Reviewerly/exHarmony huggingface dataset:
Methodology for conducting and analyzing land surveying examinations

Abstract Geoscience does not investigate the methods of producing land surveyors in the framework of court cases and reviews produced after the analysis of this expertise type. The article proposes the author’s methods of this field of study: test, technological methods and method of typical errors, which are developed and justified. The paper analyzes the errors of 176 land surveyors and provides statistics of such errors. They are classified into design errors, procedural errors, and errors of a special technical nature. The article analyzes the meaning of each error type (and introduces the concept of gross and minor errors). It proposes the author’s method of producing reviews of land surveyors which is a technological method. It is reviewed in detail; it is indicated that it is in the testing stage and has received a positive response. The study suggests the formation of reliable complete scientific research in the framework of the expertise in land disputes, which leads to more fair and well-grounded court decisions.
BIONIC PLAY FORMS IN OPEN URBAN SPACES

The typological features of bionic design forms in open urban spaces are considered for the first time. International experiences in the designing of such objects are reviewed from the perspective of not only their artistic characteristics but also play functions. The term «bionic play forms» is proposed, and these forms are broken down into play sculptures, play art objects and play landscapes. Arguments are provided in support of the application of bionic design criteria to the construction of a model of interaction between nature, man and urban environment.
Live Virtual Machine Migration Techniques in Cloud Computing

Many regular computers can be combined together for building a supercomputer so that complex problems can be solved. Cloud computing follows the same concept. Cloud computing is one of the most eminent models used in the information technology (IT) industry. Several other computing research areas are adopted by cloud computing for its functioning, which may include grid computing, virtualization, mobile computing, green cloud computing, edge computing, and so on. Virtualization is the most prime technology used in a cloud environment. It helps in obtaining various resource management targets such as system maintenance, load balancing, energy management, live virtual machine (VM) migration, and so on. Live VM migration has persistently used technique in cloud data centers because it grants load balancing, better service availability, energy management, and so on. This chapter highlights the virtualization, energy efficiency, VM migration mechanism, and types of live VM migration techniques.
Application of Artificial Intelligence Combined with Neural Network in the Research of Mobile Robot Path Planning

Abstract With the development of artificial intelligence mobile robots, it has been widely used in many fields. Mobile robots are an important development direction in the field of robots, and path planning is one of the important issues in the research of mobile robots. In the known and unknown static environment, the application of neural network technology to the path planning of mobile robots has become one of the current research hotspots. In this paper, the common algorithms of artificial neural network algorithm in mobile robot path planning process are introduced in detail. The path planning of mobile robot in known and unknown static environment is studied, and its application is prospected.
Variable Frame Rate-Based Data Augmentation to Handle Speaking-Style Variability for Automatic Speaker Verification

The effects of speaking-style variability on automatic speaker verification were investigated using the UCLA Speaker Variability database which comprises multiple speaking styles per speaker. An x-vector/PLDA (probabilistic linear discriminant analysis) system was trained with the SRE and Switchboard databases with standard augmentation techniques and evaluated with utterances from the UCLA database. The equal error rate (EER) was low when enrollment and test utterances were of the same style (e.g., 0.98% and 0.57% for read and conversational speech, respectively), but it increased substantially when styles were mismatched between enrollment and test utterances. For instance, when enrolled with conversation utterances, the EER increased to 3.03%, 2.96% and 22.12% when tested on read, narrative, and pet-directed speech, respectively. To reduce the effect of style mismatch, we propose an entropy-based variable frame rate technique to artificially generate style-normalized representations for PLDA adaptation. The proposed system significantly improved performance. In the aforementioned conditions, the EERs improved to 2.69% (conversation -- read), 2.27% (conversation -- narrative), and 18.75% (pet-directed -- read). Overall, the proposed technique performed comparably to multi-style PLDA adaptation without the need for training data in different speaking styles per speaker.
APPLICATION SECURITY PROBLEM CITIZEN BIOMETRICS

Союз криминалистов и криминологов 2020. № 4, Юридическая, Мацкевич И.М., электронная книга, печатная книга, скачать, купить, загрузить, читать, онлайн, PDF, TXT, EPUB, купить, заказать, заказ, интернет-магазин, ЛитГид, LitGid.com,
A New Optimal Ensemble Algorithm Based on SVDD Sampling for Imbalanced Data Classification

