Reviewerly/exHarmony huggingface dataset:
Toward ECListener: An Unsurpervised Intelligent System to Monitor Energy Communities

This work describes the architecture of the “Obserbot Cluster”. Obserbot is the name of a long term project resulting from the crasis of the terms “observe” and “bot” (abridged form for robot). The Obserbot Cluster is formed by a number of different components running as micro-services on an dependable and scalable platform. Obserbot main objective is to monitor the web social media to extract some knowledge without any human supervision. The service is available 24 h a day and 365 days a year. Obserbot targets are presently two: official news media and twitter. A rough mass of textual data are collected form those two sources. In-line semantics and sentiment analysis of the collected stream allows to extract information on a specific Domain of Interest (DoI). In particular, Natural Language Processing and machine learning techniques are extensively employed for recognizing named entities, performing events classification and building up taxonomies. The hardware and software architecture enabling such a collection is rather versatile and it can be exploited to accomplish several different purposes. However, the semantic analytics is strictly DoI dependent. In its present form, Obserbot is able to handle information related to essential services and monitor publications on emerging Energy Communities. Essential services are sustained by networks of utilities providing basic goods: water supply, energy supply (e.g. gas and electricity), fuel supply, fresh food supply etc. and responding to other fundamental human needs such as transports, mobility and social connectivity.
A solar resource classification algorithm for global horizontal irradiance time series based on frequency domain analysis

A novel daily and 15-min period solar resource classification algorithm, based only on the Fourier transform of global horizontal irradiance (GHI) and calculable parameters, is presented. These methods can identify GHI series that are not attenuated by clouds, whether the clouds may or may not be visible. Additional sky condition classes are also adopted in which the representative conditions of the remaining days or periods are classified. Although the proposed day classification is too conservative to be widely applicable, the 15-min period classification is generally in good agreement with the Perez sky clearness index while providing greater sensitivity to turbid conditions and algorithm flexibility. It is shown, however, that the adoption of the clearness index in the proposed 15-min period method less successfully distinguished between clear and constant overcast conditions under winter continental conditions due to relatively large solar zenith angles. It is also shown that the proposed 15-min period and Reno and Hansen statistical methods are generally in good agreement on the detection of clear conditions under temperate conditions. The classification results of the proposed method are further shown in comparison to high-resolution sky images to elucidate the character of each class and the classification algorithm flexibility.
Pedagogical technologies of machine translation skills forming on the example of bachelor students specializing in mechatronics and robotics at Don State Technical University

In this paper the authors took into consideration professional training of specialists who can use technical documentation in a foreign language. The definition of machine translation, its value, main advantages and disadvantages were examined. This research showed the importance of machine translation and the need to train students of technical specialties in MT skills. The model of a special program for training students and its theoretical and practical parts were described. Based on the results of the error analysis, the error types were categorized into 3 categories. The results of the tests were analysed and shown in the tables. A significant decrease in the number of errors was noted. Based on the findings, pedagogical implications were discussed regarding how teachers can successfully and effectively incorporate MT into the classroom studies.
CuWide: Towards Efficient Flow-based Training for Sparse Wide Models on GPUs (Extended Abstract)

In this paper, we propose an efficient GPU-training framework for the large-scale wide models, named cuWide. To fully benefit from the memory hierarchy of GPU, cuWide applies a new flow-based schema for training, which leverages the spatial and temporal locality of wide models to drastically reduce the amount of communication with GPU global memory. Comprehensive experiments show that cuWide can be up to more than 20× faster than the state-of-the-art GPU solutions and multi-core CPU solutions.
Multi-Criteria Analysis of the Mass Tourism Management Model Related to the Impact on the Local Community in Constanta City (Romania)

Traditionally and socially, the tourism in Constanta is considered to be important to the local economy. Sun and beach locations are both a draw for locals and tourists to the city, on the Black Sea. However, vacation-oriented activities in the city only have a seasonal cycle. In this paper, we proposed to analyze the mass tourist activity in Constanta, taking into account economic, social, and environmental conditions. Additionally, we attempted to build a model based on the data available. The model was developed using a PESTEL analysis to determine the supportability factor of the indicators identified. We also set out to create a projection of the activities proposed for analysis by 2050. To create a model for coastal areas, the data used in this research must be accurate and consistent. Furthermore, correctly identifying indicators and their relationships is a critical step in conducting a thorough study. Last but not least, finding the calculation coefficient for the activity in question is critical, as collecting data from various activities might be challenging when trying to find a feasible model.
Effective fine-tuning training of deep Boltzmann machine based on spatial Monte Carlo integration

