Reviewerly/exHarmony huggingface dataset:
Eigenface and SIFT For Gender Classification

In this paper, we study and analyze five different approaches for gender classification (Male or Female). The purpose of approaches is to extract the main features from face image and for each approach; we used these features as input to the Support Vector Machine SVM classifier for classification process. In the first approach, we implemented the Principal Component Analysis PCA features as input to the SVM classifier. In the second approach, the resulted parameters of Scale Invariant Features Transform approach are used as input to SVM classifier. In the third approach, we implemented Eigenfaces as input to SIFT, and then the results of SIFT are used as input to SVM classifier also. In the fourth approach, we implemented Eigenfaces to be input to Volume-SIFT (VSIFT) and then used as input to SVM classifier. The last, we modified the VSIFT approach and use as input to SVM classifier. The practical implementation results show that the proposed approach (modified VSIFT) gave us high performance of gender classification than other approaches.The training and testing images set consist of number of faces which are obtained from (Computer Vision Science Research Projects).The aim of this paper is to analyze and discuss the results of applying the eigenfaces into the new proposed approach of modifying Volume-SIFT for classifying the faces into its gender.
Short-term PV power prediction considering the influence of aerosol

Abstract To improve the accuracy of photovoltaic power prediction under haze weather, a short-term PV power prediction method combining the PM2.5 forecast and SVR model is proposed. The WRF-CHEM air quality model is used to realize the simulation calculation of PM2.5 concentration, combined with the historical output data of PV power plants, and based on the SVR machine learning model to build a direct mapping relationship model between multiple meteorological elements and PV output. A forecast experiment was carried out for a certain area in North China, and the results showed that the proposed method can effectively improve the accuracy of PV power forecasting under haze weather conditions, thereby providing strong support for power grid dispatch and operation.
ProtoChat: Supporting the Conversation Design Process with Crowd Feedback

Conversation designers use iterative design to create, test, and improve conversation flows. While it is possible to iterate conversation design with existing chatbot prototyping tools, challenges remain such as recruiting participants and collecting structured feedback on specific conversational components, hindering rapid iterations, and making informed design decisions. To address these limitations, we introduce ProtoChat, a crowd-powered design tool built to support the iterative process of conversation design for a chatbot. ProtoChat enables rapid testing with the crowd and guiding the crowd workers to provide granular feedback on specific points of conversation.
Gyroscope Fault Pattern Recognition Based on Wavelet Packet Decomposition and Fuzzy Clustering

Abstract Aiming at the problem that the sensor component’s inertial navigation gyro failure symptom is not obvious in a certain aircraft flight control system, this paper proposes a fault cluster recognition method based on fuzzy clustering. First, use wavelet packet decomposition to perform three-layer wavelet packet decomposition on the gyro output data to obtain the feature extraction of the gyro fault signal; then use the fuzzy clustering method to classify the extracted fault signal to achieve gyro fault pattern recognition in the sensor assembly the goal of.
Awards In The Field Of Information Technologies: Social And Cultural Impact

The paper studies prestigious awards in the field of computer science and information and communication technologies (ICT) and their social and cultural impact.The flagship awards of the major international technical societies Association of Computing Machinery (ACM) and Institute of Electrical and Electronics Engineers (IEEE) are considered as objects of study.The following awards are chosen after a thorough historical review: IEEE Medal of Honor, IEEE Edison Medal, ACM Turing Award.General social and psychological functions of the awards were used as the basis for the evaluation of the impact and value of a particular trophy.Such key factors determining the value were proposed: the average annual number of awards, transparency and clarity of decision-making procedure, as well as the reputation of the awarding institutions.Implementing those criteria together with some additional factors the awards in the particular ICT awards were qualitatively evaluated.For the reference with the same criteria was evaluated Nobel Prize in Physics as the most prestigious scientific award.The comparison has revealed that the leading position of the Nobel Prize in Physics is determined not just by historical and media factors, but also by well-established multistage decision-making procedure.In concluding part of the paper, the impact of the ICT scientific awards on Russian academic and professional communities is discussed.The author believes that the awards are an important tool for promoting modern technologyoriented and global worldview among the people.And it also makes the discussion on ICT awards the essential part of any ICT course.
FEATURES OF TRAINING TEACHERS ENGAGED IN INCLUSIVE PRACTICE

