Reviewerly/exHarmony huggingface dataset:
Evaluator self-efficacy analysis in a usability test

When software is made available to users, it is expected to be free of faults and good in quality. Tests such as usability can be employed to contribute to their quality. In such cases, it is possible for evaluators to analyze interfaces and note which usability errors should be corrected. Also, according to the belief of self-efficacy, those who think they are more experienced should find more errors. In this context, the purpose of this study was to perform a usability test with evaluators to verify if those considered to be more experienced were able to find more errors. A self-assessment form and a usability test were applied for this purpose. For each of the nine participants, their experience information along with the usability errors encountered by them were collected. As a result, those who thought they were capable of finding most of the errors did not match those they encountered.
Online Action Recognition from Trajectory Occurrence Binary Patterns (ToBPs)

Online action recognition is nowadays a major challenge on computer vision due to uncontrolled scenarios, variability on dynamic action representations, unrestricted capture protocols among many other variations. This work introduces a very compact binary occurrence motion descriptor that allows to recognize actions on partial video-sequences. The proposed approach starts by computing a set of motion trajectories that represent the developed activity. On that regard, a local counting process is performed over bounded regions, and centered at each trajectory, to search for a minimal number of neighboring trajectories. This process is then codified in a vector of binary values (ToBPs) that will create a regional description, at any time of the video sequence, to represent actions. This regional description is obtained by determining the most recurrent binary descriptors in a particular video interval. The final regional descriptor is mapped to a machine learning algorithm to obtain a recognition. The proposed strategy was evaluated on three public datasets, achieving an average accuracy of 70% in tasks of action recognition by using a local descriptor of only 51 values and a regional descriptor of 400 values. This compact description constitute an ideal condition for real-time video applications. The proposed approach achieves a partial recognition above 70% on average accuracy using only the 40% of videos.
A High Efficient FEMD-Based Data Hiding Algorithm

Abstract This paper proposes a high efficient FEMD-based data hiding method suitable for various complexity images. The method uses FEMD's extraction function to map a cover pixel (8 bits) and a randomizable auxiliary pixel (log 2 n +1 bits) into a data of n 2 -ary notational system, which can significantly improve the data hiding efficiency. When n =2, the embedding efficiency of FEMD is 1/8, and that of our method is 1/5, which is increased by 60% and the image quality of the two methods is almost the same. In other words, our method will have higher image quality when the embedding rate of the two methods is same. When n =4 or 8 and the dada embedding efficiency is equal, the PSNR of this method is increased by 1.3 dB or 1.1 dB compared with FEMD. In addition, the auxiliary pixel as a key can improve the security of our data hiding algorithm.
Explaining Prefix Contributions in Russian Using Frame Semantics and RSA

Variability in the interpretation of Russian verbal prefixes is traditionally regarded as an issue of lexical semantics. Grammars and dictionaries list different usages that are possible for each prefix without explaining when and why particular usages are realised. For a limited amount of prefixed verbs further information can be found in the dictionaries, but often even this is not enough for a precise interpretation. In Zinova (2017) I proposed a Frame semantic analysis that allows to compositionally construct the meaning of a complex verb. In this paper I make a further step towards a computational account of the pragmatic component of the system that would allow to predict the final interpretation of a given verb. I claim that the competition between various verbs derived from the same stem is an important part of the prefixation system that ensures its flexibility and leads to what on the surface looks like lexical ambiguity. The final interpretation of a verb depends on the availability of alternative expressions.
ACT Testbot and 4S Quality Metrics in XAAS Framework

The purpose of this paper is to analyze all Cloud based Service Models, Continuous Integration, Deployment and Delivery process and propose an Automated Continuous Testing and testing as a service based TestBot and metrics dashboard which will be integrated with all existing automation, bug logging, build management, configuration and test management tools. Recently cloud is being used by organizations to save time, money and efforts required to setup and maintain infrastructure and platform. Continuous Integration and Delivery is in practice nowadays within Agile methodology to give capability of multiple software releases on daily basis and ensuring all the development, test and Production environments could be synched up quickly. In such an agile environment there is need to ramp up testing tools and processes so that overall regression testing including functional, performance and security testing could be done along with build deployments at real time. To support this phenomenon, we researched on Continuous Testing and worked with industry professionals who are involved in architecting, developing and testing the software products. A lot of research has been done towards automating software testing so that testing of software product could be done quickly and overall testing process could be optimized. As part of this paper we have proposed ACT TestBot tool, metrics dashboard and coined 4S quality metrics term to quantify quality of the software product. ACT testbot and metrics dashboard will be integrated with Continuous Integration tools, Bug reporting tools, test management tools and Data Analytics tools to trigger automation scripts, continuously analyze application logs, open defects automatically and generate metrics reports. Defect pattern report will be created to support root cause analysis and to take preventive action.
Enhancing the performance of CNN-based blind image steganalysis approach using multi-GPU TESLA P100

