Reviewerly/exHarmony huggingface dataset:
Attribute classification for the analysis of genuineness of facial expressions

In this work we study different artificial neural network variants to classify instances of facial expressions on video according to its genuineness. This problem is a task not trivial to solve by human beings. The main analysis compares deep feed-forward neural networks with recurrent neural networks. This particular type of network capable of extracting information from a sequence and keep it through time. In that way, a video can be classified using not only its features but also the ones from its predecessors. Since the amount of videos in the dataset is rather scarce, a new metric is proposed to make a more particularized analysis. Results suggest that certain facial features that allows distinguishing a genuine expression and a faked one are too related to the subject that performs them, which suggests that developing an universal classifier (independent of the subject) seems unfeasible. Regarding the comparison between the two types of networks, although the recurrent variants cannot outperform convnets, we can observe that they achieve similar results but with a smaller amount of training epochs. The dataset used in this paper was originated for the Real Versus Fake Expressed Emotion Challenge at the ICCV 2017.
Sarcasm Discernment on Social Media Platform

Past studies in Sarcasm Detection mostly make use of Twitter datasets collected using hashtag-based supervision but such datasets are noisy in terms of labels and language. To overcome the limitations related to noise in Twitter datasets, this News Headlines dataset for Sarcasm Detection is collected from two news website. TheOnion aims at producing sarcastic versions of current events and we collected all the headlines from News in Brief and News in Photos categories (which are sarcastic). We collect real (and non-sarcastic) news headlines from Huff Post. Sarcasm Detection on social media platform. The dataset is collected from two news websites, theonion.com and huffingtonpost.com. Since news headlines are written by professionals in a formal manner, there are no spelling mistakes and informal usage. This reduces the sparsity and also increases the chance of finding pre-trained embeddings. Furthermore, since the sole purpose of TheOnion is to publish sarcastic news, we get high-quality labels with much less noise as compared to Twitter datasets. Unlike tweets that reply to other tweets, the news headlines obtained are self-contained.
S-Extension Patch: A Simple and Efficient Way to Extend an Object Detection Model

While building convolutional network-based systems, the toll it takes to train the network is something that cannot be ignored. In cases where we need to append additional capabilities to the existing model, the attention immediately goes toward retraining techniques. In this paper, I show how to leverage knowledge about the dataset to append the class faster while maintaining the speed of inference as well as the accuracies; while reducing the amount of time and data required. The method can extend a class in the existing object detection model in 1/10th of the time compared to the other existing methods. S-Extension patch not only offers faster training but also speed and ease of adaptation, as it can be appended to any existing system, given it fulfills the similarity threshold condition.
Realeasy: Real-Time capable Simulation to Reality Domain Adaptation

We address the problem of insufficient quality of robot simulators to produce precise sensor readings for joint positions, velocities and torques. Realistic simulations of sensor readings are particularly important for real time robot control laws and for data intensive Reinforcement Learning of robot movements in simulation. We systematically construct two architectures based on Long Short-Term Memory to model the difference between simulated and real sensor readings for online and offline application. Our solution is easy to integrate into existing Robot Operating System frameworks and its formulation is neither robot nor task specific. We demonstrate robust behavior and transferability of the learned model between individual Franka Emika Panda robots. Our experiments show a reduction in torque mean squared error of at least one order of magnitude. The collected data set, the plug-and-play Realeasy model for the Panda robot and a reproducible real-time docker setup are shared alongside the code. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> https://sites.google.com/ulund.org/realeasy
Continuous-Time Trajectory Estimation From Noisy Camera Poses Using Cubic Bézier Curves

Recovering the trajectory of a moving camera from image sequences usually generates a set of discrete camera poses. Applications such as view synthesis, sensor data fusion and interactive simulations, however, often require a continuous trajectory. In this work an algorithm for estimating the control points of a cubic Bézler curve is presented and evaluated on the impact of noisy measurement data. With the estimation results a continuous-time representation of the camera's trajectory is recovered and a pose interpolation between the noisy discrete camera poses is realizable. Different levels of noise on translation, rotation and velocity are analysed, whereby the use of straight line trajectories leads to special configurations. The contributions of this paper are as follows: (1) the absolute trajectory error compared on various noisy parameters in characteristic maps (2) the continuous-time trajectory generation combined with special configurations using straight line trajectories and (3) the source code of implementation and measurement examples are released, in order to make a reconstruction of the results and application in related research possible.
COGNITIVE MODELING OF THE QUALITY MANAGEMENT SYSTEM OF VOCATIONAL SECONDARY EDUCATION IN THE CONTEXT OF MODERNIZATION