The article considers the problem of training teachers for the implementation of inclusive education. It can be stated that modern educational institutions need specialists who carry out inclusive practices, are able to accept a child with disabilities, ready for the timely identification of this category of children, and the organization of various forms and types of assistance to them, taking into account individual and personal characteristics. The model of psychological and pedagogical support of inclusive education teachers on the formation of specialist’s professionally important qualities is presented and described. Thus, the training of such specialists is possible through the implementation of the described model, which assumes a holistic educational strategy and tactics aimed at personifying the future teacher’s training, teacher’s retraining as the subject of activity within the framework of inclusive education.
PyAnomaly: A Pytorch-based Toolkit for Video Anomaly Detection

Video anomaly detection is an essential task in computer vision which attracts massive attention from academia and industry. The existing approaches are implemented in diverse deep learning frameworks and settings, making it difficult to reproduce the results published by the original authors. Undoubtedly, this phenomenon is detrimental to the development of Video Anomaly detection and community communication. In this paper, we present a PyTorch-based video anomaly detection toolbox, namely PyAnomaly that contains high modular and extensible components, comprehensive and impartial evaluation platforms, a friendly manageable system configuration, and the abundant engineering deployment functions. To make it easy-to-use and easy-to-extend, we implement the architecture by hooks and registers functionality. Remarkably, we have reproduced the comparable experimental results of six representative methods as those published by the original authors, and we will release these pre-trained models with more rich configurations. To our best knowledge, the PyAnomaly is the first open-source tool in video anomaly detection and is available at https://github.com/YuhaoCheng/PyAnomaly.
USING GAMIFICATION WEB SERVICES TO ORGANIZE LEARNING AT INFORMATICS LESSONS

The article discusses the possibility of using gaming technologies in the modern educational process as effective ways of forming self-control and developing the creative potential of students, as well as organizing the educational process at mathematics lessons. The aims and characteristics of educational gaming technologies such as edutainment and gamification are considered. Game technologies have the following metrics: mechanics, aesthetics, dynamics and social interaction. Attention is paid to various educational interactive services (Learnis, Smart Suite) that allow to gamify the lesson. The characteristics and capabilities of each service are considered. The services allow to create tasks of different types: quests, quizzes, games. Services for the gamification have tools for working in real time and tools for assessing student performance.
Technological approach in training a future specialist on online math classes in a technical university

This article is devoted to the design and construction of technology for teaching future specialists in online mathematics classes. The relevance, advantages and ways of introducing into the educational process computer tools for teaching mathematics, global Internet services and their role in online education are shown. The materials of the article can be used to improve the qualifications of the teaching staff and to establish requirements for the threshold of knowledge and skills of future specialists sufficient for working with computer training tools, while developing computer training tools for academic disciplines.
From Disjoint Sets to Parallel Data to Train Seq2Seq Models for Sentiment Transfer

We present a method for creating parallel data to train Seq2Seq neural networks for sentiment transfer. Most systems for this task, which can be viewed as monolingual machine translation (MT), have relied on unsupervised methods, such as Generative Adversarial Networks (GANs)-inspired approaches, for coping with the lack of parallel corpora. Given that the literature shows that Seq2Seq methods have been consistently outperforming unsupervised methods in MT-related tasks, in this work we exploit the use of semantic similarity computation for converting non-parallel data onto a parallel corpus. That allows us to train a transformer neural network for the sentiment transfer task, and compare its performance against unsupervised approaches. With experiments conducted on two well-known public datasets, i.e. Yelp and Amazon, we demonstrate that the proposed methodology outperforms existing unsupervised methods very consistently in fluency, and presents competitive results in terms of sentiment conversion and content preservation. We believe that this works opens up an opportunity for seq2seq neural networks to be better exploited in problems for which they have not been applied owing to the lack of parallel training data.
Challenges in Docker Development: A Large-scale Study Using Stack Overflow

Background: Docker technology has been increasingly used among software developers in a multitude of projects. This growing interest is due to the fact that Docker technology supports a convenient process for creating and building containers, promoting close cooperation between developer and operations teams, and enabling continuous software delivery. As a fast-growing technology, it is important to identify the Docker-related topics that are most popular as well as existing challenges and difficulties that developers face.Aims: This paper presents a large-scale empirical study identifying practitioners' perspectives on Docker technology by mining posts from the Stack Overflow (SoF) community. Method: A dataset of 113, 922 Docker-related posts was created based on a set of relevant tags and contents. The dataset was cleaned and prepared. Topic modelling was conducted using Latent Dirichlet Allocation (LDA), allowing the identification of dominant topics in the domain. Results: Our results show that most developers use SoF to ask about a broad spectrum of Docker topics including framework development, application deployment, continuous integration, web-server configuration and many more. We determined that 30 topics that developers discuss can be grouped into 13 main categories. Most of the posts belong to categories of application development, configuration, and networking. On the other hand, we find that the posts on monitoring status, transferring data, and authenticating users are more popular among developers compared to the other topics. Specifically, developers face challenges in web browser issues, networking error and memory management. Besides, there is a lack of experts in this domain. Conclusion: Our research findings will guide future work on the development of new tools and techniques, helping the community to focus efforts and understand existing trade-offs on Docker topics.
Designing for Transformational Change in School : Digitalizing the Digitized

