Reviewerly/exHarmony huggingface dataset:
Customer Information System for performance evaluation of three Public Utilities. Case study PBL

In the present work we show an extra academic activity (EAA) developed through a university innovation project: "Design and construction of a system to evaluate the quality of municipal public services" developed at the Instituto Tecnológico Superior de Alvarado by a group of teachers of Computer Systems and Basic Sciences, with student participation of Engineering in Computer Systems. Our objective is to analyze the contribution of the ProjectBased Learning (PBL) methodology to university education and professional development of teachers. We use the qualitative research method Participatory Action Research (IAP), field experience log and portfolio of evidence for data collection and coding and scales for processing. As a result, we found that the educational experience created meaningful learning, as well as verifiable academic products, and verifiable competencies and professional experience for the mutual benefit of students and teachers. Therefore, we conclude that the ABP is an effective Strategy for Creating Meaningful Learning Experiences in technological higher education, because its results have a positive impact on university education and Professional Development of Teachers. To conclude from this experience we offer recommendations to obtain better results
A Brief Survey on AI Based Face Mask Detection System for Public Places

The corona virus which can be also called as SARS COVID-19 is spreading over the world among the peoples and led to cause a global pandemic. The spreading of the virus forced the government to pressure their peoples to follow strict lockdown, this might cause many problems to every single person in the society. The strict rules from WHO (World Health Organization) tells that the only solution for this spread of virus is wearing a face mask. Thus, to help the government and the public this proposing model ensures that every single person wears a face mask in public places. Few models work depending on the artificial intelligence, deep learning and machine learning. The model uses the Keras, Tensor-flow and OpenCV methods for execution. This device is developed with two datasets which are namely with and without facemask. The system is included with a Raspberry- PI camera which captures the live streaming video and coverts them into images, which can be used as the inputs and process the data. The system is developed with a toll-way gate which allows only if the person crossing it has a face mask worn on his/her face or it does not allow the person in. It is developed along with an alarm system which beeps with a red light if the person is not wearing a face mask or it glows with a green light. This model ensures to make the environment and the people to be safe.
The E-learning educational model, a different experience in Solfeggio classes during COVID-19

From a totally face-to-face teaching, the Solfeggio subject, like others included in the curriculum of a musician, required an improvement from models that would ensure its development in the exceptional conditions in which the world lives after the onset of the pandemic (Covid-19). This work is to recapitulate the modifications made to the instructional design of the Solfeggio for the e-learning modality in the Academic Unit of Arts of the Autonomous University of Zacatecas (UAAUAZ), Mexico. Basically, the instructional design was carried out according to the characteristics of the ASSURE model, which facilitates the continuity of the teaching-learning process and the participation of students in a synchronous and asynchronous manner.
Temporal Difference Learning as Gradient Splitting

Temporal difference learning with linear function approximation is a popular method to obtain a low-dimensional approximation of the value function of a policy in a Markov Decision Process. We give a new interpretation of this method in terms of a splitting of the gradient of an appropriately chosen function. As a consequence of this interpretation, convergence proofs for gradient descent can be applied almost verbatim to temporal difference learning. Beyond giving a new, fuller explanation of why temporal difference works, our interpretation also yields improved convergence times. We consider the setting with $1/\sqrt{T}$ step-size, where previous comparable finite-time convergence time bounds for temporal difference learning had the multiplicative factor $1/(1-\gamma)$ in front of the bound, with $\gamma$ being the discount factor. We show that a minor variation on TD learning which estimates the mean of the value function separately has a convergence time where $1/(1-\gamma)$ only multiplies an asymptotically negligible term.
Better Document-Level Machine Translation with Bayes' Rule

We show that Bayes' rule provides an effective mechanism for creating document translation models that can be learned from only parallel sentences and monolingual documents---a compelling benefit as parallel documents are not always available. In our formulation, the posterior probability of a candidate translation is the product of the unconditional (prior) probability of the candidate output document and the "reverse translation probability" of translating the candidate output back into the source language. Our proposed model uses a powerful autoregressive language model as the prior on target language documents, but it assumes that each sentence is translated independently from the target to the source language. Crucially, at test time, when a source document is observed, the document language model prior induces dependencies between the translations of the source sentences in the posterior. The model's independence assumption not only enables efficient use of available data, but it additionally admits a practical left-to-right beam-search algorithm for carrying out inference. Experiments show that our model benefits from using cross-sentence context in the language model, and it outperforms existing document translation approaches.
Protection of entanglement between two V-atoms in a multi-cavity coupling system

