Reviewerly/exHarmony huggingface dataset:
Automatic Program Repair with OpenAI's Codex: Evaluating QuixBugs

OpenAI's Codex, a GPT-3 like model trained on a large code corpus, has made headlines in and outside of academia. Given a short user-provided description, it is capable of synthesizing code snippets that are syntactically and semantically valid in most cases. In this work, we want to investigate whether Codex is able to localize and fix bugs, a task of central interest in the field of automated program repair. Our initial evaluation uses the multi-language QuixBugs benchmark (40 bugs in both Python and Java). We find that, despite not being trained for APR, Codex is surprisingly effective, and competitive with recent state of the art techniques. Our results also show that Codex is slightly more successful at repairing Python than Java.
SATLabel: A Framework for Sentiment and Aspect Terms Based Automatic Topic Labeling

In this paper, we present a framework that automatically labels Latent Dirichlet Allocation (LDA) generated topics using sentiment and aspect terms from COVID-19 tweets to help the end-users by minimizing the cognitive overhead of identifying key topics labels. Social media platforms especially Twitter are considered as one of the most influential sources of information for providing public opinion related to a critical situation like the COVID-19 pandemic. LDA is a popular topic modelling algorithm that extracts hidden themes of documents without assigning a specific label. Thus automatic labelling of LDA-generated topics from COVID-19 tweets is a great challenge instead of following the manual labelling approach to get an overview of wider public opinion. To overcome this problem, in this paper, we propose a framework named \texttt{SATLabel} that effectively identifies significant topic labels using \textit{top unigrams features of sentiment terms and aspect terms clusters from LDA generated topics} of COVID-19 related tweets to uncover various issues related to the COVID-19 pandemic. The experimental results show that our methodology is more effective, simpler, and traces better topic labels compare to the manual topic labelling approach.
Creativity of AI: Automatic Symbolic Option Discovery for Facilitating Deep Reinforcement Learning

Despite of achieving great success in real life, Deep Reinforcement Learning (DRL) is still suffering from three critical issues, which are data efficiency, lack of the interpretability and transferability. Recent research shows that embedding symbolic knowledge into DRL is promising in addressing those challenges. Inspired by this, we introduce a novel deep reinforcement learning framework with symbolic options. This framework features a loop training procedure, which enables guiding the improvement of policy by planning with action models and symbolic options learned from interactive trajectories automatically. The learned symbolic options help doing the dense requirement of expert domain knowledge and provide inherent interpretabiliy of policies. Moreover, the transferability and data efficiency can be further improved by planning with the action models. To validate the effectiveness of this framework, we conduct experiments on two domains, Montezuma's Revenge and Office World respectively, and the results demonstrate the comparable performance, improved data efficiency, interpretability and transferability.
Innovative activity of rural schools as a factor of its development

Innovative activity considered as the most important factor and as one of the main conditions for the development of all subjects of a general educational organization and its surrounding territory. In a rural (non-urban) school, innovative activity has specific features due to a number of special circumstances, including: the involvement of the educational institution in the rural society and the school's influence on the socio-cultural educational situation of the territory; the special density and closeness of business and interpersonal relations between schoolchildren and teachers, teachers and families/parents, the school and its social environment.The sources of innovation can be the problems and resources of a rural educational organization. Unlike an urban one, a rural school has a slightly different range of problems and a different set of resources for development. In determining the target guidelines, the nature, content, methods and forms of innovative activity of rural school, according to the authors of the article, one should rely on positive features, on the potentials inherent in the originality of this type of educational institutions. Are characterised development potentials inherent in one of the Karelian schools — Pryazhinskaya secondary school of the Republic of Karelia. The choice of the topic/problem of systemic innovation activity of the institution is substantiated. The idea, logic and content of the innovative work of the teaching staff of the school are revealed in the sequence of programs: «Formation of a viable student personality in rural society» (2007 2012), «School of personal growth» (2012 2018), «Basic department» (2018 — present). Technologies and forms are present, ways and means of implementing the goals and objectives of innovation in the experience of rural (non-urban) school are shown.
Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents

In complex tasks where the reward function is not straightforward and consists of a set of objectives, multiple reinforcement learning (RL) policies that perform task adequately, but employ different strategies can be trained by adjusting the impact of individual objectives on reward function. Understanding the differences in strategies between policies is necessary to enable users to choose between offered policies, and can help developers understand different behaviors that emerge from various reward functions and training hyperparameters in RL systems. In this work we compare behavior of two policies trained on the same task, but with different preferences in objectives. We propose a method for distinguishing between differences in behavior that stem from different abilities from those that are a consequence of opposing preferences of two RL agents. Furthermore, we use only data on preference-based differences in order to generate contrasting explanations about agents' preferences. Finally, we test and evaluate our approach on an autonomous driving task and compare the behavior of a safety-oriented policy and one that prefers speed.
End-to-end Document Recognition and Understanding with Dessurt