Digital technologies have gained a prominent role in education and schools, but research concerning how digital technologies can create better conditions for improved teaching and enhanced learning ...
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning

In multi-agent reinforcement learning, discovering successful collective behaviors is challenging as it requires exploring a joint action space that grows exponentially with the number of agents. While the tractability of independent agent-wise exploration is appealing, this approach fails on tasks that require elaborate group strategies. We argue that coordinating the agents' policies can guide their exploration and we investigate techniques to promote such an inductive bias. We propose two policy regularization methods: TeamReg, which is based on inter-agent action predictability and CoachReg that relies on synchronized behavior selection. We evaluate each approach on four challenging continuous control tasks with sparse rewards that require varying levels of coordination as well as on the discrete action Google Research Football environment. Our experiments show improved performance across many cooperative multi-agent problems. Finally, we analyze the effects of our proposed methods on the policies that our agents learn and show that our methods successfully enforce the qualities that we propose as proxies for coordinated behaviors.
#EndSARS: The Movement Against Police Brutality in Nigeria

Since the start of October 2020, demonstrators in the thousands have thronged Nigerian cities, calling for an end to police brutality in the country and demanding justice for victims of police violence and extrajudicial killings. The #EndSARS protests have elicited global sympathy and support, with world leaders such as United Nations Secretary-General Antonio Guterres and United States Secretary of State Mike Pompeo tweeting in support. Other political figures and celebrities have also used the hashtag or referenced the movement to either directly support the protests or demand an end to the government crackdown on protestors. These include former United States Secretary of State Hillary Clinton, U.S. president-elect Joe Biden, boxing heavyweight champion Anthony Joshua, Arsenal footballer Mesut Ozil, and American rapper Kanye West. For several days in October, the hashtag #EndSARS was the number one trending topic on global Twitter with Twitter CEO Jack Dorsey launching a special emoji for the movement.
Designing an Expert System for Online Grocery Shopping Store

In this digital era consumers prefer online shopping to save time. But in their busy schedules it can be time-consuming to choose items as per their requirements. The area of our project is to create a user friendly and easily available application for purchasing mechanism. This application will be easily available on the internet for users to download it and then install it. To start, offer only the most popular grocery items, like milk and bread, and as the business expands, so can your product line. The operating format for the app would be very easy to establish. Customers would simply log on to the app, select the items they wish to purchase, enter payment and shipping information and wait for their groceries. We propose the solution using an application designed on Android Operating System platform. We choose this platform base since Google’s Android is one of the foremost reasons of this mobile revolution.
Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study

The Gleason score is the most important prognostic marker for prostate cancer patients but suffers from significant inter-observer variability. We developed a fully automated deep learning system to grade prostate biopsies. The system was developed using 5834 biopsies from 1243 patients. A semi-automatic labeling technique was used to circumvent the need for full manual annotation by pathologists. The developed system achieved a high agreement with the reference standard. In a separate observer experiment, the deep learning system outperformed 10 out of 15 pathologists. The system has the potential to improve prostate cancer prognostics by acting as a first or second reader.
Situating Twitter Discourse in Relation to Spoken and Written Texts

Language use on Twitter is a recent and currently understudied phenomenon. The present study makes an empirical contribution towards understanding the similarities and differences between Twitter and offline registers. To this purpose, a lectometric analysis is conducted: on the basis of 236 linguistic features whose frequency information is measured in 7 309 spoken, written and Twitter texts, 10 dimensions of linguistic variation are developed. Within the space of variation circumscribed by these dimensions, Twitter is characterized not so much by radical difference as by gradual differentiation from other registers. The only dimension along which a clear distinction between Twitter and other registers can be seen is that of “colloquial markedness”. In general, language on Twitter can be described as involved and consciously informal, but not very much oriented towards either addressees or factual statements.
Investigation of Effectively Synthesizing Code-Switched Speech Using Highly Imbalanced Mix-Lingual Data