A deep Boltzmann machine (DBM) is a probabilistic deep learning model; DBM learning consists pretraining and fine-tuning stages. This study focuses on the fine-tuning stage, and it develops a new and effective fine-tuning method based on spatial Monte Carlo integration (SMCI), which is an extension of the standard Monte Carlo integration (MCI). It has been proved that SMCI is statistically more accurate than the standard MCI. Fine-tuning methods based on first-order and semi-second-order SMCI methods are formulated. The numerical experiments demonstrate that the proposed fine-tuning methods are superior to the conventional method in terms of both training and generalization errors.
Towards Efficient Masked-Face Alignment Via Cascaded Regression

Practical and efficient face alignment has been highly required and widely focused in recent years, especially under the trend of edge computation and real-time operation. And it is a critical need to deal with masked faces in the context of COVID-19 epidemic. In this paper, we propose a novel cascaded facial landmark detector towards efficient masked face alignment, which we call QCN (Quantized Cascaded Network). QCN consists of three stages: alignment, estimation and refinement. The alignment stage help to pre-align the faces to alleviate extreme poses. And the next two stages localize facial landmarks in a coarse-to-fine manner. Thanks to the Network Architecture Search and Quantization techniques, the networks of QCN are designed as efficient as possible. Specifically, QCN occupies 1.75 Mb storage and runs in 84.18 MFLOPs only. Despite costs little computations, the proposed method yields 62.62% AUC (@0.08) on test set of JD-landmark-mask, which achieves 2nd place in the Grand Challenge of 106-point Facial Landmark Localization in ICME2021.
Internal Quality Evolution of Open-Source Software Systems

The evolution of software is necessary for the success of software systems. Studying the evolution of software and understanding it is a vocal topic of study in software engineering. One of the primary concepts of software evolution is that the internal quality of a software system declines when it evolves. In this paper, the method of evolution of the internal quality of object-oriented open-source software systems has been examined by applying a software metric approach. More specifically, we analyze how software systems evolve over versions regarding size and the relationship between size and different internal quality metrics. The results and observations of this research include: (i) there is a significant difference between different systems concerning the LOC variable (ii) there is a significant correlation between all pairwise comparisons of internal quality metrics, and (iii) the effect of complexity and inheritance on the LOC was positive and significant, while the effect of Coupling and Cohesion was not significant.
A New Ensemble Method for Classifying Sentiments of COVID-19-Related Tweets

Twitter sentiment analysis enables scientists to monitor people’s attitudes to public health policies and events in the era of COVID-19. Although the pre-trained model can conduct sentiment analysis since the beginning of a pandemic or a global emergency, this model is not optimized for a specific topic. Furthermore, the way people express opinions on the topic may quickly change during the emergency. Therefore, the early-trained model may not work consistently well during the emergency. Unfortunately, the late-trained model will not be available until months after the emergency begins. In this paper, we propose an ensemble method to combine the pre-trained model and early-trained model for achieving better analysis performance on COVID-19-related tweets. The effectiveness of this method has been verified by the experiments.
Energy-Efficient Virtual Machine Migration Approach For Optimization Of Cloud Data Centres

Cloud computing inherits sharing of data from pool of resources existing in data centres when ever demanded. The imminent requirement for this purpose is proficiency of the data centre for fulfilment of this coveted objective. The pursuit of energy-efficient peak performance level is challenged by a simultaneous hike of energy consumption. To negotiate this problem of excessive energy consumption the enhancement of system's proficiency is of paramount importance. It is addressed by achieving better resource utilization by dint of the mechanism of virtual machine migration where placement of virtual machines on physical machines is suitably addressed for improved resource utilization and reduced energy consumption as a consequence. The process of virtual machine migration incurs enhanced network-related traffic hence it is challenged by constraint network capacity. Inter - data centre virtual machine migration having network capacity constraints reveals itself as an NP-Hard problem. This can be addressed by applying heuristic algorithms to secure optimal virtual machine migration solution. For attainment of green cloud computing solutions the virtual machine migration is augmented by certain other techniques which are used in data centres. These techniques minimize the operational costs, CO2 emission and power consumption by suitable resource allocation, virtualization and virtual machine migration.
Applications of blockchain technology in smart healthcare: An overview

