Reviewerly/exHarmony huggingface dataset:
A prospective view of Virtual Education in Autonomous Decentralized Governments: a case study of Honorable Provincial Government of Tungurahua

Governments distribute resources according to their strategic planning to develop studies in a variety of competence areas. Therefore, due to the COVID-19 pandemic, the need to establish citizen participation processes through training has increased, however it cannot be carried out in a complete face-to-face way due to the pandemic. In this context, the use of innovative technological tools is necessary to establish teaching-learning processes. The Honorable Provincial Government of Tungurahua through its Citizen Training Center has implemented an educational platform to provide training services in order to encourage Tungurahua citizen participation, located in the central region of Ecuador that has nine provinces and has a population of 590,600 people. By introducing innovative training processes using technology, this study attempts to solve the need for virtual training and also seeks citizen participation to strengthen and maintain training services during the pandemic. 422 people participated in this study, of which 67.54% belonged to the female gender and 32.46% to the male gender, from this total 70.14% had university education. In order to collect information about people’s training needs, an online 15 questions survey was conducted. These results made it possible to establish that 90% of the participants have an interest in digital literacy processes as well as in a variety of interesting topics that would allow them to increase their knowledge. Finally, a proposal was designed to implement the virtual environment using Moodle. The online environment was designed between the months of March and September 2020 and allowed the development of ten virtual training processes with the participation of 1,817 people, of which 47% were women and 53% men. In addition, it was found that 74% of the participants were residents and 26% were rural residents. In addition, their age ranged between 18 and 29 years, which represents 76% of the total participants.
Do Values in Online Learning in Higher Education Really Matter to Our Collective Future?

Who does the future belong to?Individuals, a collective society, or both?Furthermore, what role does online education in higher education play in that future?During 2021 over 220 million students attended colleges and universities globally with many forced to take classes online due to the COVID-19 pandemic. 1The eLearning market is projected to be 325 billion in the year 2025. 2 So do we let global forces of the pandemic and economics sweep us away or do we pause briefly to evaluate how we can and should proceed?The purpose of this reflective essay is to critique the current policy and values of online learning in high education.This is done in the context of a collective future that is built on adding positive values to the world, by the means of online learning.
ARunning: A Discussion on the Role of Augmented Reality-assisted Application in Amateur Runner Athletes

This research aims to explore and demonstrate the advantages of ARunning compared with traditional sports methods by studying the research status and development trend of augmented reality assisted sports applications, so as to realize the design and development of ARunning and enrich the form and development of ARunning. It also conducts usability testing and evaluation for the user experience, so as to realize the preliminary exploration on how AR can complement the practical application of sports psychology. In order to test the impact of ARunning on amateur runners' decision-making, experience during running, and feeling after running, we conducted live and online research on amateur runners. The results show that the popularization and application of this program will improve amateur athletes' exercise level to a certain extent and reduce their emotional burden, and it is likely to become the main motivation for users to participate in sports.
Teachers’ activities on actualization of humanistic values of military cadet corps students

In the context of the modern socio-politicof a student of the cadet military corps is a special social and state problem and requires deep consideration in theory, practice and development of special pedagogical approaches. The article reveals such approach based on the humanization of the values of cadet education. It is based on the theory of logotherapy by V. Frankl. A significant place in the formation of humanistic values of cadets is played by teachers with formed key competencies. The transition of internal potential readiness into manifested (carried out) activity is the process of actualizing teacher’s humanistic values, and the process of forming cadets’ values is based on their awareness. The process of updating teachers’ values is easier to carry out in a specially organized educational space for in-service training. The educational cluster of the military cadet corps includes the following levels: educational environment, interaction space, in-service training system, a cluster of teachers&amp;apos; competencies.
Fuzzy rule base identification using an incremental approach

A sparse fuzzy rule base provides low complexity and low memory requirements for the fuzzy system. Automatic fuzzy model generation from sample data involves two main tasks. These are structure determination and parameter identification. In this paper, we present a new approach that initially generates two rules, then gradually adds new rules to the rule base, and then finds the quasi-optimal values of the parameters using particle swarm optimization.
ANALISIS PENGGUNAAN CAMPUR KODE DALAM CERAMAH K.H. BAHAUDDIN NUR SALIM