The paper describes the updated approach to quality management of vocational secondary education (VSE) in the context of its modernization involving cognitive modeling of the educational process in the college. The authors analyzed the impact of project initiatives in the national VSE system on the quality of education in the college: the spreading of the dual education system, evaluation of education results through the WorldSkills national system of qualifications and standards, digital transformation of education. Cognitive modeling allowed studying the quantity change in the quality of education in response to variations of managing variables – project initiatives in the national VSE system – and modeling the results of the application of various strategies of development of the VSE educational institution. Based on the processing of experts' opinions matrix, the authors formed the educational process cognitive map. The paper contains the description of the cognitive model of the educational process of the college and the results of scenario modeling of the project initiatives’ impact on the quality of education. Using PEST- and SWOT-analysis, the authors described the characteristics of the educational process cognitive model. PEST-analysis revealed ten various factors (concepts) influencing the educational process in the college: the quality of education, independent evaluation of qualification, digitalization of education, academic learning, practical education, attendance, students’ academic progress, material and technical support of educational process, dual education system, and teaching staff competence level. SWOT-analysis identified cause-effect relationships between factors, evaluated their power and focus. The presented technique of cognitive modeling of the college educational process allows implementing new possibilities of strategic planning of the VSE quality management system.
Study to Conduct Engineering and TVET During Pandemic of COVID-19 in Nepal - ‘A Lesson to All'

Engineering and Technical education are heavily affected due to COVID- 19 in Nepal. There are 70 colleges providing undergraduate and graduate level of engineering education in (college.com.np, 2021), and ‘Technical education providing constituents and the affiliated institute of CTEVT are 1100 (CTEVT, 2077 BS (2020)). Altogether more than one hundred thousand students are undergoing different courses in technical and engineering education.Online education is not so viable without proper planning of practical education, this is the most problematic in the TVET and engineering sector. Online education does not give them the feeling and experience of know-how in the practical part of the education, TVET is also heavily suffered since their expertise in skill level will be lacking. How to solve this issue of the practical part is an issue of the research also.The majority of the respondents 75 % agreed to run online classes in engineering and Technical education with proper planning of practical part and said Universities, local industries, local government, and community should work hand in hand to solve the problem of TVET and engineering education. They have given a clear idea on how to go on solving this issue and given a beautiful suggestion which could be a foundation to uplift the country higher in technology. The modality of implementing the practical part of the education is designed based on the outcome of the research.
Strong Equivalence and Program's Structure in Arguing Essential Equivalence between Logic Programs

Answer set programming is a prominent declarative programming paradigm used in formulating combinatorial search problems and implementing distinct knowledge representation formalisms. It is common that several related and yet substantially different answer set programs exist for a given problem. Sometimes these encodings may display significantly different performance. Uncovering {\em precise formal} links between these programs is often important and yet far from trivial. This paper claims the correctness of a number of interesting program rewritings.
Two-Stage Fusion Model for Heavy Rain Removal on Single Image

Current single image derain methods cannot solve the heavy rain situation well. In this paper, based on the physical model of a rainy image, we build a two-stage network, TSF-Net, which combines model-driven and data-driven methods. The first stage gets the rain streaks, atmospheric light, and transmission map to obtain the coarse rain-free image by the physical model. The second stage is a fully convolutional neural network with the structure of U-Net. The proposed Multi-Scale Projection Fusion Block (MSPFB) module, which can perceive the spatial information across the scale of images, is employed to remove the residual rain and fuzzy parts in the first stage and obtain a refined clean image. Extensive experiments show that our TSF-Net achieves better accuracy and visual improvements against state-of-the-art methods.
Gender recognition from unconstrained selfie images: a convolutional neural network approach

Automated human gender recognition is an essential demographic tool. This is reflected in forensic science, surveillance systems, data retrieval and targeted marketing applications. In an earlier age, this was always driven using standard face images and hand-crafted features. Such way has achieved good results, however, the reliability of the facial images had a great effect on the findings and robustness of extracted features, where any small change in the query facial image could change the results. Nevertheless, the performance of current techniques in unconstrained real-environments is still inefficient, specially when contrasted against recent performance breakthroughs in different computer vision researches. This paper introduces  a novel technique for human gender recognition from non-standard selfie images using a deep learning technique. Selfie photos are uncontrolled partial or full-frontal body images that are usually taken by people themselves in real-life environment. As far as we know this is the first paper of its kind to identify gender from selfie photos, through a deep learning-based approach. The outcomes of the experiments reached through using the selfie dataset emphasizes the proposed technique effectiveness in recognizing gender from such images with 89% accuracy. The performance is further consolidated by testing on numerous benchmark datasets that are widely used in the field, namely: Adience, LFW, FERET, NIVE, Caltech WebFaces and CAS-PEAL-R1.
Surveillance capitalism – a new techno-economic paradigm?