Blockchain technology (BT) is a digital ecosystem which stores the series of data that is time-stamped and unchangeable. Clusters of computers manage the record of data without the ownership of a single entity or any third party (decentralized). Ranging from banking to supply chain logistics, it has also opened a new challenge in the healthcare industry. Blockchain technology offers massive opportunities for healthcare revolution to disrupt and lead a digital transformation. In this book chapter, we have overviewed the fundamental blockchain concepts and applications to be used for different aspects of smart healthcare industry and proposed a live patient monitoring system by deploying blockchain technology in the model. Keeping an eye on recent technologies in connected healthcare, we have finally presented various research factors and potential challenges where blockchain technologies can play an outstanding role to realize the concept of a smart optimization in the healthcare industry.
Application of Agile methodology in planning a joint educational program

When planning the learning process, it is increasingly possible to implement the principles of the Agile methodology. Higher education institutions that carry out a joint international specialist training program are also an exception. Thus, a joint educational project is being prepared between the Black Sea National University named after Petro Mohyla and the Yancheni Polytechnic Institute under the name "3 + 1".The use of modern software accelerates the process of managing a complex project of a specialized bachelor training program "3 + 1". The Agile approach - splitting tasks into sprints, building a Kanban board and engaging a student as a "customer", has proven itself very successful. The KanbanFlow online tool has never been used before for this task. Its use reduced the time management costs of the project and improved the engagement of an international team of Ukrainian and Chinese specialists.
A Hybrid Ostu based Niblack Binarization for Degraded Image Documents

Many Historical Documents have been preserved by libraries to protect these documents and to recover knowledge. These old documents suffer from different types of degradations, some of them are uneven illuminations, bleeding through the blur, ink stains, and different sorts of degradation. Thresholding technique is a significant phase in the image processing stage for document image scanning and processing. The main objective of binarization is to extract characters and eliminate noise from the original images. The proposed method is a classifying system to consolidate various binarization techniques for degraded document images by modifying the local and global binarization. The proposed algorithm consists of two phases: modified Otsu's and Niblack's techniques, and consolidates the advantages of modified local and global binarization techniques. The datasets used for proposed work are DIBCO 2009-2019, ICDAR 2019, and self-gathered situated image sets. Compared with two other classical algorithms, using three different evaluation techniques such as F-measure, PSNR and NRM.
Leveraging Vector Space Similarity for Learning Cross-Lingual Word Embeddings: A Systematic Review

This article presents a systematic literature review on quantifying the proximity between independently trained monolingual word embedding spaces. A search was carried out in the broader context of inducing bilingual lexicons from cross-lingual word embeddings, especially for low-resource languages. The returned articles were then classified. Cross-lingual word embeddings have drawn the attention of researchers in the field of natural language processing (NLP). Although existing methods have yielded satisfactory results for resource-rich languages and languages related to them, some researchers have pointed out that the same is not true for low-resource and distant languages. In this paper, we report the research on methods proposed to provide better representation for low-resource and distant languages in the cross-lingual word embedding space.
The modeling of memcapacitor oscillator motion with ANN and its nonlinear control application

In this study, a memcapacitor-based chaotic oscillator and its engineering applications are discussed. Also nonlinear feedback control method is applied to drive the system to equilibrium and the modeling of the memcapacitor system with Artificial Neural Networks (ANN) as engineering applications. Some dynamical properties such as phase portraits, equilibrium points and bifurcation of the proposed system are investigated. A video of the chaotic memcapacitor circuit was created using phase portraits. The image processing technique was used to determine the object in the video. ANN is trained by both a backpropagation method and the Levenberg–Marquardt method. It is shown that the states of the chaotic memcapacitor oscillator system trained with ANN can be reconstructed and modeled in this way and the results are given.
A Study on Blockchain and the Healthcare System

In an emergency healthcare situation, it is always intended to obtain the data in an easier and secure way. To accomplish this, the proposed research work has developed a study about the implementation of blockchain in healthcare system. Electronic Medical Records (EMRs) of patients are utilized to store data in the blockchain architecture. These EMRs are very sensitive, since it contains patient's personal details. Henceforth, the usage of records should take place in a secured mode otherwise hackers will attack the data. The availability of medical records will help to diagnose the diseases very easily and deliver better treatment to the patient, when they enter the critical stages like coma and unconsciousness. In this perspective, this research work analyzes different technologies and algorithms used in the existing systems.
Voices and Voids: Subverting Voice Assistant Systems through Performative Experiments

Responding to concerns such as privacy, surveillance, and the commodification of personal data with regards to voice assistants, this artistic research focuses on creating performative artifacts and vignettes that challenge artificial intelligence (AI) and machine learning (ML) technologies. By allowing AI voice assistants to listen to our most private conversations, we become receptive to their mediated care, while forgetting or ignoring how much these automated interactions have been pre-scripted. With our project Voices and Voids, we reclaim, examine, and ultimately transcode these voice assistant data through interdisciplinary performance and Post-Internet Art. In this paper, we thematically describe 12 vignettes which represent embodied and sonic experiments using a combination of design, data-driven art, cyber crafts, found-object and traditional percussion instruments, spoken word, and movement. We conclude with a discussion of how the experiments worked as a multifaceted whole, and how we used interdisciplinary methods as a central approach.
A Preprocessing Method of Facial Expression Image under Different Illumination

