Reviewerly/exHarmony huggingface dataset:
Preface for the Special Issue of the 12th Conference on Reversible Computation (RC 2020)

Adaptive Random Testing (ART) aims at enhancing the failure detection capability of Random Testing (RT) by evenly distributing test cases over the input domain. Many ART algorithms have been proposed to achieve an even spread of test cases, according to different notations. A well-known ART algorithm, namely Fixed-Size-Candidate-Set ART (FSCS-ART), chooses an element as the next test case such that it is farthest away from previously executed test cases. Previous studies have demonstrated that FSCS-ART could achieve good improvements in test effectiveness over RT, but suffers from some drawbacks such as high computational overhead and boundary effect problem. In this paper, we propose an alternative approach to enhance FSCS-ART, namely Nearest-Neighbor Divide-and-Conquer based ART (NNDC-ART), which combines the concepts of nearest-neighbor and divide-and-conquer. It attempts to reduce the computational overhead of test case generation, and also to alleviate the boundary effect problem; while maintaining the advantages of FSCS-ART. The simulation results show that compared with FSCS-ART, NNDC-ART requires much less computational overhead, and also achieves much better test case distribution; while maintaining better or comparable failure-detection effectiveness. Our empirical studies further show that the proposed approach is much more cost-effective than FSCS-ART. In addition, we further compare NNDC-ART against two enhancements of FSCS-ART based on the partitioning strategy, i.e., ART with Divide-and-Conquer (ART-DC) and ART by Bisection and Localization (ART-B-Loc), and find that our proposed approach can achieve similar or even better testing performances in some scenarios.
Science, Technology &amp; Innovation and Intellectual Property: Leveraging Openness for Sustainable Development in Africa

"Science, Technology & Innovation and Intellectual Property: Leveraging Openness for Sustainable Development in Africa." African Journal of Science, Technology, Innovation and Development, ahead-of-print(ahead-of-print), pp. 1–2
Language-Model-Based Parsing and English Generation for Unscoped Episodic Logical Forms

Unscoped Episodic Logical Forms (ULF) is a semantic representation for English sentences which captures semantic type structure, allows for linguistic inferences, and provides a basis for further resolution into Episodic Logic (EL). We present an application of pre-trained autoregressive language models to the task of rendering ULFs into English, and show that ULF's properties reduce the required training data volume for this approach when compared to AMR. We also show that the same system, when applied in reverse, performs well as an English-to-ULF parser.
Reviewer Acknowledgements

Applied Economics and Finance (AEF) would like to acknowledge the following reviewers for their assistance with peer review of manuscripts for this issue. Many authors, regardless of whether AEF publishes their work, appreciate the helpful feedback provided by the reviewers. Their comments and suggestions were of great help to the authors in improving the quality of their papers. Each of the reviewers listed below returned at least one review for this issue.Reviewers for Volume 9, Number 2 Abdulaziz Abdulahman, Marmara University, USAAlbert Henry Ntarmah, Jiangsu University, GhanaALI DARUB KASSAR, Univ. of Baghdad, IraqBabacar Seck, University of Bahrain, BahrainGetamesay Bekele Meshesha, Ethiopian Civil Service University, EthiopiaIulia Lupu, Victor Slavescu” Centre for Financial and Monetary Research, Romanian Academy, RomaniaLiao Zhu, Cornell University, USAMarco Muscettola, Independent Researcher-Credit Risk Manager, ItalyMurad Harasheh, University of Milan-Bicocca, ItalyOmer Allagabo Omer Mustafa, Sudan Academy for Banking and Financial Sciences, SudanRomeo Victor Ionescu, Dunarea de Jos University, RomaniaShahram Fattahi, Razi University, IranVictoria Cociug, Academy of Sciences of Moldova, MoldovaY. Saidi, M’sila University, Algeria Nikki GibbsEditorial AssistantOn behalf of,The Editorial Board of Applied Economics and FinanceRedfame Publishing9450 SW Gemini Dr. #99416Beaverton, OR 97008, USAURL: http://aef.redfame.com
EgoPlan: A Framework for Multi-Agent Planning Using Single Agent Planners

Planning problems are, in general, PSPACE-complete; large problems, especially multi-agent problems with required co-ordination, can be intractable or impractical to solve. Factored planning and multi-agent planning both address this by separating multi-agent problems into tractable sub-problems, but there are limitations in the expressivity of existing planners and in the ability to handle tightly coupled multi-agent problems. This paper presents EGOPLAN, a framework which factors a multi-agent problem into related sub-problems which are solved by iteratively calling on a single agent planner. EGOPLAN is evaluated on a multi-robot test domain with durative actions, required coordination, and temporal constraints, comparing the performance of a temporal planner, OPTIC-CPLEX, with and without EGOPLAN. Our results show that for our test domain, using EGOPLAN allows OPTIC-CPLEX to solve problems that are twice as complex as it can solve without EGOPLAN, and to solve complex problems significantly faster.
Drawing Illustration Using Android Studio

