Reviewerly/exHarmony huggingface dataset:
Integration of satellite data processing with seismic sections for tectonic interpretation and modeling for the breaks and omissions of continuous stratigraphic units

Abstract Satellite thematic maps in geological field studies are an important tool for the delineation, interpretation, representation, and visualization of geological processes, while the development of seismic sections offers an increasingly detailed analysis of subsurface geology and inferring and understanding the subsurface structure of the earth. This combination promoted a fast-growing research in the fields of morphotectonics and neotectonics. The area under investigation is one of the most significant Iraqi places which includes several oil fields. The study area is located in northern Iraq and administratively follows Kirkuk governorate. Landscapes in this area are characterized by asymmetrical, cylindrical and extended folds. Kirkuk, Bai-Hasan and Qarachuq Anticlines reflect these structural elements and their fold axis trends towards northwest-southeast within the Zagros folded zone. The main objective of this study is to use the visual and digital interpretation of Landsat image to discriminate the sudden breaks and omissions of rock beds in the study area. Therefore, a morphotectonic map was created, the accurate location of the sudden breaks was provided and the zones affected by tectonic deformation, which left its imprint on the successive lithological units were detected. Seismic data were used to assess the tectonic impact on subsurface formations and their reflections on exposure rocks. The seismic lines were selected based on the locations of the identified sudden breaks as interpreted from satellite images. The hypothesis derived from this study is that the sudden breaks in the study area were triggered by a series of faults. In addition, the region was affected by the vertical stresses resulting from these faults and morphological aspects of the region were determined by the temporal variations of the above stresses.
A Survey on Online Review Spammer Group Detection

Online product/service reviews are mostly used by people for their purchasing decisions. Recently, the review system has contained fake reviews that are written by people who are hired by an organization either to promote their product or to demote their competitors’ product. Nowadays, these fake reviewers work in groups to magnify their activities and to gain maximum profit. These review spammer groups occur more frequently and are even more harmful than individual review spammers in current review spamming scenarios. To detect such review spammer groups, several pieces of research have been carried out. This survey introduces a taxonomy of review spammer group detection techniques and evaluates the performance of each technique.
Learning Tenses With Multimedia For Junior High School Students

Communication is a process of interaction carried out by two or many people using language devices, which aim to interact with each other and get useful information for that person. Language is an intermediary medium in communicating directly or indirectly so that the information conveyed can be accepted and understood by the recipient. There are many languages around the world, with each country having a variety of languages. In the international world, it has been agreed that English is recognized as an international language for people who have different mother tongues, where English is used when communicating in business, political and educational matters between different countries. English is one of the subjects taught at various levels of schools in Indonesia, especially at the junior high school level. In this study, a multimedia application will be built for learning English using the MDLC (Multimedia Development Life Cycle) method. The result of this research is a multimedia application for learning English tenses at the junior high school level using the android mobile application technology.
EPNet: Efficient Patch-based Deep Network for Real-Time Semantic Segmentation

PC vision is the one that causes the machine to comprehend the highlights of different photographs and recording. The division of picture is progressively getting a matter of PC vision and Artificial Intelligence [AI] experts. Many climbing applications requires unique and more beneficial highlights: programmed driving, inward route, and reasonable edges that are not to be given explicit models. It is making incredible steps in the field of car, mechanical, and robotization. By parting the video outline for utilizing semantic segmentation, the route framework can settle on an unmistakable choice. Mental confinement is considered as the initial phase. In this research work, there are different organizations that makes semantic order characterized. While utilizing distinctive fix size things, it tends to be utilized widely. Here, this examination has showed the viable preparing of 5 × 5 based pat-net patches. The deep neural network gives an amazing commitment to semantic isolation regarding the intersection over union boundaries. In the impacts and examination area, the impacts are applied to the picture and video in 5 × 5-pixel size from CityS cape and CamVid photograph arrangements. From the examination and analysis, it has been proposed that the 5 × 5 size fix gives the union's most noteworthy junction in contrast to other profound organizations.
Adjust Planning Strategies to Accommodate Reinforcement Learning Agents