This paper look at surveillance capitalism as described in the book by Shoshana Zubof, and discuss whether surveillance capitalism represents a new stage of capitalist development. This is done by using the theory of techno-economic paradigms as a theoretical framework.
Domain Adaption in Sequence-to-Sequence Scene Text Recognition

Domain adaption techniques such as gradually vanishing bridge (GVB), have shown promising results in image classification problems. However, their efficacy in sequence-tosequence scene text recognition (STR) is yet to be known. In this paper, we combine GVB and connectionist temporal classification (CTC) techniques in STR model to improve the text recognition performance. The proposed approach is evaluated on publicly available datasets. Experimental results show the performance gain compared with state-of-the-art approaches.
Extraction of Texture Features for Content-Based Image Retrieval in the Cloud Environment

The content-based image retrieval (CBIR) is a technique used to retrieve identical images based on the features of the image. In this paper, the feature vectors of the image are extracted using gray-level co-occurrence matrix (GLCM). To secure the privacy of the images and the feature vectors in the cloud are done by AES (Advanced Encryption Standard), and Hash Indexing is the method to index the feature vectors for improving the execution of the retrieval of images. Query matching is done by Spearman rank coefficient to retrieve the similar images between the encrypted query image vector and the indexed encrypted vectors stored in the particular database. The execution performance of the proposed method is assessed by precision, time of feature extraction and time taken for retrieving images and is approved using the Corel10K dataset. The proposed image retrieval based on texture features outperformed other methods with a precision of 96.1%, 3 s for retrieving similar images and 314 s for extracting features from 2000 images in the dataset.
The Effect of Duolingo Application on Students’ Vocabulary Mastery at MTs Darul Ilmi Batang Kuis

The objective of this research to indenify the difficulties faced by the seventh gradedtusentd at MTs Darul Ilmi Batang Kuis, to identify factors of causing the studentsdifficulties in memorizing the vocabulary at the elevent grade students at MTs DarulIlmi Batang Kuis in academic year 2020/2021. This research is quantitative research.The subject of the research was the eleventh garde of MTs Darul Ilmi Batang Kuis inacademic year 2020/2021. The total of the population was 25 students and theresearcher took each experimental and controll class are 25 students of the sample. Incollecting the data the researcher used test in the form of multiple choices as theinstrument. The tests were Pre-test which was given before the treatment and the Post-test which was given after the treatment. Then, the data obtained were analysed by usingt-test in SPSS IBM 16 with 5% (0.05) significance level. Further, the reseracher foundthat the mean score of post-test in test result also presents that to (8.984) was higherthan the t-table the significance level 5% (0.05) with the degree of freedom 23 is 1.699.Therefore, the t0 result > t-table generated that H0 (null hyphothesis) was rejected andHa (alternative hyphothesis) was accepted. The calculation of the effect size was 1.37which the range of the effect criteria included to strong effect. Thus, DuolinggoApplication has significant effect on the students’ vocabulary mastery at the seventgrade of MTs Darul Ilmi Batang Kuis.
Maternar e estudar no contexto pandêmico: narrativas de estudantes mães alunas do curso de Pedagogia da Universidade Federal de Sergipe: Motherhood and study in the pandemic context : narratives of student mother students of the Pedagogy course of the Federal University of Sergipe

Esse artigo tem sua origem como parte de uma pesquisa que buscou compreender e analisar os sentidos a respeito da maternagem de alunas maes do curso de Pedagogia da Universidade Federal de Sergipe, atraves de suas narrativas. Como o estudo se desenvolveu durante o contexto marcado pela crise sanitaria instalada pela pandemia do novo coronavirus, tornou-se essencial compreender a realidade pela qual passam essas alunas, ja que e desse lugar que narram suas experiencias e expectativas. A partir da analise dos dados obtidos, foi possivel perceber como a desigualdade de genero e os estereotipos impostos as mulheres, sobretudo as maes, produzem impactos sobre a permanencia dessas alunas no curso, assim como tambem sobre a qualidade de sua formacao, consequentemente influenciando a construcao de suas subjetividades profissionais.Palavras-chave: Pandemia; Maternagem; Pedagogia  AbstractThis article has its origin as part of a research that sought to understand and analyze the meanings about the motherhood of students mothers of the Pedagogy course of the Federal University of Sergipe through their narratives. Since the study was developed during the context marked by the health crisis installed by the pandemic of the new coronavirus, it became essential to understand the reality that these students go through, since it is from this place that they relate their experiences and expectations. From the analysis of the obtained data, it was possible to perceive how gender inequality and stereotypes imposed on women,  especially on mothers, impact the stay of these students in the course, as well as the quality of their education, therefore influencing the construction of their professional subjectivities.Keywords: Pandemic, Maternalization, Pedagogy
La comprensión lectora y creatividad en estudiantes de edad escolar

