradually replace human. Therefore, it is of great significance to transform digital library into Metadata platform under the background of artificial intelligence.
Tuning Algorithms for Stochastic Black-Box Optimization: State of the Art and Future Perspectives

The focus of this paper lies on automatic and interactive tuning methods for stochastic optimization algorithms, e.g., evolutionary algorithms. Algorithm tuning is important because it helps to avoid wrong parameter settings, to improve the existing algorithms, to select the best algorithm for working with a real-world problem, to show the value of a novel algorithm, to evaluate the performance of an optimization algorithm when different option settings are used, and to obtain an algorithm instance that is robust to changes in problem specification. This chapter discusses strategical issues and defines eight key topics for tuning, namely, optimization algorithms, test problems, experimental setup, performance metrics, reporting, parallelization, tuning methods, and software. Features of established tuning software packages such as IRACE, SPOT, SMAC, and ParamILS are compared.
A Decision Support System Based on Ontology Learning from PMI’ Project Risk Management

In attempt to assist practitioners and researchers to better study the impacts of their decisions and evaluate the Project Risk (PR) as precisely as possible, we propose implementing an OWL Ontology-based Decision Support System with SWRL rules. The idea is to parse PMI’s standard for project risk management (i.e. document that embodies recommendations and PR knowledge) so as to enrich and exploit an existing PR domain ontology. The enrichment process is driven by the Ontology learning (OL) tasks. To reach this purpose, we first pre-process the unstructured text using Natural Language Processing tools (NLP) to extract the main concepts and properties. Then, we enrich the ontology not only by OWL DL axioms, but also with SWRL rules. These rules will infer recommendations aiming to provide a targeted answer for a risk-related request. Finally, to identify the similarity between terms and ontology concepts, levenshtein similarity measure is used along with word Net for the semantic matching. We have achieved the average F-measure of 0.74.
A New H–O Isotope Analysis Method for Water in Inclusions of Oxygen‐free Minerals

Acta Geologica Sinica - English EditionVolume 95, Issue 5 p. 1791-1792 Research Advance A New H–O Isotope Analysis Method for Water in Inclusions of Oxygen-free Minerals Yingxiong CAI, Corresponding Author Yingxiong CAI whcyx0226@126.com Wuhan Center of China Geological Survey, Wuhan, 430205 China Central South China Innovation Center for Geosciences, Wuhan, 430205 China Research Center for Petrogenesis and Mineralization of Granitoid Rocks, China Geological Survey, Wuhan, 430205 ChinaCorresponding author. E-mail: whcyx0226@126.comSearch for more papers by this authorJuanjuan TAN, Juanjuan TAN Wuhan Center of China Geological Survey, Wuhan, 430205 China Central South China Innovation Center for Geosciences, Wuhan, 430205 China Research Center for Petrogenesis and Mineralization of Granitoid Rocks, China Geological Survey, Wuhan, 430205 ChinaSearch for more papers by this author Yingxiong CAI, Corresponding Author Yingxiong CAI whcyx0226@126.com Wuhan Center of China Geological Survey, Wuhan, 430205 China Central South China Innovation Center for Geosciences, Wuhan, 430205 China Research Center for Petrogenesis and Mineralization of Granitoid Rocks, China Geological Survey, Wuhan, 430205 ChinaCorresponding author. E-mail: whcyx0226@126.comSearch for more papers by this authorJuanjuan TAN, Juanjuan TAN Wuhan Center of China Geological Survey, Wuhan, 430205 China Central South China Innovation Center for Geosciences, Wuhan, 430205 China Research Center for Petrogenesis and Mineralization of Granitoid Rocks, China Geological Survey, Wuhan, 430205 ChinaSearch for more papers by this author First published: 31 May 2021 https://doi.org/10.1111/1755-6724.14754Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume95, Issue5October 2021Pages 1791-1792 RelatedInformation
ANN Model Using Gradient Descent to Train Weight Vectors

Over the past decades, Artificial Neural Network (ANN) has been widely and effectively applied in various learning tasks such as image processing and emotion analysis. A main advantage of ANN is that the performance of the ANN model is not affected by the input variables of the model. Besides, certain invisible but important correlations could be recognized by the ANN model. In this paper, we would like to find out how each layer transfers data to the next layer and how it uses the data and loss to train its weight vectors in every epoch using gradient descent. The experimental results demonstrated that the proposed model achieved promising performance with the increase of epochs. Furthermore, we note that various decay of loss can be observed by verifying different learning rates.
Revising Ethical Principles and Norms in Hybrid Societies: Basic Principles and Issues

In this work, we describe the main processes by which an agent operating in a normative/ethical system may formulate a new normative/ethical rule. This is an issue that emerges as fundamental in hybrid societies, where humans and autonomous agents shall live together. In these environments, it may occur a situation that is totally unknown to the agent, who may be unable to apply existing rule and need to create new ones to deal with the novelties encountered. We employ, as a means to devise the revision operators, the defeasible deontic logic (DDL), a non-monotonic logical system that accommodates rules of ethical origin, and has been widely employed for normative and moral reasoning. New rules may be acquired by an external source, possibly an institution, or a human interacting with an intelligent agent. The agent itself may learn it in a number of manners. We introduce two revision operators for norms that are able to manage the effects of all the above-mentioned processes.
Blind Image Source Camera Identification

Advanced imaging devices that are effective and widely available make it easier to create and control digital photographs. As a consequence it is not easier to validate the authenticity of digital images in the case of legal issues. The proposed method is an effective source examination and identification procedure for an image whose source camera is being investigated, with no prior knowledge of either the source camera or the picture. Even though Photo Response Non-Uniformity (PRNU) has acquired an incredible interest in image forensics the extraction of camera fingerprint with minimum seen content is still a challenging problem. In the proposed method fingerprint is extracted using a weighted combination of two types of de-noising filters, weighted nuclear norm minimization (WNNM) based filter and wavelet filter. A set of images captured by different mobile cameras at different locations are used as the dataset. Each image is distinct and contains different seen details having varying exposures. After the fingerprint extraction process, the fingerprints are clustered using correlation clustering followed by consensus clustering and final refinement based on their origin.
African economic development: Evidence, theory, policy

It is many years since the subject of African economic development has been treated with the best insights and methods that modern social science has to offer. Cramer, Sender, and Oqubay have set a new standard in this respect. Writing from the perspective of a statistically aware, multi-methods economics, they take aim at both unjustified pessimism about Africa’s development prospects and the naïve belief—propagated by international non-governmental organizations and United Nations agencies among others—that progress in Africa is somehow exempt from the rules that have governed capitalist progress everywhere else in the world. The book (covered by OUP’s open access scheme) has 10 chapters, divided between four parts. Part I describes The Context, with a first look at the data and a commitment to clearing away the fog of conventional ‘common sense’ in development economics. This phrase refers to the significant areas of convergent policy advice between the supposedly opposed...