Abstract Blind image Steganalysis is the binomial classification problem of determining if an image contains hidden data or not. Classification problems have two main steps: i) feature extraction step and ii) classification step. Traditional blind image steganalysis approaches use handcrafted filters for the first step and use classifiers such as support vector machine (SVM) for the second step. The rapid development of steganographic techniques makes it harder to design new effective handcrafted filters, which negatively affect the feature extraction step. Recently, Convolutional Neural networks (CNNs) are introduced as an auspicious solution for this problem. CNN-based steganalysis can automatically extract features from the input images without using handcrafted filters. Although considerable success has been achieved with CNNs, CNN-based applications are considered as time consuming applications. Accordingly, it is important to quicken the CNN-based steganalysis approaches training in order to make them more applicable. This paper suggested an implementation technique of the improved Gaussian-Neuron CNN (IGNCNN) steganalysis approach on GPUs. In this paper data parallelism concept is applied to the convolutional layers while model parallelism concept is applied to the fully connected layers. Results show that the proposed method provides better performance as compared with IGNCNN [1] by an average speed up factor of 1.4 X.
Multi-engine Analytics with IReS

We present IReS, the Intelligent Resource Scheduler that is able to abstractly describe, optimize and execute any batch analytics workflow with respect to a multi-objective policy. Relying on cost and performance models of the required tasks over the available platforms, IReS allocates distinct workflow parts to the most advantageous execution and/or storage engine among the available ones and decides on the exact amount of resources provisioned. Moreover, IReS efficiently adapts to the current cluster/engine conditions and recovers from failures by effectively monitoring the workflow execution in real-time. Our current prototype has been tested in a plethora of business driven and synthetic workflows, proving its potential of yielding significant gains in cost and performance compared to statically scheduled, single-engine executions. IReS incurs only marginal overhead to the workflow execution performance, managing to discover an approximate pareto-optimal set of execution plans within a few seconds.
A location-based architecture for video stream selection in the context of IoMT

Location-Based Services (LBS) are part of our day-to-day life, assisting in a variety of tasks such as tracing better routes to a destination, traffic verification and even for safety applications such as car or people tracking. However, it may not possible visually to identify some activities like attempted theft or kidnapping in the same way that a surveillance camera with visual information. It was identified that the integration of these two information (Global Position and Video) could improve surveillance systems from a security perspective. IP cameras are common examples of multimedia sensors that have become an increasingly popular and ubiquitously installed item in urban centers applied to security. This paper proposes a location-based architecture for video stream selection that can be used to monitor entities (people or not) in a context of Internet of Multimedia Things (IoMT) through a representational model of coverage areas of IP cameras. A prototype was implemented as a proof of concept of the proposed architecture. This prototype includes a low latency video transmission service to quickly start a video transmission only when an entity is inside a visual field of view. Tests were performed on the Internet to check for video delivery latency, bandwidth consumption, and the results suggest the effectiveness of the proposal.
A Generic Dynamic Logic with Applications to Interaction-Based Systems

We propose a generic dynamic logic with the usual diamond and box modalities over structured actions. Instead of using regular expressions of actions our logic is parameterised by the form of the actions which can be given by an arbitrary language for complex, structured actions. In particular, our logic can be instantiated by languages that describe complex interactions between system components. We study two instantiations of our logic for specifying global behaviours of interaction-based systems: one on the basis of global session types and the other one using UML sequence diagrams. Moreover, we show that our proposed generic logic, and hence all its instantiations, satisfy bisimulation invariance and a Hennessy-Milner theorem.
Teaching Essay Writing Using Authentic Materials to Improve Students' Writing Performance

This study used authentic material as the tool in essay writing class. Authentic material is made not for teaching need; it is the material that bring real language knowledge. It can be brought to the class as a learning tool. There two beneficial types of authentic materials; printed and audio authentic materials. This research used authentic printed materials to improve students' writing achievement. This is a Classroom Action Research in three cycles. This study aims to explain in detail the implementation of authentic printed material in writing class and how the authentic material can improve students ' writing achievement. The result is the research has been done in three cycles, because the first and second cycle could not reach the goal. The third cycle finally could reach the criteria of success. The conclusion of this research is authentic material can improve students' writing achievement and it is recommended to use authentic material in writing class and other language classes.
Web-based Elderly Monitoring System with GIS