This research is a study of the use of code mixing in K.H's lectures. Bahauddin Nursalim downloaded on YouTube. The background of this research is the lecture has an important role in presenting oral explanations to the audience both for the present or the future. Communities will easily accept ideas from those who are adept at speaking in a variety of ways by lecturers. The purpose of this research is to describe the form of code mixing, types of code mixing, and factors for the occurrence of language code mixing in K.H’s lectures. Bahauddin Nursalim. This type of research is qualitative. The subject of this research is the speech K.H. Bahauddin Nursalim, while the object of research is mixed language code used by K.H. Bahauddin Nursalim in the lecture. Data obtained by downloading videos from YouTube channel Serambi Narukan as many as five episodes. Data collection was carried out by listening methods followed by tapping techniques, competent free involve listening techniques and note taking techniques. The method of data analysis was carried out interactively including, (1) Data reduction, at this stage the researcher listened to K.H's lecture. Bahauddin Nursalim which has been downloaded from YouTube to collect data mixed language codes. (2) Presentation of data, i.e. in this study the data is presented in tabular form so that it is easy to understand, then the data is narrated. (3) Withdrawal of Conclusions, i.e. the data are inferred based on the research objectives in a descriptive form. The results showed that (1) The form of code mixing in K.H's lectures. Bahauddin Nursalim in the form of words, phrases and clauses. (2) the type of code mix used in K.H's lecture. bahauddin Nursalim in the form of mixed code in, mixed code out, and mixed code mixed. (3) Factors of code mixing in K.H.'s lecture Bahauddin Nursalim includes speakers and speech partners in relaxed situations, speakers to show their intelligence, and the need for foreign language expressions because there is no appropriate language equivalent in use
Dialogue

Use of the app will involve the user in a number of separate interactions, often by touch. While it is essential that each individual interaction should be designed with care, it is also necessary to consider the collection of interactions as a rich dialogue between user and app. Design considerations exist that relate to single interactions, and these are discussed with respect to the Diary and the Stack of personal regions. Design issues associated with the human visual system and cognition are discussed.
Places of Summer Vacations of Academician Evfimiy Karskiy: Crimea and Beyond (for the 160th Anniversary of His Birth)

The article reconstructs the places of the summer vacancy of the academician-Slavist Evfimiy Fyodorovich Karskiy (1860—1931) during the entire time of his scientific and educational activity (since his graduation from the Nizhyn Historical and Philological Institute) on the basis of several groups of sources: reports (published and archival) on Karskiy&amp;apos;s summer trips, his scientific publications, official documents (stored in the St. Petersburg Branch of the Archive of the Russian Academy of Sciences, the Central State Historical Archive of St. Petersburg, the Polish State Archive), published minutes of the Council of the Imperial Warsaw University, correspondence of the scientist from the archives of St. Petersburg, Moscow and Prague, memoirs of members of the academician&amp;apos;s family. The favorite places of repeated summer stay of the academician&amp;apos;s family are: Grodnenskaya province (Migovo and Ponemun estates), Crimea and the village Luga in the Leningradskaya region. The first trip to the resort — to Alushta — was taken by E. F. Karskiy, as the rector of the Warsaw University, just before his retirement, at the age of 49. E. F. Karskiy&amp;apos;s second trip to the Crimea took place in the summer of 1914. The study managed to identify one of the photographs from the family archive of A. A. Karskyj as image of a dacha of the late 19th — early 20th century, owned by the hydrogeologist I. M. Peddakas, whose appearance was unknown for Alushta historians and museum workers.
Integrated Geological and Geophysical Data as an Aid to Surface Geological Mapping and Mineral Exploration of the Ibillo-Okene Area SouthWest Nigeria

Ibillo-Okene area located at the northern fringe of the Igarra Schist belt, is a part of the Basement Complex rocks of southwest Nigeria.Aerogeophysical Datasets were used in conjunction with field data in order to interpret the geology and structure of this area.Five major lithological units were identified in the study area including migmatites, biotite schist, quartz schist, granite gneiss, and calc silicate gneiss which is associated with marble.Three episodes of deformation in the study area include that captured by relics of meta-pelitic (basic) rocks caught up (as xenoliths) in the main phase gneisses, that which produced the fabric in the main phase gneisses as well as metasedimentary rocks and that which is associated with Pan-African orogenic event which apparently modified the earlier two.Orientation of dominant foliation planes gives a general outline of the closure of the ancient basin; late-stage, brittle-ductile deformation resulted in the compression of this basin and subsequent faulting.This NW-SE trending fault reported in the present study area is an extension of a goldbearing fault reported south, around Dagbala-Atte area.Also, metasedimentary rocks found here are associated with marble just like their counterpart in the southern parts of the Igarra schist belt.
5 • Traduzione: temi e immagini ricorrenti