The objective of this study is to analyze the relationship between reading comprehension and creativity in school-age students, the method used is the documentary review, analyzing quantitative and qualitative elements applied to students to identify the level of reading comprehension and the measurement of creativity. As a result, it is found that critical reading from pedagogical innovation allows evolutionary development, activates previous knowledge, thought, language and the comprehension of texts. It is concluded that the analysis of texts from a challenging, motivating and participatory perspective favors reading comprehension and helps to activate creativity processes.
Restrictive focus adverbs in contemporary varieties of Italian and European Spanish: A contrastive, corpus-based study

This paper investigates the uses of Italian and Spanish restrictive focus adverbs, concentrating on the three cognates solo, solamente and soltanto / tan solo. Our aim is to highlight the discourse similarities and differences between these cognates in contemporary varieties of Italian and European Spanish through a contrastive corpus-based analysis. A further aim is to shed light on the factors that explain the differences from both an intra- and a cross-linguistic perspective. Our data is drawnfrom existing monolingual Italian and Spanish corpora as well as a self-assembled comparable corpus of academic texts written in Italian and Spanish.
A Metric to Determine the Change Proneness of Software Classes Using GMDH Networks

Software class change proneness is the possibility that a class will undergo change in the future versions of the software, a software characteristic predicted by the internal quality attributes of the software like cohesion, coupling, inheritance, and polymorphism. We have empirically explored the interrelation between dynamic software metrics and class change proneness. While using Machine Learning (ML) techniques to construct the prediction models, we have used Open source software (OSS) “SoundHelix” for building the prediction model by using dynamic metrics as internal quality attributes of the software. From the five ML techniques for building the prediction models, which are ─ Single Decision Tree, Probabilistic Neural Network/General Regression Neural Network (PNN/GRNN), Radial Basis Function Neural Network (RBF), Logistic Regression, and Group Method of Data Handling (GMDH) polynomial network, we found that the last, or GMDH algorithm, performs best with highest accuracy. “Class Change Factor (CCF)”, a metric based on the GMDH technique and proposed to determine the change proneness of the class, can be used to find out classes with the possibility of change in the future releases of the product. To validate the proposed metric, five OSS are used. “Class Change Factor (CCF)” will help the software developers in recognition of change prone classes in the initial steps of development process, and thus they can focus their attention only on those classes for the refactoring and other maintenance activities.
Mobile Application of Electronic Medical Record (EMR) Systems using Near Field Communication (NFC) Technology

Medical records are preventive actions that are recorded into files stored by the health service providers. Medical records are needed by the doctor to find out the patient’s historical data so that they can be served further. A medical history reporting error can result in a patient’s medical care failure. To reduce administrative errors, loss of data, and the effectiveness of medical services requires tools that can provide solutions. Near Field Communication, called NFC, was the development of Bluetooth and RFID.  NFC allows cell phones to store necessary data safely and be sent to other phones equipped with NFC or read by an NFC reader. Also, this NFC allows cell phones to be developed of payment (e-money) or electronic medical record (EMR). On this study has developed an Electronic Medical Record system based on Near Field Communication and EMR with NFC is expected to the replaced conventional medical record. The advantage of NFC-based EMR is the need for a short time in use, not depending on Internet access and ease in the transmission of EMR to those in need. Patient data processing is faster, accurate, and integrated with cost management to medical services providers.
Design and Implementation of Chinese Pronunciation Automatic Scoring System Based on Speech Recognition

The development of speech recognition technology makes the interaction between human and computer possible. In view of the deficiency of pronunciation teaching in TCFL, this paper proposes the design of Chinese automatic pronunciation level evaluation system in TCFL, and describes the structure, function and process of the system in detail. This paper introduces the key technologies and steps in the implementation of the system: dynamic time warping algorithm, corpus building, phonological segmentation technology and evaluation and grading standards. Through a small range of experiments, it shows that the system has a certain reference value for the test of foreign students’ Chinese pronunciation level.