This building is implemented with Android and machine learning in mind. We can doodle or sketch on our phones. Change a pixel at a nice spot. In this project, the programme has a variety of specialised brushes and brush sets to help you with your illustration, and it is fully customisable, allowing users to design their own brushes. Choose a shading utilizing various shading models, e.g., RGB, HSV, or by utilizing a shading eye dropper. Also, color pallet generator victimization machine learning makes it simple to form a color palette from any image, therefore it will come back the color palette as the second array of colors. Which follows the k- mean bunch algorithmic program? Keywords: Drawing App, canvas, Android App, colors
Anomaly detection model based on few-shot learning and memory modules

Anomaly detection is a key issue in public security. Its accuracy is essential to identify abnormalities and take corresponding actions to ensure the safety of relevant objects, which have a broad application space. The traditional anomaly detection method based on deep learning has too strong generalization ability. At the same time, it lacks recognition ability because it only uses normal data for training. To this end, we propose an anomaly detection model based on few-shot learning, guided by memory modules and trained by a large number of normal samples combined with a small number of observed abnormalities. We introduce memory modules to record normal features, which has the function of updating and reading. When training modules, feature compactness, and separateness loss are utilized, it successfully weakens the strong generalization of CNN and improves the memory module to identify normal samples. Then, based on the few-shot learning approach, we learn a more compact normal data distribution and expand the margin between normal and anomalous events to improve the discriminant ability. Many experimental results demonstrate that our method is practical and feasible, and its performance is better than the existing detection methods.
Book review: The complete collections assessment manual: A holistic approach

"Book review: The complete collections assessment manual: A holistic approach." Journal of Access Services, 19(1), pp. 14–15
A Comparative Performance Analysis on GIS based Web Development using Geoserver versus without using Geoserver

The main objective of this paper is to use of open source geospatial and software development technologies. Technologies emerging day by day, Internet make this possible, to connect across the world from one point and collect information of whole world. We can also collect data of earth through satellite imagery. GIS played a vital role in development, it make data visualize, store, analyze and helps in making of maps. It is able to analyses past data and present data based on that make future predictions. Now a day, All the data is collected with its geographical extents, this data is prepared as Geo-spatial data, it can be easily show on maps. The portal developed based on GIS makes better experience and interactive for users. We can easily make planning and other decision making.
RECURRENT METHOD FOR CONSTRUCTING FUZZY COGNITIVE MAPS FOR FOOD SECURITY ASSESSMENT

Assessing and ensuring food security (FS) taking into account the quality of life (QL) of the population is one of the most important problems of economic security.The problem of integral estimation of the FS level can be solved by developing a family of fuzzy cognitive maps (FCM).The purpose of the article is to build such models.The FCM algorithmic core is consistently supplemented with new concepts, such as generalized QL.At each iteration, the values of the weights corresponding to the new configuration of the modified FCM graph are revised.The conceptual model of cognitive modeling FS is constructed in accordance with the identified criteria of food security, including production; consumption, QL, level of infrastructure provision and import.The constructed model allows us to evaluate the impact of additional concepts, including QL, on the evolution of the integral FS level.
Segmentation of multicorrelated images with copula models and conditionally random fields

Purpose: Multisource images are interesting in medical imaging. Indeed, multisource images enable the use of complementary information of different sources such as for T1 and T2 modalities in MRI imaging. However, such multisource data can also be subject to redundancy and correlation. The question is how to efficiently fuse the multisource information without reinforcing the redundancy. We propose a method for segmenting multisource images that are statistically correlated. Approach: The method that we propose is the continuation of a prior work in which we introduce the copula model in hidden Markov fields (HMF). To achieve the multisource segmentations, we use a functional measure of dependency called “copula.” This copula is incorporated in the conditionally random fields (CRF). Contrary to HMF, where we consider a prior knowledge on the hidden states modeled by an HMF, in CRF, there is no prior information and only the distribution of the hidden states conditionally to the observations can be known. This conditional distribution depends on the data and can be modeled by an energy function composed of two terms. The first one groups the voxels having similar intensities in the same class. As for the second term, it encourages a pair of voxels to be in the same class if the difference between their intensities is not too big. Results: A comparison between HMF and CRF is performed via theory and experimentations using both simulated and real data from BRATS 2013. Moreover, our method is compared with different state-of-the-art methods, which include supervised (convolutional neural networks) and unsupervised (hierarchical MRF). Our unsupervised method gives similar results as decision trees for synthetic images and as convolutional neural networks for real images; both methods are supervised. Conclusions: We compare two statistical methods using the copula: HMF and CRF to deal with multicorrelated images. We demonstrate the interest of using copula. In both models, the copula considerably improves the results compared with individual segmentations.
Discourse Analysis from the Perspective of Systemic Functional

