Reviewerly/exHarmony huggingface dataset:
Software Analytics for Manual Activities using Developer Work Elements

Software Engineering is a diverse and highly flexible discipline that can be practiced using a development model of the developer's choosing. Unfortunately, existing state-of-the-practice software engineering development models do not take human effort into consideration since there are no applicable metrics to gauge the associated manual activity. This study presents a novel discretization technique as a software analytic to estimate the manual effort expended on software development process. The proposed technique classifies three manual activity domains, namely, abstract, concrete, and unclassified. The units of classification are called Developer Work Elements (DevWE). The sequence of DevWE denotes a development analytic in three visual aids, namely, symbolic flow map, operation chart, and workload breakdown chart. These give rise to the determination of efforts expended which are measured by COSMIC Function Point. The result can be combined with those traditional software measurable activities to yield accurate total project effort estimation. Major contributions of this prospectus encompass (1) discretization DevWE analytic for manual effort estimation, (2) visual chart aids for operation tracing, monitoring, improving, and control, and (3) discovering that almost half of the estimation effort stems from manual activity.
A Study of Cross-Lingual Ability and Language-specific Information in Multilingual BERT

Recently, multilingual BERT works remarkably well on cross-lingual transfer tasks, superior to static non-contextualized word embeddings. In this work, we provide an in-depth experimental study to supplement the existing literature of cross-lingual ability. We compare the cross-lingual ability of non-contextualized and contextualized representation model with the same data. We found that datasize and context window size are crucial factors to the transferability. We also observe the language-specific information in multilingual BERT. By manipulating the latent representations, we can control the output languages of multilingual BERT, and achieve unsupervised token translation. We further show that based on the observation, there is a computationally cheap but effective approach to improve the cross-lingual ability of multilingual BERT.
Emotion Recognition using a Multi-view learning method

The increasing use of computing devices and applications in human daily life triggers the need for natural human-computer interaction. Emotion Recognition using multiple features using a semi-serial fusion method is proposed. The study analyses the impact of the feature combinations in the enhancement of the recognition enhancement. The paper presents the use of the multi-view learning principle to a fusion of different features for one emotion expression-based recognition. The results prove that a planned method is operative. The proposed combination method outperforms the use of one type of features and the concatenated way in recognition accuracy, improvement of execution time, and stability.
RealMonoDepth: Self-Supervised Monocular Depth Estimation for General Scenes

We present a generalised self-supervised learning approach for monocular estimation of the real depth across scenes with diverse depth ranges from 1--100s of meters. Existing supervised methods for monocular depth estimation require accurate depth measurements for training. This limitation has led to the introduction of self-supervised methods that are trained on stereo image pairs with a fixed camera baseline to estimate disparity which is transformed to depth given known calibration. Self-supervised approaches have demonstrated impressive results but do not generalise to scenes with different depth ranges or camera baselines. In this paper, we introduce RealMonoDepth a self-supervised monocular depth estimation approach which learns to estimate the real scene depth for a diverse range of indoor and outdoor scenes. A novel loss function with respect to the true scene depth based on relative depth scaling and warping is proposed. This allows self-supervised training of a single network with multiple data sets for scenes with diverse depth ranges from both stereo pair and in the wild moving camera data sets. A comprehensive performance evaluation across five benchmark data sets demonstrates that RealMonoDepth provides a single trained network which generalises depth estimation across indoor and outdoor scenes, consistently outperforming previous self-supervised approaches.
Reversible Data Hiding Technique using AMBTC based Bitmap Manipulation

In this research work, it has been proposed to use images to hide the secret data as it is difficult to extract secret data from the image.Using images to hide and transmit secret data, leads to additional overhead of increased cost associated with storage and transmission cost, as the image requires more space for storage.However, hiding data inevitably destroys the host image, even though the distortion is imperceptible.To overcome such drawbacks, image compression techniques are used for both, hiding the secret data and to reduce the storage and transmission cost.To enhance the hiding capacity and maintain the quality of the host image after embedding hidden data, we present a high payload reversible data hiding scheme that is based on the Absolute Moment Block Truncation Coding (AMBTC) compression domain.We exploit the feature of inter block redundancy in an AMBTC compressed image to improve the coding efficiency of compressed images.Normally, the AMBTC technique transforms an input image into a set of blocks and each block in turn is transformed into a set of a bit-plane and two quantizers.But in the proposed method, these blocks are categorized into Shade and Edge blocks based on the magnitude of amplitude values.For a Shade block, two values are stored, one being the block mean and the other value being the secret data.Two bytes of secret data is stored creating an illusion that the bitplane is stored.For Edge blocks, high mean and low mean are stored as two quantizers and a respective Bitplane is generated.The shade block helps in embedding 3 bytes of secret data leading to higher embedding capacity and increases the complexity of identifying the existence of secret data.It also compresses the stego-image without much degradation in the visibility of stego image.
A Novel Recommender Model Using Trust Based Networks

