Reviewerly/exHarmony huggingface dataset:
Implementation of Micro Services Architecture on Comrades Backend

Abstract This research aims to increase the scalability and maintainability of Comrades backend. The experimental method that was used in this research is Domain-Driven Design. Applying Domain-Driven Design, Comrades backend will have more scalable and high-level scalability architecture, namely, microservices architecture. The result of this research is a new model of backend architecture and a prototype to measure the performances of this new model. To achieve this result, the transformation of architecture from monolithic into several context and implementation of the context to be microservices architecture are the main steps of this research. Moreover, complete software architecture and infrastructure will be developed as the output of this research. To make it possible, the research uses several technologies including Docker, Kubernetes, and Gateway API. By using this architecture, Comrades developer are able to create Comrades backend more reliable because each functional of Comrades will have their architecture. They can use the services to give a better experience for Comrades users in accessing the features in comrades. For the conclusion, a new model of Comrades backend architecture has more scalable level without any error.
A Method to Enhance the Security Capability of Python IDE

The majority of applications running on the Internet are web applications; however, these applications are vulnerable to arbitrary code execution and database manipulation by Cross-Site Scripting or SQL injection attacks. The fundamental reason of these vulnerabilities is that web applications use a string type for assembling heterogeneous computer languages’ syntax for a particular language. To cope with these vulnerabilities, we propose a language-based scheme, in which the programming language itself provides security capabilities by a method of the syntax embedded in Python. Furthermore, the proposed solution supports backward compatibility and higher portability to other languages as well as Python. To improve the debugging difficulty caused by a language-based scheme, we propose a trace-processor that has post-mortem debug ability. We implement the proposed solution as a development environment, named Python-S, based on CPython’s source code. Python-S successfully displays the protection capabilities for the SQL injection attack.
Memories of Video: Impact of Sequencing on Rated Technical Quality for Viewed and Visualized Disruptions

In this paper we explore how memories of experience with streaming video affect Quality of Experience (QoE) indicators that are of interest to service providers and marketers. Since observations of experience are time consuming, and the effects of technical quality (TQ) are difficult to entangle from content quality (CQ), we examined the impact of a visualization methodology for assessing experiences. A study was carried out to examine how well overall technical quality (TQ) judgments for a sequence of visualized video experience (a picture of a red video playbar with yellow portions indicating disrupted video in place of actually viewed video) would correspond to overall TQ judgments made after watching a sequence of actual videos. Sequencing effects found in overall TQ ratings, made after viewing visualizations (with their overlaid disruptions) were similar to sequencing effects found after viewing actual videos. However, the sequencing effects after viewing the visualizations were less pronounced than the corresponding sequencing effects that were found after viewing actual videos. Sequences of both visualized and actually viewed videos showed significant negative end effect and trend effects (both positive and negative). There was also evidence that sequencing effects respond to relative change in TQ rather than absolute TQ.
Multilingualization of Speech Processing

Speech-to-speech translation is a technology that connects people of different languages together and its multilingualization dramatically expands the circle of people connected. “Population” in Table 1.1a shows the potential number of people who can be part of the circle, when the corresponding language benefits from the technology. However, the same table also tells us that the languages of the world are incredibly diverse, and therefore multilingualization is not an easy task. Nevertheless, methods of processing speech sounds have been devised and developed uniformly regardless of language differences. What made this possible, is the wide commonality across languages due to the nature of language—it is a spontaneous tool created for the single purpose of mutual communication between humans who basically share the same biological hardware. This chapter will describe the multilingualization of automatic speech recognition (ASR) and text-to-speech synthesis (TTS); the two speech-related components of the three that constitute the speech-to-speech translation technology.
Age and Gender Estimation of Asian Faces Using Deep Residual Network

In recent years, with the rise of deep learning and computer vision, researchers have been looking deeply into the age and gender estimation problem due to its practical influences. A lot of fields, from insurance, retails to marketing, could benefit tremendously from the presence of a reliable estimator, as it would allow companies to easily identify their customer demographics. A great number of models have been proposed, and they have achieved remarkable results. However, because of the lack of open-source, multiethnic dataset, most modern Age and Gender estimating model are trained solely based on white people with Western facial features, and thus fall short with non-Caucasian people. Therefore, in this paper, using a newly-improved Asian face database, we developed an applicable Wide ResNet model to predict the age and the gender of a person using just one image, assuming he/she comes from an Asian background. The model has shown some promising results, as it can match the performance of Microsoft’s how-old API estimator in a specific dataset.
Acoustic Characterization of Amharic Vowel Sound Units