Reading the translation as a new poetical text, a new trilogy with its own fils rouges and its internal coherence comes to light. Several themes or meaning clusters contribute to build a solid poetical structure.
Climate Bot: A Machine Reading Comprehension System for Climate Change Question Answering

Climate change has a severe impact on the overall ecosystem of the whole world, including humankind. This demo paper presents Climate Bot - a machine reading comprehension system for question answering over documents about climate change. The proposed Climate Bot provides an interface for users to ask questions in natural language and get answers from reliable data sources. The purpose of the climate bot is to spread awareness about climate change and help individuals and communities to learn about the impact and challenges of climate change. Additionally, we open-sourced an annotated climate change dataset CCMRC to promote further research on the topic. This paper describes the dataset collection, annotation, system design, and evaluation.
Constrained Adaptive Projection with Pretrained Features for Anomaly Detection

Anomaly detection aims to separate anomalies from normal samples, and the pretrained network is promising for anomaly detection. However, adapting the pretrained features would be confronted with the risk of pattern collapse when finetuning on one-class training data. In this paper, we propose an anomaly detection framework called constrained adaptive projection with pretrained features (CAP). Combined with pretrained features, a simple linear projection head applied on a specific input and its k most similar pretrained normal representations is designed for feature adaptation, and a reformed self-attention is leveraged to mine the inner-relationship among one-class semantic features. A loss function is proposed to avoid potential pattern collapse. Concretely, it considers the similarity between a specific data and its corresponding adaptive normal representation, and incorporates a constraint term slightly aligning pretrained and adaptive spaces. Our method achieves state-of-the-art anomaly detection performance on semantic anomaly detection and sensory anomaly detection benchmarks including 96.5% AUROC on CIFAR-100 dataset, 97.0% AUROC on CIFAR-10 dataset and 89.9% AUROC on MvTec dataset.
Making Sense of Raw Input (Extended Abstract)

How should a machine intelligence perform unsupervised structure discovery over streams of sensory input? One approach to this problem is to cast it as an apperception task. Here, the task is to construct an explicit interpretable theory that both explains the sensory sequence and also satisfies a set of unity conditions, designed to ensure that the constituents of the theory are connected in a relational structure. However, the original formulation of the apperception task had one fundamental limitation: it assumed the raw sensory input had already been parsed using a set of discrete categories, so that all the system had to do was receive this already-digested symbolic input, and make sense of it. But what if we don't have access to pre-parsed input? What if our sensory sequence is raw unprocessed information? The central contribution of this paper is a neuro-symbolic framework for distilling interpretable theories out of streams of raw, unprocessed sensory experience. First, we extend the definition of the apperception task to include ambiguous (but still symbolic) input: sequences of sets of disjunctions. Next, we use a neural network to map raw sensory input to disjunctive input. Our binary neural network is encoded as a logic program, so the weights of the network and the rules of the theory can be solved jointly as a single SAT problem. This way, we are able to jointly learn how to perceive (mapping raw sensory information to concepts) and apperceive (combining concepts into declarative rules).
Multi-Task Personalized Learning with Sparse Network Lasso

Multi-task learning learns multiple related tasks together, in order to improve the generalization performance. Existing methods typically build a global model shared by all the samples, which saves the homogeneity but ignores the individuality (heterogeneity) of samples. Personalized learning is recently proposed to learn sample-specific local models by utilizing sample heterogeneity, however, directly applying it in the multi-task learning setting poses three key challenges: 1) model sample homogeneity, 2) prevent from over-parameterization and 3) capture task correlations. In this paper, we propose a novel multi-task personalized learning method to handle these challenges. For 1), each model is decomposed into a sum of global and local components, that saves sample homogeneity and sample heterogeneity, respectively. For 2), regularized by sparse network Lasso, the joint models are embedded into a low-dimensional subspace and exhibit sparse group structures, leading to a significantly reduced number of effective parameters. For 3), the subspace is further separated into two parts, so as to save both commonality and specificity of tasks. We develop an alternating algorithm to solve the proposed optimization problem, and extensive experiments on various synthetic and real-world datasets demonstrate its robustness and effectiveness.
Visual Analysis and Processing of Diverse Data

Associate Editor-in-Chief Richard Zhang outlines published articles in this regular queue issue.
Interactive concept-map based summaries for SEND children