Recommended systems are highly rated recently by online users in social networking. In general, recommendation model made by existing algorithms are not reliable because these recommendations did not consider the trust factor between active users and nearest neighbors in social networks services. In order to solve this problem, new methods have been suggested based on trust-based networks with missing connections among users and elimination of cold start scenario. In our study, experiments are conducted on publicly available datasets to validate our algorithm. Through experimental results, our model is proven more robust, efficient in execution and more precise.
System-Assigned Passwords: The Disadvantages of the Strict Password Management Policies

After Morris and Thompson wrote the first paper on password security in 1979, strict password policies have been enforced to make sure users follow the rules on passwords.Many such policies require users to select and use a system-generated password.The objective of this paper is to analyse the effectiveness of strict password management policies with respect to how users remember system-generated passwords of different textual types -plaintext strings, passphrases, and hybrid graphical-textual PsychoPass passwords.In an experiment, participants were assigned a random string, passphrase, and PsychoPass passwords and had to memorize them.Surprisingly, no one has remembered either the random string or the passphrase, whereas only 10% of the participants remembered their PsychoPass password.The policies where administrators let systems assign passwords to users are not appropriate.Although PsychoPass passwords are easier to remember, the recall rate of any system-assigned password is below the acceptable level.The findings of this study explain that system-assigned strong passwords are inappropriate and put unacceptable memory burden on users.
Using Algebraic Fractals in Steganography

Steganography is a technology for hiding watermarks inside media files, which is relevant in the field of copyright protection, secret communication, etc. The effectiveness of modern methods of digital image processing allows determining the presence of embedded watermarks in a stegoimage using the original image and its statistical characteristics, as well as a priori information about the method and algorithm of embedding. In contrast to the known approaches, it is proposed to use algebraic fractals for steganographic embedding of watermarks in color images. It is proposed to use algebraic fractals in the form of medium cover image acting as a secret key, which allows the embedding to be more resistant to computer attacks, including JPEG compression. The main advantage of such use of fractals is an increase in the level of secrecy in which the attacker must know the parameters of the fractal image. Without knowledge of these parameters, it will not be enough to have the original stegoimage and a priori information about the embedding method to extract secret data. This chapter analyzes the methods and provides examples of generating algebraic fractals in the form of the Julia set using the escape time algorithm.
E-Learning, MALL and LMOOC or a New Didactic Tendency in Foreign Language Learning: A Meta-Analysis from 2014 to 2018

This meta-analysis explores the topic of formal and informal learning of foreign languages assisted by mobile devices, widely studied in international literature.The contemporary evolution of foreign language teaching shows itself in new forms such as m-learning, MALL and LMOOC.The increase of teaching/learning opportunities on e-learning platforms requires planning and didactical reflection to improve the chances of dialogue with the new generations of students.This paper analyzes the ongoing changes in terms o new pedagogy and instrumental models in the two five-year periods 2009-2013 and 2014-2018.This study adopts a meta-analysis approach to review the literature systematically, providing a complete analysis and synthesis of 156 studies, from 2014 to 2018.Previous studies focused on the massive introduction of mobile technology applied to the foreign language learning (FLL).The search in databases and citations was performed using keywords referring to FLL, MALL and LMOOC.Compared to existing reviews, the results confirm a skill-based approach to language learning with an emphasis on vocabulary, learning factors influencing students' perceptions and availability of new mobile technologies.
CAggNet: Crossing Aggregation Network for Medical Image Segmentation

In this paper, we present Crossing Aggregation Network (CAggNet), a novel densely connected semantic segmentation approach for medical image analysis. The crossing aggregation network improves the idea from deep layer aggregation and makes significant innovations in semantic and spatial information fusion. In CAggNet, the simple skip connection structure of general U-Net is replaced by aggregations of multi-level down-sampling and up-sampling layers, which is a new form of nested skip connection. This aggregation architecture enables the network to fuse both coarse and fine features interactively in semantic segmentation. It also introduces weighted aggregation module to up-sample multi-scale output at the end of the network. We have evaluated and compared our CAggNet with several advanced U-Net based methods in two public medical image datasets, including the 2018 Data Science Bowl nuclei detection dataset and the 2015 MICCAI gland segmentation competition dataset. Experimental results indicate that CAggNet improves medical object recognition and achieves a more accurate and efficient segmentation compared to existing improved U-Net and UNet++ structure.
Machine learning approach for simulation of heavy metal concentration in river water: the Crimean peninsula case study