Abstract In agent control issues, the idea of combining reinforcement learning and planning has attracted much attention. Two methods focus on micro and macro action respectively. Their advantages would show together if there is good cooperation between them. An essential for the cooperation is to find an appropriate boundary, assigning different functions to each method. Such a boundary could be represented by parameters in a planning algorithm. In this paper, we create an optimization strategy for planning parameters, through analysis of the connection of reaction and planning; we also create a non-gradient method for accelerating the optimization. The whole algorithm can find a satisfactory setting of planning parameters, making full use of the reaction capability of specific agents.
Segmentation and Feature Extraction of Infants’ Hip Joint Ultrasound Images

In this paper, we proposed a segmentation method for infant hip joint ultrasound images as well as an extraction method of interested region. The method segments the infant hip joint of ultrasound image twice to achieve the optimal segmentation result. The problem of intensities inhomogeneities existing in medical images is solved by mapping the evolved level set functions in the first segmentation procedure into a new, homogenous gray level image. In the second segmentation, shape prior and regularization term are introduced into the original CV model to optimize the segmentation results. The shape prior is used again after segmentation to extract interested regions and related features from segmented image. Experimental results show that our method achieves better segmentation results than existing methods. Besides, our method is superior to other methods on extracting regions of interest.
Reliability evaluation of command information system under different attack modes

Abstract Based on the basic structure of the typical command information system network, the model has been divided into physical layer and information layer. According to the whole network efficiency and reliability, five functional characteristics quantity are selected as the reliability evaluation indexes when the system is attacked randomly or deliberately. With the model we established, reliability changes of the constructed system are compared of different attack modes. The results provide evidence that the system reliability change obviously in different attack modes which should be special focused in practical application.
Security vs. Flexibility: Striking a Balance in the Pandemic Era

An organization's reputation is largely dependent on the work culture it provides to its employees. This is the reason, “flexibility” is becoming an inherent part of an employer's support to its employees. Due to the unprecedented outbreak of novel corona virus (COVID-19), most companies have adopted flexibility of working from anywhere, for their employees. This digital transformation took place almost instantaneously. Hence, neither the employees, nor the employers were fully prepared for this situation. This definitely makes our lives convenient. But there exists another side of the coin which is concerned with the security issues pertaining to use of personal networks and devices. The home network devices have not been configured to be secure in line with the employer's requirements. That is the reason, attackers have a larger surface to get their hands dirty on. This paper exhaustively describes the holistic view of security issues and challenges faced by employees as well as employers in remote working paradigm. This paper emphasizes on the cybersecurity threats which have emerged in this pandemic era. The work presents the challenges faced by the employees as well as their employers in these tough times. Then this paper discusses the sudden rise in volumes of cyber-attacks between January 2020 to March 2020. The company's evaluation of critical threats in the on-site working paradigm versus the remote working paradigm have also been described. Next, it describes the risks which might occur in the near future of the COVID-19 impacted world. Finally, the paper proposes some of the ways in which employers can strike an efficient balance between flexibility for employees and security of their assets.
Distributing authority – state sovereignty in the age of blockchain

According to the classical understanding of the state sovereignty, a state is sovereign when it enjoys full control over its territory, sole authority internally, and independence of outside authority. Those aspects of control and authority will further manifest themselves in law making and law enforcing. Blockchain with its distinctive characteristics can challenge state sovereignty thus seen. Its most salient feature is ability to distribute control and authority over data. Having potential to facilitate transactions without middlemen and render some legal institutions redundant, blockchain carries with itself both great promises and chilling risks for public institutions. Meanwhile, countries implement various policies related to this technology, positioning themselves among passive observers or active regulators; as outsiders or as early adopters. The argument made in this article is that, regardless of the adopted approach, blockchain poses systemic challenges to the sovereignty of states. The article examines fundamental features of blockchain that combined cause the inherent distribution of control within a blockchain environment which, in turn, challenge traditional law making and law enforcement. Finally, I make a point that those features make blockchain equally as ground-breaking as incoherent with the legal and practical functioning of sovereign states and, therefore, some trade-offs are imperative.
An Improved Cryptanalysis Algorithm for Chebyshev Map-Based Discrete Logarithm Problem

