Reviewerly/exHarmony huggingface dataset:
MODERN CONCEPTS FOR LEARNING AND TEACHING

Modern theories of learning largely explain the mechanisms of knowledge acquisition, skills building and nurturing values in a technological environment and information boom. The traditional educational paradigm is increasingly giving way to constructivism as an educational philosophy and the constructivist approach to learning. Constructivism with its varieties constructionism, social constructivism and communal constructivism are at the heart of e-learning design. The basis of modern learning theories and models for teaching and acquiring knowledge and skills is the overall personal development of the individual. Today, education is seen as a process that leads not only to qualification for the profession, but to a set of skills for successful realization and development. This holistic view is realized by the competence educational model. This paper deals with two contemporary concepts of learning and teaching - constructivism and competence approach.Methodology: A review of literary sources was made.Results: According to the theory of constructivism, new knowledge is actively constructed by learners in the process of interaction with the environment. Everything we perceive with all our senses is compared with our previous knowledge and in this way new ones are formed. The constructivist approach provides a higher degree of understanding and applying, it is more adequate in practice. The aim of the training is not only to convey information, but also to transform students from passive recipients of knowledge into active constructors of their own and other people's knowledge. Constructivism is the theoretical basis of interactive learning.Competence can be defined as a combination of knowledge, skills and abilities needed to perform a task. In short, competence is any characteristic or trait that an individual uses for successful performance of any kind. These characteristics may include a combination of the following: knowledge, skills, traits, values/beliefs, motives, way of thinking, attitudes, physical ability, social roles and aspects of the individual's self-esteem.Conclusion: The combination of the constructivist and competence approach in the planning and design of electronic forms of education leads to the development of qualified specialists with a wide range of knowledge and skills that ensure successful realization on the dynamic labor market
Enhanced adaptive trust management system for socially related IoT

The social IoT (SIoT) is a network involving heterogeneous entities like humans, and devices referred to as 'things' are connected with social relationship. Every individual thing has its own ID, f...
Language technology approach to “seeing” in Akkadian

One of the ways meanings of words can be understood is based on their distributional properties. Such methodology offers an interesting quantitative viewpoint on the study of the lexicography of long-extinct languages. This chapter explores the use of Pointwise Mutual Information (PMI), a well-known statistical word association measure used in collocation analysis. PMI is applied to the data in order to gain insights on the semantic nuances of Akkadian verbs of seeing (amāru, naṭālu, palāsu, dagālu, ḫiātu, barû, and subbû). To evaluate the data-driven results, the findings are compared to previous philological work by Ainsley Dicks. The analysis of the top-ranked PMI-extracted collocates provides a good overview of the typical semantic differences between the seven verbs of interest.
Multivariate Time Series Prediction Considering Intra-Time-Series and Inter-Time-Series Dependencies

A few artificial neural networks have been proposed so far for multivariate time series prediction and they used simple general-purpose neural networks. Therefore, they cannot achieve high prediction accuracy. In this paper, we propose an artificial neural network called DBMTSP (Dependency Based Multivariate Time Series Prediction) to predict the next element of a time series in the multivariate case. Compared to the existing methods, DBMTSP considers both intra-time-series dependencies and inter-time-series dependencies efficiently to achieve more accurate predictions. We propose a hierarchical encoder in DBMTSP to discover inter-time-series dependencies. The proposed hierarchical encoder is able to encode secondary time series into a single parameter that represents dependencies that exist between the main time series and the secondary time series. The hierarchical encoder has a scalable design such that it can accept a large number of secondary time-series. We have trained DBMTSP using 32760 data matrices. We evaluated DBMTSP using 8190 test data matrices. Our evaluations show that DBMTSP surpasses the existing methods in term of prediction accuracy.
Event Extraction as Natural Language Generation