This study proposes an approach for simulation of heavy metal concentration in river waters using machine learning techniques. A regression model was built and it captured the relationship between the concentration of heavy metal and metalloids (HMM) and several characteristics of studied catchment. Machine learning techniques allowed to simulate the annual concentration variability of HMM. This approach allows exploring the impact of different factors on studied processes.
TEACHER BEHAVIOR STRATEGY IN THE UNCERTAINTY OF THE CONDITIONS OF PROFESSIONAL DEVELOPMENT

It is proved that the current tendencies of reforming the postgraduate pedagogical education make it possible to state that the uncertainty in obtaining the results of professional development of teachers is the main condition for the emergence of risks of professional development. The concept of professional development in terms of the paradigm of education is characterized. An experimental research was organized and conducted. The purpose was to determine the personal properties of teachers, which determine the effectiveness of decision-making in a situation of uncertain of the conditions of professional development. The objectives of the experiment include: to establish the professional and personal qualities of teachers, which make decision-making in a situation of uncertainty; perform mathematical processing and interpretation of experimental research; summarize the results and make recommendations. This research presents results of definition and substantiation of the main strategies of behavior of teachers in the situation of uncertain conditions of professional development. These include: vigilance, avoidance, procrastination, dustiness. Teachers have the highest average values for the Vigilance Strategy, which indicates a careful consideration of alternatives before making a decision. The generalized results of the research characterize the educators' conscious choice of a behavioral strategy to make a decision in a situation of uncertain conditions of professional development. It is established that modern postgraduate pedagogical education has various forms of formal, non-formal and informal adult education. In the long term, we consider it necessary to analyze the potential of technologies of professional development of teachers in terms of advanced training.
Exploring the Combination of Contextual Word Embeddings and Knowledge Graph Embeddings

``Classical'' word embeddings, such as Word2Vec, have been shown to capture the semantics of words based on their distributional properties. However, their ability to represent the different meanings that a word may have is limited. Such approaches also do not explicitly encode relations between entities, as denoted by words. Embeddings of knowledge bases (KB) capture the explicit relations between entities denoted by words, but are not able to directly capture the syntagmatic properties of these words. To our knowledge, recent research have focused on representation learning that augment the strengths of one with the other. In this work, we begin exploring another approach using contextual and KB embeddings jointly at the same level and propose two tasks -- an entity typing and a relation typing task -- that evaluate the performance of contextual and KB embeddings. We also evaluated a concatenated model of contextual and KB embeddings with these two tasks, and obtain conclusive results on the first task. We hope our work may contribute as a basis for models and datasets that develop in the direction of this approach.
Dialogue-Based Relation Extraction

We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE, aiming to support the prediction of relation(s) between two arguments that appear in a dialogue. We further offer DialogRE as a platform for studying cross-sentence RE as most facts span multiple sentences. We argue that speaker-related information plays a critical role in the proposed task, based on an analysis of similarities and differences between dialogue-based and traditional RE tasks. Considering the timeliness of communication in a dialogue, we design a new metric to evaluate the performance of RE methods in a conversational setting and investigate the performance of several representative RE methods on DialogRE. Experimental results demonstrate that a speaker-aware extension on the best-performing model leads to gains in both the standard and conversational evaluation settings. DialogRE is available at https://dataset.org/dialogre/.
Specificity of Human Everyday Life in the Digital World

The paper aims to reveal peculiarities of the modern human’s everyday life transformation due to digitalization of his daily activities and intensive penetration of Internet technologies in all the spheres of social life. The article examines online-banking as an innovative social phenomenon that promotes a new type of interpersonal relations. The complicated adaptive, psychological and social nature of online-banking is revealed, its advantages and shortcomings are analysed. Scientific originality of the study involves development of logical and philosophical foundations for analysing new determinants of modern social life. Social-philosophical and scientific-technical prerequisites for transformation of social life in the modern technogenic world are considered.
An adaptive correlation based video data mining using machine learning