End-to-end text-to-speech (TTS) can synthesize monolingual speech with high naturalness and intelligibility. Recently, the end-to-end model has also been used in code-switching (CS) TTS and performs well on naturalness, intelligibility and speaker consistency. However, existing systems rely on skillful bilingual speakers to build a CS mix-lingual data set with a high Language-Mix-Ratio (LMR), while simply mixing monolingual data sets results in accent problems. To reduce the cost of recording and maintain the speaker consistency, in this paper, we investigate an effective method to use a low LMR imbalanced mix-lingual data set. Experiments show that it is possible to construct a CS TTS system with a low LMR imbalanced mix-lingual data set with diverse input text presentations, meanwhile produce acceptable synthetic CS speech with more than 4.0 Mean Opinion Score (MOS). We also find that the result will be improved if the mix-lingual data set is augmented with monolingual English data.
Cosmos : A Unified Accounting System both for the HTCondor and Slurm Clusters at IHEP

HTCondor was adopted to manage the High Throughput Computing (HTC) cluster at IHEP in 2016. In 2017 a Slurm cluster was set up to run High Performance Computing (HPC) jobs. To provide accounting services for these two clusters, we implemented a unified accounting system named Cosmos. Multiple workloads bring different accounting requirements. Briefly speaking, there are four types of jobs to account. First of all, 30 million single-core jobs run in the HTCondor cluster every year. Secondly, Virtual Machine (VM) jobs run in the legacy HTCondor VM cluster. Thirdly, parallel jobs run in the Slurm cluster, and some of these jobs are run on the GPU worker nodes to accelerate computing. Lastly, some selected HTC jobs are migrated from the HTCondor cluster to the Slurm cluster for research purposes. To satisfy all the mentioned requirements, Cosmos is implemented with four layers: acquisition, integration, statistics and presentation. Details about the issues and solutions of each layer will be presented in the paper. Cosmos has run in production for two years, and the status shows that it is a well-functioning system, also meets the requirements of the HTCondor and Slurm clusters.
Uses of Augmented Reality in Tertiary Education

Since augmented reality (AR) in educational settings hopes to enhance the process of learning, this chapter tries to answer questions of the use of AR at the university level and its impact on learners’ knowledge attainment (cognitive domain) and engagement (affective domain). Based on 21 studies (2013–2017) focusing on university level students of various fields of academic interest, meta-analysis showed that (1) the added value of AR implementation in terms of learning achievements ranges between intermediate and very positive (the lowest value of Cohen’s d = 0.42); and (2) the impact of AR on increases in learners’ motivation and autonomy, students’ collaboration and teacher-student interaction was prominent, too, although not supported by statistical testing. However, it is not known whether the effect of AR would prove stable and long-lasting, as it is probable that the aspect of novelty could play an important role during the experiments.
Greedy clustering of count data through a mixture of multinomial PCA

Count data is becoming more and more ubiquitous in a wide range of applications, with datasets growing both in size and in dimension. In this context, an increasing amount of work is dedicated to the construction of statistical models directly accounting for the discrete nature of the data. Moreover, it has been shown that integrating dimension reduction to clustering can drastically improve performance and stability. In this paper, we rely on the mixture of multinomial PCA, a mixture model for the clustering of count data, also known as the probabilistic clustering-projection model in the literature. Related to the latent Dirichlet allocation model, it offers the flexibility of topic modeling while being able to assign each observation to a unique cluster. We introduce a greedy clustering algorithm, where inference and clustering are jointly done by mixing a classification variational expectation maximization algorithm, with a branch & bound like strategy on a variational lower bound. An integrated classification likelihood criterion is derived for model selection, and a thorough study with numerical experiments is proposed to assess both the performance and robustness of the method. Finally, we illustrate the qualitative interest of the latter in a real-world application, for the clustering of anatomopathological medical reports, in partnership with expert practitioners from the Institut Curie hospital.
Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions

We study the dynamics of optimization and the generalization properties of one-hidden layer neural networks with quadratic activation function in the over-parametrized regime where the layer width $m$ is larger than the input dimension $d$. We consider a teacher-student scenario where the teacher has the same structure as the student with a hidden layer of smaller width $m^*\le m$. We describe how the empirical loss landscape is affected by the number $n$ of data samples and the width $m^*$ of the teacher network. In particular we determine how the probability that there be no spurious minima on the empirical loss depends on $n$, $d$, and $m^*$, thereby establishing conditions under which the neural network can in principle recover the teacher. We also show that under the same conditions gradient descent dynamics on the empirical loss converges and leads to small generalization error, i.e. it enables recovery in practice. Finally we characterize the time-convergence rate of gradient descent in the limit of a large number of samples. These results are confirmed by numerical experiments.