Language is an important prerequisite for social interaction, especially in interpersonal relations. The meaning of &#x0D; language itself is not only to help people to complete the most basic communication, but also to promote the emotional exchange &#x0D; between people, make society more harmonious, a symbol of social attributes. The unique charm of language is that language can &#x0D; make people get more information from language through dialogue.
The enduring relevance of philosophy in the education system. Part 2

This article focuses on the analysis of the importance of philosophy for streamlining the content of secondary education, in particular the topic of integration of subjects and integration processes in general in the field of education. Pragmatic demands for education and attempts to make it more modern and creative, to promote critical thinking, and to bring education closer to science can also be the basis for such integration. All of this is commendable, but the many ways to integrate secondary education need to be improved. First of all, some of the latest pedagogical techniques and practices have not yet received their methodological understanding within the general vision of the tasks, the interrelated ways of their implementation and the use of the potential of combining school education with extracurricular.&#x0D; Among the variety of existing integration educational projects, there are three types (their names are quite conditional): "STEM", "Four in one" and "case method". It is emphasized that the authors of these educational projects are not always correct in operating with the philosophical concepts used in the description of projects. The weak philosophical and methodological support of the projects and the insufficient methodological culture of their authors are pointed out. In particular, this manifested itself in an inadequate interpretation of the concept of integration. The content of the category "integration" in relation to the category "synthesis" is specially analyzed in the article. A philosophical and methodological analysis of these integrated educational projects was conducted, as a result of which it was shown that the shortcomings of the methodological culture can significantly affect the adequacy of the content and correctness of the wording of such projects.&#x0D; As a consequence of the analysis, the author calls for some methodological order in the pedagogical discourse, ie insists on the need to improve the philosophical and methodological culture of educational projects.
PEDAGOGICAL ASSESSMENT MODEL OF TRANSDISCIPLINARY COMPETENCES OF PRIMARY SCHOOL STUDENTS

The use of inter-/transdisciplinarity in school curricula determines the design of integrative technology for the assessment of students' transdisciplinary competences at the end of primary education. This technology is focused on the application of the integrative vision on the different levels and areas of knowledge and/or curricular areas. Integrative assessment involves a complex process focused on creating connections between different entities, such as content units, competences, skill units, which are usually formed and assessed disparately within different subjects. The assessment of school competences in primary education must be carried out as an approach to the integral knowledge of the pupil's personality, through the balanced correlation of psycho-physical, moral, aesthetic, scientific, technological and social dimensions.
Combining Super Resolution Algorithm (Gaussian Denoising and Kernel Blurring) and Compare with Camera Super Resolution

This problem addresses the problem of low-resolution image (noisy) that will proof later by PSNR number. The best way to improve this low-resolution problem is by utilizing Super Resolution (SR) algorithm methodology. SR algorithm methodology refers to the process of obtaining higher-resolution images from several lower-resolution ones, that is resolution enhancement. The quality improvement is caused by fractional-pixel displacements between images. SR allows overcoming the limitations of the imaging system (resolving limit of the sensors) without the need for additional hardware. This research aims to find the best SR algorithm in form of stand-alone algorithm or combine algorithm by comparing with the latest SR algorithm (Camera SR) from the previous research made by Chang Chen et al in 2019. Furthermore, we confidence this research will become the future guideline for anyone who want to improve the limitation of their low-resolution camera or vision sensor by implementing those SR algorithms.
Scientific TDM exception and communication to the public: did Italians do it better … or at least not worse?