Acoustic characterization of vowels has a significant role in the development of speech synthesis and recognition systems. Specifically, it reduces the improper vowel parameter selection for the concatenative and formant based speech synthesis systems. This paper studies the acoustic characteristics of Amharic vowel sound units. The characteristics of Amharic vowels are studied by considering 77 different contexts. All the seven vowels are labeled from different contexts, and then the first three formant frequencies (F1, F2, and F3) and duration acoustic features are extracted and analyzed for all vowels to characterize their quality and quantity behaviors. As a result, this study synthesizes the acoustic vowel space diagram by graphically illustrating where vowel sound units for the Amharic language are located in the acoustic space of the vocal tract tube. The effect of duration on the formant frequency values and the comparison of vowels via duration behavior are also investigated. The characterization demonstrates that F1 is inversely proportional to duration, while F2 and F3 are directly proportional to duration, and the vowels /e/, /a/, and /o/ are the long vowels, while /ix/ is a short vowel.
Psycho-akmeologikal peculiarities training for projects of the class of “mega science”

Abstract The article is devoted to psychological and akmeologickal characteristics of professional training for projects of a class “megascience”, which must be considered when implementing them. It proves the necessity of using achievements of psychology and akmeology in the training of leaders, experts, specialists involved in projects of a class “megascience”. A number of psychological and akmeologikal features are noted, in particular, a high level of requirements for them, professional uniqueness, a high degree of responsibility for future scientific and applied results, the exclusivity of their training programs. Emphasizes the need for psychological and akmeologikal support of the entire cycle of training for the implementation of scientific projects class “megascience”, a valid use of qualitative tools, psycho-ecological technologies personal and professional development of specialists of different profile. It proves the urgent need in mastering new techniques, methods and technologies of quality control personnel in research projects of a class “megascience”. It is proved that the use of personal qualimetry in research projects of a class “magicians” valid tools provided modern mathematical apparatus allows to reduce human risks, more effective use of mathematical models in solving personnel issues, aimed at improving the level of efficiency of implementation of scientific projects class “megascience”. The proposed model of measuring the competencies of a specialist through the use of expert estimation. The description of the computer model is given, which allows to obtain the necessary information regarding the process of training a specialist and to build a verified trajectory of his professional development.
Augmented Reality for Forming Technology – Visualisation of Simulation Results and Component Measurement

Abstract In sheet and bulk forming, numerical simulation has become established over the last 20 years as an indispensable tool for research and industrial practice. The numerical calculation of forming processes supports a time- and cost-efficient development in the context of a virtual product evaluation. Currently, simulation and measurement results are mainly displayed on conventional screens. However, this approach has several disadvantages. On the one hand, only cut-outs can be displayed, especially in the case of large components. A visualisation of entire components in real size is often not possible. On the other hand, no direct comparison with the real components is possible. Augmented Reality is a promising approach to solve the challenges outlined above. As part of the contribution, an innovative approach for component validation based on Augmented Reality technology will be introduced. Thereby, the visualisation using different Augmented Reality devices and the feasibility of a measurement task with Augmented Reality devices will be presented.
Measurements on encrypted and non-encrypted IoT datasets stored in relational and JSONB fields

Sensory data originating from IoT devices, after being received as encrypted over wireless channels, are plaintext stored in databases. However, if database data integrity, validity and safety must be ensured and additionally, IoT operations or actions implicitly require of non-repudiation, database encryption is a one-way path. Focusing on big IoT data, these encryption-decryption operations inflict database query time overheads, which in turn result in additional IoT operational delays and increased energy consumption at the wireless IoT motes. In this paper authors study the encryption performance of an open source database containing relational as well as non-relational JSONB IoT data fields and summarize their experimental results
SubSect—An Interactive Itemset Visualization

Itemsets and association rules are among the most simple and intuitive patterns that are being used to explore transaction datasets. However, they lack meaning without both context and domain knowledge. Typically a user has to sift through hundreds of these patterns before finding an interesting one, losing sight of the forest for the trees. We propose a novel itemset and association rule visualization that makes it possible to inspect, assess, and compare patterns at a glance. In a case study we demonstrate its ability to facilitate a user in deriving and presenting valuable insights from a real-world dataset, which can not only save time and effort, but also reduce errors introduced by misconceptions.
A Scale Normalization Algorithm Based on MR-GDS for Archaeological Fragments Reassembly