The protection of the entanglement between two V-atoms (EBTVA) in a multi-cavity coupling system is studied. The whole system consists of two V-atoms. The two V-atoms are initially in the maximum entangled state and interacts locally with its own dissipative cavity which is coupled to the external cavities with high quality factor (ECWHQF). The results show that, when there is no ECWHQF, the EBTVA can be protected effectively in the case where the V-atom and the dissipative cavity are weak coupled in large detuning, while when there are different numbers n of ECWHQF coupled to two dissipative cavities, by adjusting the parameters of the number n of ECWHQF and the coupling strength k between cavities, the EBTVA can be protected perfectly and continuously. Our result provides an effective method for protecting entanglement resources of three-level system.
Automatic Dispensing Technology of Traditional Chinese Medicine Formula Granules in Pharmacy Under the Background of Artificial Intelligence

At present, the research and application of artificial intelligence technology in the field of traditional Chinese medicine has become one of the important methods of modern inheritance strategy of traditional Chinese medicine. In this paper, under the background of artificial intelligence, the application of automatic dispensing technology of traditional Chinese medicine formula granules in pharmacy is studied. In this paper, two hospitals in a city were compared. Hospital a was the control group, and hospital B was the experimental group. Through experimental observation and data collection, it was found that the data of hospital B pharmacy using artificial intelligence automatic dispensing technology was better than that of hospital A in all aspects during the experiment. During the experiment, hospital A pharmacy processed 1056 prescriptions in total, which took 2110 min, and the experimental evaluation was 80 points, while hospital B pharmacy processed 1056 prescriptions During the experiment, 2317 prescriptions were processed in our hospital, which took 1853 min, and the experimental evaluation was 98 points. Moreover, the data of pharmacy of hospital B in dispensing accuracy, storage environment, drug control and other aspects were better than that of hospital A.
Principales retos educativos de los profesores de la Unidad Académica del Norte del Estado de Nayarit durante la pandemia de COVID-19

This article presents the educational problems and challenges that the teachers of the Academic Unit of the North of the State of Nayarit (UAEN) have manifested during the current health crisis derived from the pandemic with COVID-19, with the aim of documenting the experience and propose recommendations that rethink teaching practice. For its preparation, a questionnaire adapted from the instrument applied by the National Autonomous University of Mexico (UNAM, 2020) was used, which was applied to active teachers in the Academic Unit through a google form. In which statistical data of the personnel and the opinions of the participants are collected, related to this issue, which will allow the design of a series of actions aimed at improving the processes faced by educational institutions not only in higher education but in all the types and levels of education, but especially from the Autonomous University of Nayarit.
Development of digital literacy in basic education through virtual classes derived from Covid-19

Globally, education changed dramatically due to the SARS-CoV-2 virus pandemic. Consequently, literacy became important as an adaptive response measure of basic education students in virtual classes. In this research, it analyzes the development of digital literacy as an effective tool in the teaching-learning process in virtual education. For them, an evaluation instrument with 40 variables on a ratio scale and 5 on a nominal scale (final Cronbach's alpha of 0.950) is used. This instrument was applied to basic education teachers in Oaxaca, Aguascalientes and Durango. Statistical test was performed to determine the factor analysis and ANOVA in order to find the main variables that are related to digital literacy in the three selected states. The main conclusions were: 1) the teachers overcame the difficulties in the use of virtual platforms and digital media during the pandemic and, 2) they achieved the understanding of the contents by the students. Greater emphasis is suggested on monitoring and evaluating distance education in order to find viable strategies according to the needs of the students
Contextual Guided Segmentation Framework for Semi-supervised Video Instance Segmentation