We introduce Dessurt, a relatively simple document understanding transformer capable of being fine-tuned on a greater variety of document tasks than prior methods. It receives a document image and task string as input and generates arbitrary text autoregressively as output. Because Dessurt is an end-to-end architecture that performs text recognition in addition to the document understanding, it does not require an external recognition model as prior methods do. Dessurt is a more flexible model than prior methods and is able to handle a variety of document domains and tasks. We show that this model is effective at 9 different dataset-task combinations.
Real-Time Face Recognition System

Over the past few decades, interest in algorithms for face recognition has been growing rapidly and has even surpassed human-level performance. Despite their accomplishments, their practical integration with a real-time performance-hungry system is not feasible due to high computational costs. So in this paper, we explore the recent, fast, and accurate face recognition system that can be easily integrated with real-time devices, and tested the algorithms on robot hardware platforms to confirm their robustness and speed.
Head Pose Estimation in Complex Environment Based on Four-Branch Feature Selective Extraction and Regional Information Exchange Fusion Network

Under the severe situation of the COVID-19 pandemic, masks cover most of the effective facial features of users, and their head pose changes significantly in a complex environment, which makes the accuracy of head pose estimation in some systems such as safe driving systems and attention detection systems impossible to guarantee.To this end, we propose a powerful four-branch feature selective extraction network (FSEN) structure, in which three branches are used to extract three independent discriminative features of pose angles, and one branch is used to extract composite features corresponding to multiple pose angles.By reducing the dimension of high-dimensional features, our method significantly reduces the amount of computation while improving the estimation accuracy.Our convolution method is an improved spatial channel dynamic convolution (SCDC) that initially enhances the extracted features.Additionally, we embed a regional information exchange network (RIEN) after each convolutional layer in each branch to fully mine the potential semantic correlation between regions from multiple perspectives and learn and fuse this correlation to further enhance feature expression.Finally, we fuse the independent discriminative features of each pose angle and composite features from the three directions of channel, space, and pixel to obtain perfect feature expression for each pose angle, and then obtain the head pose angle.We conducted extensive experiments on the controlled environment datasets and a self-built real complex environment dataset (RCE) and the results showed that our method outperforms state-of-the-art single-modality methods and performs on par with multimodality-based methods.This shows that our network meets the requirements of accurate head-pose estimation in real complex environments such as complex illumination and partial occlusion. INDEX TERMS Head pose estimation,
Sentimental Evaluation of Sensitive Tweets Using Hybrid Sentiment Analysis Model

The opinions expressed by people on the social platforms are crucial in making a product or business failure or success in this information era. It was common to make decisions based on verbal feedback from known individuals like companions and relatives for their opinion or conclude from advertisements. Twitter platform has many users expressing their opinions in a free form of writing which is unstructured in nature. Here, the tweets are categorized on the scale of five. The central value is being neutral, and the other values are ranging from very negative to very positive. The tweets are preprocessed for uniformity. Semantic databases are used on which the POS tagging is done. Sentiment classification is then performed using hybrid method combining latent Dirichlet allocation and MaxEnt machine learning technique. MaxEnt-LDA’s performance over naïve Bayes on twitter dataset is compared for time required in milliseconds and accuracy they achieve.
The Effectiveness of Chemistry e-Teaching and e-Learning during the COVID-19 Pandemic in Northern Viet Nam

In the appearance of the COVID-19 pandemic, many governments had to use the adoption of severe measures to counteract the spread of the infection. Social distancing and lockdown measures have led to a sudden change in people’s habits. People are restricted from traveling and gathering in larger numbers to prevent the COVID-19 pandemic. In that context, the internet plays an important role in supporting e-teaching, remote working, online collaboration, online selling, video streaming, gaming, etc. E-learning in the schools and universities in Viet Nam began at the beginning of school closure after the Tet holiday. E-learning has been carried out simultaneously in all provinces with all educational levels and all available information technology resources. Despite being a low-middle-income country with limited resources, the simultaneous implementation of online learning in Viet Nam has achieved remarkable achievements, contributing to fighting the COVID-19 pandemic in Viet Nam. In this research, we study the effectiveness of chemistry e-teaching and e-learning during the COVID-19 pandemic in northern Viet Nam.
Preliminary study on the benefits of using the robot PALRO® in facilitating leisure programs for older adults with dementia

