Reviewerly/exHarmony huggingface dataset:
Cantor set based neighbor generation method for permutation solution representation

Metaheuristics gained world-wide popularity and researchers have been studying them vigorously in the last two decades. A relatively less explored approach in the improvement of metaheuristics is to design new neighbor generation mechanisms. Neighbor generation mechanisms are very important in the success of any single solution-based heuristic since they directly guide the search. In this study, a neighbor generation mechanism called cantor-set based (CB) method for single solution-based heuristics which use permutation solution representation is described. The inspiration for CB method stems from the recursive algorithm that constructs a cantor set which is a fractal set. Three variations of CB method are discussed (CB-1, CB-2, CB-3) considering the presented design possibilities. The computational experiments are conducted by embedding the mechanisms into the classical local search and simulated annealing algorithms, separately, to test their efficiency and effectiveness by comparing them to classical swap and insertion mechanisms. The traveling salesman problem (TSP) and quadratic assignment problem (QAP) which are very different problems that have incompatible characteristics have been chosen to test the mechanisms and sets of benchmark instances with varying sizes are chosen for the comparisons. The computational tests show that CB-2 gives very favorable results for TSP and CB-1 gives favorable results for QAP which means that CB-2 is suitable for problems that have steep landscapes and CB-1 is suitable for the problems that have flat landscapes. It is observed that CB-3 is a more generalized mechanism because it gives consistently good results for both TSP and QAP instances. The best mechanism for a given instance of the both problem types outperforms the classical neighbor generation of swap and insertion in terms of effectiveness.
ASSESSMENT OF MOBILE LEARNING TECHNOLOGIES APPLICATION FOR BUSINESS EDUCATION INSTRUCTION IN COLLEGES OF EDUCATION IN DELTA STATE, NIGERIA

The study assessed mobile learning technologies application for business education instruction in colleges of education in Delta State, Nigeria using descriptive survey research design. Three research questions and three null hypotheses guided the study. The population for the study was 312, made up of 79 business education lecturers and 233 NCE III students of business education from the two colleges of education from Delta North. Purposive sampling and simple random sampling techniques were used. The entire 79 lecturers were used from the two colleges while 61 students were sampled from FCE (T) Asaba and 42 students from College of Education Agbor, making a total of 182. The instrument for data collection was a 50-item structured questionnaire. The instrument was face-validated by three experts using Cronbach Alpha. The reliability yielded a coefficient of 0.84. The data collected were analysed using frequency, mean and t-test statistics. The findings of the study showed that the extent of utilization of m-learning technologies is relatively low. In addition, the study identified 15 instructional benefits of the use of m-learning technologies and 20 challenges undermining effective utilization of m-learning technologies for teaching and learning business education subjects in colleges of education in Delta North. The result of hypotheses showed no significant differences in the mean ratings of the responses of business education lecturers and students on 40 out of the 50 items, whereas, there were significant differences on the remaining 10 items. The study among others recommended capacity building training for lecturers on m-learning skills for effective operation of m-learning devices for teaching, learning and research purposes. Keywords: Mobile learning, Business education, Colleges of education, Benefits, Challenges.
A Distributed Clustering Approach for Heterogeneous Environments  Using Fuzzy Rough Set Theory

Vast majority of data mining algorithms have been designed to work on centralized data, unfortunately however, almost all of nowadays data sets are distributed both geographically and conceptually. Due to privacy and computation cost, centralizing distributed data sets before analyzing them is undoubtedly impractical. In this paper, we present a framework for clustering distributed data which takes into account privacy and computation cost. To do that, we remove uncertain instances and just send the label of the other instances to the central location. To remove the uncertain instances, we develop a new instance weighting method based on fuzzy and rough set theory. The achieved results on well-known data verify effectiveness of the proposed method compared to previous works.
Website Design Courses At Home

Were hoping that youve learned some lessons while living in the COVID era... some people have more time on their hands than normal. Since this is the ...
Nuove strategie per esplorare il campo emergente delle competenze digitali informali e per il mondo del lavoro 4.0

The contribution offers some reflections for the analysis of the current digital skills panorama, as well as how digital competences are interpreted at the theoretical and policy implementation level. The essay highlights how, nowadays, the tendency is to create frameworks and standard tools for coding and analyzing digital skills, but these tools may prove insufficient, in some contexts, to grasp and interpret a constantly changing environment. For this reason, especially in the fields of informal digital skills, and in those related to digital working practices in “industry 4.0” contexts, the essay proposes to integrate modeling and standardization attempts, built through top-down frameworks and tests, with bottom-up and micro-ethnography tools. These approaches, in fact, are capable of intercepting changes from below, catching informal skills, micro-innovations, local interpretations and counterculture trends, according to a non-normative but receptive vision of the digital skills for the future.
GLOBAL MESSAGE PASSING IN NETWORKS VIA TASK-DRIVEN RANDOM WALKS FOR SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGES

Abstract. The capability of globally modeling and reasoning about relations between image regions is crucial for complex scene understanding tasks such as semantic segmentation. Most current semantic segmentation methods fall back on deep convolutional neural networks (CNNs), while their use of convolutions with local receptive fields is typically inefficient at capturing long-range dependencies. Recent works on self-attention mechanisms and relational reasoning networks seek to address this issue by learning pairwise relations between each two entities and have showcased promising results. But such approaches have heavy computational and memory overheads, which is computationally infeasible for dense prediction tasks, particularly on large size images, i.e., aerial imagery. In this work, we propose an efficient method for global context modeling in which at each position, a sparse set of features, instead of all features, over the spatial domain are adaptively sampled and aggregated. We further devise a highly efficient instantiation of the proposed method, namely learning RANdom walK samplIng aNd feature aGgregation (RANKING). The proposed module is lightweight and general, which can be used in a plug-and-play fashion with the existing fully convolutional neural network (FCN) framework. To evaluate RANKING-equipped networks, we conduct experiments on two aerial scene parsing datasets, and the networks can achieve competitive results at significant low costs in terms of the computational and memory.
PENERAPAN METODE PEMBELAJARAN RESITASI UNTUK MENINGKATKAN HASIL BELAJAR BAHASA INDONESIA

Recitation learning method is a way that presents learning material by giving assignments to students to learn which are then accounted for in front of the class. The objectives of this study are (1) to describe the learning process by applying the Recitation learning model in Indonesian subjects in class IV SDN 070997 Ononamolo talafu (2) to describe the improvement in student learning outcomes by applying the Recitation learning model in Indonesian subjects in grade IV SDN 070997 Ononamolo talafu. The research was conducted at SDN 070997 Ononamolo Talafu with the research subjects of fourth grade students in odd semester 2019/2020, totaling 20 people. The research method used was classroom action research (CAR). The research instruments used were (1) observation sheets for students who were not active in the learning process (2) observation sheets for students who were active in the learning process (3) observation sheets in the learning process of teacher respondents (4) Test results for learning, (5) Documentation picture. Based on data analysis, it was found that: (1) The learning process by applying the Recitation learning model in class IV SDN 070997 Ononamolo talafu in Indonesian language subjects improved where the average reflection results at the end of the cycle reached 83% (2) Student learning outcomes increased with the application Recitation learning model in class IV SDN 070997 Ononamolo talafu in Indonesian subjects where at the end of the cycle the average student learning outcomes reached 79.32 with a percentage of completeness 88%.
Management by Zooming About [MicroBusiness]

Reports on the benefits as well as the drawbacks of working and communicating with colleagues in an era when you can search for information via digital libraries as well as attend remote meetings without ever looking through books in a library or attending conferences where one had to actually travel, book into a hotel, and attend meetings in person.
AUTOMATED CO-REGISTRATION OF INTRA-EPOCH AND INTER-EPOCH SERIES OF MULTISPECTRAL UAV IMAGES FOR CROP MONITORING

Abstract. The application of UAV-based aerial imagery has advanced exponentially in the past two decades. This can be attributed to UAV operational flexibility, ultra-high spatial resolution, inexpensiveness, and UAV-based sensors enhancement. Nonetheless, the application of multitemporal series of multispectral UAV imagery still suffers significant misregistration errors, and therefore becoming a concern for applications such as precision agriculture. Direct image georeferencing and co-registration is commonly done using ground control points; this is usually costly and time consuming. This research proposes a novel approach for automatic co-registration of multitemporal UAV imagery using intensity-based keypoints. The Speeded Up Robust Features (SURF), Binary Robust Invariant Scalable Keypoints (BRISK), Maximally Stable Extremal Regions (MSER) and KAZE algorithms, were tested and parameters optimized. Image matching performance of these algorithms informed the decision to pursue further experiments with only SURF and KAZE. Optimally parametrized SURF and KAZE algorithms obtained co-registration accuracies of 0.1 and 0.3 pixels for intra-epoch and inter-epoch images respectively. To obtain better intra-epoch co-registration accuracy, collective band processing is advised whereas one-to-one matching strategy is recommended for inter-epoch co-registration. The results were tested using a maize crop monitoring case and the; comparison of spectral response of vegetation between the UAV sensors, Parrot Sequoia and Micro MCA was performed. Due to the missing incidence sensor, spectral and radiometric calibration of Micro MCA imagery is observed to be key in achieving optimal response. Also, the cameras have different specifications and thus differ in the quality of their respective photogrammetric outputs.
Ontology-Based Reasoning for Collective Intelligence of Multi-agents System