Event extraction (EE), the task that identifies event triggers and their arguments in text, is usually formulated as a classification or structured prediction problem. Such models usually reduce labels to numeric identifiers, making them unable to take advantage of label semantics (e.g. an event type named Arrest is related to words like arrest, detain, or apprehend). This prevents the generalization to new event types. In this work, we formulate EE as a natural language generation task and propose GenEE, a model that not only captures complex dependencies within an event but also generalizes well to unseen or rare event types. Given a passage and an event type, GenEE is trained to generate a natural sentence following a predefined template for that event type. The generated output is then decoded into trigger and argument predictions. The autoregressive generation process naturally models the dependencies among the predictions -- each new word predicted depends on those already generated in the output sentence. Using carefully designed input prompts during generation, GenEE is able to capture label semantics, which enables the generalization to new event types. Empirical results show that our model achieves strong performance on event extraction tasks under all zero-shot, few-shot, and high-resource scenarios. Especially, in the high-resource setting, GenEE outperforms the state-of-the-art model on argument extraction and gets competitive results with the current best on end-to-end EE tasks.
Facial Anthropometry: A Reliable Tool for Stature Estimation in Haryanvi Population

Background: Facial anthropometry is a part of cephalometry that involves the measurements of face. Itis very helpful in reconstructive surgery, forensic medicine, orthodontics and to find ethnic differencesby defining various shapes of face. Facial features of every individual are different even in siblings.These features vary according to gender, racial and biological group, ecological and geographical region.Anthropometry can also be used for estimation of stature, where it cannot be measured directly such asspinal or lower limb deformity or any other neuromuscular disorder.So, the present study attempted to findthat is the facial parameters are reliable in estimation of stature in Haryanvi jaat population and also aimsformulate regression equation for estimation of stature in the same.Methods: A sample of total 300 adult Haryanvi jaatsof age group 20 to 60 years was taken for the presentstudy. Out of them 150 were females and 150 weremales. Anthropometric measurements of face and staturewere taken on each subject.Conclusion: All the studied facial parameters showed positive significant correlation with stature (p&lt;0.01)both in males and females So, it can be concluded that facial anthropometry is a reliable tool for statureestimation in Haryanvi population.
Knowledge Transfer across Diseases

In the field of medical data mining, collecting sufficient expert labeled datasets especially with pixel-level annotations is extremely difficult. For diseases with scarce data, supervised learning has little effect even with synthetic images for data augmentation or cross-modality knowledge transfer. In response to the problem, we propose a novel solution - knowledge transfer across diseases. Existing transfer methods either focus on mapping one domain to another or focus on generating a subspace consisting of domain invariant features. However, these methods are negative in the transfer of knowledge across diseases because they lose the private characteristics of the target disease. Motivated by this limitation, we present a reweighting network (RW-net). It can be widely applied to a domain adaptation model based on adversarial, through reweighting dynamic balance subspace generation and segmenter optimization. We compare our method to two popular transfer learning methods and baseline on the open glioma dataset (BraTS) and ischemic stroke dataset (ATLAS). Our approach achieves excellent performance, even though the two diseases have completely different imaging traits.
Framework of Goal-driven Risk Management in Software Development Projects Using the Socio-technical Systems Approach

Previous studies have identified the influence of goal agreement on requirement elicitation in software projects, but they have not addressed how it influences risks in projects. The purpose of this study is to examine the influence of goal identification and agreement on software project risks. With socio-technical systems theory as the backdrop, we used a goal-oriented perspective to understand how the social subsystem influences the technical subsystem of a software development project. In an empirical study with 411 responses from software project developers and managers in India and using structural equation modelling, the causal linkages between goal orientation and project risk variables have been examined. The results indicated that the early adoption of goal-driven risk management reduces the social context risks which, in turn, reduces the project management risk. The study also revealed that the product artefact is independent of whether the system was completed within budget and schedule. To design the risk management strategy, project managers should consider the relationship between social context risk and project management risk and the implications of these relationships on project performance.
Research on the Design and Effect of Audiovisual Elements in Online Course Videos

The impact of the epidemic has caused a rapid increase in online video courses, followed by the construction of multi-level and multi-channel online courses. Online courses are a systematic project involving multiple links. Judging from the current online course form, the shooting techniques range from amateur to professional, from single to diverse, from simple to complex, and the production skills are more abundant. All-round improvement and development in shooting mode, scene selection, post-production, etc., improved the audio-visual effects of online courses, and relatively improved teaching effects, which played a positive role in promoting the construction of college video courses. This article analyzes the audiovisual elements of online courses, one of the content of teaching resources. Different audiovisual elements play different roles in online courses and have different effects on teaching effects.
Masking and BERT-based Models for Stereotype Identication