Nowadays, imbalanced data classification is a hot topic in data mining and recently, several valuable researches have been conducted to overcome certain difficulties in the field. Moreover, those approaches, which are based on ensemble classifiers, have achieved reasonable results. Despite the success of these works, there are still many unsolved issues such as disregarding the importance of samples in balancing, determination of proper number of classifiers and optimizing weights of base classifiers in voting stage of ensemble methods. This paper intends to find an admissible solution for these challenges. The solution suggested in this paper applies the support vector data descriptor (SVDD) for sampling both minority and majority classes. After determining the optimal number of base classifiers, the selected samples are utilized to adjust base classifiers. Finally, genetic algorithm optimization is used in order to find the optimum weights of each base classifier in the voting stage. The proposed method is compared with some existing algorithms. The results of experiments confirm its effectiveness.
Enterprise Architecture Frameworks as Support for Implementation of Regulations: Approach and Experiences from GDPR

Enterprise Architecture (EA) management has been discussed as being supportive for implementation of regulations in enterprises and organizations, but the role of EA frameworks in this context has not been addressed intensely. The EU General Data Protection Regulation (GDPR) is one of the most frequently discussed regulation in industry and research, and expected to cause a shift in viewpoint of enterprises from a technological perspective dominated by information security issues to an organizational perspective governed by GDPR-compliant organizational structures and processes. A well-documented Enterprise Architecture (EA) and a working Enterprise Architecture Management (EAM) organization are expected to significantly ease the roadmap planning for GDPR implementation. Therefore, this article focuses on the practice of EA use for GDPR implementation. The main contributions of this article are (a) an analysis and comparison of existing architecture frameworks and how they address security-related issues, and (b) a case study from financial industries illustrating the use of EA for implementing GDPR compliance.
Chinese Named Entity Recognition via Adaptive Multi-pass Memory Network with Hierarchical Tagging Mechanism

Named entity recognition (NER) aims to identify text spans that mention named entities and classify them into pre-defined categories. For Chinese NER task, most of the existing methods are character-based sequence labeling models and achieve great success. However, these methods usually ignore lexical knowledge, which leads to false prediction of entity boundaries. Moreover, these methods have difficulties in capturing tag dependencies. In this paper, we propose an Adaptive Multi-pass Memory Network with Hierarchical Tagging Mechanism (AMMNHT) to address all above problems. Specifically, to reduce the errors of predicting entity boundaries, we propose an adaptive multi-pass memory network to exploit lexical knowledge. In addition, we propose a hierarchical tagging layer to learn tag dependencies. Experimental results on three widely used Chinese NER datasets demonstrate that our proposed model outperforms other state-of-the-art methods.
Multi-label classification: do Hamming loss and subset accuracy really conflict with each other?

Various evaluation measures have been developed for multi-label classification, including Hamming Loss (HL), Subset Accuracy (SA) and Ranking Loss (RL). However, there is a gap between empirical results and the existing theories: 1) an algorithm often empirically performs well on some measure(s) while poorly on others, while a formal theoretical analysis is lacking; and 2) in small label space cases, the algorithms optimizing HL often have comparable or even better performance on the SA measure than those optimizing SA directly, while existing theoretical results show that SA and HL are conflicting measures. This paper provides an attempt to fill up this gap by analyzing the learning guarantees of the corresponding learning algorithms on both SA and HL measures. We show that when a learning algorithm optimizes HL with its surrogate loss, it enjoys an error bound for the HL measure independent of $c$ (the number of labels), while the bound for the SA measure depends on at most $O(c)$. On the other hand, when directly optimizing SA with its surrogate loss, it has learning guarantees that depend on $O(\sqrt{c})$ for both HL and SA measures. This explains the observation that when the label space is not large, optimizing HL with its surrogate loss can have promising performance for SA. We further show that our techniques are applicable to analyze the learning guarantees of algorithms on other measures, such as RL. Finally, the theoretical analyses are supported by experimental results.
Novel Artificial Human Optimization Field Algorithms – The Beginning

New Artificial Human Optimization (AHO) Field Algorithms can be created from scratch or by adding the concept of Artificial Humans into other existing Optimization Algorithms. Particle Swarm Optimization (PSO) has been very popular for solving complex optimization problems due to its simplicity. In this work, new Artificial Human Optimization Field Algorithms are created by modifying existing PSO algorithms with AHO Field Concepts. These Hybrid PSO Algorithms comes under PSO Field as well as AHO Field. There are Hybrid PSO research articles based on Human Behavior, Human Cognition and Human Thinking etc. But there are no Hybrid PSO articles which based on concepts like Human Disease, Human Kindness and Human Relaxation. This paper proposes new AHO Field algorithms based on these research gaps. Some existing Hybrid PSO algorithms are given a new name in this work so that it will be easy for future AHO researchers to find these novel Artificial Human Optimization Field Algorithms. A total of 6 Artificial Human Optimization Field algorithms titled "Human Safety Particle Swarm Optimization (HuSaPSO)", "Human Kindness Particle Swarm Optimization (HKPSO)", "Human Relaxation Particle Swarm Optimization (HRPSO)", "Multiple Strategy Human Particle Swarm Optimization (MSHPSO)", "Human Thinking Particle Swarm Optimization (HTPSO)" and "Human Disease Particle Swarm Optimization (HDPSO)" are tested by applying these novel algorithms on Ackley, Beale, Bohachevsky, Booth and Three-Hump Camel Benchmark Functions. Results obtained are compared with PSO algorithm.
Overview and Comparison of Gate Level Quantum Software Platforms