Multi-Agents System (MAS) and Ontology are two technologies capable of creating intelligent reasoning and inferring new knowledge useful for decision making. In this paper we propose a platform model called Agent-SSSN whose agents reason as a human actor and collaborate with them to create a Collective Intelligence in Economic Intelligence (EI) coordinated network monitoring. To organize the knowledge in Agent-SSSN and facilitate the reasoning ability of the agent, an ontology-based approach called Onto-Agent-SSSN is presented in this paper.
Modeling and Reasoning of Contexts in Smart Spaces

Context can be defined to be a meaningful and descriptive state of a physical space, in which relevant activities of the humans and movements of objects in the same space are consecutively performed. Context aware computing thus requires the instantiation and use of a suitable context model and algorithms capable of discerning a most relevant context at the present time. In our previous research, we developed a method based on stochastic analysis, for inferring contexts from sensor datasets collected from a given smart space. By utilizing three statistical techniques including conditional probability table (CPT), K-means clustering, and Principal Component Analysis (PCA), our inference method is able to predict and generate contexts which can potentially happen. Generated contexts are then used as references when the present context needs to be found. In this paper, we build on our prior work and propose a method that analyzes the current state space and determines the present context guided by the collection of generated potential contexts. We first reconsolidate our context models and context graph in which contexts are structured, and then introduce reasoning methods which apply Euclidean distance and Cosine Similarity. PCA is also used to statistically analyze the state space, which helps to achieve better performance. Using experimental evaluation, we validated the accuracy of our proposed approach in identifying the present context.
Модель эволюции предпочтений

The paper examines the evolution of individual political preferences under the influence of the parties and political groups of which they are members. Individual preferences are considered in terms of proximity to political doctrines of the political institutions in question. The change in political views can be considered as a combination of a steady trend with the imposition of random change, and in this sense it is similar to mutations in biology. The paper shows that stationary distributions of individuals on the political spectrum scale are defined by their utility functions and initial numbers. While the initial distribution was generated by membership of the central party and two groups conventionally called “left” and “right”, the stationary distribution has the form of three narrow peaks, the maximums of which are at the points characterizing political doctrines on the political spectrum scale. In addition, it is shown that in the stationary mode, the relative number of members of the “center” party will increase as compared to the initial period, and vice versa, the total share of “left” and “right” will decrease.
Keynote: Affordable and Practical Home Context Recognition with “Image as a Document” Approach

Technologies for home context recognition have been studied for many years in the field of ubiquitous computing. The traditional ubiquitous computing employs ambient sensors (e.g., temperature, humidity, presence), wearable sensors (e.g., accelerometer, heart rate), and indoor positioning systems. In more recent years, the emerging deep learning allows the system to recognize multimedia data. Since image, voice, and text data usually contain richer information than the conventional sensor data, it is promising to use such multimedia data for recognizing the home contexts. Unfortunately, however, these existing technologies are yet far from practical use in general households, since they require expensive resources at home. It is difficult for ordinary users to operate and maintain proprietary systems at home on a daily basis. One may try to recognize home contexts via image recognition based on deep learning. However, constructing a custom recognition model dedicated for a single house requires a huge amount of labeled datasets and computing resources. Thus, there is still a big gap between research and real life. To achieve more affordable and practical home context recognition, we present a novel technique that integrates image-based cognitive API and light-weight machine learning. The cognitive API receives an image from an external application, recognizes specific information within the image, and returns the information as a set of text words called tags. Our key idea is to use these tags as features of the image, and apply light-weight machine learning techniques to infer the target context. Since every image can be considered as a document, the expensive deep learning is no more needed. We call this idea the image-as-a-document approach. To demonstrate the practical feasibility, we have conducted an experiment that recognizes contexts within our laboratory. The experimental results showed that the overall accuracy of the recognition model was 0.929. We also present further approach to improve the accuracy, by exploiting multiple cognitive APIs.
МОДЕЛЬ УПРАВЛЕНИЯ ПРОФЕССИОНАЛЬНЫМ САМООПРЕДЕЛЕНИЕМ СТАРШЕКЛАССНИКОВ В ПРОФИЛЬНОЙ ШКОЛЕ