Chebyshev map is a chaotic map frequently used in design of cryptography schemes and cryptosystems based on the hardness of the Chebyshev map-based discrete logarithm (CMDL) problem. The properties of Chebyshev map have great impact on the security of these cryptosystems. It has been known that the polynomial sequences generated by Chebyshev map defined on finite fields exhibit strong periodical features which may be utilized for cryptanalysis. This paper presents the periodical properties of Chebyshev polynomial sequences. Based on the properties, an improved cryptanalysis algorithm is proposed for the CMDL problem. It turns out that a chebyshev map-based cryptosystem using Chebyshev prime number as its modulus will have better security, where the Chebyshev prime number is defined as the prime number p satisfying that $$(p\,{+}\,1)/2$$ or $$(p\,{-}\,1)/2$$ is also a prime number. In support of cryptanalysis, fast algorithms to calculate the value of a Chebyshev polynomial and find the minimal period of a Chebyshev polynomial sequence are proposed, too. An example is given to show the process of cryptanalysis. Computational results have shown that only a small fraction of prime numbers are valid Chebyshev prime numbers.
Possibilities of deep learning neural networks for satellite image recognition

Abstract The main problem solved in this project is the analysis of big data using a system of computer processing and recognition of satellite images, based on a deep neural network architecture. The goal of the project is to develop methodological, theoretical and practical aspects of building such systems in poorly formalized subject areas, as well as to study the possibilities and advantages of building predictive models for analyzing fresh water reserves and predicting the direction, speed and nature of the spread of large fires using such systems. and assessments of the economic impact of these natural disasters.
City of Glasgow College Launch Climate-Inspired Jewellery Comp

City of Glasgow College launch an innovative jewellery design competition inspired by our climate crisis
Research of Attention-Based Bi-GRU-CRF for Slot Filling

Abstract Slot Filling (SF) is a critical part of spoken language understanding (SLU) which targets to capture semantic constituents from a specific utterance. It is considered as a sequence labeling issue. Currently, recurrent neural networks have shown promising effectiveness in this issue. Considering the effects of interrelated information within adjacent words and labels, we present that a novel approach consists of bi-directional gate recurrent unit (Bi-GRU), attention mechanism, and conditional random field (CRF). Our model can utilize interrelated information from words in the neighborhood, highlight key information, and exploit the dependencies within labels corresponding to surrounding words. The empirical experiments illustrate that our model significantly boosts the F1 score with around 1% and 5.1% relative enhancement on two public benchmark dataset ATIS and SNIPS separately.
Digital Learning Experience for University Student

Currently developments related to online learning process are developing rapidly coupled with the COVID-19 pandemic this year, so the main thing is to determine interactive learning that can support the competence of graduate graduates from undergraduate students. From the research results, it shows that the interaction of creativity is an absolute thing that must be done by the lecturer to be able to make a meaningful learning process for students.
Multidomain Sentiment Lexicon Learning Using Genre-Seed Embeddings

Sentiment lexicon is widely used resource which plays an important role in sentiment analysis tasks. Sentiment lexicon learning using minimum labeled resources for multiple domains is a challenging task. Multiple domains with similar characteristics are considered as same genre domains in our work. We present an unsupervised sentiment lexicon learning model for same-genre multiple domains. The model uses pre-learned genre-based polarity seed words as only labeled input and explore sentiment lexicon learning using different unsupervised word-embedding approaches. The pretrained embedding provides generalized word vectors, so our approach explores it further by creating domain-based word-embeddings. The experimentation involves sentiment lexicon learning using Word2Vec, GloVe, and fastText word-embeddings. The experiments are conducted on 18 same genre domains. The different word-embedding sentiment lexicon learning model results are compared across genre-base domains using standard evaluation metrics. The proposed model performs good in comparison to the existing models from the literature.
Metaheuristic Algorithm Based on Hybridization of Invasive Weed Optimization asnd Estimation Distribution Methods

Hybrid metaheuristic methods combine approaches extracted from different techniques to build a single optimization method. The design of such systems represents a current trend in the metaheuristic optimization literature. In hybrid algorithms, the objective is to extend the potential advantages of the integrated approaches and eliminates their main drawbacks. In this chapter, a hybrid method for solving optimization problems is presented. The presented approach combines (1) the explorative characteristics of the invasive weed optimization (IWO) method, (2) the probabilistic models of the estimation distribution algorithms (EDA), and (3) the dispersion capacities of a mixed Gaussian-Cauchy distribution to produce its own search strategy. With these mechanisms, the method conducts an optimization strategy over search areas that deserve a special interest according to a probabilistic model and the fitness value of the existent solutions. In the presented method, each individual of the population generates new elements around its own location, dispersed according to the mixed distribution. The number of new elements depends on the relative fitness value of the individual regarding the complete population. After this process, a group of promising solutions is selected from the set compound by the (1) new elements and the (2) original individuals. Based on the selected solutions, a probabilistic model is built from which a certain number of members (3) is sampled. Then, all the individuals of the sets (1), (2), and (3) are joined in a single group and ranked in terms of their fitness values. Finally, the best elements of the group are selected to replace the original population. This process is repeated until a termination criterion has been reached. To test the performance of the method, several comparisons to other well-known metaheuristic methods has been conducted. The comparison consists of analyzing the optimization results over different standard benchmark functions within a statistical framework. Conclusions based on the comparisons exhibit the accuracy, efficiency, and robustness of the presented approach.
Fruit Shadow Removal Algorithm Based on HSV and Region Growing

