Reviewerly/exHarmony huggingface dataset:
Optimization of Small Object detection based on Generative Adversarial Networks

Small object detection is one of the fundamental problems in computer vision applications. Existing small object detection techniques usually focus on detecting small objects with multiple scale of features with low efficiency due to high computational cost. In this paper, we investigate small object detection problem based on generative adversarial architecture that utilizes features of small objects. We propose an Optimized Perceptual Generative Adversarial Network (OPGAN) to present more features of small objects. Specifically, the generator of OPGAN learns to present the low-resolution features of the small objects to highly resolved features similar to large objects as input image of the discriminator model. After then, the discriminator of OPGAN computes the generated feature and generates a new perceptual requirement parameter into the model to train the model iteratively. Extensive experiments on the challenging benchmark data sets demonstrate the effectiveness of OPGAN in detecting small objects.
THE ROLE OF CONNECTIVISM IN TECHNICAL VOCATIONAL EDUCATION AND TRAINING

The presented study is devoted to a relatively new and so far little researched area of connectivism and its use in the field of technical vocational education and training (VET). The aim of the study is to evaluate the possibilities of using connectivism in the condition of technical VET. The first part clarifies the theoretical background and evaluates the current state of knowledge about connectivism. Special attention is focused on the relationship between the constructivism and connectivism in today's conditions. The second part is focused on the specifics of the connectivism used in technical VET with requirements of current times.The core of the problem solved is to define and characterise relationship between the connectivism and constructivism in the specific condition of the VET. The common and different elements of these concepts are identified and relevant conclusions are achieved. This conclusions adequately reflect the current requirements in the field of conception of the goals and contents of VET process at secondary vocational technical schools and universities.For the elaboration of the given topic the sources from literature review of theory of connectivism were chosen. In the field of technical VET we do not yet have any systematic study and therefore this study may become a starting point and contribution to the further research in this area of interest.Moreover, connectivism as the approach based on searching, retrieving, verifying, and processing information from the environment of electronic information systems in particular, including various information databases, media and multimedia, information channels, social networks and social media, and other available sources, is becoming an increasingly used approach, especially at present times, marked by the forced transition of many pupils and students to the electronic form of an otherwise classical form of education due to COVID-19.
Explication functions in the conditions of professionalization of thinking

The article substantiates that the explication method in conditions of professionalization of the subject’s thinking performs various functions. Explication allows for: firstly, the translation of unclear and uncertain intuitive representations (explicands) to the form of strictin categorical, semantic and logical form concepts (explicans); secondly, the conceptualization of a certain area of professional knowledge, bringing it into a coherent, effective system; thirdly, the detection and accounting of implicit dependencies between various knowable and transformable phenomena; fourthly, the implementation of supra-situational thinking that ensures the acquisition of valuable personal and professional neoformations by the subject. Explication (expression, distribution) of the selected professional thinking featuresof the subject as anbeingexplicatedphenomenonis carried out by differentiating the knowable phenomena: spiritual as a manifestation of the power and content of inner life; social as a refraction of social requirements in professional activity; environmental as the integration and understanding of those environmental conditions that become the means of operational coping with the difficulties encountered; resource as the implementation and multiplication of resources, primarily internal as the basis for professional and personal self-development of the subject.
Feature Transformation Network for Few-Shot Learning

Few-shot learning researches to learn a novel concept from a handful of labeled samples.Due to the small amount of training data, deep network has the risk of over-fitting.Although many previous approaches based on metric criterion can make significant progress to tackle this challenge, they not only ignore the association between query set and support set when learning sample representation, but also fail to focus greater attention in the target area.To cope with these issues, we propose a novel feature transformation network (FTN) for few-shot image classification.Specifically, to draw inferences about other instances from only a few examples, it is expected to learn a model that has more discriminative representation of the target attributes and robust generalization ability.To this end, we introduce an attention-based affinity matrix to transform the semantical enhanced embedding vectors of query samples by associating the support set, thereby guiding the network to learn a sample representation that embodies higher semantic information in the target area.Furthermore, aiming at highlighting the object region in the feature maps, and strengthening the pertinence of similarity measurement between samples, a global and local feature fusion module is designed to fuse the support set samples features.The comprehensive experiments validate the doable of our model, and our method achieves the state-of-the-art performance on two public benchmark datasets, namely, general object dataset mini-ImageNet and fine-grained dataset Caltech-UCSD Birds-200-2011 (CUB).
Fast And Slow Learning Of Recurrent Independent Mechanisms