In template-based archaeological fragments reassembly, the fragments and the template usually have scale variances, which needs to be eliminated by scale normalization. Traditional methods need at least two pairs of matched points for scale normalization, which is nontrivial for many fragments. In this paper, a scale normalization algorithm for template-based fragments reassembly is proposed, which solves the reassembly problem for fragments and the template with scale variances. Firstly, one pair of corresponding points between the fragment and the template is selected as the anchor points. Secondly, the Geodesic Disk Spectrum (GDS) of the fragment and the Multi-Radii Geodesic Disk Spectrum (MR-GDS) of the template are computed, respectively. Thirdly, the GDS descriptors are compared to obtain candidates of the scale variances, which will be further verified through an ICP-based strategy. At last, the fragments are registered to the template after scale normalization. Experiments on scale normalization and fragments reassembly all achieved accurate results. The proposed algorithm is suitable for the scale estimation of small fragments, which expands the application scenarios greatly for the template-based fragments reassembly.
Personal determinants of style features of pedagogical interaction as a factor of successful professional activity of the teacher

This study focuses on the problem of relationships between personality traits and stylistic characteristics of professional interaction between a teacher and student, the consideration of personal determinant of the stylistic peculiarities of their interaction, as well as the styles of professional activity of a teacher. The model of interaction of subjects of educational process, from the point of view of innovative personality-oriented education is considered. And the result of this interaction is the transformation of educational features into personal and the transition to personal meanings. It is the personal importance of the subject of interaction and the interaction itself that contributes to the formation of the subjective position of the student and the teacher. The main emphasis is placed on the subject-subject interaction of participants of the educational process, personal determinants of style features of psychological and pedagogical interaction, which contribute to the development of the success of professional activity of the teacher.
QuiltGAN: An Adversarially Trained, Procedural Algorithm for Texture Generation

We investigate a generative method that synthesises high-resolution images based on a single constraint source image. Our approach consists of three types of conditional deep convolutional generative adversarial networks (cDCGAN) that are trained to generate samples of an image patch conditional on the surrounding image regions. The cDCGAN discriminator evaluates the realism of the generated sample concatenated with the surrounding pixels that were conditioned on. This encourages the cDCGAN generator to create image patches that seamlessly blend with their surroundings while maintaining the randomisation of the standard GAN process. After training, the cDCGANs recursively generate a sequence of samples which are then stitched together to synthesise a larger image. Our algorithm is able to produce a nearly infinite collection of variations of a single input image that have enough variability while preserving the essential large-scale constraints. We test our system on several types of images, including urban landscapes, building facades and textures, comparing very favourably against standard image quilting approaches.
Randomized Evolution Model for Multi Hypothesis Kalman Filter

A new randomized approach for highly maneuvering targets based on multi hypothesis tracking is presented. The acceleration range - a parameter in current evolution models is used to design various motion models. The approach randomises this parameter to cover a wider range of maneuver characteristics. Simulation shows that the performance of the new method results in a more reliable track continuity.
Detecting and counting vehicles using adaptive background subtraction and morphological operators in real time systems

vehicle detection and classification of vehicles play an important role in decision making for the purpose of traffic control and management.this paper presents novel approach of automating detecting and counting vehicles for traffic monitoring through the usage of background subtraction and morphological operators. We present adaptive background subtraction that is compatible with weather and lighting changes. Among the various challenges involved in the background modeling process, the challenge of overcoming lighting scene changes and dynamic background modeling are the most important issues. The basic architecture of our approach is done in 3 steps: 1-background subtraction 2- segmentation module 3- detection of objects and counting vehicles. We present an adaptive background at each frame after using binary motion mask to create instantaneous image of background. To remove noises we use morphological operators and then start to segment images, detect vehicles and count them. Algorithm is eﬃcient and able to run in real-time. Some experimental results and conclusions are presented
Research on the Bended Learning Reform of Chinese Courses