The work of the authors from the Universitat Politecnica of Valencia was funded by the Spanish Ministry of Science and Innovation under the research project MISMIS-FAKEnHATE on MISinformation and MIScommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31). Experiments were carried out on the GPU cluster at PRHLT thanks to the PROMETEO/2019/121 (DeepPattern) research project funded by the Generalitat Valenciana.
A Distributed Delivery Architecture for User Generated Content Live Streaming over HTTP

Live User Generated Content (UGC) has become very popular in today’s video streaming applications, in particular with gaming and e-sport. However, streaming UGC presents unique challenges for video delivery. When dealing with the technical complexity of managing hundreds or thousands of concurrent streams that are geographically distributed, UGC systems are forces to made difficult trade-offs with video quality and latency. To bridge this gap, this paper presents a fully distributed architecture for UGC delivery over the Internet, termed QuaLA (joint Quality-Latency Architecture). The proposed architecture aims to jointly optimize video quality and latency for a better user experience and fairness. By using the proximal Jacobi alternating direction method of multipliers (ProxJ-ADMM) technique, QuaLA proposes a fully distributed mechanism to achieve an appropriate solution. We demonstrate the effectiveness of the proposed architecture through real-world experiments using the CloudLAB testbed. Experimental results show the outperformance of QuaLA in achieving high quality with more than 57% improvement while preserving a good level of fairness and respecting a given target latency among all clients compared to conventional client-driven solutions.
Online trajectory recovery from offline handwritten Japanese kanji characters

In general, it is straightforward to render an offline handwriting image from an online handwriting pattern. However, it is challenging to reconstruct an online handwriting pattern given an offline handwriting image, especially for multiple-stroke character as Japanese kanji. The multiple-stroke character requires not only point coordinates but also stroke orders whose difficulty is exponential growth by the number of strokes. Besides, several crossed and touch points might increase the difficulty of the recovered task. We propose a deep neural network-based method to solve the recovered task using a large online handwriting database. Our proposed model has two main components: Convolutional Neural Network-based encoder and Long Short-Term Memory Network-based decoder with an attention layer. The encoder focuses on feature extraction while the decoder refers to the extracted features and generates the time-sequences of coordinates. We also demonstrate the effect of the attention layer to guide the decoder during the reconstruction. We evaluate the performance of the proposed method by both visual verification and handwritten character recognition. Although the visual verification reveals some problems, the recognition experiments demonstrate the effect of trajectory recovery in improving the accuracy of offline handwritten character recognition when online recognition for the recovered trajectories are combined.
The adult visual system resists learning continuity violations

One universal property of our world is that solid objects behave under the principle of continuity: they cannot spontaneously appear or disappear. Consequently, human vision may be equipped with strong prior expectations for this basic principle (Falck et al, 2020; Flombaum & Scholl, 2006). Here, we tested people’s resistance to learning continuity violations after being trained to expect such violations. In Experiment 1, participants performed an object detection task. Each trial first showed a car or an empty space, which was then occluded by an opaque screen. Next, the screen fell, revealing a car or an empty space, and participants responded within 500ms whether the car was present or absent. They first went through a training phase of eight violation trials (i.e., car present at first, absent at reveal, or vice versa) or eight non-violation trials. They were then tested on a violation or non-violation trial. Results showed that after being trained on violations, participants’ accuracy was surprisingly near 100% both when tested on violations and non-violations. Whereas following non-violation training, their accuracy was near 100% for non-violations, 56.0% for violations. This suggests that participants could learn to expect violations through training, but could not learn to override the expectation of a non-violation. Experiment 2 asked participants to predict the car’s presence or absence and give a confidence rating before seeing outcomes in training and test trials (while no longer employing the detection task). Here, following violation training, only 70.6% participants predicted a violation, with an average confidence of 5.41/7, while following non-violation training all participants predicted non-violations (avg. confidence of 6.60/7). Taken together, our studies provide evidence for a strong prior in human vision for expecting continuity and a generic influence of this prior in learning to expect statistically improbable non-violations (Experiment 1) and statistically probable violations (Experiment 2).
Predictive Coding of Tracking and Intercepting Inferred Moving Target in Posterior Parietal Cortex