In this paper, we propose contextual guided segmentation (CGS) framework for video instance segmentation in three passes. In the first pass, i.e.,preview segmentation, we propose Instance Re-Identification Flow to estimate main properties of each instance (i.e., human/non-human, rigid/deformable, known/unknown category) by propagating its preview mask to other frames. In the second pass, i.e.,contextual segmentation, we introduce multiple contextual segmentation schemes. For human instance, we develop skeleton-guided segmentation in a frame along with object flow to correct and refine the result across frames. For non-human instance, if the instance has a wide variation in appearance and belongs to known categories (which can be inferred from the initial mask), we adopt instance segmentation. If the non-human instance is nearly rigid, we train FCNs on synthesized images from the first frame of a video sequence. In the final pass, i.e.,guided segmentation, we develop a novel fined-grained segmentation method on non-rectangular regions of interest (ROIs). The natural-shaped ROI is generated by applying guided attention from the neighbor frames of the current one to reduce the ambiguity in the segmentation of different overlapping instances. Forward mask propagation is followed by backward mask propagation to further restore missing instance fragments due to re-appeared instances, fast motion, occlusion, or heavy deformation. Finally, instances in each frame are merged based on their depth values, together with human and non-human object interaction and rare instance priority. Experiments conducted on the DAVIS Test-Challenge dataset demonstrate the effectiveness of our proposed framework. We achieved the 3rd consistently in the DAVIS Challenges 2017–2019 with 75.4%, 72.4%, and 78.4% in terms of global score, region similarity, and contour accuracy, respectively.
Contemporary NLP Modeling in Six Comprehensive Programming Assignments

We present a series of programming assignments, adaptable to a range of experience levels from advanced undergraduate to PhD, to teach students design and implementation of modern NLP systems.These assignments build from the ground up and emphasize fullstack understanding of machine learning models: initially, students implement inference and gradient computation by hand, then use Py-Torch to build nearly state-of-the-art neural networks using current best practices.Topics are chosen to cover a wide range of modeling and inference techniques that one might encounter, ranging from linear models suitable for industry applications to state-of-theart deep learning models used in NLP research.The assignments are customizable, with constrained options to guide less experienced students or open-ended options giving advanced students freedom to explore.All of them can be deployed in a fully autogradable fashion, and have collectively been tested on over 300 students across several semesters.
Development of Teaching Materials to Learn the Efficient Use of Energy

Control technology using the computer is one of the key technologies for improving energy efficiency.PWM control, which is a control technology for DC motors, is widely used as the basis of power electronics with the development of semiconductor devices.On the other hand, programming techniques are required to effectively use these techniques.In this paper, we propose teaching materials that can visualize the performance improvement by programming as the operating time performance index of the wind turbine.
Migración hacia la enseñanza virtual: Adaptación por COVID-19

The COVID-19 crisis quickly led to the closure of educational institutions at all levels around the world, from one moment to another, millions of children and young people around the world were left without receiving their face-to-face classes, and many of them went through virtual classrooms framed in a phenomenon known as emergency remote teaching (ERT). This research sought to determine that well-designed and planned online learning experiences are significantly different from virtual courses offered online in response to this crisis, in order to identify what were the main areas of opportunity that learning in this emergency brought to teachers, students and educational institutions. A 12-item instrument was applied that was analyzed under a systematic random probability sampling, obtaining a sample of 49% of the population. The results made it possible to show the difficulties that arose, as well as the tools and platforms most used during the crisis in the institution of study in order to take advantage of knowledge to improve the learning experience of students.
ADT

Data anonymization is commonly utilized for the protection of an individual's identity when his personal or sensitive data is published. A well-known anonymization model to define the privacy of transactional data is the km-anonymity model. This model ensures that an adversary who knows up to m items of an individual cannot determine which record in the dataset corresponds to the individual with a probability greater than 1/k. However, the existing techniques generally rely on the presence of similarity between items in the dataset tuples to achieve km-anonymization and are not suitable when transactional data contains tuples without many common values. The authors refer to this type of transactional data as diverse transactional data and propose an algorithm, anonymization of diverse transactional data (ADT). ADT is based on slicing and generalization to achieve km-anonymity for diverse transactional data. ADT has been experimentally evaluated on two datasets, and it has been found that ADT yields higher privacy protection and causes a lower loss in data utility as compared to existing methods.
An approach to the judicial evaluation of evidence from computers and computer systems

The evaluation of evidence derived from a complex computer system is difficult: past experience of reliable operation in general is an uncertain guide to the value of specific evidence in a particular case. By setting the evidence in question in the context of the complete transaction to which it pertains, and tracking the system’s processing path for the transaction, better guidance may be obtained. Difficulty in elucidating the processing path may itself cast doubt on the evidence in question.&#x0D; &#x0D; Index words: complexity, computer, evidence, path, transaction
The theory of nationally oriented education: on goals and principles