Equitable and inclusive quality education is a human right. It is crucial to provide for the learning needs of every child, especially those with learning disabilities. Traditional approaches to learning propose education paths performed with speech therapists. One of the most efficient strategies to help children with reading comprehension difficulties is the creation of a ``concept map'', a structured summary of the written text in a graph structure. Online tools that offer students the possibility to manually create or automatically extract concept maps from text have been created over the years. However, there is still a shortage of software that are specifically designed for children at risk and which produce a concept map that is tailored to the clinical profiles of individuals. In this Project Collaboration, we want to tackle this gap by implementing a multi-modal, online and open-access Artificial-Intelligence powered tool that could help these children to make sense of written text by enabling them to interactively create concept maps. The expected output is threefold. We will implement a new model for concept-map-based document summarization and a clinically appropriate web interface. We will evaluate them in real-world settings through user studies performed by speech therapists.
Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases (Extended Abstract)

We define and investigate new inconsistency measures that are particularly suitable for dealing with inconsistent spatio-temporal information, as they explicitly take into account the spatial and temporal dimensions, as well as the dimension concerning the identifiers of the monitored objects. Specifically, we first define natural measures that look at individual dimensions (time, space, and objects), and then propose measures based on the notion of a repair. We then analyze their behavior w.r.t. common postulates defined for classical propositional knowledge bases, and find that the latter are not suitable for spatio-temporal databases, in that the proposed inconsistency measures do not often satisfy them. In light of this, we argue that also postulates should explicitly take into account the spatial, temporal, and object dimensions, and thus define ``dimension-aware'' counterparts of common postulates, which are indeed often satisfied by the new inconsistency measures. Finally, we study the complexity of the proposed inconsistency measures.
Bayesian Auctions with Efficient Queries (Extended Abstract)

Designing dominant-strategy incentive compatible (DSIC) mechanisms for a seller to generate (approximately) optimal revenue by selling items to players is a fundamental problem in Bayesian mechanism design. However, most existing studies assume that the seller knows the entire distribution from which the players’ values are drawn. Unfortunately, this assumption may not hold in reality: for example, when the distributions have exponentially large supports or do not have succinct representations. In this work we consider, for the first time, the query complexityof Bayesian mechanisms. The seller only has limited oracle accesses to the players’ distributions, via quantile queriesand value queries. For single-item auctions, we design mechanisms with logarithmicnumber of value or quantile queries which achieve almost optimal revenue. We then prove logarithmic lower-bounds, i.e., logarithmic number of queries are necessary for any constant approximation DSIC mechanisms, even when randomized and adaptive queries are allowed. Thus our mechanisms are almost optimal regarding query complexity. Our lower-bounds can be extended to multi-item auctions with monotone subadditive valuations, and we complement this part with constant approximation mechanisms for unit-demand or additive valuation functions. Our results are robust even if the answers to the queries contain noises.
Epistemic Logic of Likelihood and Belief

A major challenge in AI is dealing with uncertain information. While probabilistic approaches have been employed to address this issue, in many situations probabilities may not be available or may be unsuitable. As an alternative, qualitative approaches have been introduced to express that one event is no more probable than another. We provide an approach where an agent may reason deductively about notions of likelihood, and may hold beliefs where the subjective probability for a belief is less than 1. Thus, an agent can believe that p holds (with probability &lt;1); and if the agent believes that q is more likely than p, then the agent will also believe q. Our language allows for arbitrary nesting of beliefs and qualitative likelihoods. We provide a sound and complete proof system for the logic with respect to an underlying probabilistic semantics, and show that the language is equivalent to a sublanguage with no nested modalities.
Analysis of E- Learning using MOORA Method

E-learning is conducted digitally through electronic media one type of learning, usually involving the Internet.Most electronics it can be accessed including a computer, laptop, tablet or Smartphone devices, wherever students are a versatile and easy way to learn.E-learning is a type of learning conducted digitally through electronic media, usually the Internet Includes.Including computer, laptop, tablet or Smartphone Most electronic devices can be accessed through it, wherever students are.A versatile and easy way to learn.Path of WebPages, under construction Webpage, Connection of Web Pages, Display of WebPages, and Colors of WebPages.Evaluation Preference: Between people, within people, between measures, Residual.The result it is seen that Colors; of WebPages and is got the first rank whereas the Connection of WebPages got is having the lowest rank.E-Learning in Rank from the result it is seen that Colors; of WebPages and is got the first rank whereas the Connection of WebPages got is having the lowest rank.
Engineering Socially-Oriented Autonomous Agents and Multiagent Systems

The emergent field of social AI is concerned with the development of autonomous agents that are able to act as part of larger community. Within this context, my research seeks to engineer meaningful social interactions among a group of agents from two different approaches. First, the societal level leverages constructs that apply to a society as a whole, like norms and social values. Second, the individual level endows agents with the ability to reason about others, by making use of Theory of Mind capabilities.