Person localization or segmentation in low resolution crowded scenes is important for person tracking and recognition, action detection and anomaly identification. Due to occlusion and lack of inter-person space, person localization becomes a difficult task. In this work, we propose a novel information fusion framework to integrate a Deep Head Detector and a body pose detector. A more accurate body pose showing limb positions will result in more accurate person localization. We propose a novel Deep Head Detector (DHD) to detect person heads in crowds. The proposed DHD is a fully convolutional neural network and it has shown improved head detection performance in crowds. We modify Deformable Parts Model (DPM) pose detector to detect multiple upper body poses in crowds. We efficiently fuse the information obtained by the proposed DHD and the modified DPM to obtain a more accurate person pose detector. The proposed framework is named as Fusion DPM (FDPM) and it has exhibited improved body pose detection performance on spectator crowds. The detected body poses are then used for more accurate person localization by segmenting each person in the crowd.
Constructing Corpus and Word Embedding for Spanish Covid-19 Data

Severe acute respiratory syndrome coronavirus 2 (COVID 19), colloquially referred to as coronavirus, escalated into a global pandemic with severe transmission and mortality rates in 2019. Despite the escalation of the virus’ worldwide impact in 2020, numerous studies on Natural Language Processing in Spanish have neglected corpus construction or word embedding, especially conspicuous in its absence being the corpora involving coronavirus or infectious diseases. Additionally, corpus construction or word embedding conducted in the medical field do not display efficacy in production pertaining to coronavirus or infectious diseases. To supplement this potentially detrimental insufficiency, this study collects Spanish Language data to build a relevant coronavirus corpus through appropriate preprocessing and then obtains a word embedding. Performance of the corpus and word embedding are then tested through word similarity evaluations, a cosine similarity evaluation, and a visualization evaluation with the existing Spanish corpus. After comparison, corpus and word embedding suitable for coronavirus will be suggested.
Membangun sistem informasi Posyandu berbasis web pada Puskesmas Pengaringan menggunakan PHP dan MySQL

Technology cannot be separated from the activities of everyday life. Technology is needed in all fields in an effort to achieve easy completion of work. One technology that is rapidly developing is the internet. Internet is part of information technology whose existence continues to grow from year to year. The large number of needs for convenience in making work easier, accompanied by an increase in internet users.The internet is used in various fields, one of which is the health sector. In the field of health, the internet is used among other things as a medium for health information. The internet is used as a medium of information, one of which is the website. A website is a tool that contains information displayed by browsers, such as Mozilla Firefox, Google, Chrome, or others. The website also provides added value as an international scope that can be accessed by everyone in the world. Apart from providing information there is also a web-based information system. One of the web-based information systems used in the health sector is the posyandu information system. The posyandu information system is designed to record posyandu activities that are routinely carried out at the Puskesmas as the closest health center in the community.      Several methods are used to obtain the required data. Among other things, Interview, Observation Method, and Reference Method. In this study, a laptop unit with laptop specifications and software for Windows 10, PHP and MySQL, XAMPP and Visual Studio Code were used.      The results obtained from this research are in the form of Information Systems. This system is called the web-based Posyandu information system at Puskesman Pengaringan. Keywords: Internet, Website, Posyandu.
No-reference Image Quality Evaluation for Multiply-distorted Images Based on Spatial Domain Coding

Considering the problem that it is difficult to accurately and effectively extract the quality features of mixed distortion image, an image quality assessment method based on spatial distribution analysis is proposed. Firstly, the brightness coefficients of the image are normalized, and the image is divided into blocks. While the Convolutional Neural Network (CNN) is used for end-to-end depth learning, the multi-level stacking of convolution cores is applied to acquire image quality perception features. The feature is mapped to the mass fraction of the image block through the full connection layer, then the quality pool is obtained by aggregating the quality of the block. Through the analysis of the spatial distribution of local quality in the quality pool, the features that can represent its spatial distribution are extracted, and then the mapping model from local quality to overall quality is established by the neural network to aggregate the local quality of the image. Finally, the effectiveness of the algorithm is verified by the performance tests in MLIVE, MDID2013 and MDID2016 mixed distortion image databases.