The article discusses the need for nationally oriented education in the unity of the processes of education, training and personal development. The relevance of the parameters of educational results most desirable for the security of Russia and the preservation of the nation is emphasized. The theory of nationally oriented education with the necessary necessity presupposes a methodology of education, training and development of a morally and patriotically mature personality, ready for socially responsible behavior and constructive activity. The theory of nationally oriented education should include: the concept of a new didactics; the concept of education in conditions of a radically changed being layer of life of children and adults; the concept of the development of intelligence as a systemic interaction of cognitive abilities and a representative picture of the world. In the prevailing geopolitical, socio-cultural and socio-economic conditions, a nationally oriented education should be a state and personally significant basis for the formation of individual and public consciousness. The goals and principles of the organization of education should be methodologically consistent. The principle of variability can be one of the central in solving the problems of education and development, but it requires the necessary restrictions in the dissemination of individual strategies for the assimilation of knowledge and their diversity. The theory of nationally oriented education should be based on the primacy of content and the need for a unified foundation of the educational space, but without the limitations of an in-depth or expanded study of the subject with the use of additional literature.
A Novel Encryption RAAM Algorithm in Different Multimedia Applications

In this era where everything is becoming digital the most challenging topic in front of us is Data Security in every aspect even in the secured communication channel. These issues can be tackled by using strong Data Encryption and the trusted third party who maintains the database. The fast development in Digital Technology also comes with the rapid crimes and the insecurity of data theft. From time to time engineers came up with many encryption techniques like Caser Ciphers, Vernam Ciphers, Vigenère Cipher which helped us in securing the data but with lots of flaws that later were exploited by the cybercriminals. So, they cannot provide sufficient security. In this research paper, we have proposed a new, more efficient encryption algorithm. This algorithm will use multiple keys during encryption or decryption so it will be very less vulnerable against the attacks like Brute force.
Fra ‘letterario’ e digitale

Father Roberto Busa is widely recognised as the founder of Humanities Computing (HC) because of his pioneering approach to text analysis, which paved the way for computational linguistics. Over the years, this early focus on linguistic analysis has evolved to include new ways of combining humanities with computing. Digital Humanities (DH) provide a common outlook that has a markedly methodological nature and an interdisciplinary focus. The aim of this paper is to review some of the key issues behind the shift from HC to DH, highlighting elements of continuity and/or change.
Caracterização espectrorradiométrica (VNIR-SWIR) do minério magnesítico da porção centro-sul do estado do Ceará

Due to the practicality, speed and low cost of operation, the use of spectroradiometry has become an important tool in determining the composition of materials, being used in the characterization of boreholes, support and orientation of mining fronts, and as support to interpretation of remote sensing images. To perform the spectroradiometric characterization of the magnesitic ore of the state of Ceara (Brazil), the FieldSpec Pro FR spectroradiometer (350 to 2500 nm) was used. The samples were collected from the mines Cabeca de Negro, Riacho Fundo and Torto, and subsequently cut and polished in order to obtain uniform surfaces, and thus minimize the effect of the sample relief and of any surface weather. Data acquisition was carried out in the laboratory, without any interference from external lighting, with three spectral measurements made at different points of each sample. The spectral features that characterized the analyzed ore were attributed to iron (350 to 750 nm, 1000 nm and 1700 nm), water/hydroxyl (1390 nm, 1440 nm and 1940 nm), to the Mg-OH bond (2070 nm and 2390 nm) and carbonate (1850 nm, 1970 nm, 2130 nm and 2300 nm), with the albedo and intensity of the features being influenced by the presence of organic matter and particle size. According to the spectral characteristics, the magnesitic ore studied could be classified into four distinct groups, called hydrated magnesite, anhydrous magnesite, ferrous magnesite and carbonaceous magnesite.
Semi-Supervised Policy Initialization for Playing Games with Language Hints

Using natural language as a hint can supply an additional reward for playing sparse-reward games.Achieving a goal should involve several different hints, while the given hints are usually incomplete.Those unmentioned latent hints still rely on the sparse reward signal, and make the learning process difficult.In this paper, we propose semi-supervised initialization (SSI) that allows the agent to learn from various possible hints before training under different tasks.Experiments show that SSI not only helps to learn faster (1.2x) but also has a higher success rate (11% relative improvement) of the final policy.