Quantum computers are available to use over the cloud, but the recent explosion of quantum software platforms can be overwhelming for those deciding on which to use. In this paper, we provide a current picture of the rapidly evolving quantum computing landscape by comparing four software platforms---Forest (pyQuil), Qiskit, ProjectQ, and the Quantum Developer Kit (Q\#)---that enable researchers to use real and simulated quantum devices. Our analysis covers requirements and installation, language syntax through example programs, library support, and quantum simulator capabilities for each platform. For platforms that have quantum computer support, we compare hardware, quantum assembly languages, and quantum compilers. We conclude by covering features of each and briefly mentioning other quantum computing software packages.
A Quantum Adiabatic Algorithm for Multiobjective Combinatorial Optimization

In this work we present a quantum algorithm for multiobjective combinatorial optimization. We show how to map a convex combination of objective functions onto a Hamiltonian and then use that Hamiltonian to prove that the quantum adiabatic algorithm of Farhi \emph{et al.} [arXiv:quant-ph/0001106] can find Pareto-optimal solutions in finite time provided certain convex combinations of objectives are used and the underlying multiobjective problem meets certain restrictions.
Privacy-Preserving Multi-keyword Search over Outsourced Data for Resource-Constrained Devices

With the rapid development of cloud computing, a variety of cloud-based applications have been developed. Since cloud computing has the features of high capacity and flexible computing, more and more users are motivated to outsource their data to the cloud server for economic savings. Users are able to search over outsourced data according to some keywords with the help of the cloud server. During data searching, the confidentiality of the relevant data could be compromised since the keywords may contain some sensitive information. However, existing privacy-preserving keyword search proposals have high computation complexity, which are not applicable to IoT-related scenarios. That is, the data processing and search trapdoor generation procedures require the users to take resource-intensive computations, e.g., high-dimensional matrix operations, which are unaffordable by resource-constrained devices. To address this issue, we propose a light-weight privacy-preserving multi-keyword search scheme. The security and performance analyses demonstrate that our scheme outperforms existing solutions and is practical in applications.
Passive Image Copy–Move Forgery Detection Based on ORB Features

Digital image acquisition is extremely easy nowadays, but this convenience comes with the illegal purposes of criminals. Therefore, it is very necessary to identify the authenticity of the image. This paper proposes an adaptive image copy–move forgery detection method based on ORB features. Firstly, the proposed method performs an adaptive image super-pixel segmentation. Secondly, adaptive image sampling is carried out for each super-pixel block according to the block size. Third, quad-tree is used to store the key-points extracted by oFAST, and then the extracted key-points are described in rBRIEF. After that, Brute Force Matcher and hamming distance are adopted in the proposed method to perform key-points matching. Finally, MorphSnake algorithm is employed to locate the forged areas. Experiments have shown that compared with the current advanced detection methods, this one has stronger robustness to the attack of brightness, color, and contrast, and shorter running time.
Two-stream FCNs to balance content and style for style transfer

Style transfer is to render given image contents in given styles, and it has an important role in both computer vision fundamental research and industrial applications. Following the success of deep learning based approaches, this problem has been re-launched recently, but still remains a difficult task because of trade-off between preserving contents and faithful rendering of styles. Indeed, how well-balanced content and style are is crucial in evaluating the quality of stylized images. In this paper, we propose an end-to-end two-stream Fully Convolutional Networks (FCNs) aiming at balancing the contributions of the content and the style in rendered images. Our proposed network consists of the encoder and decoder parts. The encoder part utilizes a FCN for content and a FCN for style where the two FCNs have feature injections and are independently trained to preserve the semantic content and to learn the faithful style representation in each. The semantic content feature and the style representation feature are then concatenated adaptively and fed into the decoder to generate style-transferred (stylized) images. In order to train our proposed network, we employ a loss network, the pre-trained VGG-16, to compute content loss and style loss, both of which are efficiently used for the feature injection as well as the feature concatenation. Our intensive experiments show that our proposed model generates more balanced stylized images in content and style than state-of-the-art methods. Moreover, our proposed network achieves efficiency in speed.
Simultaneous sorting many quDits using different input ports