Many countries have become elderly society due to the increase ratio in the elderly population. This increase causes an impact on the elderly and society in many ways. If we are to solve problems for the elderly, it is important to have elderly information that helps understand their being and quality of life. This research aims to develop the web-based elderly monitoring system with GIS that is used to store data and assess the level of quality of life of the elderly. Afterward, the proposed system provided in a spatial data format using GIS technology. We use this system to collect all the elderly in four villages (Moo 5, 6, 7, and 8). From the using of the system in 4 villages, there were 240 elderly people who participated in providing information. The result is the quality of life information and general information, both overview and personal, which the agencies related to elderly care can be used to promote the development of the quality of life for the elderly.
Visual Memory Construction for Autonomous Humanoid Robot Navigation

A visual memory (VM) is a topological map that represents an environment as a direct graph of key images. Thus, visual information acquired from cameras onboard the robot are the only data to construct the map. This work presents the construction of a VM suited for the humanoid robot navigation framework. Additionally, a genetic algorithm that estimates the epipolar geometry is proposed to tackle the problem of image matching used within the VM construction process. Experimental results using a humanoid robot dataset are presented to validate the efficacy of our approach. Further, the solution for image matching based on the proposed genetic algorithm was compared with RANSAC.
An approach for signature recognition using contours based technique

This research work addresses the problem of signature verification and recognition using contours based features and ANN classifier. In this work new features are proposed for signature verification and recognition. As extensive research works has been carried out in the area of HSVR signature verification and recognition by several researchers over the past two decades, many methodologies found in the literature survey. Signature plays a vital role in many fields, generally it is used for personal authentication or gaining control over a system or computing facility, physical entry to protected areas. The signature biometric problem has 2 different perspectives based on PR. 1) Verification and 2) Recognition. In verification, features of a test signature are contrasted with features of a limited set signatures, where the class identity is claimed, whereas, in recognition, the presence of an identity test signature in the database is ascertained. There are two types of variations in signature samples Intraclass variations Interclass variations Intraclass variations are intricate variations of a person's signature, and interclass variations are refer to variations of different person's signature. In this exploration work, problem of effective HSVR is addressed. We propose effective feature extraction and combination of features for verification and recognition of signatures. Recognition involves FM-stage that extends to entire database.
Contour Reconstruction

The method of Probabilistic Features Combination (PFC) enables interpolation and modeling of high-dimensional N data using features' combinations and different coefficients γ: polynomial, sinusoidal, cosinusoidal, tangent, cotangent, logarithmic, exponential, arc sin, arc cos, arc tan, arc cot or power function. Functions for γ calculations are chosen individually at each data modeling and it is treated as N-dimensional probability distribution function: γ depends on initial requirements and features' specifications. PFC method leads to data interpolation as handwriting or signature identification and image retrieval via discrete set of feature vectors in N-dimensional feature space. So PFC method makes possible the combination of two important problems: interpolation and modeling in a matter of image retrieval or writer identification. Main features of PFC method are: PFC interpolation develops a linear interpolation in multidimensional feature spaces into other functions as N-dimensional probability distribution functions.
Multimodal Assessment on Teaching Skills via Neural Networks

Repeated training of teaching skills for student teachers is difficult as many collaborators should be needed for rehearsal environment. Therefore, we are studying a teaching training system using virtual classroom which is constructed by virtual agents as students. In order to construct such a training system, an automatic assessment of human behaviour by the system is required. On the other hand, it is difficult to assess teaching skills in term of educational environment due to complex interactions with many students and subjectivity of the task of teaching assessment. In this study, we propose an assessment model by neural networks (NN) to learn more potential assessment features using multi-modal information: gesture and prosodic information, facial expressions, and the teacher’s intention.
Bengali Stop Phrase Detection Mechanism using Corpus Based Method

This paper discusses a corpus-based method for the detection of the stop phrase. These phrases must be detected and eliminated during NLP in the quest for attaining efficient indexing in modern Information Retrieval (IR) systems. A complete set of stop phrases for the Bengali language has not been developed yet. In this paper, a corpus-based approach is introduced for recognizing and extracting Bengali stop phrases. This proposed technique indicates that an input paragraph will be tokenized in several required manners and after that identification of stop phrases will be obtained by checking through the corpus. Accepted stop phrases will be sent for uniqueness. Outcomes of this proposed approach for stop phrases detection are notable where accuracy, precision, and recall results are observable. Eliminating these stop phrases will further reduce the time complexity of those algorithms, which were used in case of text summarizing and IR system.
SAFE EDUCATIONAL ENVIRONMENT AS AN OBJECT OF PEDAGOGICAL DESIGN