Primary school Chinese is the basis for students to learn knowledge of various subjects, is the synthesis of language and culture, it has both instrumental and humanistic. With the rapid development of educational informationization, the traditional teaching mode has been difficult to meet the needs of modern primary school Chinese teaching. According to the characteristics of Chinese teaching in primary schools, Bended learning (B-Learning) theory and learning community concept are introduced into Chinese curriculum. This paper studies the B-Learning mode of primary school Chinese, including the determination of teaching objectives, development of teaching resources, optimization of teaching design and evaluation of teaching effect, so as to optimize the teaching effect and improve the teaching quality.
Joint boundary-aware method and Bayesian Inference for object proposal

As a fundamental pre-processing procedure, object proposal focuses on selecting a small group of object candidates possibly containing most of the category-independent objects to simplify the search extent. Generally, traditional handcrafted features and CNN-based features are applied to generate object candidates. However, it is challengeable for the existing methods to achieve balance between the proposal accuracy and the computational efficiency. In this paper, we propose an effective low-level feature extractor for object proposal by combing the boundary-preserving property of super-pixels together with Bayesian Inference framework. We first compute prior probability of edge segment which is derived from clustering operation of the edge map. Then, the posterior probability of edge segment is calculated by the boundary-preserving property of super-pixel and Bayesian Inference scheme. Finally, based on the enhanced edge segment map, we apply the same mechanism used in Edge Boxes to obtain the candidate bounding boxes. Extensive experiments on PASCAL VOC2007 dataset demonstrate that the proposed method achieves good balance between proposal quality and computational efficiency.
Joining the blocks together – an NLP pipeline for CALL development

Intelligent Computer-Assisted Language Learning (ICALL) involves using tools and techniques from computational linguistics and Natural Language Processing (NLP) in the language learning process. It is an inherently complex endeavour and is multi-, inter-, and trans-disciplinary in nature. Often these tools and techniques are designed for tasks and purposes other than language learning, and this makes their adaptation and use in the CALL domain difficult. It can be even more challenging for Less-Resourced Languages (LRLs) for CALL researchers to adapt or incorporate NLP into CALL artefacts. This paper reports on how two existing NLP resources for Irish, a morphological analyser and a parser, were used to develop an app for Irish. The app, Irish Word Bricks (IWB), was adapted from an existing CALL app – Word Bricks (Mozgovoy &amp; Efimov, 2013). Without this ‘joining the blocks together’ approach, the development of the IWB app would certainly have taken longer, may not have been as efficient or effective, and may not even have been accomplished at all.
An Improved Gaussian Mixture Model Based on Prior Probability Factor for MR Brain Image Segmentation

Computer-aided algorithms play an inevitable role in medical field for disease diagnosis and preplanning for surgery. The improved Gaussian mixture model based on prior probability factor was proposed here for the segmentation of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) on MR brain data sets. The performance of the classical Gaussian mixture model was enhanced by the incorporation of prior probability smoothing factor and the results outperform the classical FCM and K-means algorithm. Experimental analysis was carried out by performance metrics like Jaccard index, probabilistic rand index, and percentage of classification ratio. The algorithms are developed in Matlab2015a and validated on BrainWeb public database images.
The use of WhatsApp to express students confirming speech act strategy to the lecturers

Following the rapid growth of the social media communication technology worldwide, WhatsApp (WA) becomes one of the most practical means to corelate students and lecturers. In fact, students are able to contact their lecturers anytime they need to confirm any other task and assigment. However, to contact the lecturers, students have frequently need the appropriate speech act strategy to convey thir intention. While several studies have addressed this issue by focusing on the use of hedgs to express politeness in WA to express politeness, and violations are found in approbation maxim. In an attempt to fill this gap, of the use of WA to express students confirming speech act strategy to the lecturers. The method of providing data was carried out by tapping method with recording technique. This tehcnique is screen shoot the written document of speech act used by students when communicating with lecturers by using WA. The investigation reveals that, the most widely strategy used by students in WA to lecturers was to seek an agreement and the lowest use was to imply or express reciprocity, give understanding, and cooperation with the lecturers. This study argues that students send WA to the lecturers with the various purposes.
An Improved Clustering Method for Multi-view Images

Existing algorithms do not meet the requirements of multi-view image clustering under big data conditions. Here, we design a multi-view clustering algorithm for massive and unstructured images. To meet the multi-view requirements, improve speed and accuracy, a response layer is introduced to the self-organizing map neural network. An online self-organizing map neural network with simple parameters and without prior training is proposed and used for the multi-view clustering process. Experiments are performed using multiple datasets. The results show that the proposed algorithm is capable of multi-view clustering of image data with high accuracy, low error rate and favorable stability.