Decomposing knowledge into interchangeable pieces promises a generalization advantage when there are changes in distribution. A learning agent interacting with its environment is likely to be faced with situations requiring novel combinations of existing pieces of knowledge. We hypothesize that such a decomposition of knowledge is particularly relevant for being able to generalize in a systematic manner to out-of-distribution changes. To study these ideas, we propose a particular training framework in which we assume that the pieces of knowledge an agent needs and its reward function are stationary and can be re-used across tasks. An attention mechanism dynamically selects which modules can be adapted to the current task, and the parameters of the selected modules are allowed to change quickly as the learner is confronted with variations in what it experiences, while the parameters of the attention mechanisms act as stable, slowly changing, meta-parameters. We focus on pieces of knowledge captured by an ensemble of modules sparsely communicating with each other via a bottleneck of attention. We find that meta-learning the modular aspects of the proposed system greatly helps in achieving faster adaptation in a reinforcement learning setup involving navigation in a partially observed grid world with image-level input. We also find that reversing the role of parameters and meta-parameters does not work nearly as well, suggesting a particular role for fast adaptation of the dynamically selected modules.
A Privacy-preserving Data Aggregation Scheme with Efficient Batch Verification in Smart Grid

This paper presents a privacy-preserving data aggregation scheme deals with the multidimensional data.It is essential that the multidimensional data is rarely mentioned in all researches on smart grid.We use the Paillier Cryptosystem and blinding factor technique to encrypt the multidimensional data as a whole and take advantage of the homomorphic property of the Paillier Cryptosystem to achieve data aggregation.Signature and efficient batch verification have also been applied into our scheme for data integrity and quick verification.And the efficient batch verification only requires 2 pairing operations.Our scheme also supports fault tolerance which means that even some smart meters don't work, our scheme can still work well.In addition, we give two extensions of our scheme.One is that our scheme can be used to compute a fixed user's time-of-use electricity bill.The other is that our scheme is able to effectively and quickly deal with the dynamic user situation.In security analysis, we prove the detailed unforgeability and security of batch verification, and briefly introduce other security features.Performance analysis shows that our scheme has lower computational complexity and communication overhead than existing schemes.
Academist Perceptions on the Use of Web 2.0 Tools Through Maslow's Needs Hierarchy: A Case Study

The theory known as the ‘Maslow Hierarchy of Needs’, which was put forward by Abraham Maslow as a result of scientific studies, describes the basic requirements that guide human behaviour. In this hierarchical order, needs in five groups are physiological needs, safety needs, belonging and love needs, esteem needs and self-actualisation needs. Nowadays, it is not difficult to observe that human life has undergone a radical metamorphosis with digital transformation. With the cultural transformation triggered by digital technologies in the postmodern world, Maslow's theory has been transformed. In light of all this, in this study, based on Maslow's transforming hierarchy of needs pyramid, it is aimed to reveal academician perceptions about the use of Web 2.0 tools. The study was conducted with a case study, one of the qualitative research methods. A case study is an empirical research method used, where more than one source of evidence or data is available. The study group of the research consists of 20 academicians working in different departments of a government state university. Academist perceptions’ interview form for ‘Use of Web 2.0 tools through the needs hierarchy of Maslow, which was developed by the researcher as a data collection tool’, was used in the research. The relevant form consists of demographic and open-ended questions. As a result, it has been observed that the views obtained from academics generally meet Maslow's Digital Needs Pyramid.
Cortex: A Compiler for Recursive Deep Learning Models

Optimizing deep learning models is generally performed in two steps: (i) high-level graph optimizations such as kernel fusion and (ii) low level kernel optimizations such as those found in vendor libraries. This approach often leaves significant performance on the table, especially for the case of recursive deep learning models. In this paper, we present Cortex, a compiler-based approach to generate highly-efficient code for recursive models for low latency inference. Our compiler approach and low reliance on vendor libraries enables us to perform end-to-end optimizations, leading to up to 14X lower inference latencies over past work, across different backends.
A neural network approach for solution of the Schrödinger equation for a particle in the Pöschl–Teller potential

We use a shallow artificial neural network (ANN) to solve the Schrödinger equation and find the ground state energy and wavefunction for a particle in the Pöschl–Teller potential. We also use the finite difference method (FDM) to find these quantities and compare the results obtained using the two methods with the analytical results. Results obtained using the neural network are in good agreement with the analytical results. Our work reaffirms the promising nature of application of artificial neural networks in quantum mechanics.
Prediction of Solar Radiation According to Aerosol Optical Depth