Background:The use of humanoid-shaped robots may have a positive influence on older adults with dementia and their caregivers.However, there is a dearth of studies with a higher level of evidence.Thus, this quasi-experimental study serves as a preliminary investigation of using PALRO, a humanoid-shaped robot, in facilities' leisure programs for older adults with dementia.Objective: To elucidate the effects of a leisure program that uses the language-capable humanoid robot PALRO on older adults with dementia.Method: A one-group pretest-posttest design was done while employing Person-Centered Care principles and the associated outcome measure, Dementia Care Mapping (DCM).Older adults with dementia from long-term care facilities and group homes participated in a PALRO-facilitated leisure program.Data was collected using methodological observation to record the behaviors and well-being.Non-parametric inferential statistics were used to determine significant changes between pre-and post-intervention.Results: Among the 77 participants, 84.4% had a significant improvement in their wellbeing after the program.This improvement indicated a 'positive state' as defined in DCM.The participants elicited behaviors within the following categories during the program: interaction with an object, expressive activities, leisure activities, intellectual activities, articulation, and physical exercises.Conclusion: Significant percentage of older adults with dementia staying in long-term care facilities and group homes exhibited a 'positive state' towards a PALRO.The robot-mediated leisure program may be able to provide an opportunity to elicit positive reactions from older adults with dementia regardless of their level of cognitive functioning or independence.
Continual Prompt Tuning for Dialog State Tracking

A desirable dialog system should be able to continually learn new skills without forgetting old ones, and thereby adapt to new domains or tasks in its life cycle. However, continually training a model often leads to a well-known catastrophic forgetting issue. In this paper, we present Continual Prompt Tuning, a parameter-efficient framework that not only avoids forgetting but also enables knowledge transfer between tasks. To avoid forgetting, we only learn and store a few prompt tokens' embeddings for each task while freezing the backbone pre-trained model. To achieve bi-directional knowledge transfer among tasks, we propose several techniques (continual prompt initialization, query fusion, and memory replay) to transfer knowledge from preceding tasks and a memory-guided technique to transfer knowledge from subsequent tasks. Extensive experiments demonstrate the effectiveness and efficiency of our proposed method on continual learning for dialog state tracking, compared with state-of-the-art baselines.
Birds Eye View Social Distancing Analysis System

Social distancing can reduce the infection rates in respiratory pandemics such as COVID-19. Traffic intersections are particularly suitable for monitoring and evaluation of social distancing behavior in metropolises. We propose and evaluate a privacy-preserving social distancing analysis system (B-SDA), which uses bird's-eye view video recordings of pedestrians who cross traffic intersections. We devise algorithms for video pre-processing, object detection and tracking which are rooted in the known computer-vision and deep learning techniques, but modified to address the problem of detecting very small objects/pedestrians captured by a highly elevated camera. We propose a method for incorporating pedestrian grouping for detection of social distancing violations. B-SDA is used to compare pedestrian behavior based on pre-pandemic and pandemic videos in a major metropolitan area. The accomplished pedestrian detection performance is $63.0\%$ $AP_{50}$ and the tracking performance is $47.6\%$ MOTA. The social distancing violation rate of $15.6\%$ during the pandemic is notably lower than $31.4\%$ pre-pandemic baseline, indicating that pedestrians followed CDC-prescribed social distancing recommendations. The proposed system is suitable for deployment in real-world applications.
Twitter sentiment analysis on Indian Government schemes using machine learning models

Sentiment analysis or opinion mining is a technique that involves building system that can identify and extract opinions from text. The process of deriving opinions from piece of text to determine writer's views towards a particular topic or product is positive, negative or neutral also termed as opinion mining. In this paper, analysis of numerous tweets related to the no plastic campaign is performed to predict the degree of polarity and subjectivity of tweets. The analysis is divided into phases: extracting data, pre-processing, cleaning, removing stop words, and calculation of sentiment scores. The machine learning approach is applied on dataset related to the no plastic campaign and analysis is done. The extraction of neutral tweets, to distinguish it from the mainstream tweets, has also been approached. Hashtags and emoticons are the relevant Twitter specific features. Emoticons can be positive or negative. Determining and extracting useful information from the emoticons is also a major aspect of this paper. Neutral tweets are also separated well from the mainstream tweets so that the accuracy of positive tweets can be extracted well to get a final overview of how the masses collectively reveal to such government-based campaigns.
Encoding and decoding of several optimal rank metric codes