Quantum sorter has gained a lot of attention during the last years due to its wide application in quantum information processing and quantum technologies. A challenging task is the construction of a quantum sorter, which collect many high-dimensional quantum systems, which are simultaneously incident on different input ports of the device. In this paper, we give the definition of the general quantum sorter of multi-level quantum systems. We prove the impossibility of the construction of the perfect quantum sorter, which works for many particles incident on any input port, while keeping their states unmodified. Further, we propose an approximate multi-particle multi-input-port quantum sorter, which performs the selection of the particles in a certain output port according to the properties of the initial states, but changing the final states. This method is useful for those situations which require high speed of quantum state sorting. Thus, the information contained in the initial states of the particles is revealed by the click statistics of the detectors situated in each output port.
Generation of system-wide parameters for Falcon cryptosystem for 256, 384, 512 bits of security

Globally, the efforts of a significant number of crypto-theorists, mathematicians and cryptologists-practitioners are focused on the NIST PQC open competition. One of the main tasks of the competition consists in development and adoption of a post-quantum ES standard or standards. The finalists of the second stage of the NIST competition were three ES mechanisms – CRYSTALS-DILITHIUM, Falcon and Rainbow. In addition, three alternative candidates were identified that require more detailed research. In general, a comprehensive analysis of the finalists is an important task for cryptologists in the global cryptocommunity. Moreover, security, i.e. brining the cryptographic stability of two finalist candidates, to the ES standard – CRYSTALS-DILITHIUM and Falcon, is based on problems in the theory and practice of algebraic lattices. Studies show that among the ES schemes on lattices it differs slightly from other candidates and has prospects for the adoption as the Falcon algorithm standard. The main and dominant approach to the design of the Falcon ES mechanism is the use of the Fiat-Shamir transformation with interruptions. The sets of system-wide parameters that ensure resistance to all known and potential attacks should be found for the safe use of the Falcon ES. In the process of forming the requirements for ES within the competition, the NIST was interested only in sets of system-wide parameters up to 256 bits of classical security inclusive. However, according to the authors of this work, in the future it is advisable to provide at least 384 and 512 bits of security for classical cryptanalysis and at least 192 and 256 bits of security for quantum cryptanalysis. The article briefly considers the essence of the Falcon electronic signature (ES) algorithm. An analysis of possible attacks on the algorithm and the mechanisms of their implementation is also performed. The process of generating system-wide parameters for 256, 384, 512 stability bits is considered. Conclusions and recommendations are given. The objective of the work is the classification and initial analysis of known attacks on the ES Falcon cryptosystem, setting limits and developing practical algorithms for calculating (generating) system-wide parameters to provide not less than 256, 384 and 512 security bits for classical and not less than 128, 192 and 256 security bits for quantum cryptanalysis.
Task-oriented Domain-specific Meta-Embedding for Text Classification

Meta-embedding learning, which combines complementary information in different word embeddings, have shown superior performances across different Natural Language Processing tasks.However, domain-specific knowledge is still ignored by existing metaembedding methods, which results in unstable performances across specific domains.Moreover, the importance of general and domain word embeddings is related to downstream tasks, how to regularize meta-embedding to adapt downstream tasks is an unsolved problem.In this paper, we propose a method to incorporate both domain-specific and taskoriented information into meta-embeddings.We conducted extensive experiments on four text classification datasets and the results show the effectiveness of our proposed method.
Typosquatting for Fun and Profit: Cross-Country Analysis of Pop-Up Scam

Today, many different types of scams can be found on the internet. Online criminals are always finding new creative ways to trick internet users, be it in the form of lottery scams, downloading scam apps for smartphones or fake gambling websites. This paper presents a large-scale study on one particular delivery method of online scam: pop-up scam on typosquatting domains. Typosquatting describes the concept of registering domains which are very similar to existing ones while deliberately containing common typing errors; these domains are then used to trick online users while under the belief of browsing the intended website. Pop-up scam uses JavaScript alert boxes to present a message which attracts the user’s attention very effectively, as they are a blocking user interface element.&#x0D; Our study among typosquatting domains derived from the Majestic Million list utilising an Austrian IP address revealed on 1219 distinct typosquatting URLs a total of 2577 pop-up messages, out of which 1538 were malicious. Approximately a third of those distinct URLs (403) were targeted and displayed pop-up messages to one specific HTTP user agent only. Based on our scans, we present an in-depth analysis as well as a detailed classification of different targeting parameters (user agent and language) which triggered varying kinds of pop-up scams. Furthermore, we expound the differences of current pop-up scam characteristics in comparison with a previous scan performed in late 2018 and examine the use of IDN homograph attacks as well as the application of message localisation using additional scans with IP addresses from the United States and Japan.