With the immense growth in the multimedia contents for education and other purposes, the availability of video contents has also increased. Nevertheless, the retrieval of content is always a challenge. The identification of two video contents based on internal content similarity highly depends on e xtraction of key frames and that makes the process highly time complex. Recently, many research attempts have tried to approach this problem with the intention to reduce the time complexity using various methods such as video to text conversion and further analysing both extracted text similarity analysis. Regardless to mention, this strategy is again language dependent and criticised for various reasons like local language dependencies and language paraphrase dependencies. Henceforth, this work approaches the problem with a different dimension with reduction possibilities of the video key frames using adaptive similarity. The proposed method analyses the key frames extracted from the library content and from the search video data based on various parameters and reduces the key frames using adaptive similarity. Also, this work uses machine learning and parallel programming algorithms to reduce the time complexity to a greater extend. The final outcome of this work is a reduced time complex algorithm for video data-based search to video content retrieval. The work demonstrates a nearly 50% reduction in the key frame without losing information with nearly 70% reduction in time complexity and 100% accuracy on search results.
A new FISH Signals Fusion Detection approach for diagnosing Chronic Myeloid Leukemia

Cancer is consider one of the worst disease in the modern era which cause a huge number of deaths each year world wide. It is generated due to the abnormal growth of attacking enormous number of cells to the human body cells that white blood cells can't defend for the human body. Regionally, the number of death as a consequence of the cancer is increasing and affect the growing economies badly. Medical imaging is a technique that visualize the inside of the human body using computerized device. Usually, manipulating the problems of medical imaging using image processing techniques. Leukemia is cancer of the body's blood-forming tissues, including the bone marrow and the lymphatic system. Chronic Myeloid Leukemia (CML) is a type of blood cancer that causes the body to produce a large number of white blood cells. In this research, FISH (Florescent In Situ Hybridization) images is the key factor of detecting abnormalities in genes. FISH usually used for detecting abnormality in chromosomes and DNA features. CML also can be detected using FISH. CML diagnosis depends on the detection of fused Red and Green signals. Our approach first detects FISH signals, their colors, and then fusion between these colors. Moreover, number of fusions need to be counted in order to specify the proper treatment as the number of fusion is important in defining the severity of the case. We prepared our dataset for our own experiment and publish it online for research usage. Our approach experimental accuracy achieved 98% which prove the efficiency of the approach when compared to similar research.
Research on multimedia technology of physical education classroom combined with computer platform

Abstract With the rapid development of the economy, the development of information technology is also relatively rapid, and informational teaching is being valued by more and more educators. The organic combination of information technology and classroom teaching can not only stimulate students’ interest in learning, but also improve students’ learning efficiency, thus enhancing the quality of teaching. Therefore, teachers should enhance the concept of high technology when teaching, and combine information technology with teaching methods. This paper mainly analyzes the application status and existing problems of information technology in physical education classroom, and discusses the application strategies of information technology in physical education, so that people can raise awareness of information technology.
Scientific and Methodological Support of the Individual Trajectory of Educators' Professional Development

Changes in the content, forms, technologies of modern education, the fulfillment by teachers of new functions require additional assistance and support both in learning and continuous training, professional competence and creativity, and the professional development of educators in the workplace based on their individual needs and requirements.All the above provides scientific and methodological support for the individual trajectory of the educators' professional development.Based on the analysis of scientific literature, the article substantiates theoretically the essence of the concepts "individual educational trajectory", "individual trajectory of professional development".The conditions of the teacher's readiness to project the individual trajectory of professional development are determined.The current study reveals the content, structure, phased technology of scientific and methodological support of projecting and realization of the individual trajectory of the educator's professional development.Vector and targeted scientific and methodological support taking into account individual requests, needs, motivation of teachers, their professional capabilities and environmental requirements (administration, educational institution, society) are presented in the parameters of the target, technological and effective components of the support.The relevance of scientific and methodological support of the individual trajectory of the teachers' professional development is proved.
Economic Dispatch Using Modified Hybrid BA/ATS

In this paper, a new modified algorithm is proposed. This modified algorithm is BA/ATS. The main modifications are including negative value into the main equation of the bee algorithm (BA) and integrating adaptive tabu search (ATS) into BA. BA/ATS aims to improve the performance of hybrid BA/TS. The economic dispatch (ED) is set as the main problem to solve with the proposed algorithm. The operation of each generator is limit by constraints. All test results indicate that the overall costs of operation when using the proposed algorithm are better than test results from other compared algorithms. This means the modified hybrid BA/ATS is a good algorithm for the solving the ED problem.