Abstract This paper presents encoding and decoding algorithms for several families of optimal rank metric codes whose codes are in restricted forms of symmetric, alternating and Hermitian matrices. First, we show the evaluation encoding is the right choice for these codes and then we provide easily reversible encoding methods for each family. Later unique decoding algorithms for the codes are described. The decoding algorithms are interpolation-based and can uniquely correct errors for each code with rank up to ⌊( d − 1)/2⌋ in polynomial-time, where d is the minimum distance of the code.
Source code auto-completion using various deep learning models under limited computing resources

Abstract Deep learning models have become state of the art in many language modelling tasks. Among such tasks, source code auto-completion is one of the important areas of research. This paper presents various methodologies for source code auto-completion using different Deep Learning models for Python and CSharp Programming Languages. In a resource-limited environment, it is paramount to reduce various overheads: one way of achieving that is to use the code sequences to train and evaluate rather than using other code structures such as semantics. This paper compares various deep learning architectures like CodeGPT [1] from Microsoft, Roberta [2] from huggingface [3] and GPT2 [4] for source code auto-completion. Different dataset strategies are employed for comparison, such as (1) treating the whole code file as a single line, (2) using each line as single individual inputs, and (3) tokenizing the codes snippets before feeding them into the models. We consider the task of autocompletion on two different datasets: (1) Python dataset; (2) CSharp dataset. The Python dataset is processed by a fine-tuned CodeGPT with an overall accuracy of 71%. For the CSharp dataset with the GPT2 model, a PPL of 2.14 and 4.082 on the training and evaluation dataset is observed. Considering the results, we discuss the strength and weaknesses of different approaches in their application in the real world programming context.
Digitalization of the Educational Process of Training Future Engineering-Teachers

This paper provides an overview of the digitalization of the educational process in Ukraine and in People’s Republic of China. Currently, digitalization of the educational process is a vital requirement of the modern education system that prepares a highly professional, flexible and mobile specialist. According to the recent research studies, trends in the development of education in the European and Asian countries, and the requirements of a modern employer, the complex use of information and communication technologies in the educational process is justified. It is highly recommendable to develop and use digital resources for educational purposes meeting the needs of the educational process and the characteristics of the content, methods and forms of education. Highlighting topical problems and analyzing the task of education digitalization, researching and refining concepts in this context, a list of components of the complex of digital educational resources has been developed. It will enhance the development of a digital educational environment aiming at the educational process modernization.
Improving response time of home IoT services in federated learning

For intelligent home IoT services with sensors and machine learning, we need to upload IoT data to the cloud server which cannot share private data for training. A recent machine learning approach, called federated learning, keeps user data on the device in the distributed computing environment. Though federated learning is useful for protecting privacy, it experiences poor performance in terms of the end-to-end response time in home IoT services, because IoT devices are usually controlled by remote servers in the cloud. In addition, it is difficult to achieve the high accuracy of federated learning models due to insufficient data problems and model inversion attacks. In this paper, we propose a local IoT control method for a federated learning home service that recognizes the user behavior in the home network quickly and accurately. We present a federated learning client with transfer learning and differential privacy to solve data scarcity and data model inversion attack problems. From experiments, we show that the local control of home IoT devices for user authentication and control message transmission by the federated learning clients improves the response time to less than 1 second. Moreover, we demonstrate that federated learning with transfer learning achieves 97% of accuracy under 9,000 samples, which is only 2% of the difference from centralized learning.
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning

In a practical setting towards better generalization abilities of client models for realizing robust personalized Federated Learning (FL) systems, efficient model aggregation methods have been considered as a critical research objective. It is a challenging issue due to the consequences of non-i.i.d. properties of client's data, often referred to as statistical heterogeneity and small local data samples from the various data distributions. Therefore, to develop robust generalized global and personalized models, conventional FL methods need redesigning the knowledge aggregation from biased local models while considering huge divergence of learning parameters due to skewed client data. In this work, we demonstrate that the knowledge transfer mechanism is a de facto technique to achieve these objectives and develop a novel knowledge distillation-based approach to study the extent of knowledge transfer between the global model and local models. Henceforth, our method considers the suitability of transferring the outcome distribution and (or) the embedding vector of representation from trained models during cross-device knowledge transfer using a small proxy dataset in heterogeneous FL. In doing so, we alternatively perform cross-device knowledge transfer following general formulations as 1) global knowledge transfer and 2) on-device knowledge transfer. Through simulations on four federated datasets, we show the proposed method achieves significant speedups and high personalized performance of local models. Furthermore, the proposed approach offers a more stable algorithm than FedAvg during the training, with minimal communication data load when exchanging the trained model's outcomes and representation.