According to actual application scenarios that may produce shadows that may affect analysis and recognition, a shadow removal algorithm based on HSV color space and region growth is proposed. First the images are transformed to HSV color space, the saturation components are extracted. Secondly the region growing threshold for the saturation component is obtained using the Otsu algorithm. Thirdly the region growth algorithm is used to separate the fruit and the background to obtain a binarized image. Finally the binary image and the original image are logically operated to obtain a de-shaded image.
Planning the use of educational and training applications

Abstract Annotation. This paper presents a method for planning the use of educational and training applications, which intended to improve the training level of military specialists. This paper provides an algorithm for determining requirements to important professional qualities and competencies of a military specialist, an integral indicator of his training level for fulfillment of his functional duties, forecasting the process of improving professional qualities and competencies and drafting a rational plan for professional development.
Towards a collaborative approach for recommendation of services using a domain of ontology and agents

Collaboration between companies provides a means of communication between individuals from different entities having a common goal. Collaborative work involves the sharing of expertise and resources. However, this sharing of resources must be done reliably and rapidly for effective decision making. In this article, an agent-based architecture for a collaborative decision support system that involves recommendation and negotiation is proposed in order to facilitate the allocation of a service by an enterprise considering fixed multi-criteria. This contribution offers a collective behavioral environment with a personalized search and time savings.
Research on the Application of Block Chain Technology in the Field of Book Sales

With the development of the Internet, the digitization and virtualization of information have become the development trend of the times. The traditional book sales model has been unable to meet the needs of consumers. Based on the analysis of the disadvantages of the traditional book sales model, this paper puts forward a scheme of applying block chain to the book sales system. The scheme uses Hyperledger super-ledger and Fabric network to solve the problems of consumers, book sellers and book manufacturers distrusting each other, obtaining information asymmetry and user information leakage in traditional book sales through block chain distributed storage, intelligent contract, consensus mechanism and other technologies. The book sales platform using this method not only changes consumers' consumption environment, consumption mode and consumption concept, but also greatly enhances users' sense of experience. At the same time, it also injects new vitality and provides a new development direction for the book sales industry.
Digital multitasking problem in modern universities educational process - review

This article is a review of recent researches on digital multitasking problem. Claiming that digital technologies are changing the traditional roles of a teacher and a student, we must clearly understand a kind of effects arised at the moment digital technologies are used in the classroom. Moreover, the methods of applying digital technologies for the success of students’ academic progress are not always obvious to the teacher himself. The goal of the research analysis on digital multitasking in education problem is to identify shifts in settings and accents from the operationalism methodology to attempts of application of analytical philosophy of consciousness methodology and / or postmodern philosophy. This allows us to analyze the intentions, motives of the educational process participants and thereby define new conceptual boundaries of digital multitasking.
Evaluation of Distribution Network Operation State Based on Spark Parallel Computing

The distribution network is an important part of the power system. Timely and accurately assessing the operation state of the distribution network is the basis for ensuring the safe and stable operation of the distribution network. The characteristics of the distribution network, including wide geographical distribution, a great variety of equipment, complex network connections, the application of information and smart technologies in the distribution network, have promoted the exponential increase of various operation monitoring data from the distribution network. It brings difficulty to the dynamic evaluation of the operation state of the distribution network. This paper proposes to evaluate the operation state of the distribution network by using the analytic hierarchy process and the Delphi method together, meanwhile calculating the indicator of static data and dynamic operating data of the distribution network based on the Spark distributed parallel computing framework. As well as the distribution network operation state evaluation software system is developed to show the operation state of the distribution network in multiple granularities. The results have verified the flexibility, continuity and practicability of the system.