In this work, we propose an image processing method which combines the limited contrast adaptive histogram equalization (CLAHE) with Gamma transform to solve the illumination problem in facial expression recognition. We apply this algorithm to professional illumination datasets (Extended Yale B) and get better visual results, compared with using CLAHE and Gamma correction separately. Moreover, we use a convolution neural network (CNN) that pre-trained on FER2013 datasets to evaluate the effect of this method in facial expression recognition. We use this preprocessing algorithm to enhance the CK+ and Oulu expression datasets, and get accuracy of 89.24% and 70.24% respectively. Compared with the datasets that have not been pre-processed, it has provided an increase in classification accuracy of 7% on the Oulu datasets.
Multimodal Feature Fusion Model For Rgb-D Action Recognition

Due to the variation and complexity of video data, human action recognition is still a challenging task. However, most of the methods based on RGB-D action recognition simply fuse the multimodal features, ignoring the potential semantic relationship between different modals. In this paper, we propose a multi-modal recognition model based on Bilinear Pooling and Attention Network (BPAN), which could effectively fuse multi-modal for RGB-D action recognition. Firstly, we adopt the efficient data preprocessing methods for RGB and skeleton. Then, a multimodal fusion network BPAN combining RGB and skeleton sequence is proposed, which could effectively compress the features of RGB and skeleton, and project them into potential subspace to obtain fusion features. In the end, a fully connected three-layer perceptron is adopted to obtain the final classification. Experimental results on two datasets demonstrate that our proposed method leads to a more favorable performance compared with the state-of-the-art methods.
Augmented Reality

Augmented reality technology holds great promise for extending and enhancing users' capabilities across numerous applications in both work and personal life. It would be easy to see AR, then, as a panacea, but thoughtful design is required if the benefits are to be realized without also realizing the nascent technology's great potential for harm. Current applications in commercial, military, and education and training settings are herein reviewed, along with consideration of potential future directions. This chapter also identifies hazards posed by poor design or haphazard application and provides recommendations and best practices for those engaged in the design of AR that seek to maximize the human utility of this rapidly maturing technology.
Test Suites as a Source of Training Data for Static Analysis Alert Classifiers

Flaw-finding static analysis tools typically generate large volumes of code flaw alerts including many false positives. To save on human effort to triage these alerts, a significant body of work attempts to use machine learning to classify and prioritize alerts. Identifying a useful set of training data, however, remains a fundamental challenge in developing such classifiers in many contexts. We propose using static analysis test suites (i.e., repositories of "benchmark" programs that are purpose-built to test coverage and precision of static analysis tools) as a novel source of training data. In a case study, we generated a large quantity of alerts by executing various static analyzers on the Juliet C/C++ test suite, and we automatically derived ground truth labels for these alerts by referencing the Juliet test suite metadata. Finally, we used this data to train classifiers to predict whether an alert is a false positive. Our classifiers obtained high precision (90.2%) and recall (88.2%) for a large number of code flaw types on a hold-out test set. This preliminary result suggests that pre-training classifiers on test suite data could help to jumpstart static analysis alert classification in data-limited contexts.
Deep learning and Its Development

Abstract Deep learning is a promising branch of machine learning. It is an algorithm that uses artificial neural networks as the architecture to characterize and learn data. In recent years, many companies, for example, Google, Microsoft and Baidu, have become interested in the field of deep learning and have set up many large-scale projects, such as Google’s Deepmind project, including alphago, which has achieved success in Go and e-sports. This article analyzes and summarizes each current research direction and approach of deep learning, with prospection about the future research direction and development of deep learning expounded. An overview of the three basic models of deep learning is given, namely multilayer perceptrons and perceptrons, convolutional neural networks and recurrent neural networks. The benefits and superiority of the deep learning algorithm are illustrated and compared with the conventional methodology used in the common applications. Further research on emerging types of convolutional neural networks and recurrent neural networks are introduced. An overview of the three basic models of deep learning is given, namely multilayer perceptrons and perceptrons, convolutional neural networks and recurrent neural networks. The current application of deep learning in various fields is summarized, such as artificial intelligence, computer vision and natural language processing applications, and some open problems for future research are also analyzed. Finally, the significance and purpose of deep learning are discussed.