In this article the actualization of the given problem is accentuated. The author offers the model of management of senior’s professional self-determination in specialized school on the basis of the motivational program-oriented approach.
Impact of Legal Requirements on Explainability in Machine Learning

The requirements on explainability imposed by European laws and their implications for machine learning (ML) models are not always clear. In that perspective, our research analyzes explanation obligations imposed for private and public decision-making, and how they can be implemented by machine learning techniques.
ВОСПИТАНИЕ ГУМАНИСТИЧЕСКИХ ЦЕННОСТЕЙ КАК ЦЕЛЕВОЙ ОРИЕНТИР ОБРАЗОВАНИЯ В ДЕМОКРАТИЧЕСКОМ ОБЩЕСТВЕ

The article is devoted to the problem of the development of values of the younger generation. The importance of the issue for the democratic society focused on the priority of the citizen’s rights and freedoms is noted. The psychological content of the concept of «value» as an element of the A.N. Leontiev’s triad: Activity - Consciousness - Personality is under analysis. It is noted that the pedagogical task of instilling of humanistic values cannot be solved without a deep understanding of the phenomenon of personal values and knowing the characteristics of their formation, development and functioning.
Development of a grey model for a medium density fibreboard dryer in ecosimpro

This work contains the design and testing of a greybox model based on first principles for the drying section of a Medium Density Fiberboard (MDF) manufacturing plant for use in a Model-Predictive Controller (MPC).The fitting of the model to experimental data has been solved with the data reconciliation technique, and the model has then been implemented and tested in simulation for proper behavior.Due to lack of information in a number of relevant process values, as well as low trustworthiness of some measurements, state and disturbance estimators have been determined as necessary to include in the final MPC control schema.
Досуговое чтение в годы Первой мировой войны (по материалам Пермской губернии)

Reading as a leisure activity became widespread at the turn of the 19th and 20th centuries. The interest in reading increased during the First World War, which is considered by the author as an important modernization factor. During the war, the contribution to the expansion of the reading audience was caused by several factors: the growth of literacy of the population, the interest in the course of military operations, the “dry” law of 1914, the opening of libraries in the counties, etc. The studied documents include statistical information of the zemstvos on the distribution of the read books on subject, sex and age, and on the social composition of library users; the diaries of library attendance; the lists of books handed out more than 10 times; reader’s forms; the reports of the county zemstvos, etc. The diversity and fragmentation of information of documents should be noted. Therefore, the author's attention is concentrated on six counties of Perm province, reflecting the complexity and specificity of the socio-economic structure of the region. Comparability of statistical data on library work in the districts in 1913–1915 made it possible to study the dynamics of the number of readers and the change of the subjects of literature, the specificity and the dependence of these processes on employment and residence of library visitors. Wartime situation did not significantly affect the topics of the most read literature. The population of the province was of little interest in geographical, historical, religious, moral, social and political literature. The periodicals, on the contrary, were in great demand among the readers. This is explained by the need of people to navigate in the course of combat. The analysis of topics of the read fiction literature made it possible to ascertain differences in cognitive and socio-cultural requests of urban and rural population. The villagers, mostly farmers, read books of adventures and historical and ethnographic subjects. Urban intellectuals read books by contemporary authors on topical social issues. The conclusion is made about the influence of the wartime situation on the process of forming a general culture of reading in the province. Key words: reading, leisure, informative needs, library, urban intellectuals, villagers, the First World War, Perm province. doi 10.17072/2219-3111-2017-4-24-34
Bibliotecas escolares em Cuba: retrospectiva e perspectivas (1900-2010)

The work aims to identify the process of emergence, evolution and perspectives of the development of the school library in Cuba.In this sense, the evolution of the actions focused on school libraries in historical periods is divided according to the country's development.It is described what happened since the beginning of the 20 th century, during the republican period, after the triumph of the revolution (1959) and the special period (90 years) until 2010.The study is based on the documentary analysis from a review of the literature, mostly collected by magazines that are no longer published.In addition, it relies on the consultation of a group of investigations carried out by the Department of Information Sciences of the Faculty of Communication of the University of Havana subscribed to the research line 'theoretical and historical studies in Information and Communication'.The results reveal the attempts over time to raise up the level of school libraries in Cuba.Many of these efforts are considered isolated, which may disrupt the fact that no major progress has been achieved.