The dependencye of aerosol optical depth on wavelength as well as the fit of the humidity, temperature and pressure approximation under atmoshperic condition at Biskra city of Algeria has been investigated. Our work consists of measuring and modeling solar radiation on the horizontal area to create a mathematical model of global solar radiation which depends on the aerosol optical depth data between two wavelengths: 550 and 1250 nm. Simultaneous measurements of global solar radiation were carried out and recorded on the horizontal zone on an urban site (Biskra, Algeria) to characterize the radiative effect of atmospheric aerosols from January to December 2013. In addition, the effect of meteorological parameters such as: humidity, ambient temperature, and time durations were studied. This relationship constitutes an alternative tool to estimate AOD at the routine lighting measurements available at many radiometric stations around the world. Finally, a comparative study was established between the theoretical results and the experimental data which leads at an excellent correlation by a low relative error which is limited by the interval 2 and 15%.
RBI and the Indian Crypto Industry

RBI, the Central Bank of India has been in a cold war with the Indian crypto industry. Being concerned with India’s ability to absorb financial shocks, RBI has time and again tried to unfurl the disadvantages of using cryptocurrencies. The same concern was shared by other Indian regulators’ and governmental agencies. The industry has felt that the RBI is reacting in a hyperbolic fashion. When eventually RBI banned the access of crypto industry to the banking channel in India, the case eventually got to the Supreme Court of India which not only dealt with the issue, but also with certain paramount questions with regards to the RBI’s regulating powers. This article deals with journey of RBI with the Indian crypto industry including the case of IMAI & Ors. vs RBI where Supreme Court eventually lifted the ban imposed by RBI. The Achilees’ heel in RBI’s approach is the delusion towards the fact that it is possible to ban cryptocurrencies whereas looking at all the other nations’, it is much better to regulate it and mitigate the risks. A bill regarding banning cryptocurrencies is still in parliament which if approved, will suffice the RBI’s objective.
Bit Error Robustness for Energy-Efficient DNN Accelerators

Deep neural network (DNN) accelerators received considerable attention in past years due to saved energy compared to mainstream hardware. Low-voltage operation of DNN accelerators allows to further reduce energy consumption significantly, however, causes bit-level failures in the memory storing the quantized DNN weights. In this paper, we show that a combination of robust fixed-point quantization, weight clipping, and random bit error training (RandBET) improves robustness against random bit errors in (quantized) DNN weights significantly. This leads to high energy savings from both low-voltage operation as well as low-precision quantization. Our approach generalizes across operating voltages and accelerators, as demonstrated on bit errors from profiled SRAM arrays. We also discuss why weight clipping alone is already a quite effective way to achieve robustness against bit errors. Moreover, we specifically discuss the involved trade-offs regarding accuracy, robustness and precision: Without losing more than 1% in accuracy compared to a normally trained 8-bit DNN, we can reduce energy consumption on CIFAR-10 by 20%. Higher energy savings of, e.g., 30%, are possible at the cost of 2.5% accuracy, even for 4-bit DNNs.
Publisher Correction: A computational reward learning account of social media engagement

A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-22067-6
Digital Copies for Anthropological Research: Virtual Models and Databases

In the last two decades, a large number of anthropological papers have been focused on digital copies of pa-laeoanthropological materials rather than original skeletal remains. According to some foreign scholars, “virtual anthropology” has taken a shape of a separate field of anthropological science. One of the main advantages of “virtual anthropology” is the possibility to develop databases, datasets, digital collections and catalogues accessi-ble to the scientific community worldwide. Digitization of research objects facilitates organizational side of studies, provides access to wider data, expands the toolkit of available research methods, and also provides safety to the original materials. At the same time, the variability of types of virtual models along with the absence of generally accepted protocols complicate verification of morphometric and structures data. The main goal of this review pa-per is to structure the available information on virtual palaeoanthropological databases and the materials they contain. 3D-scanning technologies can be generally divided into surface scanning (including photogrammetry) and tomographic scanning. The first group of technologies provide 3D models of the shape of an object, accurate enough to be used in morphometric studies if resolution of the equipment is adequate for the size of the object and aims of the study. The second group is designed to scan the whole form of an object, which allows the ex-amination of its internal structures or tissues, small surface structures or dental material. Both methods have their strengths and weaknesses: while surface scans are cheaper and easier to obtain, CT scans provide information unavailable from the former technique. Assessment of qualitative and quantitative characteristics of digital copies depends on objectives of the study. The article provides an overview of 17 databases of virtual paleoanthro-pological models, which comprise either surface or CT scans, or both. These materials can be used in various fields of study, including human evolution, primatology, palaeoanthropology, palaeopathology, forensic science, human anatomy, as well as in teaching of these subjects. For each collection, approximate number of objects and terms of use have been specified.
Feeling Closer Despite the Distance