Purpose of the study: The main task of this research is to analyze the scientific points of view on a safe educational environment.&#x0D; Methodology: When writing this scientific work, an analysis was conducted of various sources (scientific monographs and publications) on the topic of creating a safe educational environment. Such methods as analysis and synthesis were used, a systematic approach to data analysis was also applied.&#x0D; Main Findings: The pedagogical reflection of safety is focused on the problem of providing safe conditions for the reproduction of a viable generation, which actualizes the need for the special design of a safe educational environment for students and the preparation of bachelors of education in this direction. The need to design an educational environment to ensure the safety of students stems from their lack of ability to ensure personal safety and the targeted educational priorities in reproducing a viable generation.&#x0D; Novelty/Originality of this study: The design of a safe educational environment in its turn is regarded as a special creation of opportunities for human self-development, which involves the introduction of innovations in the organization of the educational process to minimize risks to physical, mental and social well-being, as well as to ensure the quality of education.
ПРОЄКТУВАННЯ ПРОГРАМИ РОЗВИТКУ ПЕДАГОГІЧНИХ ЗДІБНОСТЕЙ: ПРОЦЕСУАЛЬНІ ТА ЗМІСТОВІ АСПЕКТИ

The article has been devoted to one of the most critical issues of the modern psychological science – development of pedagogical skills. The described program is a response to the lack of effective models of development of pedagogical skills. Besides the presentation of the program, the article also has described the integration of the existing models of professional development into one program, which encouraged positive changes among lecturers and teachers. It has been emphasized that awareness of the own mission in relation to pedagogical activity is critical in the development of pedagogical skills. The program included reflexive discussions, informational sessions and seminars aimed at updating and elaborating various content aspects of pedagogical activity, pedagogical thinking and self-awareness of the pedagogues.
Circular Vector Field Analysis for the Adaptive Diffusion Flow Snakes Applied to Ultrasound Images of Breast Cancer

A number of popular methods for segmentation are based on a generalized gradient vector flow (GGVF) snakes. One of the most successful recent extensions of this idea is the adaptive diffusion flow (ADF) snake. However, the good choice of the control parameters of the ADF such as the Gaussian smoothing coefficients and the weights associated with harmonic hypersurface functional and the infinity Laplacian are hard to select. In turn, the wrong choice often leads to inappropriate results. In this paper, we propose a new method to select the control parameters of ADF based on the vector field analysis (VFA). The experimental results obtained on a set of 40 US images of breast tumor show that the ADF combined with the VFA outperforms the original ADF.
Poster

Despite the pervasive use of real-time video telephony services, their quality of experience (QoE) remains unsatisfactory, especially over the mobile Internet. We conduct a large-scale measurement campaign on \appname, an operational mobile video telephony service. Our analysis shows that the application-layer video codec and transport-layer protocols remain highly uncoordinated, which represents one major reason for the low QoE. We thus propose \name, a machine learning based framework to resolve the issue. We train \name with the massive data traces from the measurement campaign using a custom-designed imitation learning algorithm, which enables \name to learn from past experience following an expert's iterative demonstration/supervision. We have implemented and incorporated \name into the \appname. Our experiments show that \name outperforms state-of-the-art solutions, improving video quality while reducing stalling time by multi-folds under various practical scenarios.
Moving Object Detection Using Integrated Spatial and Motion-Based Method

Moving object detection is one of the key tasks in image processing due to its important role in many applications nowadays. In spite of the fact that many relevant active researches exist, these methods still have drawbacks. This paper proposes an integrated method to detect moving object in video sequence frames. To make the detection more robust, we adopt two well-known methods namely, optical flow and active contour, and added a new adaptive spatial thresholding. The optical flow is first applied on two sequence frames to differentiate the shift of flow vectors of these two. Then, the lost areas are retrieved by our adaptive spatial thresholding. Depending upon the intensity and spatial correlation of individual pixel, they can improve the accuracy and robustness of segmentation process. In the final step, active contour converges to unite moving areas into one. The experimental results show that our method outperforms the previous motion-based methods, especially in term of processing time.