The present contribution relates to the research work of the Team F.I.L.M. 4.0 in the context of the IUSTeC Excellence Project ‘Diritto, Tecnologie e Cambiamenti’ (Law, Technologies and Changes) of the Department of Law, University of Verona, Italy. In the context of Directive (EU) 2019/790 on copyright and related rights in the Digital Single Market (hereinafter ‘Dir. DSM’), the normative exceptions for text and data mining (TDM) introduce a harmonized discipline for such computational activity,1 which has a strategic role for the development of new digital services, including those based on artificial intelligence technologies.2 Beyond its technical complexity and leaving aside any further issue related to the (EU) agenda on digital markets and services,3 TDM has to face the interference with IP rights that may protect the mining sources, including copyright, in its alleged extension to any partial—if not minimal—reproduction of the work, and the...
The Application of the Concept Walkability in The City of Makassar In Terms of Behavioral Aspects

Presenting an environmentally friendly pedestrian path within the framework of the walkability concept is not easy to implement.Especially in Makassar City where the increase population and community mobility is quite high and has resulted in the emergence of various socio-economic and environmental problems, thus affecting the comfort level of pedestrians.One of the factors that affect the level of pedestrian comfort is the behavior of people which do not understand the importance of the existence of pedestrian paths.This study aims to identify the factors that hinder application of the Walkability concept in Makassar City, in terms of behavioral aspects, used in this study uses a descriptive qualitative approach which aims to reveal events or facts, phenomena, variables and circumstances that occurred during the research by presenting what actually happened, while the behavior mapping technique used is the Place centered mapping technique which aims to identify how humans, individually and in groups, utilize, use or accommodate their behavior in a particular time and place situation.From the results of the study, it was found that there are several factors that influence people's behavior, so that the area becomes not walkable according to the perception of pedestrians.Among them are environmental factors such as weather, the presence of street vendors, the surface of the pedestrian path and security.Meanwhile, accessibility factors such as the distance traveled by pedestrian paths, disabled friendly and parking areas, are influencing factors that are not user friendly and not walkable so that it is deemed necessary for the government and stakeholders to improve the area into an area that can be applied to the walkability concept.
Teaching history in Chinese universities: the need for reform

The article presents a critical analysis of the existing situation related to teaching history in Chinese universities. It is noted that according to the curriculum, most of the academic time is allocated to theoretical courses. The main way of transferring knowledge is a traditional lecture passively perceived by students. There are no interactive forms of classroom organization. Little attention is paid to the development of students' self-learning skills, independent search for the necessary information and creative attitude to the educational process. The existing examination system, based on written examinations and dentistry in preparation for them, does not contribute to the disclosure of students' abilities for generalization, classification, reflection and comparison of historical facts. Students are content with the facts presented in the textbooks, they have little interest in the latest achievements of historical science. In the final choice of profession, graduates of historical faculties rarely work in their specialty, preferring other areas of activity. The article presents the author's proposals for reforming the system of historical education in Chinese universities. There is a need for a serious adjustment of the current curriculum, reorientation of it from exams as a means of monitoring the assimilation of knowledge to the development of students' creative abilities. History teachers are encouraged to move away from traditional lectures and to make greater use of interactive teaching methods. It was concluded that it is necessary for each university history teacher to speak a foreign language, which should not only increase the professional competence of the teacher himself, but also seriously affect the inclusion of students in foreign historical science. It is noted the need to develop the creative skills of students, cultivate an innovative spirit, mobilize enthusiasm and initiative of students, develop the ability to identify problems, ask questions and solve them. It is emphasized that a modern history teacher should not only have solid professional skills, but also have good methodological training, skills of extracurricular communication with students. It is emphasized that closed, noncomminicative history teachers are not able to educate creative, competitive students.
Response Attempts of Traditional Publishing Industry to Emerging Industries Such as Internet

Established in 1985, Social Sciences Academic Press (hereinafter referred to as “SSAP”) is a professional academic publishing agency in the field of social sciences directly under the Chinese Academy of Social Sciences, and is committed to being a knowledge service provider in the field of social sciences. Aiming at comprehensively promoting the serialization, scale production, marketization, digitization and internationalization of think tank products and professional academic achievements, SSAP covers traditional publishing, digital publishing and international publishing in terms of the business scope.
The Msr-Video to Text Dataset with Clean Annotations

Video captioning automatically generates short descriptions of the video content, usually in form of a single sentence. Many methods have been proposed for solving this task. A large dataset called MSR Video to Text (MSR-VTT) is often used as the benchmark dataset for testing the performance of the methods. However, we found that the human annotations, i.e., the descriptions of video contents in the dataset are quite noisy, e.g., there are many duplicate captions and many captions contain grammatical problems. These problems may pose difficulties to video captioning models for learning underlying patterns. We cleaned the MSR-VTT annotations by removing these problems, then tested several typical video captioning models on the cleaned dataset. Experimental results showed that data cleaning boosted the performances of the models measured by popular quantitative metrics. We recruited subjects to evaluate the results of a model trained on the original and cleaned datasets. The human behavior experiment demonstrated that trained on the cleaned dataset, the model generated captions that were more coherent and more relevant to the contents of the video clips.