In the framework of “togetherness” as a psychophysiological experience of social presence, the current chapter highlights the importance of work environments to socializing. The absence of such physical collective spaces impacts group-dynamics and team performance in online meetings, which also tend to prioritize task-solving discussions and limit non-verbal exchanges. Interpersonal coordination (or “social glue”), characterized by a spontaneous mutual attunement, both in speech and gestures, is classically observed during collective events where social-bonding and affiliation are promoted. This chapter will review the cognitive, behavioral, and physiological consequences of togetherness and integrate those in the context of recent technological advancements in computer-mediated interaction which have culminated in the advent of virtual and augmented reality. Given the potential of such methods to increase embodied interactions, they have been coined as “empathy machines” and could be seen as a technological solution to restore the experience of togetherness in the workplace.
Towards a hybrid ecosystem of blended learning within university contexts

The socio-health emergency of covid-19 has affected the use of distance learning The massive use of technologies and digital media in higher education has profoundly revolutionized the perceptions of the actors involved and creating innovations within practices that are already in use Starting from the didactic experiences in digital learning environments that have already been tested in recent years, it was possible to summarize in a single model the trajectories of future experimentation in e-learning for universities The purpose of this article is in fact to propose an overall ecosystem of blended learning for university teaching, within the new phase of resumption of activities in the post-covid world The key elements brought into play by the pandemic concern training, i e , (1) users, (2) market rules and (3) didactics, further modified in relation to time, place, technology and teaching content (for practical purposes, we will call these last four categories extrinsic characteristics of the educational process), which are affected by the contextual needs that have emerged The result is a proposal for the application of a hybrid ecosystem of higher university education © 2021 Copyright for this paper by its authors Use permitted under Creative Commons License Attribution 4 0 International (CC BY 4 0)
Poster: Technological University Faculty Attitudes Towards Online Education

The paper discusses the implementation of a research project funded by the Russian Science and Higher Education Ministry during which 20 blended faculty continuing education programs were designed and delivered at IDPO KNITU in November – December 2019. Two surveys of faculty members were conducted within 15 months, the first in October 2018 and the second in March 2020. The paper focuses on the impact that participation of the faculty in the IDPO KNITU project has had on instructors’ assessment of factors that impede and motivate participation in online learning as well as their assumptions about the conditions for effective online delivery and its best practices.
Model Optimization of Computer-Aided English Teaching in Local Area Networks

With the continuous development of science and technology, more and more technologies such as computers have been put into actual teaching work.Li's computer-assisted music teaching model can just make up for the single teaching model of exam-oriented education.Based on computer-assisted teaching technology, this article carries out the design and development of a digital music course teaching system.The system can easily digitally process the recorded sound and present it in the form of sound waves on the computer.It can visually edit the audio and save the specific training status in the database comprehensively, providing more information for teaching and training.Intuitive visual information and all parameters transform the theory and methods of skills into vivid image data information.At the same time, analysis and discussion on computer technology are carried out.Using computers, music production can be operated in an intuitive and visual environment, clarifying the theme of music, developing the structure, and post-production.It has been proved through practice that this system can not only provide for teaching and training More intuitive and vivid visual information and all parameters, which transform skill theory and methods into vivid image data information, can also significantly expend efficiency of music learning and the training of professional skills, which is worthy of vigorous promotion and practice.
Learner and Instructor’s Perception on Discussion Technique in ELT

This study aimed to investigate on how to optimize discussion technique. This is a descriptive qualitative study. The data gained through open ended interview and processed by using data reduction, display data, and conclusion drawing/verification. This study answered two research question. What is the students’ perception on how discussion will be effectively implemented? and What is the teachers’ perception on how discussion will be effectively implemented? The result shows that learning styles in discussion found to be important to consider on how the teacher should treat the students and what teaching technique is best to match their need. The standard of participating in discussion not only when students giving opinion it further whether the students pay attention in discussion. Not only the seating setting is important but also the number of students should be considered on how to implement better discussion class. Learning styles do affect what treatment should be given to the students. Teacher lack affection shows the ineffectiveness of the discussion. Therefore, discussion is considered to be effective if it meets some standards of its effectiveness.